ALPS: A Bluetooth and UltrasoundPlatform for Mapping and Localization

Patrick Lazik Niranjini Rajagopal Oliver Shih Bruno Sinopoli Anthony RoweElectrical and Computer Engineering Department

Carnegie Mellon University

{plazik,niranjir,oshih,brunos,agr}@ece.cmu.edu

AbstractThe proliferation of Bluetooth Low-Energy (BLE)

chipsets on mobile devices has lead to a wide variety of user-installable tags and beacons designed for location-aware ap-plications. In this paper, we present the Acoustic LocationProcessing System (ALPS), a platform that augments BLEtransmitters with ultrasound in a manner that improves rang-ing accuracy and can help users configure indoor localizationsystems with minimal effort. A user places three or morebeacons in an environment and then walks through a calibra-tion sequence with their mobile device where they touch keypoints in the environment like the floor and the corners of theroom. This process automatically computes the room geom-etry as well as the precise beacon locations without needingauxiliary measurements. Once configured, the system cantrack a user’s location referenced to a map.

The platform consists of time-synchronized ultrasonictransmitters that utilize the bandwidth just above the humanhearing limit, where mobile devices are still sensitive and candetect ranging signals. To aid in the mapping process, thebeacons perform inter-beacon ranging during setup. Eachbeacon includes a BLE radio that can identify and triggerthe ultrasonic signals. By using differences in propagationcharacteristics between ultrasound and radio, the system canclassify if beacons are within Line-Of-Sight (LOS) to themobile phone. In cases where beacons are blocked, we showhow the phone’s inertial measurement sensors can be usedto supplement localization data. We experimentally evaluatethat our system can estimate three-dimensional beacon loca-tion with a Euclidean distance error of 16.1cm, and can gen-erate maps with room measurements with a two-dimensionalEuclidean distance error of 19.8cm. When tested in six dif-ferent environments, we saw that the system can identifyNon-Line-Of-Sight (NLOS) signals with over 80% accuracyand track a user’s location to within less than 100cm.

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DOI: http://dx.doi.org/10.1145/2809695.2809727

1 IntroductionIn order to improve indoor localization, low-cost beacon-

ing systems like Gimbal [1] and iBeacon [2] allow users toinstrument their environment. These devices typically per-form approximate ranging using Received-Signal-Strength-Indicator (RSSI) values measured from short-range commu-nication like Bluetooth Low Energy (BLE). BLE has gainedtraction in localization applications because unlike previ-ous generations of Bluetooth, mobile devices can scan andrapidly detect tags without needing to be paired. The phone’soperating systems can scan for tags in the background andselectively push notifications to an application when certainconditions are met. This ability of BLE to operate trans-parently while the phone is sleeping has enabled a numberof location-aware services. For example, there are multipleBLE door locks available that periodically transmit proxim-ity beacons to grant access if an authorized user is nearby.Unfortunately, BLE’s ability to estimate distance (proxim-ity) is based on radio signal strength that is affected by an-tenna type, orientation, environment specific path-loss andobstructions. This makes it difficult for BLE to act as afine-grained localization source. Even if the ranging datais accurate, as demonstrated in [3], there are still significantbarriers involved in setup and configuration of localizationsystems. It is extremely difficult for non-experts to createaccurate maps of the environment and precisely survey bea-con locations.

In this paper we present ALPS, a platform that augmentsBLE proximity beacons with ultrasonic transmitters in amanner that can help non-expert users quickly install andconfigure a precise and robust indoor localization system. Auser simply installs three or more ALPS beacons in a spaceand then launches an app on their phone that interactivelyguides them through a configuration process. Once the spaceis configured, users can enter the space and the app will de-termine their location and can directly plot it relatively toa map of the area. As part of this training process, ALPSalso characterizes the environment in terms of Line-Of-Sight(LOS) and Non-Line-of-Sight signal features such that it canfilter out NLOS signals at run time.

The system consists of time synchronized beacons thattransmit ultrasonic chirps similar to those described in [4]and [5]. These chirps are inaudible to humans, but are stilldetectable by most modern smartphones. The phone can usethe Time-Difference-Of-Arrival (TDOA) of chirps to mea-

sure distances. As described in [5], if enough beacons arevisible, a mobile phone can use TDOA to back compute thebeacon transmit time in order to synchronize its clock withthe infrastructure. Once synchronized it is possible to di-rectly measure the Time-Of-Flight (TOF) for any new sig-nals until the clocks drift apart. In contrast to previous work,ALPS uses BLE on each node to send relevant timing infor-mation. This both simplifies the design and allows for theentire ultrasonic bandwidth to be used exclusively for rang-ing. The approach from [4] was demonstrated to performwith an accuracy better than 2m at the IPSN 2014 localiza-tion competition [3]. These errors were significantly largerthen what would be expected by TOF and likely a result ofmulti-path as well as incorrect beacon locations. Both ofthese sources of error are the key motivations for this work.Our updated approach of using BLE for data and the entireultrasonic bandwidth for ranging improved performance tobetter than 30cm accuracy in the 2015 version of the com-petition. However, in both of these tests the receiver hadsufficient beacons within LOS to perform TDOA ranging.

One major benefit of the evolving BLE ecosystems is thatany user can rapidly deploy and annotate tags in a region ofinterest to build location-aware services. In some cases, theuser can even define a location on a crowd-sourced map if aninterior floor plan exists. ALPS takes this concept one stepfurther and allows users to place three or more beacons in anarea and then walk through a configuration process that gen-erates a 3D map of the space with the precise location of eachof the beacons. The approach is similar in nature to range-only Simultaneous Localization and Mapping (SLAM). Anapp on the smartphone guides the user through a process thatallows the system to determine the dimensions of the roomby placing the phone in key locations where it performs rang-ing measurements. Each beacon not only transmits BLE andultrasound, but can also receive ultrasonic messages in or-der to perform inter-beacon ranging. The inter-node rangeinformation is required to solve the beacon mapping prob-lem. Once the mapping process is complete, the systemcan leverage inertial measurements from new mobile usersto precisely localize them in the space even if a subset oftransmitters are obscured.

If the exact geometry of the beacons is known, a systemneeds three beacons in order to compute a two-dimensionallocation. If the geometry is not known, for example duringinstallation, the system needs at least four beacons in orderto perform the mapping operation. After profiling the abilityto timestamp BLE packets, it was apparent that there is toomuch jitter in timing to use BLE as a precise starting pointfor the ultrasonic transmission (it is good enough to iden-tify Time-Division-Multiple-Access (TDMA) slots). Duringthe configuration phase we ask the user to hold their phonedirectly next to one of the beacons. We then use this bea-con at zero-distance to synchronize with the infrastructureinstead of requiring another beacon. The audio time syn-chronization during setup allows the user to use TOF rangingin order to localize the beacons and room anchor points in 3-dimensions. Time synchronization experiments on phones in[5] show that a smartphone can stay synchronized for tens ofminutes before drift causes significant ranging error. Once

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the geometry is determined, users that enter the space cansimply use TDOA (or TOF once synchronized to the infras-tructure) and tracking to compute their locations.

One of the major limitations of ultrasonic ranging sys-tems is error due to interpreting NLOS or multi-path signalsas LOS signals. As part of the configuration process, thesystem captures the signal strength of both BLE and ultra-sonic transmissions along with the ultrasonic TOF data ofeach ultrasonic transmission. The user is asked to captureinstances where the phone is in clear LOS of all beacons,as well as a few NLOS cases where beacons are obstructed.Using this data and the difference in multi-path attenuationproperties of RF and ultrasound, the system is able to clas-sify if a received signal is LOS or NLOS. When given directLOS, ultrasonic ranging systems are highly accurate and canmeasure distances to less than 10cm of error. ALPS is ableto ignore NLOS data and interpolate the true position us-ing inertial measurement assisted tracking when beacons areblocked.

In summary, the contributions of this paper are (1) a hard-ware and software platform that augments BLE with ultra-sonic transmissions for fine-grained localization, (2) a pro-cedure that leverages this platform to help users automati-cally generate maps of the environment without any manualmeasuring and (3) an enhanced location tracking approachthat uses machine learning to filter out NLOS signals whenlocalizing users after the installation phase.

2 Related WorkResearch on the topic of localization can be broadly clas-

sified into two main categories of range-based approaches [6,7, 8, 9] and range-free approaches [10, 11, 12, 13]. Range-free approaches typically attempt to match either syntheticor naturally occurring signatures to a particular location oruse tracking techniques like on-board inertial measurementdata [14, 15, 16]. Range-based approaches use measureddistances or angular estimates to known anchor points tocompute a position. In this paper, we focus primarily onrange-based technologies including Time-of-Arrival (TOA),TDOA, TOF and tracking approaches that use inertial data.For a more detailed general overview, we refer to [17]. Ourapproach uses TOF for setup and then uses TDOA with iner-tial tracking for run-time localization.

There is a large body of work in the mobile computingdomain on TOF [18] systems that compute distances basedon how long it takes for a signal to propagate from a senderto a receiver. For example, [19] and [20] both compute dis-tances by measuring the Round-Trip-Time-of-Flight (RTOF)by recording a signal’s departure and the return time dividedby the propagation speed. This assumes that the receiverwill retransmit a return signal within a fixed amount of time.BeepBeep [20] uses this approach on cellular phones to com-pute inter-device ranges. Even though smartphones are typi-cally sensitive to ultrasonic sound, their speakers are highlydirectional in those frequencies, which lead [20] to use au-dible frequencies. The authors also focused on peer-to-peerranging rather than infrastructure to device ranging.

TDOA systems can remove the requirement of know-ing exactly when a signal was transmitted by using whatis known as pseudo-ranging. Pseudo-ranging computes dis-tances by looking at the relative differences between the ar-rival of several signals, assuming they were all transmittedsimultaneously or at known offsets. As compared to TOAand TOF approaches, this requires one additional transmit-ter to allow the common distance from all broadcasting de-vices to be estimated. GPS [7] is the most popular ex-ample of this ranging approach. Similar approaches havebeen applied towards ultrasonic communication [21, 22, 4].The Dolphin [21, 22] system adopts a pseudo-ranging ap-proach using a 50kHz carrier with Direct-Sequence-Spread-Spectrum (DSSS) modulation. While extremely accurate,this approach requires custom hardware and is not applicableto standard smartphones. In [23], the authors expand uponDolphin (while still requiring custom hardware) by adding aself-training deployment approach based on filtered motionwithin the space. This work in part inspired our inter-beaconranging capability. In [24] the authors identify the locationof a cellular phone in a car using ultrasonic pseudo-rangingfrom the car’s audio speakers. This approach used fixed fre-quency tones in an extremely controlled environment wheredata transfer was not required.

In [4], we introduced an ultrasonic TDOA ranging ap-proach that is able to perform ranging between speakers dis-tributed in the environment and mobile devices. The sys-tem utilizes commercial tweeters and is evaluated on pre-vious generations of smartphones with a wider frequencyrange above 20kHz than the current generation (iPhone 4 asopposed to 5-6). The approach also only supports pseudo-ranging and not TOF. Followup work [5] extended the ap-proach with clock synchronization to enable TOF rangingand simplify the modulation scheme to accommodate newerphones with less available bandwidth above 20kHz. We fur-ther extend upon this work by completely replacing the datacommunication component of the system with BLE such thatthe ultrasound is only used for ranging. We also focus on theconfiguration elements of the system by providing a mecha-nism to rapidly set up and map spaces. In the previous work,NLOS signals were a significant source of error. In this work,we show an approach to both detect NLOS signals as wellas improve tracking in their presence by fusing inertial datafrom the phone’s on-board sensors.

The radio and communications community has studieddifferentiating LOS and NLOS signals in depth. A surveyof this work can be found in [25]. The most common ap-proaches either use the coherence of the signal [26] or theylook at the distribution of multiple consecutive signals forclassification [27]. A recent approach that utilized featuresdirectly derived from RSS to identify and mitigate NLOSsignals can also be found in [28]. As described in Section 4,we saw that the coherence of the ultrasound signal was moresignificantly influenced by the environment rather than theexpected multi-path component of a NLOS signal. As sug-gested by [27], the distribution of LOS data has much lessvariance as compared to NLOS data, but in our case the datarate is so low that it would take tens of seconds to arrive ata reliable confidence interval. In contrast, we utilize envi-ronment specific training along with the fact that we havetwo significantly different transmission media to help clas-sify LOS and NLOS from a single transmission.

The robotics community has developed multiple ap-proaches for SLAM in indoor environments. Early workin SLAM required range and bearing measurements fromthe landmarks. Our system provides range information aswell as inter-node ranges which can aid in mapping. [29]proposes techniques to localize connected nodes with noisyrange measurements. [30] proposes utilizing a mobile nodeto map beacons that are sparsely connected. In this paper, wepresent a technique for mapping three transmitters with inter-node ranging in a single area. In case of larger spaces withconnected rooms, varied number of nodes in each space, andsparse connectivity between the beacons, we can draw upontechniques from [30] and [29]. Finally, Google’s projectTango and sensors like Occipital’s Structure use depth sen-sors to scan and map 3D environments. We believe that ourapproach can help augment these techniques during the map-ping process (improving both techniques) and then can beused for localization once the mapping is complete.

3 ArchitectureA typical ALPS setup consists of three or more transmit-

ters deployed in the target area as seen in Figure 1. Place-ment of the transmitters is flexible, however in our currentimplementation each beacon should be within LOS of eachother and placed such that LOS coverage is maximized. Inmost deployments, this typically means mounting them tothe ceiling. The transmitters are time synchronized using802.15.4 radios that listen to periodic transmissions from amaster node. The timing master node can be one of theinstalled beacons. Current closed-source BLE implemen-tations limit access to the lower levels of the stack whichmakes it difficult to use BLE for tight timing.

3.1 HardwareWe developed an embedded hardware platform for our

transmission infrastructure shown in Figure 2, which con-sists of the following main components: (1) An ultrasoundtransceiver board with an 802.15.4 radio shown in Fig-ure 3(a), (2) a BLE daughter board shown in Figure 3(b), (3)a piezo bullet tweeter with attached omni-directional hornand (4) a battery pack for optional battery powered opera-tion.

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Figure 2. ALPS beacon

The ultrasound transceiver uses an Atmel At-mega256RFR2 SoC with an on-chip 802.15.4 radio.The CPU drives a Texas Instruments TLV320AIC312024-bit 192kHz audio codec via an I2S interface, which weemulate using an SPI port and a timer output. The codec isconnected to an Akustica AKU340 MEMS microphone forreceiving ultrasound and includes an on-chip 1.6W (into 8Ohms) class-D amplifier for driving the piezo tweeter. Thesystem is able to achieve an audio sampling rate of 125kHzfor playback and recording, which is limited by the clockspeed of the microcontroller. This tightly coupled designallows for negligible end-to-end jitter from reception of an802.15.4 packet to playback through the speaker of less than20µs.

The BLE daughter board shown in Figure 3(b) contains aTI CC2640 SoC with on-chip BLE radio and an ARM M3core which attaches to the ultrasound transceiver board viatwo on-board connectors that supply it with power and con-nect I2C and GPIO interfaces. BLE advertising transmis-sions can be triggered by the ultrasound transceiver througha GPIO interrupt to synchronize BLE and ultrasound packettransmission. The audio is produced by a low-cost (< $2.50)Goldwood GT-400CD bullet piezo tweeter capable of pro-ducing sound well above our required frequency range of20−21.5kHz.

Current Power Time Energy(mA) (mW) (ms) (mJ)

100% Vol, Chirp 58.04 174.12 50 8.71100% Vol, Tone 56.04 168.12 50 8.4050% Vol, Chirp 33.31 99.30 50 4.9750% Vol, Tone 32.77 98.31 50 4.97BLE Idle 2.48 7.44 n/a n/aBLE Adv. 20ms 3.08 9.24 n/a n/a

Table 1. Beacon power consumption

The power consumption of our prototype beacon is sum-marized in Table 1. The values for playback show the aver-age power consumption of only the ultrasound transceiverboard while continuously transmitting and the BLE num-

TLV320'Audio'Codec' Atmega256RFR2'SoC' RF'Amplifier'Microphone'

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BLE Antenna802.15.4 Antenna CC2460 SoC

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Figure 3. Ultrasonic beacon PCBs

bers include the isolated BLE average power consumption.All currents were measured at a supply voltage of 3V . Bothboards draw a negligible amount of current (< 800nA com-bined) when put into a deep-sleep mode. At 100% vol-ume the beacon is capable of transmitting ultrasound sig-nals to an off-the-shelf smartphone over a range of roughly40m. With ultrasound operating for 12h per day in a 7 slotTDMA schedule at half volume and BLE advertising contin-uously during these 12 hours, each beacon can operate froma 20AH lithium (Tadiran D-cell) battery for approximately212.6 days. We believe that this can be optimized by a fac-tor of 3-5x with more aggressive duty-cycling and improvedBLE management. The audio efficiency could also be im-proved with a more efficient custom driver that resonates atour target frequencies.

3.2 Horn DesignIn a typical loudspeaker, as the audio frequency increases,

the spatial spread of the signal decreases, eventually forminga narrow beam. In our system, we ideally want an omni-directional speaker that has a flat frequency response acrossthe 18− 24kHz frequency band that can uniformly deliverdata without distortion. Since no such speaker was commer-cially available, we designed a custom transducer based ona multi-sector omni-directional horn design shown in Fig-ure 2. This turned out to be a non-trivial effort that requiredsignificant experimentation.

We initially evaluated multiple commercial speakers inorder to determine suitable driver components and geome-tries. In terms of frequency response, we found that ribbontweeters had an excellent frequency response and horizontaldispersion pattern. Unfortunately, they require large mag-nets that are both heavy and expensive ($50+). They alsohave a narrow vertical beam pattern. In certain scenarios,

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Figure 5. Ultrasonic beam patterns

they could be an ideal transducer but are too expensive forgeneral purpose indoor localization applications. We alsoevaluated piezo electric tweeter elements since they are low-cost (< $2.50) and have a reasonably linear frequency re-sponse. Unfortunately, without a horn to guide the signal,they are quite directional. The top two rows in Figure 5 showa comparison of the vertical and horizontal beam patterns ofa ribbon tweeter and piezo driver.

The acoustic literature has many models that describe awide variety of speaker designs [31]. Most of the commondesigns tend to be for audible frequencies and exhibit con-fined beam patterns. In order to design a custom horn, weinitially modeled a cone based on standard horn equations.These models specify the width of the horn’s mouth to be4.76mm in diameter to support frequencies above 20kHz.The resonant chamber needs to be at least 1 wavelength,or 1.6cm in length. The horn throat then needs to be sized

in order to reduce distortion while having sufficient ampli-fication. A point source (pin-hole speaker) would be ideal,except that the volume would be insufficient. Figure 4(e)shows the basic geometry of our omni-directional horn. Inorder to evaluate performance, we varied the horn angle, theheight of the top of the horn and experimented with differentnumbers of internal sectors. Each horn variant was printedon an SLA 3D printer and then tested using a pan-tilt mech-anism that allowed automatic frequency response measure-ments to be taken at different angles. We tested 12 differenthorn designs generating a vertical and horizontal frequencyresponse plot measured using a swept sine deconvolution ap-proach recorded on a measurement microphone.

We define two metrics to compare different speaker con-figurations. These metrics are computed from the gain val-ues at different frequencies and directions, as seen in Fig-ure 5. To measure the flatness of frequency response, wecompute the frequency distortion. The frequency distortionof a speaker in a particular direction is the difference betweenthe maximum and minimum gain in the frequency band ofinterest. We average this metric across all directions tocompute the frequency distortion (lower plots in Figure 4(a-d)). To measure the deviation from omni-directionality fora speaker, we first find the gain in a particular direction byaveraging the gain across the frequency band. We then com-pute the average deviation from the mean gain across all di-rections to arrive at the directional distortion (upper plots inFigure 4(a-d)). Both these metrics are averaged across thehorizontal and vertical orientations for each speaker.

Frequency distortion as well as directional distortion bothdirectly impact the SNR at the receiver. Frequency distor-tion will create a mismatch between the recorded signal andthe template used during matched filtering, while directionaldistortion will vary the signal level with respect to the an-gle between the beacon and the receiver. A decrease in SNRincreases timing jitter when determining the TOA of the re-ceived ultrasound transmissions, which in turn negativelyimpacts ranging and localization performance.

Since frequency response and amplitude can be compen-sated for through equalization and amplification (within rea-son), the most important factor is the directionality of thehorn. Since the horn without sectors and the six sectored ver-

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sion with a 30◦ angle and a height of 10mm (compression ra-tio (throat area/mouth area) of 70) performed almost exactlythe same in this respect, we selected the sectored version forincreased mechanical stability. Figure 5(e) and Figure 5(f)show the beam pattern and frequency response across 140◦of our final design.3.3 Data and Ranging

In our previous work [4] and [5], we present two methodsusing ultrasonic chirps for modulating data and ranging in-formation onto an ultrasound carrier. Similar to the systemdescribed in [5], we use TDMA to multiplex the transmissionof our ultrasound transmitters over time and transmit ultra-sonic chirps for precise ranging. Instead of encoding datausing chirps, ALPS relies on BLE advertisement packets inan iBeacon compatible format to signal the current TDMAtime slot. This eliminates the need for a complicated andmore processing intensive demodulation step on the phoneand makes the ultrasound signals shorter and more likely tobe detected correctly. Receivers are also able to obtain BLERSSI and iBeacon range measurements from these packetsfor detecting when a beacon is not within LOS.

Our ultrasound ranging signals consist of a 50ms up-chirpbetween 20kHz and 21.5kHz followed by a 50ms period ofsilence to wait for any reverberations to decay significantly.The silence duration as well as the volume is adjustablebased on the room size and is determined during the config-uration process. In the following time slot we broadcast anorthogonal 50ms down-chirp between 21.5kHz and 20kHzto further minimize possible interference from reverberationfrom the previous time slot and to allow the periods of si-lence between transmission to be kept to a minimum.

The primary requirements for a smartphone to be able tofunction as an ALPS receiver are that it is able to receive au-dio signals between at least 20−21.5kHz and delivers BLEadvertisement packets to the application layer with low la-tency. In [32], the authors profile the frequency responseof the microphones of 10 iOS and Android smart devices,and show that all of them provide adequate response in the20−21.5kHz range.

In order to better understand the impact of the environ-ment, we evaluated the ultrasound TOF and iBeacon rangingperformance of our beacons in six different spaces. Figure 6shows the ranging error in a free space and in a confinedcorridor setting. The data was collected by time synchroniz-ing an iPhone 5S to the beacon by holding it directly at thespeaker while it was playing evenly spaced 50ms chirp sig-

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nals and then placing it at a known distance away from thebeacon. The beacon would then transmit 500 additional pe-riodic chirp signals per sampled distance after a known timedelay, for which we calculated the measured distance basedon the propagation time of the signal. We collected samplesat 10 different beacon to receiver distances in every envi-ronment. 100 iBeacon distance samples were collected atthe same time from the distance being reported in iOS. TheiBeacon power level was calibrated by measuring its averageRSSI at a 1m distance as recommended by Apple. For thefree space case using ultrasound TOF a mean absolute rang-ing error of 8.9cm with 95% of the distance samples below33.5cm in error was observed. The mean absolute ranging er-ror for using iBeacon in this environment was 403.4cm with95% of the distance samples below 845.6cm in error. For thecorridor case a mean absolute ranging error of 17.9cm with95% of the distance samples below 34.2cm in error was ob-served. The iBeacon distance measurements showed a meanabsolute ranging error of 1209.9cm with 95% of the distancesamples below 1861.3cm in error in this environment. Wesee that both BLE and ultrasound are negatively impacted bymulti-path. This indicates that it is important to use the roomgeometry information to set transmit power.

In order to map received ultrasound transmissions to theirrespective transmitters, our beacons transmit periodic BLEadvertisement packets that contain a counter value τtx indi-cating the time offset from the broadcast of the BLE adver-tisement packet to the beginning of the TDMA cycle shown

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Figure 8. BLE advertisement packet reception latency

in Figure 7 (a), (b). Mobile receivers can synchronize tothe TDMA cycle by timestamping the BLE packet receptionτrx (Figure 7 (c)) and subtracting the received counter valuefrom τrx. While BLE advertisement intervals can be as lowas 20ms, there is an indeterministic latency associated withreceiving them in an application running on a smartphone.Typical smartphones such as the iPhone 5S do not allow low-level access to their BLE stack for accurate timestampingand also time-multiplex hardware resources between theirWiFi, Bluetooth classic and BLE receivers, allowing themto only listen for BLE advertisements intermittently. On theiPhone 5S, received BLE advertisements are passed to theapplication roughly once a second, but it is unclear how of-ten the phone receives BLE packets and how long it takesbefore they signal applications.

In order to evaluate the feasibility of time-synchronizingthe phone to the TDMA cycle of the broadcasting infras-tructure, we measured the latency between BLE advertise-ment packets and the audio input of an iPhone 5S. We setup a beacon to toggle a GPIO pin that was connected to thephone’s microphone input when a new TDMA cycle startedand simultaneously started broadcasting BLE advertisementpackets containing τtx. The phone timestamped the recep-tion of each BLE packet in the application and subtracted τtxto determine when the GPIO pin was toggled in its frameof reference. Simultaneously the phone was recording theGPIO trigger in an audio waveform, which was preciselytimestamped to within 1ms using the technique detailed in[5]. Figure 8 shows the BLE advertisement packet receptionlatency for 20,50 and 100ms advertisement intervals across1000 packets. When set to a 20ms interval, we measured anaverage latency of 25.1ms with a maximum of 72.4ms, whichis well below our 100ms long TDMA slot length, hence al-lowing slot-accurate time synchronization via BLE. The lessfrequent intervals provided unacceptable worst-case latencyof 169.3ms and 275.1ms (50 and 100ms intervals respec-tively).3.4 Inter-beacon Ranging

In order to assist in determining the locations of the bea-cons, we require accurate direct inter-beacon measurements.Each beacon is equipped with a MEMS microphone con-nected to its audio codec which can stream audio to the net-work master node via 802.15.4. We implemented an inter-beacon TOF ranging procedure, where two beacons at a timelisten for a trigger from the network master via 802.15.4, af-

ter which one of them transmits an ultrasonic ranging signalwhile the other records and streams the recording back tothe network master for processing. The propagation time ofthe ultrasound signal can then simply be calculated from thereceived recording. Due to the higher sampling rate of theaudio-codec (running at 125kHz) and the direct RF time syn-chronization, this procedure provides range measurementswith errors below 5cm. We discuss how these measurementsare used in Section 5.

4 Non-Line-of-Sight FilteringA major source of error in TOF ranging systems is incor-

rect measurements due to NLOS signals. Failing to identifythe NLOS signals can introduce estimation errors in rangingand thus seriously affect the localization performance. Theidentification of LOS/NLOS signals not only facilitates theprocess of selecting the right measurements, but also helpsto further mitigate the ranging bias. Most of the identifica-tion techniques deal with the problem based on the rangeestimates or channel pulse response (CPR), but are often in-feasible in real world since large amount of training data isrequired for characterization. The Cricket system [6] wasone of the first efforts that noticed that the difference be-tween two transmission media could be used to possibly in-fer NLOS transmissions. In Cricket, the frequency was quitehigh and the transmitters where highly directional, whichlikely made the correlation between RSSI distance and ultra-sonic TOF more obvious. At lower frequencies, with chirpencoding and omni-directional transmitters, the ultrasounddiffracts significantly more, making the distinction betweenLOS and NLOS more difficult.

In this section, we discuss the creation of a binary clas-sifier for NLOS detection that is able to learn the charac-teristics of a space with relatively little training data. Duringour experiments, we collected 3600 samples of LOS data and1200 samples of NLOS data from arbitrary locations in morethen 6 environments. The unbalanced amount of LOS dataand NLOS data are designed to model the real world sce-nario where LOS data is much easier to collect during theinstallation process. Since the rate of position updates is rel-atively low, we ideally want to find a set of features that canbe extracted from a single measurement. The key insight toour approach is that we are able to detect ultrasonic TOF, ul-trasonic RSSI and iBeacon RSSI, which are different in LOSand NLOS cases. In Table 2 we show classification accuracy

Features Set Accuracy{Fus} 0.644{FiB} 0.925{Fwave} 0.767{Fdelay} 0.753{FiB,Fwave} 0.779{Fus,FiB} 0.965{Fdelay,Fwave} 0.787{Fus,FiB,Fdelay} 0.959

{Fus,FiB,Fdelay,Fwave} 0.779Table 2. Identification accuracy with multiple features

NLOS Accuracy FP FN Prec. Recall1% 0.805 0 0.195 1.00 0.8054% 0.826 0 0.175 1.00 0.8267% 0.837 0.007 0.156 0.992 0.843

10% 0.841 0.016 0.143 0.982 0.855Table 3. Impact of training samples on FiB and Fus per-formance

with different combinations of features, where Fus is the ra-tio of RSSIus to DiB, FiB is the ratio of RSSIiB to DiB, Fwav isthe normalized waveform of the received ultrasonic signal,and Fdelay is the root mean square (RMS) delay spread of theultrasonic signal. DiB is the distance estimate returned byiBeacon, RSSIus and RSSIiB are RSSI values from ultrasonicand iBeacon respectively.

Based on the results in Table 2, we selected Fus and FiBbecause they perform best with the least amount of trainingdata. A Support Vector Machine (SVM) classifier is trainedwith 10-fold cross validation and grid search on selectingthe best parameters in order to prevent over-fitting. Otherfeatures like the shape of the ultrasonic waveform performedpoorly in our experiments.

In Table 3 we summarize the identification performanceon our dataset while using 10% of the LOS data for train-ing and varying the amount of NLOS data. We see that evenwith 1% of the NLOS data used for training, we are ableto achieve 80% classification accuracy. In any one mappingcollection cycle, this corresponds to about 300 LOS sam-ples (which are easily captured while holding the phone inthe open during the mapping phase) and 12 NLOS sampleswhich the user is instructed to collect. However, we shouldnote that most of classification error results from false nega-tive (FN) instead of false positive (FP) due to the unbalanceddata set, which can seriously decrease the performance of ourlocalization algorithm. With an increased number of NLOSdata samples in the training phase, we observe a slight in-crease in overall accuracy while FN probability greatly de-creased as a trade-off with more data collection time.

As shown in Section 6, the ability to filter out NLOS mea-surements significantly increases overall localization perfor-mance.

5 User-Assisted MappingAny beacon-based localization system requires the loca-

tion of the beacons with respect to the floor plan to pro-vide meaningful location estimates. Most systems assumethese beacon positions can be easily determined, but in prac-tice this can be quite difficult. Errors in the position of

the beacons can cause significant end-to-end localization er-rors. Generating beacon positions is a labor-intensive time-consuming process which involves either taking extensiverange measurements to walls using laser rangers or employ-ing other equipment like a robotic system with accurate mo-tion control equipped with the ability to sense the signal fromthe beacons. What makes this process difficult is that thefloor plan information itself may not be provided to the in-staller. We propose a semi-automatic mapping process wherethe installer deploys the beacons and walks around the roomtaking a few measurements to aid the mapping process.

The goal of the proposed mapping process is to (a) mapthe beacons with respect to the floor plan, and (b) generatethe floor plan using landmarks such as the corners if it is notalready available. This process can be performed by a non-expert user in a few minutes for a single area.

5.1 ProcedureThe process for mapping three beacons in a single area is

given below. The approach can be extended to more beaconsin a single area and conceptually also multiple areas. Thoughnot currently implemented, the app could potentially take ex-isting floor plan images and determine anchor points withinthem. Our mobile app guides the user through these steps:

1. Deploy the three beacons such that they provide goodcoverage of the area and are in LOS of each other.

2. Hold the phone close to one of the beacons and selectthe Sync option in the app and wait for 10 seconds whilethe phone synchronizes to the beacons.

3. Identify three points on the floor such that all three bea-cons are visible from each point. Place the phone ateach location, and select the Floor reference point op-tion.

4. If the floor plan is not provided, walk around the roomand go to each corner and select the Corner referencepoint option. This will compute line segments betweenthe corner points.

5. Specify an origin and the orientation of the x− y coor-dinate space. One way to do this is to select one of thecorners as the origin and an adjacent corner to be on thex or y axis.

5.2 AlgorithmThe basic principle of the 3-D mapping process is that

we make use of the following three types of information touniquely solve for the beacon positions (a) ultrasonic-basedinter-node ranging (b) estimation of z− plane using the threeground measurement points (c) user specified x−y plane ori-gin and orientation. The algorithm for mapping three bea-cons is as follows:

1. Given inter-node ranges r12, r23, r13 between the threebeacons B1, B2, B3, define a 3-D coordinate systemR3

a such that the three beacons are on the z = 0 plane,B1 is the the origin [0,0,0], and B2 is along the xaxis [r12,0,0]. Coordinates of B3 can be obtained as

[r13 cos(α),r13 sin(α),0], where α= arccos( r212+r2

13−r223

2r12r13)

Beacon  1   Beacon  2   Beacon  3  Phone  

Area  A   Area  B   Area  C  Wall  1   Wall  2  

Figure 9. Panorama of automatically configured kitchen area using three beacons

2. Estimate the coordinates of the three ground measure-ment points with respect to the beacons in R3

a.

3. Define a new coordinate system R3b such that the plane

that contains the three ground points is the new z = 0plane in R3

b.

4. The x− y plane of R3b can be defined by its origin and

one of the axes. This can be chosen arbitrarily since wewould re-assign the x−y plane after generating the floorplan. In our implementation, we did the following: Theprojection of B1 on the x− y plane is assigned as theorigin (0,0,0) of R3

b. The projection of B2 on this planeis assigned to lie on the y-axis of the new plane. Thex-axis of R3

b is found to be normal to the y and z axes.

5. Estimate the location of all the corner points in R3b using

trilateration.

6. The x− y coordinates of the required 2-D coordinatesystem are specified by the user during the calibrationprocess. Either apply an affine transformation on R3

bto get the final coordinate system, or for better accu-racy, apply non-linear transformations to minimize er-ror across all reference points if more than two refer-ence points are available.

5.3 EvaluationWe evaluated our mapping process in half a dozen areas:

a kitchen and lounge space, a lab, and in four office areas.The largest space in terms of area and number of cornerswas a lounge and kitchen space, as shown in Figure 9, with atotal area of around 775 sq ft. and 10 corners. The generatedmap is shown in Figure 10. Note that this process requiresall the corners to be in LOS of the three beacons. Some ofthe boundaries in Figure 10 were not physical walls but wereeither 1.5m tall partitions or were chosen to ensure all cor-ners are in LOS. The results of the mapping process for thekitchen setup and averaged across all six experimental se-tups are shown in Table 4. Our system can determine three-dimensional beacon location with a Euclidean distance errorof 16.1cm averaged over the three beacons, and can gener-ate maps with room measurements with a two-dimensionalEuclidean distance error of 19.8cm averaged over all the cor-ners. We observe that while mapping the beacons, the overallerror in the height is around 13.5cm, while the error in the xor y coordinate is less than 4cm. This is because the height

Beacon Error (cm) Corner Error (cm)Setup Avg. x y z Avg. Max

Kitchen 13.9 2.2 1.4 13.4 26.8 43.6Lab 18.2 5.4 3.6 13.6 13.0 25.2

Office 1 17.5 4.6 3.5 15.0 10.7 13.9Office 2 17.2 5.0 1.6 15.1 22.8 34.0Office 3 15.5 2.3 1.7 11.1 18.9 40.9Office 4 14.1 3.4 3.1 12.9 26.5 31.4Overall 16.1 3.8 2.5 13.5 19.8 43.6

Table 4. Mapping error

of the beacons were within 1m of each other whereas theywere well separated in the x− y plane. Hence the height ismore sensitive to errors.

6 Localization and TrackingOnce the beacons are mapped, they are capable of lo-

calizing a user in the region. During the mapping phase,as explained in Section 5.1, the user first places the mobilephone close to one of the beacons in order to synchronizewith the infrastructure. However, we cannot expect this syn-chronization when the system is used for localization. In a3D space with the beacons synchronized to each other, butnot to the mobile phone, we must instead perform TDOA-based pseudo-ranging. In the presence of 3 beacons we can-not uniquely use trilateration to estimate the locations of themeasurement points. We assume the height of the phone isbetween 0.9 and 1.2m and perform the multilateration. Ifthere are regions where four or more beacons are located, wecan adopt the technique in [5] to synchronize the phone tothe beacons. This is done by first determining the phone’sposition using TDOA ranging and multilateration and thencalculating the distance to at least one beacon. Since theultrasound transmissions are periodic, the beginning of theTDMA cycle can be determined based on the distance to abeacon, the TOA of the transmission in the phone’s record-ing buffer and the time slot of the transmission. Since thephone’s ADC has a free running clock, we can synchronizeit to the transmission cycle of the beacons by determining thesample in the recording buffer that corresponds to the begin-ning of the TDMA cycle. This then allows for TOF rangingto be used instead of TDOA. To solve for the location withonly three beacons, we search through the region and findthe 3D position that gives the minimum mean square errorin TDOA for the obtained measurements. We can determinethe bounds of the region in which we should perform this

−5 0 5 10

0

2

4

6

8

x (m)

y (m

)Area A

Area B

Area C

Wall 2

Wall 1

(a) Overhead view

−50

5100

24

68

0

1

2

3

x (m)

y (m)

z (m

)

BeaconsEstimated Beacon LocationsCornersEstimated Corner Locations

(b) 3D mapping result

Figure 10. Kitchen area mapping output

search based on the set of beacons where we receive BLEdata. We perform the search in an iterative manner, first on a1m×1m grid, then 20cm×20cm and finally 2cm×2cm grid.

As can be seen in Figure 11(b), the system provides a lo-calization accuracy within 30cm 90% of the time. However,in situations where the user blocks one or more transmitterswhile walking, or when a NLOS signal is detected, the sys-tem cannot update the location estimate. In these situationswe make use of the Inertial Measurement Unit (IMU) sen-sors on the phone and a motion model to track the user andprovide location updates as explained in the next section.6.1 Implementation of Extended Kalman Fil-

ter (EKF) for TrackingWe implement an EKF to filter the location estimates of a

mobile user by utilizing the phone’s IMU sensors for track-ing. For step count and direction we use the step count fromthe iPhone’s accelerometer and the direction from the com-pass which already fuses the magnetometer with the rategyros. The details of our process model and measurementmodel for the EKF are given below.

Our objective is the estimate the 2-D position (xt ,yt) ofthe mobile device at time t. We define the state vector as:

Xt =

[xtyt

]∼N (µt ,Σt)

where µt is the expected value of Xt and Σt is the uncertaintyin the state. The EKF generates estimates of µt and Σt basedon the prediction from the previous state Xt−1 and the processmodel, and then updates this estimate based on measurementZt and the measurement model. A time step of t = 1 is thetime a person takes for one step while walking.

6.1.1 Process ModelThe input ut to this system is given by:

ut =

[∆Dtθt

]with noise vt such that:

vt =

[vD

tvθ

t

]∼N (0,Mt)

Mt =

[σ2

D 00 σ2

θ

]∆Dt is the step length of mobile device and θt is the heading.The step length and heading of the mobile device can be esti-mated from its IMU sensors and are used as input to the filter.σ2

D and σ2θ

are the variance in the step length and heading re-spectively. The focus of our work is not on implementing anaccurate step length and heading estimation method, so forour model we conservatively assumed that 2σD is 10cm and2σθ as 45◦ (For a normal distribution 95.45% of the valueslie within 2σ of the mean)

The process model is given by[xtyt

]=

[xt−1yt−1

]+

[(∆Dt + vD

t )cos(θt + vθt )

(∆Dt + vDt )sin(θt + vθ

t )

]The process model is linearized and µt and Σt are updated as:

µt = g(µt−1,ut)

Σt = GtΣt−1GTt +Rt

where

g(µt−1,ut) = Gtµt−1 +

[∆Dt cos(θt)∆Dt sin(θt)

]Gt =

[1 00 1

]Rt =VtMtV T

t

Vt =∂g(µt−1,ut)

∂ut

Vt =

[cos(θt) −∆Dt sin(θt)sin(θt) ∆Dt cos(θt)

]6.1.2 Measurement Model

Though the actual measurements from our system are theTDOA values from the set of visible transmitters, these cannot be directly used with an EKF due to the linear approxi-mation of the TDOA equations. Instead, we first estimate theposition using the TDOA measurements, and use this esti-mate as our measurement. Our measurement model is givenby:

Zt =

[xtyt

]+wt

wt ∼N (0,Qt)

where Zt = [xt , yt ]T is obtained by multilateration. From Fig-

ure 11, we observe that 90% of the range errors are less than

−5 0 5 10

0

2

4

6

8

10

12

x (m)

y (m

)

True pathDead ReckoningLocalizationLocalization + Tracking

(a) Tracking path

0 20 40 60 80 100 1200

20

40

60

80

100

Position error (cm)

Per

cent

age

of e

stim

ated

loca

tions

(%

)

LocalizationDead ReckoningLocalization and Tracking

(b) CDF without obstacles

0 50 100 150 200 250 3000

20

40

60

80

100

Position error (cm)

Per

cent

age

of e

stim

ated

loca

tions

(%

)

LocalizationDead ReckoningLocalization and Tracking

(c) CDF with obstacles

Figure 11. Tracking performance in the kitchen with and without obstructions. Note scale of x-axis.

30cm. We assume that the errors in the xt and yt are uncorre-lated and assign Qt = σzI where σz = 30cm

In case one or more transmitters are blocked, or if thephone identifies that one of the signals from the beacons isa NLOS signal, it does not update its measurement Zt . Inthis case, we assign Qt = σnI where σn is a large number,such that the filtering effectively updates the estimate of thelocation based purely on tracking.

6.2 EvaluationWe evaluated the accuracy of our system, and the localiza-

tion and tracking algorithm in the same 6 experimental envi-ronments where we performed the beacon mapping. We usedthe map that was generated by our mapping process. In eachtest a user held an iPhone 5S and took approximately 30 stepsin the area. We collected data from the compass and read stepvalues. Ultrasonic measurements from the beacons were alsocollected at every step. We analyzed the data offline usingMatlab. Results from our largest scenario (the kitchen area)are presented in Figure 11. The Localization line refers toposition estimated based on only the ranges from the bea-cons, the Pedestrian Dead Reckoning line refers to positionestimates purely based on the IMU sensors and the motionmodel, the Localization and Tracking line refers to the out-put of the EKF explained above. Figure 11(b) shows thattracking does not improve the accuracy much as comparedto using only localization since the localization is much moreaccurate (error less than 30cm 90% of the time) than the esti-mates from the motion model. We then simulated situationswhen the user blocks one transmitter by removing some ofthe range measurements from a beacon in the data-set. TheLocalization line in Figure 11(a) shows the localization es-timates under this case. The location does not update wheninsufficient measurements are received. We observe that insuch cases the system benefits from tracking, as seen in Fig-ure 11(c), and the error is less than 50cm 90% of the time.7 Limitations

While promising, there are still a number of open chal-lenges with respect to ALPS. Users are required to installthree beacons per LOS location. If the beacons share thesame height then z-axis resolution will be limited. TheNLOS detection system is still environment dependent andit can be difficult to capture a comprehensive data set. Allbeacons require LOS in order to accurately determine their

distances as part of the setup process. The proposed mappingprocess works for a single space covered by three beacons.In the future, we intend to look at using tracking of the mo-bile device as part of the mapping process to link multiple re-gions, possibly connected by corridors or separated by wallsor doors. The power requirement of the ultrasonic transmit-ters is still relatively high compared to BLE-only solutions,which require larger packaging or more frequent battery re-placement. This approach also requires two radios in orderto synchronize and communicate with mobile phones. Webelieve that BLE alone could provide synchronization be-tween beacons in the future. Another consideration is thatthe system transmits in a frequency range that is audible toanimals. We believe that the duration, duty-cycle and vol-ume of the system can be set low enough that the impact onanimals would be minimal. Some motion detectors alreadyoperate at this frequency.

8 ConclusionsIn order for indoor localization systems to gain traction,

they need to be precise and simple to install. This paperpresents a platform called ALPS that uses a combination ofultrasound and BLE to rapidly bootstrap precise localizationin small and medium sized areas. After placing three or morebeacons on the ceiling of an area, the devices communicatewith each other and a phone app walks the user through acalibration and mapping process. In our experiments, userswere able to map room corners and the beacon positions withan average error of 19.8cm and 16.1cm respectively, with-out having to manually measure any distances. They wouldsimply capture key locations by placing their phone as in-structed by an app. Once the map has been generated, thesystem can perform precise localization using TDOA datafrom ultrasonic transmitters that utilize bandwidth just abovethe human hearing range, but can still be detected by modernsmartphones. When beacons are blocked, the system is ableto continue estimating positions based on inertial data fromthe phone as well as filter out NLOS signals using an SVMclassifier that looks at the ratios between BLE RSSI, ultra-sonic RSSI and ultrasonic TOF. We designed and evaluated astand-alone hardware platform that is able to broadcast timesynchronized ultrasonic signals along with BLE packets.

9 AcknowledgementsThis research was funded in part by the Bosch Research

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