Abstract— The ability to safeguard domestic shipping and waterside facilities from threats associated with surface and underwater threats (vessels as well as divers) is critical to ensuring security for the maritime domain. Stevens Institute of Technology has been conducting numerous studies and associated field experiments of passive acoustic sensor applications for the detection, characterization, and tracking of surface and underwater threats, which lead to the development of the Stevens Passive Acoustic Detection System (SPADES). The extended diver detection tests were conducted in Newport, Rhode Island and in Den Helder, The Netherlands. Tests involving surface boats were conducted in various locations, including the New York Harbor, Miami, and San Diego. Acoustic tests in Lake Hopatcong, NJ were also conducted in controlled conditions using six distinct boats, including a Panga, a Go Fast boat, a jet ski, and a quiet electrical boat.
Keywords-passive acoustic, detection, divers, small bboats.
I. INTRODUCTION
The Maritime Domain is defined as "all areas and things of, on, under, relating to, adjacent to, or bordering on a sea, ocean, or other navigable waterway, including all maritime-related activities, infrastructure, people, cargo, and vessels and other conveyances.” The ability to safeguard domestic shipping and waterside facilities from threats associated with surface and underwater vessels and divers is critical to ensuring a viable Maritime Domain Awareness. Small surface vessels and divers have already been employed as weapon delivery vehicles elsewhere in the world. The Report of the Committee on Homeland Security, House of Representatives stated [1]: “The systems used in U.S. coastal areas, inland waterways, and ports - AIS, radar, and video cameras - have more difficulty tracking smaller and non-commercial vessels because they are not required to carry AIS equipment and because of the technical limitations of radar and cameras.” Last year, a lot of attention was paid to unmanned surface and underwater vehicles as a new emerging threat to waterside security [2]. There is also some information of concern regarding divers [3].
A number of various technologies were developed for MDA [4-8] and many of them are combined in an integrated system. Examples of similar systems include a European system for enhanced operational maritime border control and maritime domain awareness [6] and the Joint Harbor Operations Center (JHOC), an experimental fusion center organized by the US Coast Guard and Navy for the protection of ports, where the Navy had a large fleet presence [4,7]. Inside
the center, homeland security personnel capture radar and sonar signals, track video and vehicle data, take phone calls from the field, listen to radio traffic from patrols and commercial ships at sea, etc. to prevent a terrorist strike or assist in maritime rescues [7].
The research being conducted in the National Center for Secure and Resilient Maritime Commerce (CSR), a DHS S&T National Center of Excellence for Port Security examines basic science issues and emerging technologies to improve the security of ports as well as coastal and offshore operations. CSR work relies on a layered approach utilizing above water and underwater surveillance techniques. The investigated layers include satellite-based wide area surveillance; HF Radar systems providing over-the-horizon monitoring; and nearshore and harbor passive acoustic surveillance. Integration of these systems is aimed at achieving surface and underwater threat detection, classification, identification, and tracking at various scales.
The acoustic part of the CSR research is aimed at the investigation of applying passive acoustic methods to surface and underwater threat detection, classification and tracking in coastal zones. Acoustics is the only tool that provides detection of underwater threats, and Stevens work has concentrated on passive acoustic methods that are much simpler and cheaper than conventional sonar techniques mainly applied for underwater threat detection. Acoustic research at Stevens is conducted in Stevens’s Maritime Security Laboratory that provides the capabilities of experimental research of physical phenomena connected with acoustic wave generation and propagation in the realistic environment of the Hudson River Estuary. Initially, the focus was on threats posed by surface and subsurface intruders including SCUBA divers [8] and later was extended to small boats by using passive acoustic techniques. Part of the uniqueness of Stevens’ Maritime Security Laboratory is its location on the Hudson River tidal estuary, which is a key waterway that defines the Port of New York/New Jersey, one of the busiest harbors in the U.S. From a scientific perspective, this harbor embodies a high degree of complexity due to variability of currents, salinity, temperature, winds, turbidity, as well as man-made factors including ambient noise due to surface and air traffic, construction noise, and various forms of electromagnetic radiation. All of these enter into the analysis of above and below surface threats.
The majority of the acoustic tests were conducted using Stevens Passive Acoustic Detection System (SPADES) and the previous version of the system is described in [9]. The SPADES is based on the acquisition and analysis of sound generated by various threats; it does not radiate any sound itself. The system uses just four hydrophones and provides simultaneous acquisition and analysis of acoustical signals. The analysis function includes arbitrary digital filtering, spectral analysis and cross-correlation for simultaneous processing of signals from several hydrophones, acoustical source separation and determination of bearing for different targets relative to the central underwater mooring. The system also records and stores the complete raw acoustical data set, enabling further research and analysis of the acoustic signatures.
The system components include a land-based computer and an in-water system. The two sub-systems are connected via an underwater cable that provides power and communication between the two sub-systems. The Stevens Passive Acoustic Detection System (SPADES-2) is divided into two parts, underwater equipment and shore equipment. The underwater equipment consists of a set of 4 hydrophones (ITC model 6050C) arranged on a collapsible aluminum frame. A data hub is centered in this frame. Three of the four hydrophones are joined to cylindrical hubs through three ~2.4 m collapsible extruded aluminum legs arranged with 120° between each leg. All the hydrophones are connected to the central hub with BH4M SubConn wet-mateable connectors. The hydrophones and supporting frame have a weight in air of ~45 lbs. Usually the stands provide the hydrophone placement at a height of 60 cm above the bottom.
Figure 1. The schema of the SPADES.
Figure 2. Pictures of the SPADES.
Figure 3. SPADES onboard of a boat prepared for delpoyement.
The data hub (see Figure 4) houses:
• custom made 4-channel signal conditioner, • USB 2.0 Data Acquisition Board DT9816 , • USB 2.0 to fiber media converter ICRON USB
Ranger 2224. • Custom made DC-DC converter to supply power for
components. The mooring has a diameter of 8 inches and is 13 inches
long. The weight is about 25 pounds.
4- Channel Signal Conditioner
16 –bit USB DAQ
USB to Fiber Optics Converter
DC-DC Converter
Figure 4. The schema of the underwater mooring of the SPADES.
The underwater equipment is connected to the shore equipment using a combined fiber-optic/electrical cable. Necessary power and data transmission between the underwater nodes and the surface are handled through a MacArtney Underwater Technology type 3444 electro/optical cable. The data is handled using 4 multimode optical fibers 50/125 μm. Two of the fibers are used for data transmission, the third is used for a pulse per/second (PPS) signal from the GPS unit for precise signal timing and a fourth fiber is reserved. The power is handled using 2 pairs of two 1.0 mm2 stranded tinned copper conductors insulated with polyofin supplying ~ 24 V DC, 2 A. The overall length of the electro/optic cable is 250 meters.
The dry end of the cable is connected to a data storage computer. This part of shore equipment contains not only a computer, but also Fiber Optics to USB 2.0 converter and a GPS receiver and is enclosed in weatherproof Pelican briefcase.
The software developed by Stevens was used for acoustic target detection, classification and tracking. A single SPADES node provides passive acoustic detection and line of bearing. The main method for acoustic source detection and bearing
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determination is based on the calculation of cross-correlation of acoustic signals recorded by various pairs of hydrophones.
For target localization and tracking, two or more such systems are required. Each system is able to determine bearing to source of acoustic signal and find the azimuth estimate towards the target. By intersecting the bearings, two systems are able to estimate target location. Those intersections are tracked by a linear Kalman filter using multiple hypotheses tracking (MHT), which allows multiple targets to be tracked simultaneously. Those tracks can be overlaid on a map.
III. SPADES APPLICATION FOR DIVER DETECTION
Stevens Institute of Technology conducted numerous diver detection tests. The initial tests were conducted in the Hudson River [8] and later the intensive tests were run in Newport, RI and in the Royal Netherlands Navy harbor of Den Helder.
Below, we present a few results from Den Helder test conducted in 2010 in cooperation with TNO (Netherlands). The test was performed in a harbor that presents a central area that is about half a kilometer wide and 10 meters deep on average. Besides allowing access between the sea and the various facilities, this area also serves as the entrance to the harbor.
During the trial, the harbor was operating as usual. Care was taken to ensure the safety of the personnel involved during the trial, the divers in particular, but no effort has been made to make the test site a quieter place, which would constitute more favorable conditions for passive acoustic detection.
Several examples of the various diver run records are presented below. Figure 5 shows the SPADES display for one diver run. Figure 5a shows spectrogram of the recorded signal. The detection distance of the diver was 440 m in this case and the first appearance of the diver signal is shown on the spectrogram.
The cross-correlogram shown in Figure 5b shows the time difference between signals received by two hydrophones that were used to estimate bearing to the diver. The bearing to the diver was determined for the first time during this run at a distance of 340 m.
Figure 5c shows the time variation of the Diver Classification Algorithm (DCA) confidence level. This algorithm is based on measurements of the diver’s breathing rate and was briefly described in [9]. It is seen that the automated diver detection took place at a diver distance of 350m.
Figure 5. SPADES system display: a) First detection on spectrogram at a
distance R=440 m. b) First presence of the diver signal in the cross-correlogram allows direction finding a, R= 340 m . c) Diver Classification Algorithm (DCA) confidence level. Alarm threshold of 10 is crossed at a
distance R=350 m.
Acoustic Track
GPS Track
Figure 6. Example of the acoustic diver tracking. The white line is the
acoustic tracking and the yellow line shows ground truth from a GPS towed by the diver. The shaded area indicates a low-resolution zone.
The tracking of the diver was conducted using triangulation from two separated pairs of the SPADES. The example of the tracking compared with ground truth GPS data is shown in Figure 6. It is seen that the passive acoustic method provides a good localization capability.
a
b
c
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Figure 7. Spectrogram of the open circuit diver emissions (a), spectrogram
normalized to the ambient noise showing SNR of the recorded signal (b).
The open circuit (OC) diver signature was measured using TNO’s hydrophones that provided a recording of the acoustic signal in the frequency band up to 200 kHz. The spectrogram of the recorded signal is shown in Figure 7a. Various frequencies having higher amplitudes than the ambient noise are clearly visible. The spectral density of the ambient noise is lower in the high frequency band and the Signal/Noise Ratio is higher in the lower band. Figure 7b presents the spectrogram of the recorded signal normalized to the ambient noise. This is the spectra presentation of the SNR that allows one to find the optimal frequency band for diver detection.
IV. SURFACE BOAT DETECTION TRACKING AND
CLASSIFICATION
The developed SPADES acoustic system can detect, track and classify surface boats. Stevens deployed SPADES in the Hudson River for several months and collected thousands of acoustic boat signatures. The intensive tests were conducted on the Port of Miami and some of the vessels with collected acoustic signatures are presented in Figure 8.
Figure 8. Some vessesles with colleccted acoustic signatures in the Miami
experiment.
Tests in controlled conditions with six various boats were conducted at the lake of Hopatcong, NJ. The controlled conditions allowed recalculation of the boat acoustic signature to the distance of 1 m. It was done by comparison of the recorded vessel signal with the signal from the calibrated emitter placed at the same point were the boat signal was measured. Several examples of recorded signatures are presented below.
Figure 9 shows the spectral density (Source Level) of the fast small boat with three engines recalculated to distance of 1 m from the boat for low and high speed. Figure 10 shows the acoustic signature of Panga (Source Level) for low and high speed. Usually, sound noise increases with speed of the boat but for the Panga it was differently – the Sound Level for the high speed boat (Fig. 10c) was less than that of the low speed boat (Fig.10b). It is probably due to smaller part of the boat touching the water at high speed.
a
b
Frequency [kHz]
Frequency [kHz]
Time
Time
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Figure 9. Acoustic signatures of the small fast boat (a) for speed of 10 knots (b) and 44 knots (c).
Figure 10. Acoustic signatures of the Panga (a) for speed of 8 knots (b) and 22
knots (c).
Target localization was conducted using bearing triangulations from two SPADES nodes. Figure 11 shows example of acoustic tracking of the Panga in comparison with ground truth from GPS carried by the Panga. It is seen that acoustic tacking provided the target localization close to the real target position.
a
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Figure 11. Acoustic track of the Panga during the San Diego test and
comparions with GPS ground truth.
Additional ship signature information was extracted from a spectrum of the ship noise envelope. The noise radiated by a ship is modulated at a rate dictated by some parameters of the propeller and engine (number of blades and rotational speed). Evaluation of that modulation provides information on the ship, such as the shaft rotation frequency, that can be used for ship classification. The method for estimation of the envelope modulation is known as DEMON (Detection of Envelope Modulation on Noise) and a number of various DEMON boat signatures collected by Stevens was published in [10,11].
Figure 12. The spectrum of acoustic radiation (a) and DEMON spectrum (b)
of the Stevens research vessel Savitsky.
Acoustic measurements allow finding of the specific parameters of the boat such as shaft rotation frequency (RPM), propeller rotation frequency and blade frequency (propeller rotation multiplied to number of blades). Figure 12 presents the recorded acoustic signature (Figure 12a) and the DEMON spectrum (Figure 12b) of Stevens Research vessel Savitsky.
Knowledge of the shaft and blade frequencies allowes the calculation of the engine gear ratio as G=N·Fshaft / Fblade where N is the number of blades of the propeller (for Savitsky N=4), Fshaft is the shaft rotation frequency and Fblade is the blade frequency. Substituting measured frequencies Fshaft=54.2 Hz, Fblade=34 Hz, N=4, we have estimated Savitsky gear ratio G=2.51. The engine manual gives the ratio G=2.54 that is very close to the ratio determined by acoustics.
ACKNOWLEDGMENT
This work was funded partially by the U.S. Department of Homeland Security under Grant Award no. 2008-ST-061-ML0002. The views and conclusions contained in this paper are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied of the U.S. Department of Homeland Security.
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