Pengda Huang & Yiming Pi University of Electronic Science & Technology of China
ABSTRACT
Our aim at improving GPS receivers' availability in an urban environment, weak GPS signal acquisition becomes
one key issue in satellite navigation signal processing. In special signal propagation environments, such as indoors, urban crayons, and woods, various factors such as signal propagation power loss, amplitude attenuation, and phase, time, and frequency delay distortions produce at the signal receiving end, what is now known in the literature as, the GPS signal near-far effect, which results in severe degradation of the GPS signal acquisition performance. To mitigate the near-far effect, we focus on four main factors, which are the signal strength attenuation; the Doppler frequency distortion; carrier
phase uncertainty; and mUlti-path distribution because a proper optimization of these factors appears to improve the acquisition performance effectively.
INTRODUCTION
In normal GPS signal propagation environments such as outdoors or open air, the Line of Sight (LOS) GPS signal power level of the L 1 frequency is approximately at - 1 60 dBw [ 1 ] which enables normal GPS CIA code signal acquisition at 1 ms GPS CIA code repetition period and 20 ms GPS data transition period. However, in other challenging or difficult environments such as indoors, urban canyons, and woods, GPS signal propagation encounters
Author's Current Address: P. Huang and Y. Pi , University of Electronic Science & Technology of China, Chengdu, Sichuan, 6 1 1 7 3 1 , P.R. China.
Manuscript received May 25, 20 1 0 ; revised December 27, 20 1 0, January 2 1 , 201 1 , January 24, 2 0 1 1 , and January 29, 201 1 . Review was handled by I.F. Progri.
reflection, refraction, diffraction, and scattering, which induce multi-path fading.
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To test GPS signal propagation degradation in these challenging environments, Lachapell et al [2] studied signal strength fading caused by concrete walls whose measurements resulted in about 23 dB loss in GPS signal strength. However, the strong difference between GPS LOS signal received in open air and degraded GPS signal propagation received through a concrete wall is larger than the maximum out-of-phase cross-correlation peak at approximately 1 5 dB for GPS CIA code in Progri 's work [3, 4] makes it almost impossible to receive a GPS signal through a concrete wall. Progri [4] provides a clear, methodical, and well-analyzed explanation of this effect.
In conventional wireless communications signal processing, the signal strength difference is usually caused by different relative distance between receivers and transmitters. The signal from the nearer transmitter is stronger than the signal from the next consecutive further transmitter and so on; and therefore, when this signal propagation difference becomes larger than the allowable CIA code separation between those two GPS signals, the stronger GPS signal comin�from the near transmitter will block receiving the weaker GPS signal from the farther (or next consecutive farther) transmitter from properly being received at the receiver channel, which is the so-called near-far effect. An illustration of complete definition, explanation, and analysis of the near-far effect are Progri et al [3] and especially Progri ' s Ph.D. Dissertation [4] . The near-far effect in the field of mobile communication has been studied by Yao et al [5] and Glisic et al [6] . Yao et al [5] studied the impact on throughput of direct sequence spread spectrum communication induced by the near-far effect. The work of Glisic et al [6] focuses on mitigating the near-far effect by proposing the novel spread spectrum scheme, which combines the advantageous points of direct sequence spread spectrum code and frequency hopping spread spectrum code. Yao et al and Glisic et al attribute near-far effect to the
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amplitude difference caused by the distance between receiver and transmitter.
However, the near-far effect in the field of mobile communications is not totally identical to the near-far effect in Global Navigation Satellite System (GNSS) navigation signal processing due to inherent differences in signal structure of the two systems and overall system architecture. These signal design and system architecture differences are explained in Progri ' s book on Geolocation of RF Signals: Principles and Simulations [7] . Just to name a few system architecture and signal design differences we have:
J) the strength of the mobile phone in civil use is larger than satellite navigation signal;
2) one link can support the work of a cellular phone while GPS receiver needs at least 4 satellites in sight; and
3) huge Doppler shift will not occur in mobile communications, while the carrier frequency of GPS signal might shift dramatically as a result of Doppler shift·
Progri and his colleagues have made huge contributions on near-far effect [3, 4, 7] in indoor geolocation systems, which is similar to GPS. The definition of near-far effect for geolocation systems is presented, and novel indoor geolocation systems are provided and assessed to eliminate the near-far effect [3] . In [4], comprehensive studies on indoor geolocation systems can eliminate the near-far effect in pseudo lite signal acquisition are performed and presented. Even though indoor geolocation systems are similar to GPS or GNSS, there still exists obvious differences between the two systems, like the Doppler shift range.
Lopez-Risueno et al [8] have also analyzed the near-far effect for GPS pseudo lite signal acquisition by proposing successive cancellation methods to mitigate near-far effect. However, the effectiveness of their cancellation methods are limited to handling the near-far effect caused by GPS signal amplitude and Doppler shift difference. They appear to ignore the near-far effect caused by multi-path distribution, and more importantly, the geo-spatial distribution.
Madhani et al [9] have studied the near-far effect caused by cross-correlation when acquiring indoor GPS signals. The acquisition performance, when signal detection encounters near-far effect, is analyzed; the near-far effect detector is defined and near-far effect mitigation methods are also proposed. Indeed the principle of the near-far effect mitigation methods are similar with the ideas presented in Lopez-Risueno et al [8] , which acquires weak GPS signal after cancelling the interference from the strong GPS signal. The difference between Madhani et al [9] and Lopez-Risueno et al [8] lies in the types of processed location signal, real GPS signal, and GPS pseudo lite signal. S imilar to the
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approach of Lopez-Risueno [8], the work of Madhani et al [9] is also limited to the near-far effect caused by amplitude and Doppler shift difference.
We can reasonably conclude that the near-far effect on GPS signal acquisition is somewhat understood based on a number of credible conference proceedings, j ournal articles, books, book reviews, Ph.D. Dissertations, and other credible works which are not referenced herein due to space limitations. As a result of this investigation the near-far effect was first studied in the field of wireless communications [2] . Second, an attempt is made to explain the gap in acquisition principle blocks of the near-far effect that occurs in wireless communications which overlap with GPS signals acquisition; hence, the near-far effect in indoor geolocation systems is deeply studied [3 , 4 , 7] . However, this approach requires further refinements to account for other difficult and challenging environments for GPS signal acquisitions. An explanation of these reasons for near-far effect in these challenging environments is required. This provides another attempt to verity the findings of the previous works on the near-far effect of GPS signal acquisition and provides new analysis and simulation results of our findings. Simulation results demonstrate that the probability of detecting weak signals improves from 0 .8 to about 0.98 at the same SNR, when encountering amplitude differences caused by near-far effect. Under I kHz Doppler shift, the new method achieves 0.9 detection probability at 30 dB versus 33 . 8 dB for traditional methods, when handling Doppler shift caused by near-far effect. As for multi-path caused near-far effect, the new method achieves 0.9 detection probability at 32 dB versus correlation-based solutions.
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Fig. 1 . Near-Far Effect for GPS Signal
The most common implementation approach of GPS signal acquisition results from the correlation between the received GPS signal and its identical locally-generated replica; after that, thresholding is employed to detect the CIA
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code maximum correlation peak to decide the existence of the desired GPS signal . In fact, besides signal strength difference, other factors also affect correlation results, during the procedure of detecting GPS signal by correlation. Other factors include difference in time delay and in carrier frequency. Figure I illustrates the principle by which near-far effect in GPS signal acquisition occurs as a result of four main possible near-far effect factors such as amplitude difference, carrier frequency delay, carrier phase distortion, and multi-path effects.
In the Introduction, the four factors of near-far effect for GPS signal acquisition are introduced; the GPS Signal Near-Far Effect in Degraded Environment presents corresponding solutions to different near-far effects; in the Solutions to GPS Acquisition Near-Far Effect, simulations are performed to test the effectiveness of the solutions.
GPS SIGNAL NEAR-FAR EFFECT IN DEGRADED ENVIRONMENT
As mentioned previously, thresholding should be lowered to let weak GPS signal reception in challenging environments . However, low thresholding can cause saturation from an unexpected GPS signal at strong strength; e.g., LOS GPS signal from an unexpected satel l ite. Leaking strong but unexpected GPS signals into the receiver induces channel obstruction in GPS receiver channels.
Near-Far Effect Caused by Amplitude Difference Amplitude difference induces the conventional near-far
effect which is often encountered in mobile communication signal processing such as GSM signal processing in the second generation (or 2G) of mobile communications and COMA signal processing in the third generation (or 3G). However, there is also a huge difference between mobile communications signal processing and GPS signal processing which is the basis of this discussion and investigation.
A mobile phone signal is received at a larger power level than a satellite navigation signal . Moreover, during the acquisition phase of mobile communication signals huge signal strength, carrier frequency shift, and ambiguity of initial phase of pseudo-random noise code can be ignored as the result of the new mobile communications architecture and mobile communications acquisition scheme. Also, huge Doppler shifts seldom occurs for mobile communications. Contrast this with GPS signal acquisition in which the received GPS signal power is much lower than the received mobile communications signal power [6] . In such challenging environments, the differences existing in amplitude, Doppler shift estimation accuracy, and initial code phase estimation accuracy are assessed to explain the differences in acquisition schemes of the two systems.
Herein, near-far effect due to signal strength factor is studied. To focus on the effect coming from GPS signal amplitude difference, the estimation error of pseudo-random code initial phase and carrier frequency are ignored to be discussed in later sections.
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To illustrate near-far effect due to amplitude difference, Figure 2 demonstrates correlation ratio versus amplitude ratio. The x-axis denotes the ratio between the amplitudes of the expected satellite and the unexpected satellite. The y-axis denotes the ratio between auto-correlation results and
Fig. 2. Correlation Ratio Curve Versus Amplitude Ratio, between No. 8 and No. 3 Satellite
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Fig. 3. Auto-correlation Peak Fading Curve Versus Doppler Shift
cross-correlation results. Auto-correlation result is obtained by correlation between expected received GPS signal and local generated identical replica of expected GPS signal . Cross-correlation is performed between unexpected received GPS signal and local replica of expected GPS signal . In Figure 2, the signal from the No. 3 satellite is expected and the signal from the No. 8 satellite is taken as unexpected.
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At 0 dB point of x-axis, auto-correlation is larger than cross-correlation by 1 1 .6 dB; However, at the 22.S dB point on x-axis, cross-correlation result almost equals auto-correlation. In this case, the expected signal submerged in the unexpected signal . Correspondingly, conventional acquisition methods based on correlation cannot achieve successful acquisition. In fact, concrete walls will degrade the GPS signal acquisition by 20-30 dB [2], in which situation algorithm improvement is needed to handle the amplitude caused by near-far effect.
Near-Far Effect Caused by Doppler Shift Reference [ 1 ] mentions possible Doppler frequency
shifts in ranges from - 1 0 kHz to + 1 0 kHz when received GPS signal is in high dynamic state. In fact, these ranges can cover the entire Doppler shift frame, when acquiring indoor GPS signals. Even in the Doppler shift range of -S kHz to +S kHz, poor (or severely poor) correlation results are observed due to the near-far effect caused by Doppler shift.
Figure 3 illustrates correlation loss caused by possible Doppler shift, with a window of data length of I ms. From Figure 3, under the conditions of T = I ms and S kHz Doppler shift, the envelope of correlation peak decreases by I S dB, which will cause difficulty in the GPS signal acquisition.
In fact, Doppler frequency shift will change the chip length of pseudo-random code, which causes correlation results to decrease further. To il lustrate waveform distortion caused by Doppler shift, the correlation ratio between auto-correlation results and cross-correlation results are depicted in Figure 4. In the simulation, the waveform of expected GPS signal is distorted by Doppler shift and unexpected GPS signal stays at the ideal carrier frequency.
From Figure 4, as expected signal encounters 1 kHz Doppler shift, correlation ratio approaches 0 dB, which
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Fig. 5. Correlation Result After Encountering Multi-Path
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Near-Far Effect Caused by Multi-path When acquiring GPS signals indoors and in urban streets,
the mUlti-path effect becomes one main factor resulting in poor GPS acquisition performance. Two signal paths are considered in the received data; the main path and first reflection path (or vice path). F igure 5 demonstrates the correlation results between received GPS signal and locally-generated replica under multi-path effects.
Figure SA il lustrates the cases that the symbols of amplitudes for the main path and the vice path are the same with each other. Figure 5B illustrates the case that the
Fig. 6. Correlation Result Versus Carrier Phase Difference
Phase Oifference(rad) o -1
Time OeIay(ms)
Fig. 7. Correlation Loss Versus Phase Difference and Time Delay
symbols of the amplitude for the main path and the vice path are opposite one another. The dash curve illustrates the case that the initial code phase difference of the main path lags behind the vice path within one chip. The real curve illustrates the case that the initial code phase difference is larger than one chip.
From Figure 5, if the symbols of amplitudes in the two paths are the same and the initial phase difference of PRN code is larger than a chip, the multi-path degrades correlation results slightly; if the amplitude symbols are reversed and initial PRN code phases are located within a chip delay, the degradation would be worse.
From the above discussion, the symbols of amplitudes and initial PRN code phase differences will bring unstable
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acquisition performance. Indeed, the initial carrier phase will also cause unstable correlation results. Figure 6 demonstrates the effect of carrier phase difference on correlation results. The Y-axis denotes the ratio between auto-correlation and cross-correlation. The X-axis denotes the carrier phase difference.
To comprehensively demonstrate the effect of initial PRN code phase difference and the initial carrier phase difference, Figure 7 shows us the result of correlation fading in the two dimensions. The x-axis, labeled as Time Delay, denotes the initial code phase difference. The y-axis, labeled as Phase Difference, denotes the initial carrier phase difference.
In Figure 7, the correlation result does not change according to the monotonic trend. In local parts of the simulated correlation results, the two aspects increase correlation results slightly; on the whole, correlation results decrease hugely. In some special cases, the decrease approaches -2 OdB, which would be fatal to GPS signal acquisition.
SOLUTIONS TO GPS ACQUISITION NEAR-FAR EFFECT
We present a partial conclusion on the work performed and described in the previous section. Four main factors inducing GPS signal acquisition near-far effect are presented: s ignal strength difference, which is mainly caused by partially blocking the GPS signal; Doppler frequency shift difference; Carrier phase difference; and whether GPS signal confronts multipath effect. To understand these factors three corresponding solutions are presented herein, respectively.
Fig. 8. Adaptive Step-wise GPS Signal Acquisition Scheme
Adaptive GPS Receiver Capable of Getting Strong Signals
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As for signal strength caused near-far effect, a stepwise structure is a good advantageous candidate method. More details of this method are provided. First, an acquisition of a strong signal is performed and one channel of the GPS receiver turns, into the state of tracking, the acquired GPS signal . Afterwards, the receiver begins to acquire the weak
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Received Data
Correlat.or 2
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Thresholding Decision
Fig. 9. Partial Correlation Processing Flows
GPS signal from the data which the stronger GPS signal is getting rid of. To remove the stronger GPS signal, the initial PRN code phase and Doppler shift from the code and frequency tracking loops in the receiver are utilized. When the GPS receiver turns from a strong GPS signal acquisition to detecting a weak GPS signal, thresholding must be modified correspondingly; otherwise the high probability of a missed detection will happen. Herein, an adaptive scheme ?ased on cells average constant false alarm ratio (CA-CF AR) IS employed to modify thresholding adaptively. Figure 8 demonstrates the block diagram of the receiver.
As depicted in Figure 8, from the radio front-end, c?rrelation results, between received data, containing strong sIgnals and local replica, is pushed into a register stack which includes M cells. Statistic Z, the maximum value among M cells, is used to judge the existence of a GPS signal . The average value of M cells without Z is treated as the estimated noise power which is used in thresholding determination.
Set the false alarm probability as Pfa• The thresholding in the CA-CF AR structure can be computed by the formula: Th = -V-2NolnP:a [ 1 0] . To detect the existence of a GPS signal, compare statistic Z with Th; if the result is false, shift the initial PRN code phase and carrier frequency of local replica; if true, one channel of the receiver becomes a tracking state and the strong GPS signal is acquired successfully. To get rid of the effect from the strong GPS signal, local replica code is generated according to two outputs from the strong GPS signal tracking loop, carrier frequency, and initial time; by multiplying the replica of the strong GPS signal with received data bit by bit, and the stronger GPS signal is cleaned.
Solution to High Doppler Shift Caused Near-Far Effect Herein, the solution to correlation loss caused by Doppler
shift estimation error is studied. When Doppler-time product JdT becomes larger than the threshold, the peak value of the
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............ Partial Correlation on 1/2 Code
Partial Correlation on 1/4 Code
Partial Correlation on 1/8 Code
Partial Correlation on 1/ 16 Code
Fig. 1 0. Doppler Tolerance on Doppler Shift
correlation result between the received signal and identical locally-generated replica will decrease. Traditionally, such Doppler shift problems are solved by using a bank of local GPS replicas. The bank of local replicas contains frequency range covering possible Doppler shift with equal frequency intervals. If any carrier frequency or member among the bank of local
.replica approaches the factual carrier frequency of
the received data, the correlation peak is largest. The carrier fre�uency of the local members is the most accurately estimated value for the real carrier frequency. Such a traditional method is effective but may require additional expensive hardware.
In this section, partial correlation is employed to solve large Doppler-time products. Differing from conventional methods, the underlying partial correlation reduces Doppler-time product in the temporal dimension T, not in the frequency domain h Divide the received GPS signal with the length To into M equal sub-sections at the length of T, MT = To. Such a division is also performed on local replicas. After the division, perform correlation between each data sub-section of the received data and divided local data. Afterwards, select the maximum correlation value to compare with thresholding. If the mru:i��m correlation value is smaller than thresholding, shift the Imtlal PRN code phase of local replica, until cover all code phase bins are covered. The block diagram of partial correlation-based GPS signal acquisition algorithms is il lustrated in Figure 9.
The correlation module in F igure 8 can be substituted by the partial correlation structure to solve the problem caused by Doppler shift. With the improved structure by partial correlation, the adaptive receiver depicted in Figure 8 can elevate the survivability in high Doppler shift environments. To demonstrate the advantage in the high dynamic environment further, different tolerances on Doppler shifts corresponding to different partial correlation lengths are i l lustrated in Figure 1 0.
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Fig. l 1A. Cyclic Spectrum at Cyclic Frequency
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Fig. l I B. Cyclic Spectrum at Cyclic Frequency
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Fig. 1 1 . Cyclic Spectrum of Expected GPS Signal
In the simulation of Doppler shift tolerance test in Figure 1 0, the received GPS signal is sampled at 5 MHz. From Figure 1 0, if the partial correlation length is 5 1 2 points, half power bandwidth tolerates 6 kHz Doppler shift which covers all possible Doppler shift indoors.
Solutions to Multipath Caused Near-Far Effect As for multi path effects, two categories of solutions are
often adopted :
• Depressing signal at vice paths, and
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!
Iteration Times
Fig. 12. Time Delay Estimation Error Curve versus Iteration Times
• Accumulating signals on multi-path by diversity technology.
In fact, indoors and on urban streets, signal strength becomes quite weak. Furthermore, LOS signal at the main path might not exist. Therefore, accumulating non-LOS signal at vice paths is precious to improve signal noise ratio .
Diversity technologies mainly include spatial diversity and temporal diversity, which are widely studied [7] . Spatial diversity requires more antenna elements than temporal diversity, which will inevitably increase hardware complexity and cost of the receiver. Therefore, temporal diversity is discussed following.
Prior to accumulating mUlti-paths in temporal dimension, time delay estimations at different paths are needed. A good number of research contributions have been discovered; however, basic and effective methods are based on correlation peak searching [ I I ] ; MUSIC algorithm [ 1 2] , and the method based on high order statistics [ 1 3 ] are also used in time delay estimation. The correlation method is sensitive to noise; MUSIC algorithm has the trade-offs between computation burden and super-resolution estimation; time estimation method based on high order statistic performs well in Gaussian white noise background but is not effective in environments with color noise. Fortunately, PRN code in GPS signal is cyclostationary. Noise and interference do not have the same cyclostationary characteristics. This selects cyclostatistic characteristic [ 1 4] of GPS signals to implement in time delay estimation.
Figure I I illustrates the advantages of cyclostatistic on depressing background noise and interference. White Gaussian noise at -I l l dBm is imposed on expected signal at - 1 74 dBm. An unexpected satellite signal is also put on the
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received data. Figure 1 1 demQnstrates the cyclic spectrum .of expected satellite at cyclic frequencies ex = 0 and ex = 21Te.
ObviQusly, the cyclic spectrum at ex = 21Te depresses background nQise and interference effectively which is demQnstrated in Figure l I B. The peak value denQtes the cyclic spectrum .of the expected signal which can be easily distinguished frQm the backgrQund. However, the peak value in Figure I I A submerges in the background. From Figure 1 1 , the partial cQnclusiQn can be drawn that cycl ic spectrum at high .order cyclic frequency is effective tQ detect expected GPS signals. FurthermQre, Figure 1 2 demQnstrates the effectiveness of time delay estimatiQn methQds based .on cyclQstatistics.
The estimatiQn errQr curve versus iteratiQn times are plQtted in Figure 1 2 . Signal tQ Interference and NQise RatiQ (SINR) is 32 dBHz. From Figure 1 2, cyclQstatistic-based methQds can achieve successful time delay estimation after abQut 1 00 times .of iteratiQn, while the errQr .of estimated time delay based .on cQrrelatiQn methQds dQes nQt cQnverge.
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SIMULATION
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Herein, simulatiQn is perfQrmed tQ test the effectiveness .of sQlutiQns tQ near-far effect. The signal S3(t) frQm the NQ. 3 GPS sateIlite is expected and the signal Ss(t) frQm the NQ. 8 satellite is unexpected. The expected signal S3(t) and unexpected Ss(t) are piled up tQ get the received signal S(t) = S;(t) + Ss(t) . The NQ. 8 GPS satellite signal is strQnger than NQ. 3 by 1 3 dB .
TQ measure the carrier nQise ratiQ (CNR), the amplitude .of the NQ. 3 is set as standard. Based on the standard, Gaussian white nQise ro(t) is impQsed .on the received
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Fig. 14. Acquisition Probability Curve of Partial Correlator
signal at a certain CNR and get the received data r(t) = S(t) + ro(t).
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Take r(t) as input .of adaptive receiver in Figure 8. In cQntrast, the cQnventiQnal receiver which dQes nQt get rid of the strQng signal is also used in GPS signal acquisitiQn. Figure 13 shQWS us the detectiQn prQbabi lity curve versus CNR.
In the area IQwer than 20 dBHz, bQth receivers cannQt achieve successful acquisition .of expected GPS signals. This is because bQth strong and weak signals submerge in nQise. When CNR gQes beyQnd 32 dBHz, the autQmatic receiver rids itself .of the strQng signal and reaches 0.95 detectiQn prQbability . HQwever, the perfQrmance curve of the cQnventiQnal receiver is nQt better than 0 .8 . That is because nQise degrades the signal slightly with higher CNR, but degradatiQn frQm the strong signal always exists which limits detectiQn prQbability .
In .order tQ i l lustrate the perfQrmance .of the partial cQrrelatQr .on Doppler shift, the signal SQurce generates .one set .of No. 8 satel lite signal Ss(t) , and twQ sets Qf NQ. 3 sateIlite signals S' 3(t) and S" 3(t) . S' 3(t) and S" 3(t) enCQunter 1 kHz and 4 kHz DQPpler shifts. DQPpler shift is absent in the NQ. 8 signal . The amplitude of the NQ. 8 satel l ite signal Ss(t)
is same as with the NQ. 3 and Ss(t) are added with S' 3(t) and S" ;(t) , respectively tQ get tWQ sets .of received signals S' (t) =
S'3(1) + Ss(t) and S"(t) = S" 3(1) + Ss(t) . White Gaussian nQise is alsQ impQsed .on the tWQ sets .of received signals. F igure 1 4 shQWS the acquisitiQn curves denQte the perfQrmances .of ful l and partial cQrrelatiQns, respectively . The length .of each sub-sectiQn in partial cQrrelatiQn equals ' /4 pseudQ code period.
In the case that the No. 3 satell ite signal enCQunters 1 kHz DQPpler shifts, the partial correlatQr implements 0.95 detectiQn prQbability at 32 dBHz; the ful l cQrrelatQr reaches
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Fig. 1 5. Acquisition Probability Curve of Temporal Collection
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0.95 detection probability at 37 dBHz; when 4 kHz frequency shift occurs in the No. 3 satellite signal, the partial correlator implements 0.95 detection probability at 39 dBHz CNR; however, the full correlator reaches no better than 0.74 detection probability .
As introduced previously, high order cyclostatistic can be adopted to estimate mUlti-path time delay, because high order cyclostatistic Gaussian white noise equals zero. Color noise and interference either possess no high order cyclostationary characteristic or possess a different cyclostationary characteristic from the expected signal . In simulation, the No. 3 satellite signal S3(t) contains one main path and one vice path in which the initial time is later than the main path by 0.587I1S, and the amplitude of the vice path is 0.6 times that of the main path. The No. 8 satellite signal S8(t) has only one main path and no vice path. The received signal S(t) = S3(t)
+S8(1} is degraded by color noise ro(t), which is the response from the FIR system with white noise as input. Also, the signal from the NO. 3 GPS satellite is expected to be acquired, and the signal from the No. 8 GPS satellite serves as interference.
After the time delay estimation on the GPS signal from the No. 3 satellite is completed, the initial code phase of the vice path is modified to be identical with the main path and the two paths are combined. Figure 1 5 shows the detection performance after high order cyclostatistic-based time delay estimation. The detection result, without temporal collection technology results and with correlation-based temporal collection technology, are also performed as contrast.
From Figure 1 5 , GPS acquisition algorithm, based on temporal collection technology outperforms the algorithm without collection technology, which is caused by the destruction from the vice path. Furthermore, an acquisition algorithm based on high order cyclostatistic temporal collection reaches 0.95 detection probability at 32 dBHz,
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which is because cyclostatistic-based methods are immune to noise and inference from the No. 8 satellite signal .
CONCLUSION
To improve GPS signal acquisition performance indoors and in urban streets, this has discussed the near-far effect which degrades signals dramatically. Different from conventional mobile communications signal processing, more factors conducting GPS signal acquisition near-far effects have been investigated, such as the signal strength difference, the Doppler shift difference, the carrier phase, and the mUlti-path effect. Furthermore, corresponding solutions are presented. For strength difference, the step-wise receiver getting rid of the strong signal is proposed; for high Doppler shift, the partial correlator is employed to reduce the correlation loss; for multi-path fading, the high order cyclostatistic-based temporal collection technology is proposed to elevate the SNR of the expected signal . The simulation is carried out to test the effectiveness of the solutions. Simulation results illustrate that these solutions are useful to handle near-far effects which increase survivability of GPS or other satell ite navigation receivers indoors, and in other urban environments. Furthermore, the idea of handling near-far effect in GPS signals might be employed in other direct spread spectrum signals .
ACKNOWLEDGMENTS
The authors thank the Editor and anonymous reviewers for their valuable time, reviews, ideas, and contributions made to this article.
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