A new FFT acquisition scheme based on partial matched filter in GNSS receivers for harsh environments

Accepted Manuscript A New FFT Acquisition Scheme Based on Partial Matched Filter in GNSS Receivers for Harsh Environments

Wenfei Guo, Xiaoji Niu, Chi Guo, Jingsong Cui

PII: DOI: Reference:

S1270-9638(16)31127-0 http://dx.doi.org/10.1016/j.ast.2016.11.017 AESCTE 3834

To appear in:

Aerospace Science and Technology

Received date: Accepted date:

17 June 2016 21 November 2016

Please cite this article in press as: W. Guo et al., A New FFT Acquisition Scheme Based on Partial Matched Filter in GNSS Receivers for Harsh Environments, Aerosp. Sci. Technol. (2016), http://dx.doi.org/10.1016/j.ast.2016.11.017

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A New FFT Acquisition Scheme Based on Partial Matched Filter in GNSS Receivers for Harsh Environments Wenfei Guo1; Xiaoji Niu1; Chi Guo1* ; Jingsong Cui2 (1, GNSS Research Center, Wuhan University; 2, Computer School of Wuhan University) Abstract: FFT acquisition scheme based on Partial Matched Filter is an important search method in GNSS receivers, which can scan the code phase and Doppler frequency by parallel search scheme. However, the detection probability reduces due to the coherent combing loss and scalloping loss. To solve this problem, we propose a new FFT acquisition method based on Partial Matched Filter (PMF-FFT) in GNSS receiver, in which, the IF signal is divided and central frequency is shifted before the matched filters, and the FFT results are combined to search the Doppler frequency. The performance of the scheme is analyzed by amplitude response of the system. Both analysis and simulation results show that the new scheme has a larger acquisition range without adding the FFT points, which is quite appropriate for dynamic and weak signal environments. We also do a comparison based on the collected data test, showing that the PMF-FFT scheme outperforms the other three methods generally used in the GNSS receivers. Moreover, we apply a FPGA&DSP hardware receiver to verify the practicability and superiority of our method, which turns out our method can extremely reduce the acquisition time in a cold start condition, compared to the serial scheme. Keywords: Acquisition Range; High Dynamic; Weak Signal; Coherent Combining Loss; Scalloping Loss.

1. Introduction Coarse synchronization, usually called acquisition, is an essential step in GNSS signal reception㸪through which visible satellites are discovered and the first rough estimates of signal code delay and carrier frequency deviation are obtained [1]. The basic principal estimating parameters of the signal is realized by correlating the signal with a local signal with preset code phase and frequency. According to the differences presented during the implementation of schemes, a macro classification of classical acquisition methods can be done [2, 3]. Due to the independence of signal parameters to be estimated, the most elementary idea of signal acquisition, named serial scheme, is to scan and search over a predicted time-frequency uncertainty zone in the time domain by digital correlators [4]. Advantages of this method are that only few correlators are needed and they can also be used to track signals in the tracking process. Consequently, the complexity of hardware design is greatly reduced. However, for the reason that this scheme only searches a unit instead of a set of units each time thus the searching process is too slow to meet people’s requirements. In order to improve the acquisition speed, the intuitive idea is to use parallel correlators, a matched filter to scan multiple code phases simultaneously [5]. According to the frequency response of the matched filter, another method called parallel code phase search scheme is also proposed [3, 6]. The use of FFT reduces the searching time dramatically during the signal acquisition process. Although the parallel search is faster than the serial one in the time domain, the huge computational load, coming with conducts of two Fourier transforms as well as one Fourier inversion, makes the latter more favorable for software-based implementation in general. For when hardware resources needed in hardware-based design as well as its complexity are made into consideration, the size of FFT block is far too large. Actually, experiments show that 3536 ALUTs and 229,376 Memory Bits are required for an 8192 point FFT with 8bit data even if Burst I/O Data Flow is adopted. If the FFT results of the local code is calculated first and store in the hardware, the computational load could be reduced. However, storage resources will be increased significantly in this way. That is to say, there are about 31.96875 Kbits will be extra needed for each satellite signal. Another fast search scheme based on parallel frequency is more suitable for hardware implementation, for its code First author: [email protected]; Corresponding author: [email protected].

phases of receiving signals are scanned by correlators like the serial scheme. However, as for parallel frequency search, all results of its correlation calculations are implemented by using FFT [7]. Compared to the parallel code scheme, the computational load of this scheme is reduced obviously, for on the one hand, the frequency bins to be searched is far smaller than the uncertain code phases scope; on the other hand, Inversion Fourier Transform is not needed. Hence, this method is widely used in actual engineering receivers and its performance is researched. With the improvement of TTFF requirement, some efforts are made to combine the matched filter in code phase and the FFT based frequency estimator to further enhance the signal searching speed. It is equal to conduct parallel search both in time domain and frequency domain respectively [8]. In order to fit in with the needs of implementation in hardware receivers, the length of matched filters should be shorted. Thus a new method named Partial Matched Filters with FFT algorithm is formed, which provides a good trade-off between acquisition performance and complexity [9, 10]. This method is proposed initially in the communication literatures [11]. However, its acquisition range is limited and its acquisition sensitivity degrades due to the coherent combining loss and scalloping loss, resulting in a low detection probability. As a consequence, the algorithm performance will degrade in some harsh environments, such as high dynamic or weak signal conditions. To overcome these limitations, a new FFT acquisition scheme based on Partial Matched Filter is proposed in this paper. In this scheme, the IF signal is divided and frequency is shifted before the matched filters, and the FFT results are combined to search the Doppler frequency. This new scheme has a larger acquisition range and higher sensitivity than the traditional one, making it meet the requirements of the high dynamic and weak signal environmental applications better. The rest of the paper is organized as follows: the signal and system model of our scheme are presented in Section II. Section III presents the performance analysis by the amplitude, including the acquisition range, detection probability and speed performance. The simulation and test results are given in Section IV. Finally, conclusions are drawn in Section V. 2. Signal and system model 2.1 FFT acquisition based on partial matched filter structure The input of the acquisition processing block is generally an IF digital signal, which can be written in the form:

r ( i ) = A ( iTs ) D ( iT ) C ( iT − τ ) ⋅ cos ª¬ 2π ( f IF + f d ) Ts i + φ º¼ + n ( i )

(1)

where r(i) is the received IF GNSS signal at time t=Ts*i, A(t) is the signal amplitude, D(t) is the navigation message bits, C(t) is the spreading code sequence for GNSS with a chip duration Tc, IJ is the code delay in samples, fd is the carrier Doppler frequency shift in hertz, ‫ ׋‬is the carrier phase in rad/s, Ts is the sampling time interval in seconds, fIF is the IF in Hertz, and n(i) is the additive band-limited white Gaussian noise (AWGN) with a one-sided power spectral density (PSD) of N0 and bandwidth of 2/Tc. The code phase delay IJ and the carrier frequency Doppler shift fd are the main parameters to be estimated in the acquisition stage. As shown in Fig. 1, the implementation structure of the algorithm can be divided into two parts: partial matched filter of the parallel code phase search in time domain and FFT-based Doppler frequency parallel search in frequency domain. First, the local carrier wave numerical controlled oscillator (NCO) would generate two-way mutually orthogonal signal sin(wt) and cos(wt), which would be utilized to multiply the IF digital signal respectively to get the complex baseband signal with a Doppler frequency fd shift. With branch I and branch Q being regarded as real part and imaginary part respectively, the result of frequency mixing can be expressed as:

x ( i ) = I ( i ) + jQ ( i ) = ª¬ aD ( i − τ ) C ( i − τ ) + nb ( i ) º¼ e

j 2π ( f d Ts i +θ )

(2)

with a representing the amplitude and nb(i) representing the complex baseband noise. Note that the high frequency component of the frequency mixer output is ignored in the equation, for the following matched filter can be regarded as a low pass filter.

Fig. 1 FFT acquisition based on partial matched filter structure.

Then this baseband signal is filtered by a series of matched filters with given local C/A codes being coefficients, whose output can be indicated as: M

M

i =1

i =1

Ccor = ¦ x ( i ) Li = ¦ ª¬ aD ( i − τ ) C ( i − τ ) + nb ( i ) º¼ e

j 2π ( f d Ts i +θ )

Li

(3)

Here M is the length of each matched filter. Assuming the matched filter correlator is implemented under a navigation message bit D and the local code Li is aligned with signal C/A code, the normalized amplitude response of each MF correlator with a noise free signal can be written as: M

M

Ccor = ¦ x ( i ) Li =

¦ ae i =1

i =1

j 2π ( f d Ts i +θ )

M

¦ ae

j 2πθ

=

1 sin (π f d MTs ) M sin ( f d MTs )

(4)

i =1

In the equation above, the output of the MF correlator is influenced by the Doppler shift fd and the length M. Considering that the acquisition system in Fig. 1 consists of P complex partial matched filter (PMF) correlators, and each of them has M coefficients placed according to the C/A code sequence, the partial correlation results of the pth (p=1,ĂˈP) partial matched filter as shown in Fig. 1 can then be derived from Equation (3):

C ( p) =

PM

¦

i =1+ ( p −1) M

x ( i ) Li

p = 1, P

(5)

The discrete Fourier transform of the received signal in terms of magnitude and phase can then be written as: P −1

Y (k ) = ¦C ( p) e p =0

−j

2π kp N

k = 0, , N − 1

(6)

With k being the index number of output bins and N (NıP) being the length of the FFT block. In the ideal case, if the code phase offset is captured, the parallel frequency scheme will obtain the carrier frequency spectrum. As a result, the selection of the maximum signal at the FFT output is identical to the selection of the frequency closest to the actual Doppler shift. Accordingly, the Doppler offset is estimated as well as the code phase. Meanwhile, the acquisition is achieved. 2.2 Coherent combining loss and scalloping loss effect The frequency response of the PMF-FFT depicted in Fig. 1 architecture can be derived using Eq. (4), (5) and (6) [11]:

Y ( fd , k ) 1 Y ( fd , k ) = = Y ( 0, 0 ) PM

N −1

¦ C (n) e n=0

−j

2π kn N

=

1 sin (π f d MTs ) sin (π f d PMTs − π kP / N ) ⋅ PM sin (π f d Ts ) sin (π f d MTs − π k / N )

(7)

Where k=0,ĂˈN-1, and it is assumed that the local code is synchronized with the incoming signal code and the signal is in a noise free situation. Fig. 2 is the amplitude response drawn from Equation (7), in which the processing data is 1ms, and the number of PMFs as well as the length of the FFT is 8. It should be noted that the real observed Doppler range should be restricted in [-4 KHz, 4 KHz] due to the sampling frequency of the matched filter outputs is 8 KHz. 1 0.9

Normalized PMF FFT output

0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

0

1000

2000

3000

4000 5000 6000 Doppler frequency/Hz

7000

8000

9000

10000

Fig. 2 Output amplitude changing with Doppler frequency offsets.

As shown in Fig. 2, the normalized amplitude response of the PMF-FFT output is mainly influenced by two factors: one is the coherent integration time of each PMF, deciding the total amplitude response envelope; the other is the processing data length, deciding the main lobe width of the frequency responses in each bin. In terms of output amplitude response envelope attenuation, namely coherent combining loss, it is obvious that the acquisition system with more PMFs in a confirmed processing data length has a wider bandwidth and it could scan a larger Doppler shift scope. Nevertheless, acquisition system with more PMFs will lead to a complex FFT with more points, bringing about a larger computational load. Besides the limited search scope caused by coherent combing loss, another amplitude attenuation, namely scalloping loss could also be observed between the bins of the FFT from Fig. 2. More exactly speaking, the FFT estimator outputs will decline sharply when the carrier Doppler frequency offsets of the received signal deviate from frequency intervals, especially when the Doppler frequency offset falls between two bins of FFT. And of course, when compared to signals which occur in the center of the closest given bin, the probability of detection for signals with these Doppler offsets is reduced. 2.3 A new partial matched filter based on FFT acquisition structure To alleviate coherent combining loss and scalloping loss, we propose a new Partial Matched filter based on FFT acquisition scheme, as depicted in Fig. 3. Different from the traditional one, the IF signal is down converted to two special base frequencies, and then is filtered by two groups of partial matched filters. The partial filter outputs are zero-padded and transferred in turn to frequency domain separately by an N point FFT module. The outputs of two FFT are aligned in the index and combined. At last, we search the peak value to decide the Doppler frequency.

Fig. 3 FFT acquisition based on New Partial matched filter structure.

On the one hand, when the local frequency is shifted by ±1/(2*Tp), where Tp is the coherent integration time in each partial matched filter, the peak value at the edge of Doppler range will be greatly improved. Thus, the acquisition performance in high dynamic case is improved. On the other hand, long coherent integration time is often adopted to improve the weak signal, resulting in a narrow range of acquisition. Thus this new scheme is also useful for the weak signal condition. Moreover, the peak value in the center of two frequency bins rises because of the zero-padding. It could improve the detection probability of these Doppler signals. 3 Performance analysis 3.1 Frequency Response of the New PMF-FFT The amplitude response of the new PMF-FFT can be easily derived from Equation (7):

­ ° ° ° ° 1 ° PM ° ° ° Y ( f d , k ) °° Y ( fd , k ) = =® Y ( 0, 0 ) ° ° ° ° ° 1 ° PM ° ° ° °¯

cos (π f d MTs )

⋅ π · § sin ¨ π f d Ts + ¸ 2M ¹ © π P ( N − 2k ) · § sin ¨ π f d PMTs + ¸ 2N © ¹ πk · § cos ¨ π f d MTs − ¸ N ¹ ©

, fd < 0

(8)

cos (π f d MTs )

⋅ π · § sin ¨ π f d Ts − ¸ 2M ¹ © π P ( N + 2k ) · § sin ¨ π f d PMTs − ¸ 2N © ¹ πk · § cos ¨ π f d MTs − ¸ N ¹ ©

, fd > 0

As depicted in Fig. 4, the whole amplitude response of the system is combined from two FFT outputs. The acquisition range of the new scheme is double of the traditional one. Therefore, it can efficiently avoid the coherent combining loss of the PMF-FFT acquisition algorithm, with a same point FFT. In term of the scalloping loss, essentially, the scalloping loss is the manifestation of barrier effects and spectrum leakage caused by truncation and barrier effects in the FFT processor. Zero-padding FFT could effectively improve the barrier effects by increasing the sampling in frequency domain. For example, 16 point FFT of 8 partial matched filter correlator results followed by 8 zeros has 16 peaks, which is as twice as that of the results with directly FFT. As a result, there will be a peak value rather than a valley value between the two bins as previous. It is clear that further levels of padding will continually reduce the scalloping loss that occurs. However, the rate of improvement will decrease rapidly with the increasing amount of padding. Fig. 5 shows the comparison of amplitude response between the new PMF-FFT with zero-padding and the old one. As shown in Fig.5, the scalloping loss is significantly mitigated by the zero-padding. Thus, we can conclude that the new PMF-FFT algorithm could acquire all the frequency signals during the scope with almost the same performance. This characteristic is very important in the high dynamic conditions where the Doppler frequency is large. Besides, it is also useful for the weak signal environments where the coherent integration time could be very long, resulting in a relative small acquisition range [12].

Amplitude response of the 1st FFT Amplitude response of the 2nd FFT Amplitude response of the whole system

1 0.9

1 Normalized PMF FFT output

Normalized PMF FFT output

0.8

0.8

0.6

0.4

0.7 0.6 0.5 0.4 0.3 PMF-FFT without Zero-padding New PMF-FFT without Zero-padding PMF-FFT with Zero-padding New PMF-FFT with Zero-padding

0.2

0.2

0.1

0 -8000

-6000

-4000

-2000 0 2000 Doppler frequency/Hz

4000

6000

0 -8000

8000

Fig. 4 Amplitude response of the new PMF-FFT

-6000

-4000

-2000 0 2000 Doppler frequency/Hz

4000

6000

8000

Fig. 5 Amplitude response of the new PMF-FFT with zero-padding

3.2 Probabilities of False Alarm and Detection Assuming the total length of the processing data is T, each correlator output will have a zero mean noise with variance N0*M/T. Neglecting the effect of noise correlation by zero-padding in FFT, the noise power of the N point FFT should be N0/T. Thus, when there is no signal, the magnitude of the FFT outputs should be Rayleigh distributed with Probability

Density Function (PDF): v2

− v fn ( v ) = e 2 N0 /T ( N0 / T )

(9)

Giving a consent threshold vt, the probability of false alarm can be derived under the assumption. And the noise of

each frequency bin is independent and identical distributed (i.i.d): v ª º − t 2 N0 /T º » f n ( v ) dv = 1 − «1 − e ¼» «¬ »¼ 2

N

Pfa = 1 − ª ³ ¬« 0

vt

N

(10)

Fig. 6 shows the probability of false alarm, where the total processing time is 1ms, and the CN0 is 45 dB-Hz, the signal power is normalized to be 1, and the number of matched filters is 8. It can be seen that the New PMF-FFT false alarm is a little higher than the traditional one, because the new algorithm has a larger acquisition range, which is equivalent to double detected bins. When the desired signal is present, the magnitude of the FFT outputs should be Rice distributed with Probability

Density Function (PDF):

fs ( v ) =

v

σ

2 n

e

−

v 2 + ak2 2σ n2

§ va · I 0 ¨ 2k ¸ © σn ¹

(11)

Where a and are the signal magnitude and noise variance of the output of the FFT respectively. Thus, the detection probability of different Doppler frequency signal can be derived as follows:

Pd = ³ f s ( v ) dv ª ³ f n ( u ) du º vt ¬« 0 ¼» ∞

vt

N −1

v ª − t2 º 2σ n » = «1 − e « » ¬ ¼ 2

N −1

v ª − t2 º 2σ n » ⋅ Q ( , ) = «1 − e σn σn « » ¬ ¼

a

vt

2

N −1

ª 1 § a − vt · º ⋅ «1 − erfc ¨¨ ¸¸ » «¬ 2 © 2σ n ¹ »¼

(12)

The detection probabilities of four different schemes present in Fig. 7, where the false alarm is set to 1e-4, and the total processed data is 1ms. As can be seen, the different algorithms almost have the same detection probabilities when the signal is within the acquisition range. However, from the amplitude response shown in Fig. 5, signals with different Doppler frequency have different magnitude because of the coherent combining and scalloping loss. As shown in Fig. 8., the CN0 is

40dB-Hz, the processed data is 10ms, presenting a weak signal acquisition method. It can be easily concluded that the zeropadding FFT method can significantly improve the detection probability in some special frequency points, and the new PMF-FFT scheme has a double acquisition range without almost the same sensitivity performance. 1 1

0.8 Probability of detection

Probability of false alarm

0.8 0.7 0.6 0.5 0.4 0.3 0.2

0.9 0.8

0.7 0.6 0.5 0.4

0

0.1

0.2

0.3

0.4

0.5 0.6 Threshold

0.7

0.8

0.9

1

0.7 0.6 0.5 0.4

0.3

0.3

0.2

0.2

0 30

PMF-FFT PMF-FFT with Zero-padding New PMF-FFT New PMF-FFT with Zero-padding

0.1

0.1

0.1 0

0.9

PMF-FFT PMF-FFT with Zero-padding New PMF-FFT New PMF-FFT with Zero-padding

0.9

PMF-FFT PMF-FFT with Zero-padding New PMF-FFT New PMF-FFT with Zero-padding

Probability of detection

1

32

34

36

38

40 42 CN0/dB-Hz

44

46

48

50

0 -800

-600

-400

-200 0 200 Dopper frequency/Hz

400

600

800

Fig.6 Comparison of false alarm probability Fig. 7 Comparison of Detection Probability Fig. 8 Detection probability of different Doppler frequency signals

3.3 Implementation parameter setting and acquisition time analysis Assuming pseudo-code period is T, do coherent integration during c code periods, then the Pre-detection Integration Time PIT = cT. Dividing the shift register into P segments averagely, each segment point number M is just the length of the matched filter. In this case, the coherent integration time of each segment will be TP=PIT/P=cT/P. As each result of partial correlators is regarded as a point of FFT input, TP is the sampling interval of FFT. Normally, assuming that the range of the acquisition Doppler offsets scope is [-fd, +fd], the coherent time of the traditional PMF-FFT scheme should satisfy the following equation:

1 P = ≥ 2 fd Tp cT

(13)

Taking GPS for example, the period of C/A code is 1ms, with 1023 chips. If the range of the acquisition Doppler offset scope is set to [-10 KHz, +10 KHz], and code phase offset accuracy is less than half chip, the depth of the shift register should be 2046c. The points of FFT and each correlator in the traditional PMF-FFT can be derived:

N ≥ P ≥ 20c ­ ° 2046c 2046c ® °¯ M = P ≤ 20c = 102.3

(14)

Hence, in order to cover the range of Doppler frequency scope, the depth of each matched filter cannot be greater than 102. Due to the number of C/A code half chip in a period is 2046=22*93, the appropriate length of the partial matched filter can be set to 93.Thus the number of FFT points is 22c. However, the matched filter number in the new PMF-FFT scheme can be reduced to 11c due to its double acquisition range. What’s more, according to the general knowledge in frequency analysis, the frequency resolution of FFT ѐf=1/tlen is determined by the input data length tlen. If the estimation error of the Doppler frequency is expected to be less than 1 KHz, the sampling time should be no shorter than 1 ms, i.e. a C/A code cycle, at least. In term of the acquisition time, considering the acquisition occurs on a single channel, in general, the processing data length is set to 1ms within the searched Doppler frequency range [-10 KHz, +10 KHz]. In the PMF_FFT acquisition discussed above, the depth of the shift register is set to 2046, with half C/A code chip sampling interval in a C/A code period, and 1ms received signal can be stored so that the code phase can be completely searched simultaneously. When the system is processing, the receiving IF signal glides across the local C/A code registers with half a chip every time, and the partial matched filters produce P PMF results, followed by an N point FFT. Thus the acquisition time for each satellite is 1ms in ideal condition. The first satellite acquisition needs 2ms if we consider the system reset time because the signals fill the shift registers by 1ms. Each other acquisition can be completed within another 1ms. Certainly, when it is

implemented, some other time should be consumed to run the system or to enhance the detection probability, such as incoherent integration of the results. 4. Simulation and test results 4.1 Simulation results Simulation and test with live GPS data on MATLAB aim to validate the superiority of the new PMF-FFT search scheme. First, a simulation is done to compare the performance of the new PMF-FFT scheme with that of the traditional one. In the simulation, parameters are set according to the analysis in 3.3; the processed signal is 1ms with 8 KHz Doppler and CN0 45 dB-Hz. As shown in Fig. 10, the traditional scheme can’t acquire the signal due to the range is restricted in [-4 KHz, 4 KHz] while the new scheme could search the frequency as shown in Fig. 11.

Fig.9 Acquisition results with traditional PMF-FFT

Fig.10 Acquisition results with new PMF-FFT

Besides, compared to the serial acquisition and two other parallel search methods described in Section I, tests with live GPS data on MATLAB aim to validate the acquisition time superiority of the new PMF-FFT search scheme. The signal is sampled by a rate fs = 16.3676 MHz at an intermediate frequency 4.1304 MHz. In the simulation, the signal is searched from the 22nd millisecond and coherent integral time in each method is set to 1ms, without additional incoherent integration time. Normally, the expected accuracy of the acquisition for the phase estimation is within 0.5 chip and the Doppler frequency estimation is within 1 KHz. To make the statistical time has nothing to do with the processing platform, in this paper, the capture time cell is designated as the sampling interval. The mean acquisition time is presented in Table 1, compared to the other three methods. It is worthwhile to note that, all the schemes scan all the 2046 half code phases of 32 satellites in the test. The traditional three schemes search frequency scope [-11 KHz, 10 KHz] with the frequency step 1 KHz while the new method with 11/16 KHz. In addition, to be more clearly, only one channel is adopted in the serial scheme. And multiple channels generally using in actual projects will be tested in the next section by hardware platform. As shown in the table 1, the consumed time is agreement with the theoretical value. Therefore, the new scheme outperforms the other methods greatly. Table 1 Mean acquisition time comparison of the four schemes Methods

Serial scheme

Parallel frequency

Parallel code

New PMF-FFT

Time/s

1136.534

56.488

0.524

0.033

4.2 Hardware receiver test results We have been implemented the hardware GPS receiver based on the new PMF-FFT acquisition scheme on the FPGA&DSP platform. We will present a test of the acquisition module using a real time GPS signal in this section. As shown in Fig.11, we take a transponder for our test platform to connect an outdoor GPS antenna to broadcast the indoor

signal. Thus, a real GPS receiver based on FPGA&DSP can receive and process the GPS signal in the room. It consists of a RF frontend and a baseband processing board.

Fig. 11 Actual connection of test receiver

Fig. 12 Acquisition result by new PMF-FFT scheme

Fig. 12 shows us the result of the new acquisition scheme, validating its practicability. In order to compare the performance of speed, we test the general serial search and new PMF-FFT search scheme separately on the same hardware platform in a cold start condition. Both methods are mainly implemented in the FPGA chip and are controlled by DSP. The serial search scheme uses twelve parallel channels while the PMF-FFT scheme only employs one channel. According to the tests, both methods can capture nine satellite signals successfully. However, only about 0.23s is used in the PMF-FFT scheme. It uses 45s in the serial scheme. Compared to 0.033s in time analysis, the reason for consuming more time in the test is that it consists of communication time between DSP and FPGA, local code generated and written time, as well as other hardware consumption such as multipliers and adders. With the comparison of statistic time above, it can be concluded that the new PMF-FFT scheme in the hardware platform can capture the signal with a far less time than serial scheme generally used in the receiver. 5 Conclusions We propose the new partial matched filter based on FFT acquisition method in GNSS receiver, aiming to meet the requirements of high dynamic and weak signal environments. Through analyzing the performance of the new scheme, we found that both analysis and simulation results prove that the new scheme owns a larger acquisition range without adding the FFT points. What’s more, compared to the other three acquisition methods generally known, we test the speed performance on the Matlab simulation platform with the collected GPS IF data. The results turns out that the new PMF-FFT scheme is superior to the other methods distinctly. Besides, compared to the serial scheme, we implement a FPGA&DSP hardware platform, verifying the practicability and superiority of the new scheme. 6 Acknowledgements This work was supported by the National Key Research and Development Program of China (2016YFB0501804), the National High Technology Research and Development Program (863 Program) major project (2013AA12A206, 2013AA12A204), the National Natural Science Foundation of China (41604021, 61273053), and National Natural Science Foundation of Hubei province (2014CFB727). References [1]

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