Interference Mitigation for the GPS Receiver Utilizing the Cyclic Spectral Analysis and RR-MSWF Algorithm

Dang, Xiaoyu; Yan, Hong-Sen; Hu, Yong; Song, Maozhong

doi:10.13164/re.2017.0798

Cited by 6 publications

(7 citation statements)

References 24 publications

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“…Substitute (16) and 17into (15), and take D = N, we can get the proposed FACA method as ( ; , ) 1 ( , ) ( ; , )…”

Section: Derivation Of the Faca Methodsmentioning

confidence: 99%

“…where s(t) is the SOI, w(t) is the interfering signal including the noise. For simplicity, we assume that w(t) is a weak stationary signal and not cyclic correlated with s(t) in the following analysis, otherwise if w(t) is also cyclostationary and stronger than s(t), we can use the presented FACA method to get the cycle frequencies of w(t) first, and then cancel it from x(t) with the FRESH filtering [16], [17] to get the signal of s(t).…”

Section: Caf and Scfmentioning

confidence: 99%

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Efficient Cycle Frequency Acquisition of a Cyclostationary Signal with the FACA Method

Hu¹,

Yu²,

Deng³

et al. 2019

RADIOENGINEERING

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The cycle frequencies of a cyclostationary signal can be used for the signal identification and classification, separation of the overlapped signals in cycle domain, and so on. Efficient cycle frequency acquisition depends on the fast measurement of cyclic autocorrelation function (CAF) or spectral correlation function (SCF) of the signal. Presently the relative efficient CAF and SCF measuring methods mainly include the cyclic correlogram, the well-known fast Fourier transform accumulation method (FAM), and so on. Motivated by these methods, a new efficient cycle frequency acquisition method which integrates the fast Fourier transform (FFT) algorithm with the autocorrelated cyclic autocorrelation function, named FACA, is presented. With the presented method, we can acquire the cycle frequencies of a cyclostationary signal more efficiently with a given level of reliability. Meanwhile, by enlarging the FFT window width of the FACA method we can get the same cycle frequency resolution as the benchmarked method FAM, but the computation cost still can be spared at this case.

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“…Substitute (16) and 17into (15), and take D = N, we can get the proposed FACA method as ( ; , ) 1 ( , ) ( ; , )…”

Section: Derivation Of the Faca Methodsmentioning

confidence: 99%

Section: Caf and Scfmentioning

confidence: 99%

Efficient Cycle Frequency Acquisition of a Cyclostationary Signal with the FACA Method

Hu¹,

Yu²,

Deng³

et al. 2019

RADIOENGINEERING

Self Cite

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“…at is to say, the 14 frequencies can support the usage of 28 satellites. Multipath propagation phenomenon is inevitable in the wireless communication system because of the radio signal reflection or refraction [32,33]. e GLONASS terminal receives the satellite signal with two or more paths including the line-of-sight component.…”

Section: Glonass Signal and Acquisitionmentioning

confidence: 99%

Sparse GLONASS Signal Acquisition Based on Compressive Sensing and Multiple Measurement Vectors

Song

Zhang

et al. 2020

Mathematical Problems in Engineering

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A sparse global navigation satellite system (GLONASS) signal acquisition method based on compressive sensing and multiple measurement vectors is proposed. The nonsparse GLONASS signal can be represented sparsely on our proposed dictionary which is designed based on the signal feature. Then, the GLONASS signal is sensed by a normalized orthogonal random matrix and acquired by the improved multiple measurement vectors acquisition algorithm. There are 10 cycles of pseudorandom codes in a navigation message, and these 10 pseudorandom codes have the same row sparse structure. So, the acquisition probability can be raised by row sparse features theoretically. A large number of simulated GLONASS signal experiments show that the acquisition probability increases with the increase in the measurement vector column dimension. Finally, the practical availability of the new method is verified by acquisition experiments with the real record GLONASS signal. The new method can reduce the storage space and energy loss of data transmission. We hope that the new method can be applied to field receivers that need to record and transmit navigation data for a long time.

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“…Multipath disturbance affect the measuring of propagation time, is a major error that limits the accuracy of GPS precise positioning. Some techniques have been proposed for mitigating the multipath error including narrow correlators [21], multipath estimating delay lock loop [22] and reduced rank multistage Wiener filtering algorithm [23], etc. In this paper, we first examine the GPS signal which takes the multipath into account, however, how to mitigate the multipath components is out the scope of this paper.…”

Section: Gps Signalmentioning

confidence: 99%

GPS Signal Acquisition Based on Compressive Sensing and Modified Greedy Acquisition Algorithm

Song

et al. 2019

IEEE Access

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The first task of GPS receiver is to acquire the satellite signal, which tests the all likely 2-D hypotheses of the code phase and the Doppler frequency of the incoming GPS signal. Compressive sensing is a new signal sampling theory, for sparse signal, the signal can be sampled at a lower sampling rate than Nyquist sampling theory. In this paper, a new GPS signal acquisition method is proposed based on compressive sensing. We designed a GPS signal sparse representation dictionary according to the satellite; the position of sparse spike corresponds to the code phase and Doppler frequency. The modified greedy acquisition algorithm that is different from the compressive sensing signal reconstruction, which is used to acquire the GPS satellite. The numerical simulations illustrate that the new method can efficiently acquire the satellite with artificial or real GPS signal. The new method can compress the GPS signal greatly and reduce the transmission energy consumption accordingly, which is hoped to be applied to aircraft or animal monitoring that have little interference noise and need to record GPS data for a long time.

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Interference Mitigation for the GPS Receiver Utilizing the Cyclic Spectral Analysis and RR-MSWF Algorithm

Cited by 6 publications

References 24 publications

Efficient Cycle Frequency Acquisition of a Cyclostationary Signal with the FACA Method

Efficient Cycle Frequency Acquisition of a Cyclostationary Signal with the FACA Method

Sparse GLONASS Signal Acquisition Based on Compressive Sensing and Multiple Measurement Vectors

GPS Signal Acquisition Based on Compressive Sensing and Modified Greedy Acquisition Algorithm

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