A low complexity parameter estimation algorithm of LFM signals

Zhu Lin Xiong; Ce Lun Liu; Jian Ping An

doi:10.11999/JEIT140166

A low complexity parameter estimation algorithm of LFM signals

Zhu Lin Xiong, Ce Lun Liu^*, Jian Ping An

^*Corresponding author for this work

School of Cyberspace Science and Technology

Beijing Institute of Technology

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Abstract

A quadratic estimation algorithm is proposed to reduce the complexity of accurate Linear Frequency Modulation (LFM) parameter estimation. First, the frequency rate and initial frequency are estimated coarsely by short time coherent integral and incoherent accumulation. Then, the parallel Partial Matched Filters combined with FFT (PMF-FFT) and quadratic interpolation are utilized to estimate the residuals of the frequency rate and initial frequency. Last, the final estimated values are obtained by synthesizing the results of both estimations. Simulation shows that the proposed algorithm has a low SNR threshold, and the accuracy is close to Cramer-Rao Lower Bound (CRLB). The complexity and hardware consumption of the proposed algorithm are much less than the frequency rate test algorithm and joint estimation algorithm based on interpolation.

Original language	English
Pages (from-to)	489-493
Number of pages	5
Journal	Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology
Volume	37
Issue number	2
DOIs	https://doi.org/10.11999/JEIT140166
Publication status	Published - 1 Feb 2015

Keywords

Cramer-Rao Lower Bound (CRLB)
Linear Frequency Modulation (LFM)
Low complexity
Quadratic estimation
Signal proessing

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title = "A low complexity parameter estimation algorithm of LFM signals",

abstract = "A quadratic estimation algorithm is proposed to reduce the complexity of accurate Linear Frequency Modulation (LFM) parameter estimation. First, the frequency rate and initial frequency are estimated coarsely by short time coherent integral and incoherent accumulation. Then, the parallel Partial Matched Filters combined with FFT (PMF-FFT) and quadratic interpolation are utilized to estimate the residuals of the frequency rate and initial frequency. Last, the final estimated values are obtained by synthesizing the results of both estimations. Simulation shows that the proposed algorithm has a low SNR threshold, and the accuracy is close to Cramer-Rao Lower Bound (CRLB). The complexity and hardware consumption of the proposed algorithm are much less than the frequency rate test algorithm and joint estimation algorithm based on interpolation.",

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T1 - A low complexity parameter estimation algorithm of LFM signals

AU - Xiong, Zhu Lin

AU - Liu, Ce Lun

AU - An, Jian Ping

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N2 - A quadratic estimation algorithm is proposed to reduce the complexity of accurate Linear Frequency Modulation (LFM) parameter estimation. First, the frequency rate and initial frequency are estimated coarsely by short time coherent integral and incoherent accumulation. Then, the parallel Partial Matched Filters combined with FFT (PMF-FFT) and quadratic interpolation are utilized to estimate the residuals of the frequency rate and initial frequency. Last, the final estimated values are obtained by synthesizing the results of both estimations. Simulation shows that the proposed algorithm has a low SNR threshold, and the accuracy is close to Cramer-Rao Lower Bound (CRLB). The complexity and hardware consumption of the proposed algorithm are much less than the frequency rate test algorithm and joint estimation algorithm based on interpolation.

AB - A quadratic estimation algorithm is proposed to reduce the complexity of accurate Linear Frequency Modulation (LFM) parameter estimation. First, the frequency rate and initial frequency are estimated coarsely by short time coherent integral and incoherent accumulation. Then, the parallel Partial Matched Filters combined with FFT (PMF-FFT) and quadratic interpolation are utilized to estimate the residuals of the frequency rate and initial frequency. Last, the final estimated values are obtained by synthesizing the results of both estimations. Simulation shows that the proposed algorithm has a low SNR threshold, and the accuracy is close to Cramer-Rao Lower Bound (CRLB). The complexity and hardware consumption of the proposed algorithm are much less than the frequency rate test algorithm and joint estimation algorithm based on interpolation.

KW - Cramer-Rao Lower Bound (CRLB)

KW - Linear Frequency Modulation (LFM)

KW - Low complexity

KW - Quadratic estimation

KW - Signal proessing

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A low complexity parameter estimation algorithm of LFM signals

Abstract

Keywords

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