MIMO radar range-angular-doppler sidelobe suppression using random space-time coding

Zhou, Shenghua; Liu, Hongwei; Wang, Xu; Cao, Yunhe

doi:10.1109/taes.2013.120681

Cited by 24 publications

(14 citation statements)

References 21 publications

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“…The question is how to estimate the generalized target reflectivity density function in Equation (11). This is a deconvolution process with the prior knowledge including the sampling cross-ambiguity function χ c (τ) and the auto-ambigity function χ(τ) of the transmitted signal.…”

Section: Iterative Deconvolution-time Reversal Methodsmentioning

confidence: 99%

“…In this paper, this situation can be met as shown in Equation (11) and then the iterative deconvolution is achieved using Equation (18). This is a similar procedure as iterative time reversal processing.…”

Section: Iterative Deconvolution-time Reversal Methodsmentioning

confidence: 99%

“…Therefore, the capability of noise reduction, range sidelobe level and range resolution are the three key issues considered by active radar/sonar. The usual way to solve the problems of high sidelobe level or low resolution is radar/sonar waveform design [5][6][7][8][9][10][11]. Generally speaking, different waveforms have different resolution performances.…”

Section: Introductionmentioning

confidence: 99%

“…After years of research, great progress has been made in MIMO radar waveform design. For the current state of art, sidelobe suppression merely by means of waveform design seems to achieve a limit [11]. Therefore, other approaches, such as the coherent CLEAN algorithm [12] and coherence factor filtering [13,14] have been reported to suppress the sidelobe.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the processing gain to combat noise and the peak-to-sidelobe level are greatly improved. Instead of transmitting multiple waveforms [5][6][7][8][9][10][11][12][13][14][15][16], the transmitted signal is a single pulse of LFM in the paper.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

An Iterative Deconvolution-Time Reversal Method with Noise Reduction, a High Resolution and Sidelobe Suppression for Active Sonar in Shallow Water Environments

Guo

Zhao

2020

Sensors

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Matched filtering is widely used in active sonar because of its simplicity and ease of implementation. However, the resolution performance generally depends on the transmitted waveform. Moreover, its detection performance is limited by the high-level sidelobes and seriously degraded in a shallow water environment due to time spread induced by multipath propagation. This paper proposed a method named iterative deconvolution-time reversal (ID-TR), on which the energy of the cross-ambiguity function is modeled, as a convolution of the energy of the auto-ambiguity function of the transmitted signal with the generalized target reflectivity density. Similarly, the generalized target reflectivity density is a convolution of the spread function of channel with the reflectivity density of target as well. The ambiguity caused by the transmitted signal and the spread function of channel are removed by Richardson-Lucy iterative deconvolution and the time reversal processing, respectively. Moreover, this is a special case of the Richardson-Lucy algorithm that the blur function is one-dimensional and time-invariant. Therefore, the iteration deconvolution is actually implemented by the iterative temporal time reversal processing. Due to the iterative time reversal method can focus more and more energy on the strongest target with the iterative number increasing and then the peak-signal power increases, the simulated result shows that the noise reduction can achieve 250 dB in the “ideal” free field environment and 100 dB in a strong multipaths waveguide environment if a 1-ms linear frequency modulation with a 4-kHz frequency bandwidth is transmitted and the number of iteration is 10. Moreover, the range resolution is approximately a delta function. The results of the experiment in a tank show that the noise level is suppressed by more than 70 dB and the reverberation level is suppressed by 3 dB in the case of a single target and the iteration number being 8.

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Section: Iterative Deconvolution-time Reversal Methodsmentioning

confidence: 99%

Section: Iterative Deconvolution-time Reversal Methodsmentioning

confidence: 99%