Fast imaging algorithm for the multiple receiver synthetic aperture sonars

Zhang, Xuebo; Dai, Xuntao; Yang, Bo‐Suk

doi:10.1049/iet-rsn.2018.5040

Cited by 21 publications

(11 citation statements)

References 22 publications

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“…The Class A noise Viterbi decoder can be established by Equation (13). The algorithm equation can be depicted as soft decision based on Class A noise PDF simplified model, as shown in Equation (18). The bit error rate formula under the influence of Class A noise can be obtained as…”

Section: Performance Of Viterbi Decoder Under Class a Noisementioning

confidence: 99%

“…The noise distribution [1,2] plays an important role in developing underwater signal processors. Traditional signal processors such as underwater localization [3][4][5][6][7][8][9][10][11][12][13], underwater tracking [9,14,15], sonar imaging [16][17][18][19][20][21][22][23][24][25][26][27], direction of arrival (DOA) estimation [28][29][30][31][32], and underwater acoustic communication (UAC) are mostly based on Gaussian noise, which can be supported by a central limit theorem. Besides, the Gaussian model is just determined by the first-order and second-order statistics [33].…”

Section: Introductionmentioning

confidence: 99%

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A Viterbi Decoder under Class A Modeled Noise in Shallow Water

Wang

Fan

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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A traditional Viterbi decoder is primarily optimized for additive white Gaussian noise (AWGN). With the AWGN channel, it offers good decoding performance. However, the underwater acoustic communication (UAC) channel is extremely complicated. In addition to white noise, there are a variety of artificial and natural impulse noise that occur suddenly. The traditional Viterbi decoder cannot obtain the optimum performance under this case. In order to solve this problem, this paper introduces a novel Viterbi decoder with the impulsive noise, which is considered to be subjected to Middleton Class A distribution in shallow ocean. Since Middleton Class A noise is very complicated, a simplified model is first introduced. Then, the error analysis of simplified model under various parameters is discussed in detail. The analysis shows that the simplified one just leads to slight error. Hereafter, a novel Veterbi decoder using the simplified model is discussed. Compared to a traditional decoder, a preprocessing is just required. The performance of soft decision-based decoder in the Middleton Class A noise channel (MAIN) and AWGN are further compared. Based on our simulations, the new decoder can significantly improve the performance in comparison with conventional one, which further validates our presented method.

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Section: Performance Of Viterbi Decoder Under Class a Noisementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Viterbi Decoder under Class A Modeled Noise in Shallow Water

Wang

Fan

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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“…Compared to traditional sidescan sonar or multibeam sonar, multireceiver synthetic aperture sonar (SAS) [1][2][3] exploiting a single transmitter and a receiver array brings us highresolution images across the wide-swath scenario. Thanks to its high resolution, SAS has been widely applied to target recognition [4], seabed mapping [5] and marine archaeological surveying [6].…”

Section: Introductionmentioning

confidence: 99%

“…Synthetic aperture image formation is an inverse process of echo generation, and the transfer function also named point target reference spectrum (PTRS) is a key of frequency-based imaging algorithms [5]. To analytically deduce the PTRS, some approximations such as phase centre approximation (PCA) [2] and Loffeld's bistatic formula (LBF) [7][8][9][10] are preferred, as slight approximation error is generated.…”

Section: Introductionmentioning

confidence: 99%

Wide‐bandwidth signal‐based multireceiver SAS imagery using extended chirp scaling algorithm

Zhang

Yang²,

Huang

et al. 2021

IET Radar Sonar & Navi

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Traditional chirp scaling algorithm is often developed based on the quadratic approximation of point target reference spectrum, which neglects the higher-order coupling between range and azimuth dimensions. Besides, the scaling operation is conducted based on quadratic term of reference range, and the residual quadratic error increases with target range far away from reference range. The analysis of phase error shows that both errors are noticeable under the case of wide-bandwidth signal, and the imaging performance would be seriously distorted. To solve this problem, the higher-order coupling and quadratic error are compensated by exploiting sub-block processing method in the range dimension when the chirp scaling algorithm is exploited to reconstruct targets. Compared to traditional methods, the presented method significantly improves the focussing performance based on the simulation results. The processing of real data further shows that the performance of presented method is superior to that of traditional method.

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“…In general, these methods can be grouped into two classes including the frequency‐domain method and time‐domain method. In order to satisfy the requirements of the real‐time processing, the frequency‐domain methods [8–12] are more preferred by most users as these methods are fast. However, these methods often need the approximation of the point target reference spectrum (PTRS), because the accurate PTRS cannot be obtained analytically.…”

Section: Introductionmentioning

confidence: 99%