Concepts for Synthetic Aperture Sonar Performance Prediction and Mission Planning

Campbell, Jason; Chang, Enson

doi:10.1109/oceans.2005.1639730

Cited by 2 publications

(5 citation statements)

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“…With the R-D algorithm, secondary range compression (SRC) is conducted to deal with the quadratic coupling between cross-track and along-track dimensions. Inspecting the quadratic coupling in (12), it is a function of range, Doppler frequency, and instantaneous frequency. To simplify the compensation, quadratic coupling at the reference range is often exploited by the traditional R-D algorithm [27].…”

Section: Residual Quadratic Coupling Errormentioning

confidence: 99%

“…Remote Sens. 2021, 13, x FOR PEER REVIEW 12 of 28 the quadratic coupling in (12), it is a function of range, Doppler frequency, and instantaneous frequency. To simplify the compensation, quadratic coupling at the reference range is often exploited by the traditional R-D algorithm [27].…”

Section: Residual Quadratic Coupling Errormentioning

confidence: 99%

“…To compensate for the PTRS error and residual quadratic coupling error, this section discusses an improved multi-receiver SAS R-D algorithm based on ( 9) and (12).…”

Section: Imagerymentioning

confidence: 99%

“…Originating from synthetic aperture radar (SAR) [1][2][3][4][5][6][7][8][9][10], synthetic aperture sonar (SAS) [11][12][13][14][15][16] attracts investigators' interests due to its high resolution in the underwater field. Nowadays, it is widely applied to underwater mapping [14,15,17,18], target recognition [19][20][21][22], and so on.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Imaging Algorithm for Multi-Receiver SAS System with Wide-Bandwidth Signal

Zhang

Yang²

2021

Remote Sensing

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When the multi-receiver synthetic aperture sonar (SAS) works with a wide-bandwidth signal, the performance of the range-Doppler (R-D) algorithm is seriously affected by two approximation errors, i.e., point target reference spectrum (PTRS) error and residual quadratic coupling error. The former is generated by approximating the PTRS with the second-order term in terms of the instantaneous frequency. The latter is caused by neglecting the cross-track variance of secondary range compression (SRC). In order to improve the imaging performance in the case of wide-bandwidth signals, an improved R-D algorithm is proposed in this paper. With our method, the multi-receiver SAS data is first preprocessed based on the phase center approximation (PCA) method, and the monostatic equivalent data are obtained. Then several sub-blocks are generated in the cross-track dimension. Within each sub-block, the PTRS error and residual quadratic coupling error based on the center range of each sub-block are compensated. After this operation, all sub-blocks are coerced into a new signal, which is free of both approximation errors. Consequently, this new data is used as the input of the traditional R-D algorithm. The processing results of simulated data and real data show that the traditional R-D algorithm is just suitable for an SAS system with a narrow-bandwidth signal. The imaging performance would be seriously distorted when it is applied to an SAS system with a wide-bandwidth signal. Based on the presented method, the SAS data in both cases can be well processed. The imaging performance of the presented method is nearly identical to that of the back-projection (BP) algorithm.

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Section: Residual Quadratic Coupling Errormentioning

confidence: 99%

Section: Residual Quadratic Coupling Errormentioning

confidence: 99%

“…To compensate for the PTRS error and residual quadratic coupling error, this section discusses an improved multi-receiver SAS R-D algorithm based on ( 9) and (12).…”

Section: Imagerymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Improved Imaging Algorithm for Multi-Receiver SAS System with Wide-Bandwidth Signal

Zhang

Yang²

2021

Remote Sensing

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“…Ongoing sensor research continues to advance the capabilities for large-area search. For example, synthetic aperture sonar payloads provide increased resolution, allowing for better discernment of underwater munition targets without sacrificing detection range or swath width (Hansen et al, 2003;Campbell and Chang, 2005;Willcox et al, 2009). Chemical detectors, discussed below, also hold promise for increasing the specificity of detection.…”

Section: Auv Sensors For Localization Characterization and Monitoringmentioning

confidence: 99%

AUV Sensors for Real-Time Detection, Localization, Characterization, and Monitoring of Underwater Munitions

Camilli¹,

Bingham²,

Jakuba³

et al. 2009

mar technol soc j

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A B S T R A C TThis technical paper describes existing capabilities and new research results for autonomous underwater vehicles (AUVs) for use in locating, characterizing, and monitoring underwater munitions. The authors introduce advances in sensor technologies and search methods pertinent to AUV-based underwater munition mitigation operations. Results are presented from a series of trials using in situ chemical sensors to detect both conventional and nonconventional underwater munitions in real time. These technologies are considered within the context of chemical plume tracing and biomimetic search algorithms. This paper concludes with a look toward future AUV sensor payloads with more extensive real-time situation awareness. Advancement of these technologies and methods will be critical for realizing the potential of AUV platforms to manage the risks posed by underwater munitions sites.

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Concepts for Synthetic Aperture Sonar Performance Prediction and Mission Planning

Cited by 2 publications

References 1 publication

An Improved Imaging Algorithm for Multi-Receiver SAS System with Wide-Bandwidth Signal

An Improved Imaging Algorithm for Multi-Receiver SAS System with Wide-Bandwidth Signal

AUV Sensors for Real-Time Detection, Localization, Characterization, and Monitoring of Underwater Munitions

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