Target Motion Parameter Estimation for LOFARgrams Based on Waveguide Invariants

Li, Jian; Han, Guangjie; Zhou, Defu; Tang, Kai; Han, Qingbang

doi:10.1007/978-981-10-2422-1_12

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…First, we employed the lofargram [28, 29] to qualitatively evaluate the performance of the three ALEs for passive sonars. A 2 s short‐time Fourier transformation (STFT) with the overlapping length of 1 s was used to generate the lofargrams.…”

Section: Performance With Simulationsmentioning

confidence: 99%

Sparsity‐based adaptive line enhancer for passive sonars

Chi

Qiu

et al. 2019

IET Radar, Sonar & Navigation

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Section: Performance With Simulationsmentioning

confidence: 99%

Sparsity‐based adaptive line enhancer for passive sonars

Chi

Qiu

et al. 2019

IET Radar, Sonar & Navigation

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“…Low frequency analysis recording (LOFAR) is a classic passive sonar signal processing method in which the tones are detected in a time-frequency spectrogram (i.e., the “lofargram”) [ 17 , 18 ]. The lofargram is obtained by the short-time Fourier transform (STFT) of the received signal.…”

Section: Multi-beam Lofargrammentioning

confidence: 99%

A Broadband Beamformer Suitable for UUV to Detect the Tones Radiated from Marine Vessels

Wang

Zou

et al. 2018

Sensors

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In this paper, the issue that the underwater unmanned vehicle (UUV) with a sonar array achieves the passive detection of vessel targets by detecting the tones radiated from the targets is considered. The multi-beam low-frequency analysis and recording method is widely applied in a manned sonar system. The sonar operator provides an auxiliary decision to extract the target tones from the multiple beams output. However, the complexity of the multi-dimensional information fusion makes it difficult to apply the multi-beam processing in the unmanned sonar system. Aiming at this problem, we introduce the self-adjusting characteristics of adaptive line enhancer to a time domain broadband beamformer and then propose a self-steering broadband beamformer. The proposed beamformer can adaptively steer the main beam to the direction-of-arrival (DOA) of the tonal target. There is no need to pre-form the multiple beams. The complexity of the UUV-based tone detection is reduced. Theoretical derivation and simulation experiments verify that the main beam of the proposed beamformer can track the DOA of tonal target which is rapidly changing. Meanwhile, the tonal interferences as well as the wide-band noise are well suppressed.

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“…Lourens [11] used software that was written to place markers on the patterns and fitted the exact curve through the markers, which was manual and might introduce a subjective element. Works [4,12]- [14] estimated the parameters of parabolas and extracted the striations utilizing the Hough transform which is an image processing technique. Nevertheless, when the signals received near the CPA present wider interference striations on lofargram and the signal-to-noise ratio (SNR) of the image is low provoked by the huge underwater noise environment, Hough transform may no longer be applicable.…”

Section: Introductionmentioning

confidence: 99%

A method for extracting interference striations in lofargram based on decomposition and clustering

Wang

Tian

et al. 2023

IET Image Processing

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In complex underwater noise environment, target detection, recognition, and tracking are proceeded through the frequency spectrum analysis of the signals received by sonar systems, in which the lofargram plays a major role. Typically, if the distance of the target experiences far‐near‐far variation, there will be parabolic interference striations presented in the lofargram. However, the existing striations extraction methods sometimes lack objectivity and fail to extract the wide striations accurately. In this paper, a novel method for wide type interference striations extraction is developed based on efficient decomposition and ensemble clustering. To obtain valuable information, the lofargram is decomposed into smooth background, striations area, and noise, then a multi‐phase ensemble clustering algorithm is employed to extract parabolas from the decomposed striations area pixels. Experimental results on simulated and real‐life datasets exhibit the effectiveness of the proposed method, and verify that it has comparable performance with prevalent Hough transform while extracting striations.

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Target Motion Parameter Estimation for LOFARgrams Based on Waveguide Invariants

Cited by 3 publications

References 4 publications

Sparsity‐based adaptive line enhancer for passive sonars

Sparsity‐based adaptive line enhancer for passive sonars

A Broadband Beamformer Suitable for UUV to Detect the Tones Radiated from Marine Vessels

A method for extracting interference striations in lofargram based on decomposition and clustering

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