&lt;title&gt;Statistical analysis of sonar data for target detection&lt;/title&gt;

Robertson, Candace J.

doi:10.1117/12.241216

Cited by 4 publications

(5 citation statements)

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“…Each ping, representing one second in time, encompasses 3 along track out to a range to port and starboard and to the ocean surface and bottom {1]- [3]. A run typically has 800 to 900 pings.…”

Section: Multi-channel Processingmentioning

confidence: 99%

“…The circular receive array consists of 1 20 contiguous stave elements which circumnavigate the diameter of the towbody. The toroidal volume search sonar ( TVSS ) system has demonstrated an excellent detection capability in low reverberant environments [1]- [3]. However, of special interest is the very high reverberation cases where conventional signal processing performs very poorly.…”

Section: Introductionmentioning

confidence: 99%

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<title>Target detection from TVSS shallow-water data using a multichannel subband adaptive filtering scheme</title>

et al. 1997

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In processing the Toroidal Volume Search Sonar ( TVSS ) data, the primary challenge is to detect targets in highly reverberant environments. This may be done by processing each sonar channel separately and estimating the time delays associated with the specular returns and then isolating these components from the background noise prior to beamforming. However, if there is significant reverberation it is difficult to accurately estimate time delays using only one sonar channel. Therefore, a multi-channel method is introduced that exploits the interdependency ofadjacent sonar stave data. The method starts by enhancing signal to noise by combining multiple stave data after suitable phase compensation for array geometry. Next, time delays of the specular returns are estimated from the discrete wavelet sub-bands of the combined signal using a least squares method. The new method has been tested on several TVSS data sets, and the results are promising for even the most reverberant cases.

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Section: Multi-channel Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

<title>Target detection from TVSS shallow-water data using a multichannel subband adaptive filtering scheme</title>

et al. 1997

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“…Generally, to cover a large area, TVSS needs to collect 800-900 pings of data per run [22]. This workload is beyond the capabilities of human operators.…”

Section: High-order Correlation For Postprocessingmentioning

confidence: 99%

“…It will alleviate the burden of sonar operator, help to provide a high target detection rate, and reduce the incident of false reports. In [22], statistical measures, i.e., mean, standard deviation, skewness, and kurtosis were calculated from the digitized beamformed sonar data. The combination of these statistical measures provides an additional clue about the presence of a target versus background.…”

mentioning

confidence: 99%

Underwater target detection using multichannel subband adaptive filtering and high-order correlation schemes

Yuan

Azimi-Sadjadi²,

Wilbur³

et al. 2000

IEEE J. Oceanic Eng.

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Abstract-In this paper, new pre-and post-processing schemes are developed to process shallow-water sonar data to improve the accuracy of target detection. A multichannel subband adaptive filtering is applied to preprocess the data in order to isolate the potential target returns from the acoustic backscattered signals and improve the signal-to-reverberation ratio. This is done by estimating the time delays associated with the reflections in different subbands. The preprocessed results are then beamformed to generate an image for each ping of the sonar. The testing results on both the simulated and real data revealed the efficiency of this scheme in time-delay estimation and its capability in removing most of the competing reverberations and noise. To improve detection rate while significantly minimizing the incident of false detections, a high-order correlation (HOC) method for postprocessing the beamformed images is then developed. This method determines the consistency in occurrence of the target returns in several consecutive pings. The application of the HOC process to the real beamformed sonar data showed the ability of this method for removing the clutter and at the same time boosting the target returns in several consecutive pings. The algorithm is simple, fast, and easy to implement.

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“…This makes identification and navigation with over-simplified physical models unrealistic. Statistical approaches are therefore highly desirable in order to study sonar responses to targets, and extract useful information about the environment (Robertson, 1996; Vicente Martinez Diaz, n.d.). …”

Section: Introductionmentioning

confidence: 99%

A Unified Analysis of Structured Sonar-Terrain Data Using Bayesian Functional Mixed Models

et al. 2017

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Sonar emits pulses of sound and uses the reflected echoes to gain information about target objects. It offers a low cost, complementary sensing modality for small robotic platforms. While existing analytical approaches often assume independence across echoes, real sonar data can have more complicated structures due to device setup or experimental design. In this paper, we consider sonar echo data collected from multiple terrain substrates with a dual-channel sonar head. Our goals are to identify the differential sonar responses to terrains and study the effectiveness of this dual-channel design in discriminating targets. We describe a unified analytical framework that achieves these goals rigorously, simultaneously, and automatically. The analysis was done by treating the echo envelope signals as functional responses and the terrain/channel information as covariates in a functional regression setting. We adopt functional mixed models that facilitate the estimation of terrain and channel effects while capturing the complex hierarchical structure in data. This unified analytical framework incorporates both Gaussian models and robust models. We fit the models using a full Bayesian approach, which enables us to perform multiple inferential tasks under the same modeling framework, including selecting models, estimating the effects of interest, identifying significant local regions, discriminating terrain types, and describing the discriminatory power of local regions. Our analysis of the sonar-terrain data identifies time regions that reflect differential sonar responses to terrains. The discriminant analysis suggests that a multi- or dual-channel design achieves target identification performance comparable with or better than a single-channel design.

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<title>Statistical analysis of sonar data for target detection</title>

Cited by 4 publications

References 0 publications

<title>Target detection from TVSS shallow-water data using a multichannel subband adaptive filtering scheme</title>

<title>Target detection from TVSS shallow-water data using a multichannel subband adaptive filtering scheme</title>

Underwater target detection using multichannel subband adaptive filtering and high-order correlation schemes

A Unified Analysis of Structured Sonar-Terrain Data Using Bayesian Functional Mixed Models

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