Adaptive Matched Field Processing for Source Localization Using Improved Diagonal Loading Algorithm

Tran, Phu Ninh; Trinh, Khanh Dang

doi:10.1007/s40857-017-0089-4

Cited by 6 publications

(6 citation statements)

References 7 publications

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“…In these shallow conditions, the propagation can be represented using a waveguide, and the spatial complexities of the acoustic field are utilized in a MFP algorithm, which is a generalized beam-forming method to localize the active sources as a function of range, depth. Specifically, MFP is a spatial filter that compares the received signal and a replica signal modeled using Green's function in a waveguide [19]. It can be implemented using conventional or adaptive formulations for both narrowband and wideband signal models.…”

Section: Introductionmentioning

confidence: 99%

Underwater Target Localization Using Opportunistic Ship Noise Recorded on a Compact Hydrophone Array

et al. 2021

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In this research, a new application using broadband ship noise as a source-of-opportunity to estimate the scattering field from the underwater targets is reported. For this purpose, a field trial was conducted in collaboration with JASCO Applied Sciences at Duncan’s Cove, Canada in September 2020. A hydrophone array was deployed in the outbound shipping lane at a depth of approximately 71 m to collect broadband noise data from different ship types and effectively localize the underwater targets. In this experiment, a target was installed at a distance (93 m) from the hydrophone array at a depth of 25 m. In this study, a matched field processing (MFP) algorithm is utilized for localization. Different propagation models are presented using Green’s function to generate the replica signal; this includes normal modes in a shallow water waveguide, the Lloyd-mirror pattern for deep water, as well as the image model. We use the MFP algorithm with different types of underwater environment models and a proposed estimator to find the best match between the received signal and the replica signal. Finally, by applying the scatter function on the proposed multi-channel cross correlation coefficient time-frequency localization algorithm, the location of target is detected.

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Section: Introductionmentioning

confidence: 99%

Underwater Target Localization Using Opportunistic Ship Noise Recorded on a Compact Hydrophone Array

et al. 2021

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“…Generally, there are two types of localization methods; one is Matched Field Processing (MFP) that matches to the environment using a modelbased approach; another is based on time-delay including multiple signal classification (MUSIC), estimation of signal parameters via rational invariance Techniques (ESPRIT), Minimum Variance Distortion Response (MVDR), Root Multiple Signal Classification (ROOT MUSIC) algorithms Multi-channel Cross-Correlation Coefficient (MCCC) that they are reviewed in [1][2][3][4][5] MFP is a special beamforming technique that uses the spatial complexities of acoustic fields in an ocean waveguide to localize the active sources in range and azimuth. Also, it is a spatial filter with a correlation between the received signal and a replica, where the replica is constructed using Green's function for a waveguide [6], [7]. Green's function is used to represent the replica signal based on environmental parameters, and can be modelled using different techniques the most important of which are the normal mode for shallow water waveguide, the Lloyd-Mirror Pattern (LMP), and the Image Method [8].…”

Section: Introductionmentioning

confidence: 99%

Underwater Target Localization using the Generalized Lloyd-Mirror Pattern

Hotkani

Seyedin

Bousquet

2021

mjee

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“…Also, localization algorithms related to DOA, such as MUSIC, ESPRIT, ROOT MUSIC and MVDR are reviewed in [11][12][13]. There are other famous localization methods such as matched field processing (MFP) [14], that are not included in these three categories. The MFP algorithm has commonly been used in shallow underwater conditions for source detection and localization using a hydrophones array.…”

Section: Introductionmentioning

confidence: 99%

Underwater Object Localization using the Spinning Propeller Noise of Ships Based on the Wittekind Model

Hotkani,

Seyedin,

Bousquet

2020

IJEAT

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The purpose of this article is to localize underwater objects based on the noise reflection of the propeller rotation in cavitation mode. In the proposed method, the propeller noise, which plays the role of pings in active sonar, is modeled by the Wittekind method. As such, an echo is continuously received by a vertical and uniform linear hydrophone array due to reflection from the underwater targets. The challenges associated with the underwater channels are simulated by the ocean model in COMSOL. Specifically, to model the propagation of underwater acoustic in this channel, the Helmholtz equation is solved using COMSOL. Finally, localization is performed by comparing the Delay & Sum algorithm and the multiple signal classification (MUSIC) algorithm in MATLAB. According to the simulation results, the proposed method is able to detect the position of the target and the propeller approximately, although the multipath phenomenon causes adverse effects on the results. The narrowband MUSIC algorithm is used in the proposed method at the frequency of the strongest intensity.

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Adaptive Matched Field Processing for Source Localization Using Improved Diagonal Loading Algorithm

Cited by 6 publications

References 7 publications

Underwater Target Localization Using Opportunistic Ship Noise Recorded on a Compact Hydrophone Array

Underwater Target Localization Using Opportunistic Ship Noise Recorded on a Compact Hydrophone Array

Underwater Target Localization using the Generalized Lloyd-Mirror Pattern

Underwater Object Localization using the Spinning Propeller Noise of Ships Based on the Wittekind Model

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