Track-Before-Detect Bearings-Only Localization Performance in Complex Passive Sonar Scenarios: A Case Study

Northardt, Thomas; Nardone, S.

doi:10.1109/joe.2018.2811419

Cited by 37 publications

(7 citation statements)

References 12 publications

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“…For lower complexity, PF samples the state-space to estimate the probability density function for target reflections [11]. Tracking of an underwater moving source by PF is demonstrated in [22], while [23] demonstrated a PF-based TkBD algorithm on a multi-target passive sonar scenario. In turn, the PMHT approach [24] models a combination of target and noise using the expectation maximization (EM) algorithm, and estimates the target's path by a Kalman smoother.…”

Section: Related Workmentioning

confidence: 99%

Sonar Point Cloud Processing to Identify Sea Turtles by Pattern Analysis

Kipnis¹,

Levy²,

Diamant³

2022

Preprint

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Abundant in coastal areas, sea turtles are affected by high-intensity acoustic anthropogenic sounds. In this paper, we offer a pattern analysis-based detection approach to serve as a warning system for the existence of nearby sea turtles. We focus on the challenge of overcoming the low signal-to-clutter ratio (SCR) caused by reverberations. Assuming that, due to low SCR, target reflections within the point cloud are received in groups, our detector searches for patterns through clustering to identify possible 'blobs' in the point cloud of reflections, and to classify them as either clutter or a target. Our unsupervised clustering is based on geometrical and spectral constraints over the blob's member relations. In turn, the classification of identified blobs as either a target or clutter is based on features extracted from the reflection pattern. To this end, assuming reflections from a sea turtle are stable but include spectral diversity due to distortions within the turtles body, we quantify the stability of the blob's members and their spectral entropy. We test our detector in both modeled simulations, and at sea, for the detection of sea turtles released after rehabilitation. The results show robustness to highly-fluctuating target intensity and ability to detect at low SCR.

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Section: Related Workmentioning

confidence: 99%

Sonar Point Cloud Processing to Identify Sea Turtles by Pattern Analysis

Kipnis¹,

Levy²,

Diamant³

2022

Preprint

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“…Passive acoustic localization has attracted significant attention for its multitudinous applications in localization, navigation, surveillance and other fields. However, it is a challenging problem due to the nonlinearity of the measurements, low signal-to-noise ratio (SNR), high false alarm, and high miss detection rate [1][2]. Traditional localization can be achieved by utilizing the estimated parameters related to the position of the target, such as time difference of arrival (TDOA), angle of arrival (AOA), frequency difference of arrival (FDOA), and the strength of the received signal [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

A Track-before-Detect Strategy for Multi-sensor Passive Acoustic Localization

Wang

et al. 2023

J. Phys.: Conf. Ser.

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This paper focuses on the multi-sensor passive acoustic localization problem. The traditional solution for the problem is to estimate the parameter of the received signal, such as the time difference of arrival (TDOA). Then the position of the target is solved by the estimated parameter. This solution is suboptimal because estimations are carried out at each sensor separately, and the fact that the received signals of different sensors originate from the same target is ignored. In this paper, we propose a track-before-detect (TBD) method. The proposed method adopts a particle filter that defines its likelihood function as the product of the output of the cross-correlation (CC) between the signal of different sensors conditioned on the particle’s state. This likelihood function is designed to ensure that the processing gain of multi-sensors can be fully obtained. Moreover, the proposed method circumvents the challenging measurement-to-track association problem faced by the method based on the estimated parameter.

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“…Therefore, the passive tracking process sometimes is called the bearing-only tracking in a 2D scenario and angle-only tracking in a 3D scenario. The observability analyses are comprehensively studied by several researchers [ 9 , 10 , 11 ], and the conclusion is made that the angle-only measurements by a single static observer is not sufficient to guarantee the system to be fully observable. In order to make a tracking system observable to satisfy the robustness requirements of the passive tracking system, the single observer must maneuver with much more agility than the target or more observers are needed.…”

Section: Introductionmentioning

confidence: 99%

3D Underwater Uncooperative Target Tracking for a Time-Varying Non-Gaussian Environment by Distributed Passive Underwater Buoys

Hou

Zhou

Yang

et al. 2021

Entropy

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Accurate 3D passive tracking of an underwater uncooperative target is of great significance to make use of the sea resources as well as to ensure the safety of our maritime areas. In this paper, a 3D passive underwater uncooperative target tracking problem for a time-varying non-Gaussian environment is studied. Aiming to overcome the low observability drawback inherent in the passive target tracking problem, a distributed passive underwater buoys observing system is considered and the optimal topology of the distributed measurement system is designed based on the nonlinear system observability analysis theory and the Cramer–Rao lower bound (CRLB) analysis method. Then, considering the unknown underwater environment will lead to time-varying non-Gaussian disturbances for both the target’s dynamics and the measurements, the robust optimal nonlinear estimator, namely the adaptive particle filter (APF), is proposed. Based on the Bayesian posterior probability and Monte Carlo techniques, the proposed algorithm utilizes the real-time optimal estimation technique to calculate the complex noise online and tackle the underwater uncooperative target tracking problem. Finally, the proposed algorithm is tested by simulated data and comprehensive comparisons along with detailed discussions that are made to demonstrate the effectiveness of the proposed APF.

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Track-Before-Detect Bearings-Only Localization Performance in Complex Passive Sonar Scenarios: A Case Study

Cited by 37 publications

References 12 publications

Sonar Point Cloud Processing to Identify Sea Turtles by Pattern Analysis

Sonar Point Cloud Processing to Identify Sea Turtles by Pattern Analysis

A Track-before-Detect Strategy for Multi-sensor Passive Acoustic Localization

3D Underwater Uncooperative Target Tracking for a Time-Varying Non-Gaussian Environment by Distributed Passive Underwater Buoys

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