An Improved Particle Filter Based Track-Before-Detect Method for Underwater Target Bearing Tracking

Jin, Shenglong; Huang, Haining; Ren, Yufei; Wang, Yujie; Zhong, Rongxing

doi:10.1109/oceanse.2019.8867231

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…The particle filter (PF) is a technique for estimating the hidden states of non-linear and/or non-Gaussian systems and is very accurate. As a result, a particle filter-based track-before-detect approach is proposed, in which the particle posterior density distribution is directly estimated using beam-former output energy rather than bearing measurements, avoiding measurement-to-track interaction issues [117]. Regarding the estimation of movable systems from partial observations of internal states, particulary when random disturbances are present in the sensors [118], the gap in the filtering design was resolved.…”

Section: Particle Filter (Pf)mentioning

confidence: 99%

A Survey for Recent Techniques and Algorithms of Geolocation and Target Tracking in Wireless and Satellite Systems

et al. 2021

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A single Radio-Frequency Interference (RFI) is a disturbance source of modern wireless systems depending on Global Navigation Satellite Systems (GNSS) and Satellite Communication (SatCom). In particular, significant applications such as aeronautics and satellite communication can be severely affected by intentional and unintentional interference, which are unmitigated. The matter requires finding a radical and effective solution to overcome this problem. The methods used for overcoming the RFI include interference detection, interference classification, interference geolocation, tracking and interference mitigation. RFI source geolocation and tracking methodology gained universal attention from numerous researchers, specialists, and scientists. In the last decade, various conventional techniques and algorithms have been adopted in geolocation and target tracking in civil and military operations. Previous conventional techniques did not address the challenges and demand for novel algorithms. Hence there is a necessity for focussing on the issues associated with this. This survey introduces a review of various conventional geolocation techniques, current orientations, and state-of-the-art techniques and highlights some approaches and algorithms employed in wireless and satellite systems for geolocation and target tracking that may be extremely beneficial. In addition, a comparison between different conventional geolocation techniques has been revealed, and the comparisons between various approaches and algorithms of geolocation and target tracking have been addressed, including H∞ and Kalman Filtering versions that have been implemented and investigated by authors.

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Section: Particle Filter (Pf)mentioning

confidence: 99%

A Survey for Recent Techniques and Algorithms of Geolocation and Target Tracking in Wireless and Satellite Systems

et al. 2021

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“…t is the i-th particle for the j-th imputation at time t and w t . Finally, by substituting Equation (43) into Equation (42) we obtain a form to practically compute an approximation of the posterior PDF when observations are missing and replaced by imputations:…”

Section: Handling Missing Detectionsmentioning

confidence: 99%

“…Albeit defined as a tracking-before-detection, the same principles have been applied in other domains as well. One notable example is underwater acoustic signal processing in the work of Jin et al [ 42 ] where the low signal-to-noise ratio, random missing measurements, multiple interference scenarios, and merging-splitting contacts in measurement space are found to pose challenges for common target tracking algorithms. The authors of this paper propose a tracking-before-detection particle filter that estimates particle likelihood functions directly using the beam-former output energy and adopts crossover and mutation operators to evolve particles with a small weight.…”

Section: Related Workmentioning

confidence: 99%

Cooperative Multi-Sensor Tracking of Vulnerable Road Users in the Presence of Missing Detections

Dimitrievski

Hamme

Veelaert

et al. 2020

Sensors

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This paper presents a vulnerable road user (VRU) tracking algorithm capable of handling noisy and missing detections from heterogeneous sensors. We propose a cooperative fusion algorithm for matching and reinforcing of radar and camera detections using their proximity and positional uncertainty. The belief in the existence and position of objects is then maximized by temporal integration of fused detections by a multi-object tracker. By switching between observation models, the tracker adapts to the detection noise characteristics making it robust to individual sensor failures. The main novelty of this paper is an improved imputation sampling function for updating the state when detections are missing. The proposed function uses a likelihood without association that is conditioned on the sensor information instead of the sensor model. The benefits of the proposed solution are two-fold: firstly, particle updates become computationally tractable and secondly, the problem of imputing samples from a state which is predicted without an associated detection is bypassed. Experimental evaluation shows a significant improvement in both detection and tracking performance over multiple control algorithms. In low light situations, the cooperative fusion outperforms intermediate fusion by as much as 30%, while increases in tracking performance are most significant in complex traffic scenes.

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An Improved Particle Filter Based Track-Before-Detect Method for Underwater Target Bearing Tracking

Cited by 2 publications

References 9 publications

A Survey for Recent Techniques and Algorithms of Geolocation and Target Tracking in Wireless and Satellite Systems

A Survey for Recent Techniques and Algorithms of Geolocation and Target Tracking in Wireless and Satellite Systems

Cooperative Multi-Sensor Tracking of Vulnerable Road Users in the Presence of Missing Detections

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