A Track Initiation Method for the Underwater Target Tracking Environment

The traditional centroid tracking method over-relies on the accuracy of segment, which easily lead to loss of underwater moving target. This paper presents an object tracking method based on circular contour extraction, combining region feature and contour feature. Through the correction to circle features, the problem of multiple solutions causing by Hough transform circle detection is avoided. A new motion prediction model is constructed to make up the deficiency that three-order motion prediction model has disadvantage of high dimension and large calculation. The predicted position of object centroid is updated and corrected by circle contour, forming prediction-measurement-updating closed-loop target tracking system. To reduce system processing time, on the premise of the tracking accuracy, a dynamic detection method based on target state prediction model is proposed. The results of contour extraction and underwater moving target experiments demonstrate the effectiveness of the proposed method.

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Shifted Rayleigh filter: a novel estimation filtering algorithm for pervasive underwater passive target tracking for computation in 3D by bearing and elevation measurements

International Journal of Pervasive Computing and Communications ◽

10.1108/ijpcc-06-2021-0138 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Lakshmi M. Kavitha ◽

Rao S. Koteswara ◽

K. Subrahmanyam

Keyword(s):

Target Tracking ◽

Long Range ◽

Target Location ◽

Unscented Kalman Filter ◽

Three Dimensional ◽

Content Type ◽

Solution Convergence ◽

Passive Target Tracking ◽

Underwater Target ◽

Target Motion Analysis

Purpose Marine exploration is becoming an important element of pervasive computing underwater target tracking. Many pervasive techniques are found in current literature, but only scant research has been conducted on their effectiveness in target tracking. Design/methodology/approach This research paper, introduces a Shifted Rayleigh Filter (SHRF) for three-dimensional (3 D) underwater target tracking. A comparison is drawn between the SHRF and previously proven method Unscented Kalman Filter (UKF). Findings SHRF is especially suitable for long-range scenarios to track a target with less solution convergence compared to UKF. In this analysis, the problem of determining the target location and speed from noise corrupted measurements of bearing, elevation by a single moving target is considered. SHRF is generated and its performance is evaluated for the target motion analysis approach. Originality/value The proposed filter performs better than UKF, especially for long-range scenarios. Experimental results from Monte Carlo are provided using MATLAB and the enhancements achieved by the SHRF techniques are evident.

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Performance of amplitude information PMHT algorithm for underwater target tracking

2010 IEEE 18th Signal Processing and Communications Applications Conference ◽

10.1109/siu.2010.5653697 ◽

2010 ◽

Author(s):

Serkan Ceylan ◽

Murat Efe

Keyword(s):

Target Tracking ◽

Underwater Target

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Linear Multi-Target Integrated Probabilistic Data Association Filter With Automatic Track Management for Autonomous Vehicles

Volume 2: Control and Optimization of Connected and Automated Ground Vehicles; Dynamic Systems and Control Education; Dynamics and Control of Renewable Energy Systems; Energy Harvesting; Energy Systems; Estimation and Identification; Intelligent Transportation and Vehicles; Manufacturing; Mechatronics; Modeling and Control of IC Engines and Aftertreatment Systems; Modeling and Control of IC Engines and Powertrain Systems; Modeling and Management of Power Systems ◽

10.1115/dscc2018-8930 ◽

2018 ◽

Author(s):

Andinet Hunde ◽

Beshah Ayalew

Keyword(s):

Target Tracking ◽

Tracking System ◽

Data Association ◽

Tracking Algorithm ◽

Probabilistic Data ◽

Probabilistic Data Association ◽

Address Data ◽

Multi Target Tracking ◽

Track Management ◽

Track Initiation

Target tracking in public traffic calls for a tracking system with automated track initiation and termination facilities in a randomly evolving driving environment. In addition, the key problem of data association needs to be handled effectively considering the limitations in the computational resources onboard an autonomous car. In this paper, we discuss a multi-target tracking system that addresses target birth/initiation and death/termination processes with automatic track management feature. The tracking system is based on Linear Multi-target Integrated Probabilistic Data Association Filter (LMIPDAF), which is adapted to specifically include algorithms that handle track initiation and termination, clutter density estimation and track management. The performance of the proposed tracking algorithm is compared to other single and multi-target tracking schemes and is shown to have acceptable tracking error. It is further illustrated through multiple traffic simulations that the computational requirement of the tracking algorithm is less than that of optimal multi-target tracking algorithms that explicitly address data association uncertainties.

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An Improved Kernelized Correlation Filter Algorithm for Underwater Target Tracking

Applied Sciences ◽

10.3390/app8112154 ◽

2018 ◽

Vol 8 (11) ◽

pp. 2154 ◽

Cited By ~ 2

Author(s):

Xingmei Wang ◽

Guoqiang Wang ◽

Zhonghua Zhao ◽

Yue Zhang ◽

Binghua Duan

Keyword(s):

Target Tracking ◽

Adaptive Filter ◽

Poor Performance ◽

Image Sequences ◽

Correlation Filter ◽

Tracking Errors ◽

The Poor ◽

Continuous Scale ◽

Underwater Target ◽

Update Strategy

To obtain accurate underwater target tracking results, an improved kernelized correlation filter (IKCF) algorithm is proposed to track the target in forward-looking sonar image sequences. Specifically, a base sample with a dynamically continuous scale is first applied to solve the poor performance of fixed-scale filters. Then, in order to prevent the filter from drifting when the target disappears and appears again, an adaptive filter update strategy with the peak to sidelobe ratio (PSR) of the response diagram is developed to solve the following target tracking errors. Finally, the experimental results show that the proposed IKCF can obtain accurate tracking results for the underwater targets. Compared to other algorithms, the proposed IKCF has obvious superiority and effectiveness.

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