scholarly journals Tracking and Estimation of Multiple Cross-Over Targets in Clutter

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 741 ◽  
Author(s):  
Sufyan Memon ◽  
Myungun Kim ◽  
Hungsun Son
Keyword(s):  
Data Association ◽  
Fixed Interval ◽  
Tracking Performance ◽  
Estimation Accuracy ◽  
Current Time ◽  
Unknown Number ◽  
Detection Probabilities ◽  
Multiple Cross ◽  
Backward Tracking ◽  
Target Data

Tracking problems, including unknown number of targets, target trajectories behaviour and uncertain motion of targets in the surveillance region, are challenging issues. It is also difficult to estimate cross-over targets in heavy clutter density environment. In addition, tracking algorithms including smoothers which use measurements from upcoming scans to estimate the targets are often unsuccessful in tracking due to low detection probabilities. For efficient and better tracking performance, the smoother must rely on backward tracking to fetch measurement from future scans to estimate forward track in the current time. This novel idea is utilized in the joint integrated track splitting (JITS) filter to develop a new fixed-interval smoothing JITS (FIsJITS) algorithm for tracking multiple cross-over targets. The FIsJITS initializes tracks employing JITS in two-way directions: Forward-time moving JITS (fJITS) and backward-time moving JITS (bJITS). The fJITS acquires the bJITS predictions when they arrive from future scans to the current scan for smoothing. As a result, the smoothing multi-target data association probabilities are obtained for computing the fJITS and smoothing output estimates. This significantly improves estimation accuracy for multiple cross-over targets in heavy clutter. To verify this, numerical assessments of the FIsJITS are tested and compared with existing algorithms using simulations.

scholarly journals Variational Bayesian Based Adaptive Shifted Rayleigh Filter for Bearings-Only Tracking in Clutters

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1512 ◽  
Author(s):  
Jing Hou ◽  
Yan Yang ◽  
Tian Gao
Keyword(s):  
Target Tracking ◽  
Data Association ◽  
Tracking Performance ◽  
Computation Complexity ◽  
Variational Bayesian ◽  
Target State ◽  
Scenarios Simulation ◽  
Simulation Results

This paper considers bearings-only target tracking in clutters with uncertain clutter probability. The traditional shifted Rayleigh filter (SRF), which assumes known clutter probability, may have degraded performance in challenging scenarios. To improve the tracking performance, a variational Bayesian-based adaptive shifted Rayleigh filter (VB-SRF) is proposed in this paper. The target state and the clutter probability are jointly estimated to account for the uncertainty in clutter probability. Performance of the proposed filter is evaluated by comparing with SRF and the probability data association (PDA)-based filters in two scenarios. Simulation results show that the proposed VB-SRF algorithm outperforms the traditional SRF and PDA-based filters especially in complex adverse scenarios in terms of track continuity, track accuracy and robustness with a little higher computation complexity.

Modeling Based on Combinatorial Optimization Multi-Target Data Association and Bonic Algorithm

2014 ◽  
Vol 624 ◽  
pp. 505-508
Author(s):  
Xin Fu
Keyword(s):  
Combinatorial Optimization ◽  
Data Association ◽  
Simulator Training ◽  
Flight Simulator ◽  
Multiple Target ◽  
Research Focus ◽  
Multiple Targets ◽  
Training Course ◽  
Flight Formation ◽  
Target Data

Group operations is a emerging key problem for flight simulator training course. No matter the radar array in the ground or flight formation in the air, all need to track multiple targets at the same time. That means analyzing the correlation between detecting targets of sensors and known multiple aircraft is essential. So Multiple target data association is the research focus in this paper. Aimed at the research focus, this paper does research for modeling based on combinatorial optimization multi-target data association and bionic algorithm.

scholarly journals Underwater Bearing-Only Multitarget Tracking in Dense Clutter Environment Based on PMHT

2020 ◽  
Vol 38 (2) ◽  
pp. 359-365
Author(s):  
Xiaohua Li ◽  
Ya'an Li ◽  
Xiaofeng Lu ◽  
Chenxu Zhao ◽  
Jing Yu
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Data Association ◽  
Estimation Accuracy ◽  
Multitarget Tracking ◽  
Single Observation ◽  
Multiple Observations ◽  
Multiple Hypothesis Tracking ◽  
Kalman Filter Algorithm ◽  
Present Algorithm

Underwater bearing-only multitarget tracking in clutter environment is challenging because of the measurement nonlinearity, range unobservability, and data association uncertainty. In terms of the principle of expectation maximization, combining the extended Kalman filter (EKF) and unscented Kalman filter algorithm(UKF), a new bearing-only multi-sensor multitarget tracking via probabilistic multiple hypothesis tracking(PMHT) algorithm is proposed. The PMHT algorithm introduces an association variable to deal with the data association uncertainty problem between the measurements and the targets. Furthermore, the EKF-based PMHT for multi-sensor multitarget system is simplified, which obviate the need to "stack" the synthetic measurements and can reduce the computation cost. The estimation accuracy of the EKF based on PMHT approach and UKF based on PMHT approach in simulation experiments for underwater bearing-only cross-moving targets and closely spaced targets for the case of stationary multiple observations and maneuvering single observation under dense clutter environment is analyzed. The experimental results demonstrate that the present algorithm is very well in a highly clutter environment and its computational load is low, which confirms the effectiveness of the algorithm to the bearing-only multitarget tracking in dense clutter.

Real Time Multi Sensor Multi Target Data Association Over Sensor Network

2021 ◽  
Author(s):  
Surabhi Sethi ◽  
Piyush Abhishek ◽  
Sandipan Sarkar ◽  
H K Ratha
Keyword(s):  
Real Time ◽  
Sensor Network ◽  
Data Association ◽  
Target Data
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