Abstract
The problem of two-dimensional bearings-only multisensor-multitarget tracking is addressed in this work. For this type of target tracking problem, the multidimensional assignment (MDA) is crucial for identifying measurements originating from the same targets. However, the computation of the assignment cost of all possible associations is extremely high. To reduce the computational complexity of MDA, a new coarse gating strategy is proposed. This is realized by comparing the Mahalanobis distance between the current estimate and initial estimate in an iterative process for the maximum likelihood estimation of the target position with a certain threshold to eliminate potential infeasible associations. When the Mahalanobis distance is less than the threshold, the iteration will exit in advance so as to avoid the expensive computational costs caused by invalid iteration. Furthermore, the proposed strategy is combined with the two-stage multiple hypothesis tracking framework for bearings-only multisensor-multitarget tracking. Numerical experimental results verify its effectiveness.
Highlights
Multitarget tracking (MTT) refers to jointly estimating the number of targets and their states in the presence of false alarms and missed detections using single or multiple sensors [1]
In centralized Multisensor-multitarget tracking (MSMTT), the raw measurements from all sensors are sent to the fusion center (FC) where data association is followed by filtering, while in distributed MSMTT, each sensor first processes its own measurements and sends the results to FC for further processing
Multiple hypothesis tracking (MHT) is performed at each sensor, and only the measurements used to update the tracks are sent to the FC
Summary
Multitarget tracking (MTT) refers to jointly estimating the number of targets and their states in the presence of false alarms and missed detections using single or multiple sensors [1]. The standard JPDA algorithm evaluates the association probabilities of measurement-totrack and combines them to obtain the state estimate of the target [15], which means that one observation may contribute to updating multiple tracks [16]. In centralized MSMTT, the raw measurements from all sensors are sent to the fusion center (FC) where data association is followed by filtering, while in distributed MSMTT, each sensor first processes its own measurements and sends the results to FC for further processing Both frameworks have their own advantages and disadvantages in terms of communication requirements, computational complexity, performance, robustness, etc.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
