Abstract
If equipped with several radar emitters, a target will produce more than one measurement per time step and is denoted as an extended target. However, due to the requirement of all possible measurement set partitions, the exact probability hypothesis density filter for extended target tracking is computationally intractable. To reduce the computational burden, a fast partitioning algorithm based on hierarchy clustering is proposed in this paper. It combines the two most similar cells to obtain new partitions step by step. The pseudo-likelihoods in the Gaussian-mixture probability hypothesis density filter can then be computed iteratively. Furthermore, considering the additional measurement information from the emitter target, the signal feature is also used in partitioning the measurement set to improve the tracking performance. The simulation results show that the proposed method can perform better with lower computational complexity in scenarios with different clutter densities.
Highlights
As a means of avoiding the complicated problem of data association, the probability hypothesis density (PHD) filter [1] has received considerable attention in multi-target tracking [2,3,4,5,6,7,8,9]
Assuming that the sensors and targets are in a uniform Cartesian coordinates system, the target statement vector is denoted as X ~(x,x_,y,y_)T, which contains the positions and velocities of the X-axis and Y-axis
To address the problem of measurement set partitioning in the extended target PHD filter (ET-PHD) filter, this paper proposes a fast partition method based on hierarchy clustering
Summary
As a means of avoiding the complicated problem of data association, the probability hypothesis density (PHD) filter [1] has received considerable attention in multi-target tracking [2,3,4,5,6,7,8,9]. The standard PHD filter assumes that each target produces at most one measurement per time step. With the application of high-resolution sensors, one object (e.g., large airplane and ship) may yield several measurements at each time step and is denoted as an extended target. Without considering the spawned target, the Gaussian-mixture implementation of the ET-PHD filter can be given by the following three steps [3, 12, 15] which present a closed form solution to the PHD recursion. Assume that the posterior intensity at time k–1 is a Gaussian-mixture form Dk{1jk{1(xk{1)~. Ð1Þ where xk{1 represents the target statement at time k–1, v(ki{) 1 is the weight of the ith component, and N (x; m,P) denotes a Gaussian density with mean m and covariance P. The predicted intensity at time k is given by.
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
