Multiple extended target tracking algorithm based on GM-PHD filter and spectral clustering

Abstract

With the increase of the resolution of modern radars and other detection equipments, one target may produce more than one measurement. Such targets are referred to as extended targets. Recently, multiple extended target tracking (METT) has drawn a considerable attention. However, one crucial problem is how to partition the measurement sets accurately and rapidly. In this paper, an improved METT algorithm is proposed based on the Gaussian mixture probability hypothesis density (GM-PHD) filter and an effective partition method using spectral clustering technique. First, the density analysis technique is introduced to eliminate the disturbance of clutter, and then the spectral clustering technique based on neighbor propagation is used to partition the measurements. Finally, the GM-PHD filter is implemented to achieve the METT. Simulation results show that the proposed algorithm has a better performance, especially a better real-time performance, than the conventional distance partition and K-means++ methods.

Gaussian mixture probability hypothesis density filter for multipath multitarget tracking in over-the-horizon radar

“…From the above analysis, we can see that the computational load required by the proposed MP-PHD filter is similar to that of the extended targets PHD filter [18][19][20][21][22]. Note that some methods, such as K-means++ method [19] and spectral clustering [20], have been suggested for the implementation of the extended targets PHD filter to reduce the number of partitions.…”

“…Note that some methods, such as K-means++ method [19] and spectral clustering [20], have been suggested for the implementation of the extended targets PHD filter to reduce the number of partitions. However, these methods that applied in the extended targets are measurement dependent.…”

“…Note that W does not include null set. For instance, Z k = {z 1 , z 2 , z 3 } can be partitioned as follows [19][20][21]]…”

“…In 2009, Mahler derived a series of "second-generation" PHD/cardinalized probability hypothesis density (CPHD) filters [17] which addressed the tracking problems for nonstandard multitarget measurement model, such as extended targets [18][19][20][21], unresolved target [22], unknown clutter [23], and superpositional sensors [24]. As for the multipath propagation in OTHR, the receiver can obtain more than one measurement from the same target, which means that it violates the second assumption of the standard measurement model.…”

Affinity Propagation Clustering of Measurements for Multiple Extended Target Tracking

“…In a multiple extended targets scene, it is very difficult to obtain the relationship between measurements and targets, and divide measurements from one target into the same partition. To address the difficulty of measurement partitioning for multiple extended target tracking, Granstrom proposed a partitioning method based on the distances between measurements [ 6 , 7 ], Zhang proposed an algorithm using the fuzzy ART model [ 10 ], and Yang used a spectral clustering algorithm for partitioning [ 11 ]. However, the problem of how to accurately and effectively partition the measurements of the extended target with clutter remains unsolved.…”