A1 R. Danchick

A1 G.E. Newnam

PB

T1 Reformulating Reid's MHT method with generalised Murty

JN IEE Proceedings - Radar, Sonar and Navigation

VO 153

IS 1

SP 13

OP 22

AB The authors reformulate Reid's multiple hypothesis tracking algorithm to exploit a K-best ranked linear assignment algorithm for data association. The reformulated algorithm is designed for real-time tracking of large numbers of closely spaced objects. A likelihood association matrix is constructed that, for each scan, for each cluster, for each cluster hypothesis, exactly and compactly encodes the complete set of Reid's data association hypotheses. The set of this matrix's feasible assignments with corresponding non-vanishing products is shown to map one-to-one respectively onto the set of Reid's data association hypotheses and their corresponding probabilities. The explicit structure of this matrix is a new result and leads to an explicit hypothesis counting formula. Replacement of the likelihood association matrix elements by their negative natural logs then transforms the data association matrix into a linear assignment problem matrix and recasts the problem of data association into efficiently finding sets of ranked assignments. Fast polynomial time Murty ranked assignment algorithms can thus replace Reid's original NP-hard exhaustive hypothesis identification, probability evaluation, and branch-and-prune methods and can rapidly determine the maximally likely data association hypothesis, the second most likely, etc. Results from two high fidelity surveillance sensor simulations show the validity of the proposed method.

K1 Reids MHT method

K1 electro-optical surveillance satellite

K1 radar sensor system application

K1 probability evaluation

K1 K-best ranked linear assignment

K1 data association

K1 interceptor aircraft-missile

K1 cluster hypothesis

K1 recursive hypothesis identification

K1 reformulated algorithm

K1 multiple hypothesis tracking

K1 generalised Murty algorithm

K1 branch-and-prune method

K1 likelihood association matrix

K1 explicit hypothesis counting formula

DO https://doi.org/10.1049/ip-rsn:20050041

UL https://digital-library.theiet.org/;jsessionid=ca4t69bh42ba.x-iet-live-01content/journals/10.1049/ip-rsn_20050041

LA English

SN 1350-2395

YR 2006

OL EN