Passive-sensor data association for tracking: a PC software

Publisher Website
Google Scholar
Abstract
This paper describes a PC software package for the following problem : We are given multiple passive sensors, each with a number of detections at a given time. With each detection, there is an associated line-of-sight measurement originating from a source. The source can be either a real target, in which case the measurements are azimuth and elevation angles of the target, plus some measurement noise, or a spurious one, i.e., a false alarm. Position estimates of targets can be formed by associating measurements originating from it. Mathematically, the measurement-target association problem leads to a generalized assignment problem. The problem of forming position estimates of multiple targets in a dense cluster, from passive sensor measurements at a given time, require at least three sensors. However, for three sensors, the three-dimensional assignment is known to be NP-complete, i.e., the complexity of the optimal algorithm increases exponentially with the size of the problem. The association problem is solved as a maximum likelihood estimation procedure; the likelihood function is maximized through the use of a near-optimal, iterative, and polynomial-time three dimensional assignment algorithm which employs Lagrangian relaxation technique that successively solves a series of (polynomial time) generalized two-dimensional assignment subproblems. The algorithm is coded in Fortran and available as an interactive PC software package PASSDAT. In this paper we present performance results and graphical scenario description of a representative test case solved by PASSDAT.

This publication has 0 references indexed in Scilit: