Efficient multisensor fusion using multidimensional assignment for multitarget tracking

Abstract

In this paper we present the development of a multisensor fusion algorithm using multidimensional data association for multitarget tracking. The work is motivated by a large scale ground target surveillance problem, where observations from multiple asynchronous sensors with time-varying sampling intervals (e.g., electronically scanned array radars) are used for centralized fusion. The combination of multisensor fusion with multidimensional assignment is done such as to maximize the 'time-depth,' in addition to 'sensor-width' for the number S of lists handled by the assignment algorithm. The time-depth results from the simultaneous use of multiple frames of measurements obtained at different time instants. The sensor- width comes from the geographically distributed nature of the sensors. A procedure, which guarantees maximum effectiveness for an S-dimensional data association (S greater than or equal to 3), i.e., maximum time-depth (S-1) for each sensor without sacrificing the fusion across sensors, is presented. Using a sliding-window technique (of length S), the estimates are updated after each frame of measurements. The algorithm provides a systematic approach to automatic track formation, maintenance and termination for multitarget tracking using multisensor fusion with multidimensional assignment for data association. Estimation results are presented for simulated data.