A discrete-time neural network multitarget tracking data association algorithm

Abstract

This paper describes an alternative solution to that proposed earlier by Sengupta and Iltis (1989). The earlier work exploited widely known stability results for analog continuous-time neural networks. Such results are not known in general for analog, discrete-time networks. Therefore it is not a straightforward issue to transport a continuous-time solution into a purely discrete-time domain. In this paper, we define a particular analog, discrete-time network which is structurally similar to the earlier continuous-time form. We show that, with a weight structure analogous to that earlier obtained from Liapunov function considerations, and with a few mild constraints, this discrete-time network has qualitative stability properties that can be similarly exploited. Permission to copy without fee all or part of this material is granted provided that tbe copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish requires a fee and/or specific permission. The resulting system is applied, in an analogous way to that of Sengupta and Iltis, to the data association problem in multitarget tracking. An advantage of the proposed approach is that it is more amenable to inclusion in clocked, or digital systems. Such an implementation will also run much faster than a discretized version of the earlier algorithm.