A hardware design for real-time multiple target tracking

This paper describes the use of a simulated real time system of a feed-forward neural network, a recurrent neural network and a set of expert rules in solving the problem of multiple target tracking. It is assumed that data is provided in the from of blips, taken off 3 consecutive focal plane arrays, operating at visible or infrared wavelengths. In this paper, the task of multiple target tracking is transformed from one of blip-frame data association to one of target clustering, which in turn is broken down and solved in four stages. Each stage is described and mapped with the use of a feed-forward, a recurrent neural network or a set of fuzzy rules. The first and second stages of the solution procedure involve the use of two feed-forward neural network modules, while the third and forth stages use a recurrent neural network module and a set of expert rules module. The multiple target tracking solution procedure is simulated through use of FORTRAN code. In principle the number of targets that can be tracked with the routine is unlimited. However, in reality, the number of targets is dictated by the number of neurons, which in turn is constrained by hardware requirements. Software simulation results shows that the multiple target tracking code is capable of tracking an arbitrary number of targets very efficiently. The program was tested and debugged for use in the tracking of sets of multiple targets; ranging from 2 to 14. Results indicated that once the average acceleration of the targets is adequately evaluated, track files could be developed with 100% accuracy.