Data association consists of assigning measurements to the predicted track positions in the multiple target tracking application. The data association problem can be structured in a basic framework very similar to that of the classic travelling salesman problem (TSP). The derivation of the energy function is presented, and the solution is based on a modified Hopfield network which uses the Runge-Kutta method and Aiyer (1990) network's structure. We demonstrate the feasibility of applying neural network technology to multitarget tracking using simulated and real-life radar data. The modified Hopfield tracker is also observed to have better performance than the original Hopfield network.<>
Data association for multiple target tracking using Hopfield neural network
01.01.1994
244867 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Data Association for Multiple Target Tracking using Hopfield Neural Network
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