Performance analysis of NNF-class target tracking algorithms applied to benchmark problem

In target tracking system, accurate target state estimation is required to accomplish efficient radar beam pointing control even in a cluttered environment. State estimation accuracy is degraded by false alarms due to clutters and jamming by intelligent targets. The NNF (nearest neighbor filter) is widely used for tracking a target in a cluttered environment for its computational simplicity. One drawback of the NNF stems from the fact that the selected NN is a false measurement. To improve the performance of the NNF, the PNNF is suggested to consider the probability of the event that the selected NN is the target-originated measurement. The PNNF-m is a new data association with the NN by incorporating the number of validated measurements into design of the PNNF. In this paper, tracking filter algorithms combined with nearest neighbor data association have been applied to the benchmark problem of target tracking algorithms for MPAR (monopulse phased array radar) to compare the tracking performance in the realistic situation where the spatial density of false measurements in the validation region is unknown. Also the performance comparison of the NNF-class filters and the PDAF is included.