Waveform-agile multiple target tracking using probability hypothesis density filtering

We investigate a waveform-agile technique for tracking multiple targets using the probability hypothesis density filter (PHDF). We assume that waveforms can be chosen from a class of linear frequency-modulated signals with varying parameters and implement the PHDF sequentially using particle filtering. The selection process myopically chooses the transmit waveform for each target by minimizing the predicted mean-squared error (MSE) across a pre-computed waveform library. Using simulations, we compare the performance of the proposed waveform-agile PHDF tracker with a fixed waveform PHDF tracker, demonstrating improved MSE tracking performance.