OFDM radar waveform design for sparsity-based multi-target tracking

We propose a sparsity-based approach to track multiple targets using an orthogonal frequency division multiplexing (OFDM) radar. The use of an OFDM signal increases the frequency diversity of our system as different scattering centers of a target resonate variably at different frequencies. We observe that in a particular pulse interval the targets lie at a few points on the delay-Doppler plane. Hence, we exploit that inherent sparsity to develop a tracking procedure. The nonzero entries of the sparse vector in our model correspond to the target scattering coefficients at different OFDM subcarriers. Therefore, the sparse vector and associated sparse measurement model exhibit block-sparsity property. We design the spectral weights of the transmitting OFDM waveform to minimize the block-coherence measure of the sparse model. In the tracking filter, we develop a block version of the compressive sampling matching pursuit (CoSaMP) algorithm. We present numerical examples to show the performance of our sparsity-based tracking approach and compare with that of a particle filter (PF). The proposed sparsity-based tracking algorithm takes significantly less computational time and provides equivalent, and sometimes better, tracking performance in comparison with the PF-based tracking.