A low-complexity sparsity-based multi-target tracking algorithm for urban environments

In this paper, we propose a low-complexity sparsity based multi-target tracking algorithm. We develop a finite dimensional representation of the received signal when the radar is operating in an urban environment. The dimensionality of the representation denotes the extra degrees of freedom that an urban environment offers. We employ spread-spectrum signaling to exploit the full diversity offered by the environment. We then develop a block-sparse measurement model by discretizing the delay-Doppler plane and prove that the dictionary of the block sparse model exhibits a special structure under spread-spectrum signaling. This structure enables an efficient support recovery of the sparse vector, by projecting the measurement vector on the row space of the dictionary. Numerical simulations show that our tracking procedure takes significantly less time, while giving good tracking performance.