Unambiguous Sparse Recovery of Migrating Targets with a Robustified Bayesian Model

The problem considered is that of estimating unambiguously migrating targets observed with a wideband radar. We extend a previously described sparse Bayesian algorithm to the presence of diffuse clutter and off-grid targets. A hybrid-Gibbs sampler is formulated to jointly estimate the sparse target amplitude vector, the grid mismatch and the (assumed) autoregressive noise. Results on synthetic and fully experimental data show that targets can be actually unambiguously estimated even if located in blind speeds

An unambiguous radar mode with a single PRF wideband waveform

In this paper, we consider the problem of unambiguously estimating targets, including in blind velocities, using a single-low-PRF wideband radar signal. We present a Bayesian sparse recovery algorithm able to estimate the amplitude and location of range-migrating targets possibly straddling range-velocity bins embedded in colored noise. Numerical simulations on synthetic data and experimental data show that the proposed algorithm is able to mitigate velocity ambiguity and estimate targets in blind velocities