sparse Fourier representation of off-grid targets with application to experimental radar data. Signal Processing, Elsevier, 2015Elsevier, , vol. 111, pp. 261-273. <10.1016Elsevier, /j.sigpro.2014 Any correspondence concerning this service should be sent to the repository administrator: AbstractThe problem considered is the estimation of a finite number of cisoids embedded in white noise, using a sparse signal representation (SSR) approach, a problem which is relevant in many radar applications. Many SSR algorithms have been developed in order to solve this problem, but they usually are sensitive to grid mismatch. In this paper, two Bayesian algorithms are presented, which are robust towards grid mismatch: a first method uses a Fourier dictionary directly parametrized by the grid mismatch while the second one employs a first-order Taylor approximation to relate linearly the grid mismatch and the sparse vector. The main strength of these algorithms lies in the use of a mixed-type distribution which decorrelates sparsity level and target power. Besides, both methods are implemented through a Monte-Carlo Markov chain algorithm. They are successfully evaluated on synthetic and experimental radar data, and compared to a benchmark algorithm.

Within the scope of sparse signal representation, we consider the problem of velocity ambiguity mitigation for wideband radar signal. We present a Bayesian robust algorithm based on a new sparsifying dictionary suited for range-migrating targets possibly straddling range-velocity bins. Numerical simulations on experimental data demonstrate the ability of the proposed algorithm in mitigating velocity ambiguity.

In this paper, we study the problem of estimating the target scene via a signal sparse representation (SSR) scheme in the range-Doppler domain. As compared to a range-gate by range-gate SSR analysis, this bidimensional approach can take into account targets straddling two range-gates. Here, we propose a robust SSR Bayesian algorithm that considers the well known grid mismatch problem in both the range and Doppler dimensions. Our algorithm implements a bidimensional approach to a previous described algorithm. Numerical simulations are performed with synthetic and experimental data to demonstrate the benefit of estimating the grid mismatch with the proposed technique.978-1-4799-8232-5/151$31.00@2015IEEE

In this paper, we consider the problem of estimating a signal of interest embedded in noise using a sparse signal representation (SSR) approach. This problem is relevant in many radar applications. In particular, estimating a radar scene consisting of targets with wide amplitude range can be challenging since the sidelobes of a strong target can disrupt the estimation of a weak one. Within a Bayesian framework, we present a new sparse-promoting prior designed to estimate this specific type of radar scene. The main strength of this new prior lies in its mixed-type structure which decorrelates sparsity level and target power, as well as in its subdivided support which enables the estimation process to span the whole target power range. This algorithm is implemented through a Monte-Carlo Markov chain. It is successfully evaluated on synthetic and semiexperimental radar data and compared to state-of-the-art algorithms.

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.