Search

International audience; Various correlation-based receivers have been proposed in passive bistatic and active monostatic radar exploiting information-bearing orthogonal frequency-division multiplexing (OFDM) transmissions, but too little has been dedicated to establishing their relations and advantages over each other. Accordingly, this paper provides an analytical comparison of the most commonly encountered filters, along with a performance analysis regarding three criteria: computational complexity, signal-to-interference-plus-noise-ratio and resilience to ground clutter. The last two especially assess the possible detrimental effects of the random sidelobes (or pedestal) induced by the data symbols in the range-Doppler map. Although simulations show that none of the filters performs unanimously better, the ones employing circular correlations globally evidence attractive results.

Compressive sensing (CS) has been widely used in multiple-input-multiple-output (MIMO) radar in recent years. Unlike traditional MIMO radar, detection/estimation of targets in a CS-based MIMO radar is accomplished via sparse recovery. In this article, for a CS-based colocated MIMO radar with linear arrays, we attempt to improve the target detection performance by reducing the coherence of the associated sensing matrix. Our tool in reducing the coherence is the placement of the antennas across the array aperture. In particular, we choose antenna positions within a given grid. Initially, we formalize the position selection problem as finding binary weights for each of the locations. This problem is highly nonconvex and combinatorial in nature. Instead, we find continuous weight values for each location and interpret them as the probability of including an antenna at the given location. Next, we select antenna locations randomly according to the obtained probability distribution. We formulate the problem for the general case of a MIMO radar with independent transmit and receive arrays for which we propose an iterative algorithm. For the special case of a transceiver array, the solution is obtained through a convex optimization approach. Our experiments show that the proposed method achieves a superior detection performance compared to a uniform random placement of the antennas within the array aperture.

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.

We consider a target selection problem for multitarget tracking in a multifunction radar network from a gametheoretic perspective. The problem is formulated as a noncooperative game. The radars are considered to be players in this game with utilities modeled using a proper tracking accuracy criterion and their strategies are the observed targets whose number is known. Initially, for the problem of coordination, the Nash equilibria are characterized and, in order to find equilibria points, a distributed algorithm based on the bestresponse dynamics is proposed. Afterwards, the analysis is extended to the case of partial target observability and radar connectivity and heterogeneous interests among radars. The solution concept of correlated equilibria is employed and a distributed algorithm based on the regret-matching is proposed. The proposed algorithms are shown to perform well compared to the centralized approach of significantly higher complexity

Array processing is widely used in sensing applications for estimating the locations and waveforms of the sources in a given field. In the absence of a large number of snapshots, which is the case in numerous practical applications, such as underwater array processing, it becomes challenging to estimate the source parameters accurately. This paper presents a nonparametric and hyperparameter, free-weighted, least squares-based iterative adaptive approach for amplitude and phase estimation (IAA-APES) in array processing. IAA-APES can work well with few snapshots (even one), uncorrelated, partially correlated, and coherent sources, and arbitrary array geometries. IAA-APES is extended to give sparse results via a model-order selection tool, the Bayesian information criterion (BIC). Moreover, it is shown that further improvements in resolution and accuracy can be achieved by applying the parametric relaxation-based cyclic approach (RELAX) to refine the IAA-APES&BIC estimates if desired. IAA-APES can also be applied to active sensing applications, including single-input single-output (SISO) radar/sonar range-Doppler imaging and multi-input single-output (MISO) channel estimation for communications. Simulation results are presented to evaluate the performance of IAA-APES for all of these applications, and IAA-APES is shown to outperform a number of existing approaches.

In this paper, we address the problem of detecting multiple Noise-Like Jammers (NLJs) through a radar system equipped with an array of sensors. To this end, we develop an elegant and systematic framework wherein two architectures are devised to jointly detect an unknown number of NLJs and to estimate their respective angles of arrival. The followed approach relies on the likelihood ratio test in conjunction with a cyclic estimation procedure which incorporates at the design stage a sparsity promoting prior. As a matter of fact, the problem at hand owns an inherent sparse nature which is suitably exploited. This methodological choice is dictated by the fact that, from a mathematical point of view, classical maximum likelihood approach leads to intractable optimization problems (at least to the best of authors' knowledge) and, hence, a suboptimum approach represents a viable means to solve them. Performance analysis is conducted on simulated data and shows the effectiveness of the proposed architectures in drawing a reliable picture of the electromagnetic threats illuminating the radar system., 37 pages, 18 figures