Suppression of radar-to-radar jammers, especially the mainbeam jammers, has been an urgent demand in vehicular sensing systems with the expected increased number of vehicles equipped with radar systems. This paper deals with the suppression of mainbeam deceptive jammers with frequency diverse array (FDA)-multiple-input multiple-output (MIMO) radar, utilizing its extra degrees-of-freedom (DOFS) in the range domain. At the modelling stage, false targets, which lag several pulses behind the true target, are considered as a typical form of mainbeam jammers. To this end the data-independent beamforming is performed to suppress false targets by nulling at the equivalent transmit beampattern with an appropriate frequency increment. However, the suppression performance degrades in the presence of transmit spatial frequency mismatch, which could be induced by quantization errors, angle estimation errors and frequency increment errors. To solve this problem, a preset broadened nulling beamformer (PBN-BF) is proposed by placing artificial interferences with appropriate powers around the nulls of the equivalent transmit beampattern. In such a way, effective suppression of deceptive jammer can be guaranteed owing to the broadened notches. At the analysis stage, numerical results in a scenario with multiple unmanned aerial vehicles (UAVs) are provided to illustrate the effectiveness of the devised data-independent BF, and the signal-to-interference-plus-noise ratio is improved compared with the conventional data-independent BF.

This paper deals with the problem of adaptive target detection in the presence of homogeneous Gaussian interference with frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. Adaptive detectors are devised according to the generalized likelihood ratio test (GLRT) criterion, where the position of the target within each range cell is assumed unknown. To obtain the maximum likelihood (ML) estimate of the target incremental range under the H1 hypothesis, three different optimization strategies are pursued. They are respectively based on Semi-definite programming (SDP), discrete grid search, and Newton-method. At the analysis stage, a detection performance comparison is carried on among the new proposed adaptive detectors, benchmark and mismatched receivers. Numerical results corroborate the effectiveness of the developed receivers.

This article deals with the problem of angle and incremental range (i.e., the target range offset with respect to the center of the cell under test) estimation with a frequency diverse array multiple-input multiple-output (FDA-MIMO) radar exploiting a single data snapshot as observable. Starting from the observation that the maximum likelihood (ML) estimation entails a 2-D grid search over the parameters of interest, three approximated ML techniques are designed resorting to the coordinate descent algorithm and the adaptive monopulse criterion (employing either real or complex slope/bias corrections). At the analysis stage, the estimation performance of the proposed methods, including the tapered and double-step monopulse versions, is also assessed in comparison with the Cramér–Rao lower bound. Numerical results corroborate the effectiveness of the considered estimation strategies in some diverse simulated scenarios.

Suppressing the main-beam deceptive jamming in traditional radar systems is challenging. Furthermore, the observations corrupted by false targets generated by smart deceptive jammers, which are not independent and identically distributed because of the pseudo-random time delay. This in turn complicates the task of jamming suppression. In this paper, a new main-beam deceptive jamming suppression approach is proposed, using nonhomogeneous sample detection in the frequency diverse array-multiple-input and multiple-output radar with non-perfectly orthogonal waveforms. First, according to the time delay or range difference, the true and false targets are discriminated in the joint transmit-receive spatial frequency domain. Subsequently, due to the range mismatch, the false targets are suppressed through a transmit-receive 2-D matched filter. In particular, in order to obtain the jamming-plus-noise covariance matrix with high accuracy, a nonhomogeneous sample detection method is developed. Simulation results are provided to demonstrate the detection performance of the proposed approach.

This paper addresses the joint design of the transmit parameters (i.e., radar code/frequency increments) and the receive filter in a Frequency Diverse Array (FDA)-Multiple-Input Multiple-Output (MIMO) radar system. The operating environment includes clutter, namely signal-dependent interference tied up to the FDA transmitted waveforms and the antenna array features, along with conventional thermal noise. The chosen optimization policy relies on the constrained maximization of the Signal-to-Interference-plus-Noise Ratio (SINR) which for Gaussian interference is tantamount to maximizing the radar detection performance. In this context, a bespoke Minorization-Maximization (MM)-Maximum Block Improvement (MBI) algorithm is proposed to tackle the resulting constrained non-convex optimization problem. The convergence properties of the resulting procedure are rigorously proven, along with a thorough investigation of the computational complexity for its implementation. Finally, numerical results are provided to show the effectiveness of the new technique under diverse clutter scenarios of practical relevance and in comparison with some counterparts.