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.

In the intelligent transportation system, it is an efficient way for the intelligent vehicle to use the integrated radar and communications system (IRCS) to obtain the range and velocity estimations of other vehicles and simultaneously communicate with other facilities, such as vehicles and base stations. In the IRCS, the transmitted waveform is the orthogonal frequency division multiplexing (OFDM) integrated radar and communications waveform that contains communications information. Due to the existence of communications information, the traditional range and velocity estimation methods in radar can not be directly utilized in the IRCS. Moreover, to improve the resolution of range and velocity estimations, the signal bandwidth and coherent processing interval (CPI) are usually required to be increased, which will result in the increase of system cost and the reduction of update rate. To solve these problems, a auto-paired super-resolution range and velocity estimation method is proposed by using the OFDM integrated radar and communications waveform. First, the communications information in the received signals is compensated. Then, the frequency smoothing is performed to reduce the correlation between the echoes reflected by different targets. Finally, the auto-paired super-resolution range and velocity estimations are obtained by exploiting the translational invariance structure of the underlying signal model in both frequency and pulse domains. Furthermore, Cramr-Rao bounds (CRBs) on range and velocity estimations with the OFDM integrated radar and communications waveform are derived. Several numerical examples are presented to verify the effectiveness of the proposed method.

An adaptively smart wireless power transfer (WPT) system is proposed by using a 2-bit programmable metasurface (PMS), in which an indoor dynamic charging application scenario is described and near-field focused (NFF) technique is adopted to transmit wireless powers. Theoretical analyses of NFF PMS are presented to illustrate its WPT capabilities. To implement dynamic WPTs, digital phase-shift quantization of PMS element is investigated, indicating that 2-bit quantization scheme is a preferred solution. As experimental demonstrations, a 2-bit 12 × 12 PMS operating at 5.8 GHz is designed, fabricated, and tested. High-power focal spots that can be regulated to focus at different near-field positions are measured in an anechoic chamber. We conduct multiple WPT experiments by using the 2-bit PMS as the transmitter and a 4 × 4 microstrip array antenna as the receiver. Experimental results show that the proposed 2-bit PMS can continuously transmit the wireless powers to the receiver even though the target is moving, and the WPT efficiency is maintained over 9.1%, which has about 16.3 dB improvement at the end of the trajectory compared to the fixed NFF beam. This work is one of the first attempts to thoroughly examine the possibility of the dynamic WPTs using PMS, offering a brand-new manner for the adaptively smart WPT systems.

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.

In this paper, we investigate the problem of target localization with a multiple-input multiple-output (MIMO) and frequency diverse array MIMO (FDA-MIMO) dual-mode radar system. The signal model for this newly introduced radar system is presented in detail. On this basis, a computationally efficient method for joint angle and range estimation is proposed by taking advantage of the subspace principle. First, the direction-of-arrival (DOA) of each target is determined by utilizing the fused signal data received via this dual-mode radar. In order to solve the coupling of target range and direction-of-departure (DOD) in FDA MIMO radar mode, we use the monostatic-like characteristics of the dual-mode radar to develop an effective decoupling method. More specifically, by employing the same eigenmatrix corresponding to MIMO and FDA-MIMO radar data, the coupling of DOD and range in the FDA-MIMO mode can be easily overcome, and the parameters can be successively estimated with automatic paring. Further, to make full use of the transmit array for DOD estimation, we propose to compensate the range information in the transmit array steering vector to achieve an improved estimate of target DOD. Extensive simulation results are performed to show that the proposed approach can provide superior DOD and range estimation accuracy while maintaining DOA estimation performance under the presented dual-mode radar system, and does not require the design of additional parameter pairing strategies with low computational cost, exhibiting excellent target localization behavior.