Root-MUSIC Based Angle Estimation for MIMO Radar with Unknown Mutual Coupling

Direction of arrival (DOA) estimation problem for multiple-input multiple-output (MIMO) radar with unknown mutual coupling is studied, and an algorithm for the DOA estimation based on root multiple signal classification (MUSIC) is proposed. Firstly, according to the Toeplitz structure of the mutual coupling matrix, output data of some specified sensors are selected to eliminate the influence of the mutual coupling. Then the reduced-dimension transformation is applied to make the computation burden lower as well as obtain a Vandermonde structure of the direction matrix. Finally, Root-MUSIC can be adopted for the angle estimation. The angle estimation performance of the proposed algorithm is better than that of estimation of signal parameters via rotational invariance techniques (ESPRIT)-like algorithm and MUSIC-like algorithm. Furthermore, the proposed algorithm has lower complexity than them. The simulation results verify the effectiveness of the algorithm, and the theoretical estimation error of the algorithm is also derived

Sparse Recovery for Bistatic MIMO Radar Imaging in the Presence of Array Gain Uncertainties

A sparse recovery based transmit-receive angle imaging scheme is proposed for bistatic multiple-input multiple-output (MIMO) radar. The redundancy of the transmit and receive angles in the same range cell is exploited to construct the sparse model. The imaging is then performed by compressive sensing method with consideration of both the transmit and receive array gain uncertainties. An additional constraint is imposed on the inverse of the transmit and receive array gain errors matrices to make the optimization problem of the CS solvable. The image of the targets can be reconstructed using small number of snapshots in the case of large array gain uncertainties. Simulation results confirm the effectiveness of the proposed scheme

Advanced Ground-Based Real and Synthetic Aperture Radar

Ground-based/terrestrial radar interferometry (GBRI) is a scientific topic of increasing interest in recent years. The GBRI is used in several field as remote sensing technique for monitoring natural environment (landslides, glacier, and mines) or infrastructures (bridges, towers). These sensors provide the displacement of targets by measuring the phase difference between sending and receiving radar signal. If the acquisition rate is enough the GBRI can provide the natural frequency, e.g. by calculating the Fourier transform of displacement. The research activity, presented in this work, concerns design and development of some advanced GBRI systems. These systems are related to the following issue: detection of displacement vector, Multiple Input Multiple Output (MIMO) and radars with 3D capability

Robust Target Positioning for Reconfigurable Intelligent Surface Assisted MIMO Radar Systems

The direction of arrival (DOA) based multiple-input multiple-output (MIMO) radar technique has been widely utilized for ubiquitous positioning due to its advantage of simple implementability. On the other hand, reconfigurable intelligent surface (RIS) has received considerable attention, which can be deployed on the walls and objects to strengthen the positioning performance. However, RIS is usually not equipped with a perception module, which results in the tremendous challenge for RIS-assisted positioning. To tackle this challenge, this paper propose the fundamental problem of DOA-based target positioning in RIS-assisted MIMO radar system. Unlike conventional DOA estimation systems, the beneficial role of RIS is investigated in MIMO radar system, where a nonconvex $\ell _{p}$ promoting function is exploited to estimate DOA task. By adjusting the reflecting elements of the RIS, the proximal projection iterative strategy is developed to obtain the feasible solution. Both theoretical analysis and simulation results illustrate that the proposed scheme can achieve remarkable positioning performance and shed light on the benefits offered by the adoption of the RIS in terms of positioning performance

Signal Identification and Entrainment for Practical FMCW Radar Spoofing Attacks

This paper proposes a method of passively estimating the parameters of frequency-modulated-continuous-wave (FMCW) radar signals with a wide range of structural parameter values and analyzes how a malicious actor could employ such estimates to track and spoof a target radar. When radars are implemented to support automated driver assistance systems, an intelligent spoofer has the potential to substantially disrupt safe navigation by inducing its target to perceive false objects. Such a spoofer must acquire highly accurate estimates of the target radar's chirp sweep, timing, and frequency parameters while additionally tracking and compensating for time and Doppler shifts due to clock errors and relative movement. This is a difficult task for millimeter-wave radars due to severe Doppler shifts and fast sweep rates, especially when the spoofer uses off-the-shelf FMCW equipment. Algorithms and techniques for acquiring and tracking an FMCW radar are proposed and verified through simulation, which will help guide future decisions on appropriate radar spoofing countermeasures