Multi-input Multi-output Radar System Research Articles

Joint Transmit Waveform and Reflection Design for RIS-Assisted MIMO Radar Systems

In this letter, we investigate to deploy multiple reconfigurable intelligent surfaces (RISs) in multi-input multi-output (MIMO) radar systems for achieving better clutter suppression and multi-target detection performance. In particular, the space-time transmit waveform and receive filter of the radar and the reflection coefficients of the RISs are jointly optimized to maximize the minimum radar signal-to-interference-plus-noise ratio (SINR) under the constant-modulus power constraint and the restrict of RISs. We develop an efficient alternating algorithm to solve the resulting complicated non-convex problem. Simulation studies illustrate the advantages of deploying RISs in MIMO radar systems and the effectiveness of the proposed algorithm.

Geometric Optimization of Distributed MIMO Radar System for Accurate Localization in Multiple Key Subareas

In this paper, we study a geometric optimization problem of distributed multi-input multi-output (MIMO) radar system. We aim to enhance both the system surveillance and localization performance by adjusting the node positions. Different from existing researches, from a practical perspective, we consider the importance differences of the performance in different subareas and the coupled relationship between different radar performance. To optimize the node placement scheme, we first establish evaluation metrics respectively for surveillance and localization performance. Then, we formulate a multi-objective geometric optimization problem with complex coupled constraints. Considering that the final optimization problem is difficult to tackle due to its high dimensionality, non-convexity, and especially the complex coupled constraint, we propose a novel self-constrained multi-objective particle swarm optimization (SC-MOPSO) algorithm for fast computation. The SC-MOPSO algorithm can be efficiently applied to the established optimization problem with the complex coupled constraint satisfied. Moreover, the obtained Pareto-optimal solution set is more complete in comparison with the state-of-the-art algorithms. Finally, various numerical results show that the proposed method can effectively enhance both the system surveillance and localization performance while the complex coupled constraints are satisfied.

Salp swarm optimization in hybrid beamforming for MIMO radar

The Multi Input Multi Output (MIMO) radar waveform diversity Significantly improves parameter identifiably than phased-array radar performance. Precoding, combining and spatial multiplexing techniques improves the data throughput and reliability of the transmission in MIMO systems. But increment in transmit and receive elements in MIMO antenna array induces considerable increase in required power for hardware and computation cost. Hybrid beamforming employs fewer RF-to-baseband chains. With conscious selection of the weights for pre-coding and combining, hybrid beamforming establishes perfect trade-off between complexity, performance, cost, and power consumption in practical applications. Performance of MIMO radar system can be improved using newly developed bio inspired metaheuristic algorithms as compared to conventional and adaptive beamforming algorithms. In this work the Salp Swarm algorithm (SSA) is implemented to optimize the performance of hybrid beamforming using Raleigh channel and considering the bit error rate and normalized array power parameters. The swarming behavior of salps when navigating and foraging in oceans is the inspiration behind the SSA optimization algorithm. The obtained results are compared with the conventional phase-shift as well as adaptive linearly constrained minimum variance beamforming algorithms on simulation platform with standard considerations. It is observed that this new approach of Salp swarm algorithm is having improved and much better performance with the considered parameters.

Target Detection in Intelligent Reflecting Surface Aided Distributed MIMO Radar Systems

Motivated by the intelligent reflectingsurface (IRS) applications in multi-input multi-output (MIMO) communication systems which can improve capacity and energy performance, we apply the IRS in the distributed MIMO (DMIMO) radar systems to improve the target detection performance. In the IRS-aided DMIMO radar systems, each IRS is controlled by its receiver, and the receivers can receive the signals reflected from the target and the IRS simultaneously. In the proposed method, the IRS is first optimized, and then the target detection is carried out. The detection probability, false alarm probability, and the detector's divergence are analyzed and compared with the traditional DMIMO radar. Simulations verify the superiorities of the proposed IRS-aided DMIMO radar.

Geometric Optimization of Distributed MIMO Radar Systems With Spatial Distance Constraints

We consider a geometric optimization problem of distributed multi-input multi-output (MIMO) radar systems with widely separated radar nodes in this article. The aim is to maximize the radar surveillance performance in a given area of interest by adjusting the node positions, while satisfying practical spatial distance constraints among radar nodes. Typical constraints can be, for example, the maximum distance constraints between nodes and fusion centers (FCs) due to limited communication and the minimum distance constraints to ensure a better system spatial diversity. To achieve this goal, we first derive an analytical expression for a weighted coverage ratio (WCR) metric to evaluate the system surveillance performance. Then, using the WCR metric as the objective function, we formulate a spatial constrained geometric optimization problem for three typical MIMO radar system architectures, each of which has a unique expression of distance constraints. However, the formulated optimization problem is computationally intractable for practical scenarios due to its high dimensionality, non-convexity and especially the complex spatial constraints. To solve this problem, we propose an enhanced particle swarm optimization (PSO) algorithm, and different from the standard PSO, the particles of the proposed enhanced PSO can properly consider the distance constraints within themselves during swarm optimization process. Finally, various numerical studies show that the proposed method can effectively maximize the surveillance performance while satisfying the complex distance constraints.

First Demonstration of Joint Wireless Communication and High-Resolution SAR Imaging Using Airborne MIMO Radar System

Special attention has been devoted to joint wireless communication and radar sensing systems in recent years. However, since communication and radar have conflict requirements in terms of waveforms, transceiver developments, and signal processing algorithms, realization of this system concept is still a great challenge. In this paper, we introduce an airborne multi-input multi-output (MIMO) radar, along with the modified orthogonal frequency-division multiplexing (OFDM) and space–time coding (STC) waveform schemes, for the implementation of the joint wireless communication and synthetic aperture radar (SAR) imaging. The proposed system, which simultaneously transmits multidimensional waveforms with reconfigurable channels, can acquire adequate degrees of freedom. Thereby, it becomes a feasible method to perform both data transmission and high-resolution SAR imaging at the same time without intramodal interference. Theoretical analysis is validated by laboratory and flight experiments. Through our analysis, we aim to open up a new perspective of using MIMO radar to realize joint wireless communication and SAR imaging.

Generalized Ambiguity Function for MIMO Radar Systems

In this paper, a generalized signal model is presented to accommodate both narrowband and wideband signals in a multi-input multi-output (MIMO) sensor system scenarios. The derived model is then used to define a MIMO ambiguity function based on the Kullback–Leibler divergence. Moreover, the proposed formulation is parametrized using the signal and channel correlation matrices to account for different waveform and sensor placement designs, thereby allowing a flexible modeling approach. A comparison between the proposed definition and the more conventional approach of summing the squared matched filter outputs is presented for different sensors and waveforms configurations.

Design and Analysis of a UWB MIMO Radar System with Miniaturized Vivaldi Antenna for Through-Wall Imaging

The ultra-wideband (UWB) multi-input multi-output (MIMO) radar technique is playing a more and more important role in the application of through-wall detection because of its high resolution, lower antenna requirements, and efficient data capturing ability. This paper develops a novel UWB MIMO radar system using a stepped-frequency continuous-wave (SFCW) signal, which is designed to detect human targets behind the regular brick and concrete wall. In order to balance high range resolution and wall-penetration depth, a novel miniaturized Vivaldi antenna with desired bandwidth of 0.5–2.5 GHz was designed, simulated, manufactured, and successfully used in through-wall imaging. To suppress the artifacts in the focused image and reduce the computing complexity, the cross-correlation-based time domain back projection (CC-TDBP) algorithm was developed. In addition, a through-wall imaging model was established, based on which the effects of the wall on the refraction of electromagnetic (EM) waves and the reduction of velocity are compensated. Finally, different experiments were conducted for multiple stationary targets utilizing the designed radar system, and the improved BP-based algorithms are applied to focus the targets behind the wall more accurately. The reconstructed two-dimensional (2D) images illustrate that the designed MIMO radar system can successfully detect and image human targets in the air and behind the wall.

DOA and DOD Estimation Based on Double 1-D Root-MVDR Estimators for Bistatic MIMO Radars

This paper deals with direction of arrival (DOA) and direction of departure (DOD) estimation of existing targets based on polynomial root finding estimators with minimum variance distortionless response (MVDR) criterion in bistatic multi-input multi-output radar systems. First, the presented estimator transforms the conventional two-dimensional searching approach into double one-dimensional polynomial root-MVDR approach for DOA and DOD estimation. Thus, the pairing operation can be obtained automatically. Second, to mitigate the influence of noise corruption and the estimate radial bias, we also presented a differential polynomial root-MVDR estimator to obtain more accuracy estimation performance. Finally, simulation results are provided to verify the efficiency of the proposed estimators.

THREE-DIMENSIONAL MICROMOTION SIGNATURE EXTRACTION OF ROTATING TARGETS IN OFDM-LFM MIMO RADAR

In monostatic radars systems, only the micromotion signatures projected onto the radar line-of-sight (LOS) can be observed from echoes. As a result, the obtained micromotion signatures (e.g., the radius length of rotation) are sensitive to the radar LOS. In this paper, we propose a method for the accurate estimation of three-dimensional (3-D) micromotion signature with the orthogonal frequency division multiplexing | linear frequency modulation (OFDM-LFM) multi-input multi-output (MIMO) radar technique, which makes use of the advantages of the multi-view of MIMO radar systems and the broad bandwidth of the OFDM-LFM signals. In the proposed method, the Hough transform and Orthogonal Matching Pursuit (OMP) algorithm are introduced to extract the m-D curve features from echoes, and then the 3-D micromotion signatures of the rotating targets are obtained by solving nonlinear multivariable equation systems. The extracted 3-D micromotion signatures are no longer sensitive to the radar LOS, and can provide realistic feature information for target recognition. Simulations are given to validate the efiectiveness of the proposed method.

Adaptive subspace detector for multi-input multi-output radar in the presence of steering vector mismatch

This study studies the problem of adaptive target detection by multi-input multi-output (MIMO) radar with multiple antennas at each of the receiver sites. MIMO radar has been postulated to have a number of advantages over its monostatic counterpart. However, many previous publications have not necessarily made valid assumptions in assessing performance potential. Here, the authors consider the issue of the overall MIMO radar system steering vector which should be aligned with the desired pointing direction. However, misalignment can occur because of calibration errors, receiver channel imperfections and beam pointing errors. To account for this misalignment, the steering vector is assumed to belong to a known linear subspace. At the design stage, the authors resort to a generalised likelihood ratio principle and Rao test criterion. Subsequently, MIMO versions of the subspace generalised likelihood ratio test (MIMO-SGLRT), the subspace adaptive beam-former orthogonal rejection test (MIMO-SABORT) and the subspace Rao (MIMO-SRao) detector are developed to improve the robustness of MIMO radar detection performance for the case of steering vector mismatch. The constant false alarm rate properties of the three detectors are demonstrated. Finally, the performance of the MIMO-SGLRT, MIMO-SABORT and MIMO-SRao detectors in both the matched and mismatched steering vector cases are numerically evaluated. The results show the robustness of the proposed detectors to steering vector mismatch.

Range Compression and Waveform Optimization for MIMO Radar: A CramÉr–Rao Bound Based Study

A multi-input multi-output (MIMO) radar system, unlike standard phased-array radar, can transmit via its antennas multiple probing signals that may be correlated or uncorrelated with each other. This waveform diversity offered by MIMO radar enables superior capabilities compared with a standard phased-array radar. One of the common practices in radar has been range compression. We first address the question of ldquoto compress or not to compressrdquo by considering both the Cramer-Rao bound (CRB) and the sufficient statistic for parameter estimation. Next, we consider MIMO radar waveform optimization for parameter estimation for the general case of multiple targets in the presence of spatially colored interference and noise. We optimize the probing signal vector of a MIMO radar system by considering several design criteria, including minimizing the trace, determinant, and the largest eigenvalue of the CRB matrix. We also consider waveform optimization by minimizing the CRB of one of the target angles only or one of the target amplitudes only. Numerical examples are provided to demonstrate the effectiveness of the approaches we consider herein.