Multi-input Multi-output Radar Research Articles

Adaptive detection with training data in partially homogeneous environments for colocated MIMO radar

The issue of adaptive detection is considered for the colocated multi-input multi-output (MIMO) radar in this paper. Meanwhile, the background is partially homogeneous environments (PHE), where the power mismatch is present between the training data and test data. The training data in PHE are utilized to derive effective detectors on the basis of generalized likelihood ratio test, Rao, Wald, Gradient, and Durbin tests. Since the Durbin test-based detector coincides with the Rao test-based detector, the two-step design approach for the Durbin test is used for deriving the new detector. The outcomes of simulation experiments illustrate that the proposed detectors achieve superior effectiveness to existing approaches. Furthermore, the results also show that when the signal mismatch exists, the Wald and Durbin tests maintain robust characteristics. Meanwhile, the Rao test ensures selective property.

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.

Joint DOD and DOA detection for MIMO radar based on signal subspace reconstruction and matching

In this study, the Direction Of Departure (DOD) and Direction Of Arrival (DOA) of signals detection for Multi-Input Multi-Output (MIMO) radar is discussed. A novel signal subspace reconstruction model to match the signal subspace obtained based on the covariance matrix of the array output is developed to enhance the performance of the DOD and DOA detection. In the developed scheme, the technology of beamforming is first introduced to define an objective space in mathematics for the targets to be detected. By considering the orthogonality between the signal subspace and the noise subspace and defining a reconstruction index of the signal subspace, a multi-dimensional objective function of the DOD and DOA is established. Therefore, the problem of DOD and DOA detection is transformed into an optimization of the multi-dimensional objective function. Subsequently, the Quantum-Behaved Particle Swarm Optimization (QPSO) is employed to optimize the multi-dimensional objective function and to determine an optimal signal subspace. At the same time the DOD and DOA can be fast captured. A series of simulations demonstrate that the proposed method provides significant accuracy improvements in DOD and DOA detection, especially for low signal-to-noise ratio thresholds and small snapshots.

Joint Strategy of Power and Bandwidth Allocation for Multiple Maneuvering Target Tracking in Cognitive MIMO Radar With Collocated Antennas

In this article, we propose a joint strategy of power and bandwidth allocation (JSPBA) for multiple maneuvering target tracking (MMTT) in the multi-input multi-output (MIMO) radar with collocated antennas. The basis of our strategy is to optimally allocate the transmitted resources of power and effective bandwidth by the prior information in the closed-loop system of cognitive tracking. On account of the predicted conditional Cramér-Rao lower bound (PC-CRLB) offering a more accurate and time-sensitive lower bound than the standard posterior CRLB (PCRLB), the PC-CRLB of the range, Doppler frequency, and direction-of-arrival (DOA) is derived, normalized and adopted as the optimization criterion. Moreover, in order to solve the nonconvex problem, the initial nonconvex problem is converted into a series of convex problems, which are further formulated as the standard semi-definite programming (SDP) problems and then be solved, by introducing the convex relaxation technique and the two-step solution technique. Simulations confirm the superiority of the proposed JSPBA algorithm, in terms of the overall tracking accuracy in the MMTT scenario.

Target Detection for Distributed MIMO Radar With Non-Orthogonal Waveforms in Cluttered Environments

Orthogonal radar waveforms originating from spatially distributed transmitters (TXs) usually arrive at a receiver (RX) in non-orthogonal forms, as they propagate through different paths with distinct delays and Doppler frequencies in distributed multi-input multi-output (MIMO) radar. Non-orthogonal waveforms complicate the composition of the target and clutter returns, making it more challenging to separate one from the other. In this paper, we consider joint target detection and clutter mitigation in distributed MIMO radar. We first present a general signal model for distributed MIMO radar in cluttered environments. Next, we propose three families of detection solutions, including non-coherent detectors that require no phase estimation and are relatively simple to implement, coherent detectors that offer enhanced detection performance given accurate phase information, and hybrid detectors that are a compromise of the former two, requiring only local phase coherence but no explicit phase estimation. In addition, approximate solutions are proposed in each category with further complexity reduction, using high clutter-to-noise (CNR) approximation or convex relaxation. Simulation results are presented to demonstrate the performance of the proposed detectors, which outperform their earlier counterparts that neglect the presence of clutter.

Hybrid Beamforming for Dual Functioning Multi-input Multi-output Radar using Dimension Reduced-baseband Piecewise Successive Approximation

A reliable and effective hybrid beamforming design for dual functioning multi-input multi-output (MIMO) radar is a challenging research problem because of the concerns related to limited user capacity, interference, and lack of performance trade-off. Due to the shortage of available spectrum, radar frequency spectrum sharing has become vital in emerging 5G communication systems. This will reduce spectrum congestion, therefore receiving significant attention. The existing hybrid beamforming methods reduce the radio frequency (RF) chains but improving user capacity is still a major concern. Future dual radar-communication designs are having challenges in enhancing the user capacity with minimum RF chains, interference mitigation, and hardware cost reduction. This work proposes a novel approach to a hybrid beamforming mechanism for dual-functioning MIMO radar. This mechanism uses the dimension-reduced baseband piecewise successive approximation integrated with a digital precoder. At the analog precoder, the piecewise successive iterative approximation approach is applied to perform the analog beamforming. The novel hybrid beamforming with lens antenna array integration improves the user capacity and reduces power requirement, interference, and expenses. The simulation results showed improved performances compared to existing state-of-the-art methods in terms of bit error rate, spectral efficiency, energy efficiency, and response time.

Signal Accumulation Method for High-Speed Maneuvering Target Detection Using Airborne Coherent MIMO Radar

The airborne coherent multi-input multi-output (MIMO) radar benefiting from coherent integration (including intra-channel integration and inter-channel integration) of multi-channel echoes can obtain superior detection performance for high-speed maneuvering targets. However, due to the coupling motion characteristic between multiple airborne platforms and high-speed targets, the range walk (RW) and Doppler walk (DW) will occur within the intra-channel integration. Besides, the inter-channel signals also exist envelope and phase differences, which will lead to the difficulty in multi-channel integration. We make contributions towards addressing these limitations. On the one hand, we establish a three-dimensional (3-D) geometrical model for high-speed maneuvering target detection with airborne coherent MIMO radar and derive the mathematical expression of radar echoes, while the generalized Radon-Fourier transform (GRFT) is utilized to simultaneously correct the RW and DW for acquiring intra-channel integration. On the other hand, we analyze the properties of GRFT outputs and propose a GRFT-domain integration method to achieve the multi-channel coherent integration. More specifically, the proposed method first constructs a set of coupling equations for the estimation of target motion parameters (i.e., position, velocity and acceleration), then, the target's motion parameters can be estimated by Newton-Raphson method and solving the linear equations. Second, in order to settle the envelope and phase differences between multi-channel echoes, the envelope alignment and phase compensation functions of inter-channel echoes are constituted based on the estimated parameters. Finally, the coherent accumulation of multi-channel echoes can be implemented. Numerical simulations demonstrate the effectiveness of the proposed method.

Spectrally Compatible Waveform Design for Large-Scale MIMO Radar Beampattern Synthesis With One-Bit DACs

Multi-input multi-output (MIMO) radar with massive antennas is promising for high resolution applications. However, a big challenge of this system is that the hardware cost and power consumption will increase significantly, if high-resolution quantizers are adopted. In this article, we consider MIMO radar deployed with one-bit digital-to-analog converters, and investigate the problem of designing one-bit transmit sequence with good spatial and spectral properties. Specifically, the one-bit waveform design problem is formulated by minimizing the mean-square error between the desired and designed transmit beampatterns, subject to spectral constraints. The resulting problem, including a nonconvex quartic objective and a nonconvex discrete constraint, is NP-hard, and an alternating optimization (AltOpt) framework with the aid of “almost equivalent” criterion is thereby developed to handle it. Particularly, in the AltOpt framework, a low-complexity algorithm is developed based on the alternating direction method of multipliers approach. Numerical simulations are provided to show the advantages of the proposed method over the state-of-the-art techniques in terms of the spatial and spectral properties as well as computational complexity.

Research progress on sparse array MIMO radar parameter estimation

Making full use of the virtual aperture expansion property with the sparse array multi-input multi-output (MIMO) radar, we can achieve positive contributions for parameter estimation. We focus on the design of the sparse array MIMO radar and the optimization of conventional algorithms. According to the joint subspace-based method and compressive sensing theory, we study the analysis and design of the sparse arrays, direction of arrival estimation, and joint angle estimation for improved performance and low complexity, aiming to establish systematic parameter estimation theory for the sparse array MIMO radar. Combining the aspects of array optimization design, mixed target parameter estimation, nonideal factor suppression, and distributed platform construction, the future development trend of the sparse array MIMO radar is discussed.

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.

Mutual Coupling Compensation for Compact MIMO Radar

A calibration method to compensate for the mutual coupling and channel imbalance in a compact multi-input multi-output (MIMO) radar array based on turn table measurements is proposed. Two mutual coupling matrices, one for the transmit array and the other for the receive array, are used to model the relationship between the ideal manifold and the measured manifold. The practical factors such as the nonisotropic element pattern and the displacement of the array phase center are also taken into account. All the unknowns are determined by solving the nonlinear least-squares fitting problem iteratively. Using two coupling matrices consists of the mutual coupling mechanism in an MIMO array and has better calibration performance than using one matrix for the virtual array. It also reduces the dimension of the matrix and hence the computation load of calibration. Measurements by a microstrip array with two transmitters and four receivers are used to verify its effectiveness in angle estimation, beamforming, and superresolution of close targets.

Cascaded Regional People Counting Approach Based on Two-Dimensional Spatial Attribute Features Using MIMO Radar

A cascaded regional people counting approach based on two-dimensional (2-D) spatial attribute features using multi-input multi-output (MIMO) radar is presented in this letter. The main task is to detect whether there is a target, and to classify the number of people in a closed metal ramp. The difficulty to accurately detect the number of people lies in the presence of strong static clutter and the strong multipath effects in the environment. In this letter, range-angle spectrum is first computed and exploited to extract the 2-D spatial attribute features, namely the effective peaks of range-angle spectrum, the spacing features and the energy block features. These features reflect the relative position relationships between the people and the environment as well as the physical structure of a certain environment. Then, a maximum likelihood classifier and a random forest classifier are cascaded and applied to detect the target presence and classify the number of people. The experiment results show that the average classification accuracy of 0 person, 1 person, and multiple persons is above 92.55%, which outperforms the existing people counting approaches.

Regional People Counting Approach Based on Multi-Motion States Models Using MIMO Radar

The existing radar-based regional people counting methods usually perform a single prediction model to classify the number of people. However, signals of different motion states of the target vary greatly, which results in some features aliasing among different numbers of people. It is difficult to find an optimal single prediction model to adapt the different motion states of the target, especially in the case of limited training data. To address this problem, we propose a regional people counting approach based on multi-motion states models using multi-input multioutput (MIMO) radar. We first use the state classification model with the 2-D spatial attribute features to classify the motion state of the target and then cascade the corresponding motion state model to classify the number of people. In particular, we incorporate the mixed state into the proposed approach to deal with the situations of mixed motion states of targets. The experimental results show that the proposed approach can be implemented in an embedded operating system, and its average classification accuracy of zero person, one person, and multiple persons is above 97.45% and much better than that of the existing regional people counting methods in the metal ramp with strong static clutter and strong multipath, which is a small room and too small for many people.

Adaptive Detectors for Colocated MIMO Radar With Training Data

In this letter, we consider the problem of target detection in unknown Gaussian noise for a colocated multi-input multi-output (MIMO) radar. To improve the detection performance, we adopt the training data, which were not utilized in existing references for the considered problem. We derive the generalized likelihood ratio test (GLRT) and Rao and Wald tests. Moreover, the corresponding analytical expressions for the probabilities of detection (PDs) and probabilities of false alarm (PFAs), which indicate that the proposed detectors have constant false alarm rate (CFAR) properties. Simulation results show that the proposed detectors can provide higher PDs than the existing detectors which do not utilize training data.

Delay Compensation for Distributed MIMO Radar With Non-Orthogonal Waveforms

Distributed multi-input multi-output (MIMO) radar with non-orthogonal waveforms has become a critical problem because waveform orthogonality may be lost as waveforms experience distinct delays and Doppler across different transmit-receive propagation paths. In such cases, the widely used matched filter (MF) cannot perfectly separate the waveforms, and it outputs the filtered echo of the desired waveform (auto term) as well as multiple undesired waveform residuals (cross terms). In this paper, a transmit delay compensation scheme is proposed by employing a set of transmit delay compensation variables to control the cross terms so that they can be utilized to enhance target detection. Specifically, the probability of detection is maximized by optimizing the delay parameters. To solve the resulting nonconvex problem, an optimum solution based on multi-dimensional search and a computationally efficient suboptimal method are proposed. Simulation results show that the proposed delay compensation approach can substantially improve the target detection performance.

MIMO Radar Aided mmWave Time-Varying Channel Estimation in MU-MIMO V2X Communications

Robust channel estimation in time-varying channels is used to guarantee the quality of communication services, especially for Vehicle-to-Everything (V2X) scenarios. To improve the channel estimation accuracy and reduce the pilot overhead, multi-input multi-output (MIMO) radar is deployed to assist millimeter wave (mmWave) channel estimation. In this paper, we propose a MIMO radar aided channel estimation scheme using deep learning (DL) for the uplink mmWave multiuser (MU)-MIMO communications. To allocate pilot resources reasonably, we design a transmission frame structure of joint radar module and communication module, which divides the estimation scheme into two stages, i.e., the arrival/departure (AoA/AoDs) estimation stage and the gain estimation stage. In view of the imperfections of array elements in practice, we propose an AoA/AoDs estimation algorithm based on subspace reconstruction in the AoA/AoDs estimation stage named two-step angle estimation (TSAE) algorithm. In the gain estimation stage, a DL based channel gain estimator is designed. An autoencoder combined with residual structure named residual denoising autoencoder (RDAE) is proposed to eliminate the noise on wireless signals, which is passed into the least square (LS) estimation module to obtain gains. Simulation results demonstrate that the MIMO radar aided and DL-based channel estimator provides the efficient estimation performance of the high-mobility mmWave channel with fewer training resources.

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.

Spatial- and Range- ISLR Trade-Off in MIMO Radar Via Waveform Correlation Optimization

This paper aims to design a set of transmitting waveforms in cognitive colocated Multi-Input Multi-Output (MIMO) radar systems considering the simultaneous minimization of spatial- and the range- Integrated Sidelobe Level Ratio (ISLR). The design problem is formulated as a bi-objective Pareto optimization under practical constraints on the waveforms, namely total transmit power, peak-to-average-power ratio (PAR), constant modulus, and discrete phase alphabet. A Coordinate Descent (CD) based approach is proposed, in which at every single variable update of the algorithm we obtain the solution of the uni-variable optimization problems. The novelty of the paper comes from deriving a flexible waveform design problem applicable for 4D imaging MIMO radars which is optimized directly over the different constraint sets. The simultaneous optimization leads to a trade-off between the two ISLRs and the simulation results illustrate significantly improved trade-off offered by the proposed methodologies.

Signal Detection in Distributed MIMO Radar With Non-Orthogonal Waveforms and Sync Errors

Although routinely utilized in literature, orthogonal waveforms may lose orthogonality in distributed multi-input multi-output (MIMO) radar with spatially separated transmit (TX) and receive (RX) antennas, as the waveforms may experience distinct delays and Doppler frequency offsets unique to different TX-RX propagation paths. In such cases, the output of each waveform-specific matched filter (MF), employed to unravel the waveforms at the RXs, contains both an \auto term and multiple cross terms, i.e., the filtered response of the desired and, respectively, undesired waveforms. We consider the impact of non-orthogonal waveforms and their cross terms on target detection with or without timing, frequency, and phase errors. To this end, we present a general signal model for distributed MIMO radar, examine target detection using existing coherent/non-coherent detectors and two new detectors, including a hybrid detector that requires phase coherence locally but not across distributed antennas, and provide a statistical analysis leading to closed-form expressions of false alarm and detection probabilities for all detectors. Our results show that cross terms can behave like foes or allies, respectively, if they and the auto term add destructively or constructively, depending on the propagation delay, frequency, and phase offsets. Regarding sync errors, we show that phase errors affect only coherent detectors, frequency errors degrade all but the non-coherent detector, while all are impacted by timing errors, which result in a loss in the signal-to-noise ratio (SNR).