Colocated MIMO Radar Research Articles

A Novel Method based on Sequential Unconstrained Programming for Transmit Beamforming in Colocated MIMO Radars

A Novel Method based on Sequential Unconstrained Programming for Transmit Beamforming in Colocated MIMO Radars

One-Bit Target Detection in Colocated MIMO Radar with Colored Background Noise

One-Bit Target Detection in Colocated MIMO Radar with Colored Background Noise

Height-Adaptive Human Gait Model Through Radar-Driven Pipeline With Two Co-Located mmWave MIMO Radars

Height-Adaptive Human Gait Model Through Radar-Driven Pipeline With Two Co-Located mmWave MIMO Radars

A Nested-Nested Sparse Array Specially for Monostatic Colocated MIMO Radar with Increased Degree of Freedom.

This paper mainly investigates the problem of direction of arrival (DOA) estimation for a monostatic MIMO radar. Specifically, the proposed array, which is called a nested-nested sparse array (NNSA), is structurally composed of two nested subarrays, a NA with N1+N2 elements and a sparse NA, respectively, with N3+N4 elements. The design process of NNSA is optimized into two steps and presented in detail. Setting NNSA as transmitter/receiver arrays, we derive the closed-form expression of consecutive DOFs and calculate the mutual coupling coefficient. Eventually, extensive simulations are carried out and the results verify the superiority of the proposed array over the previous arrays in terms of consecutive DOFs, array aperture and mutual coupling effect.

LPI-constrained collaborative transmit beampattern optimization and resource allocation for maneuvering targets tracking in colocated MIMO radar network

Considering tracking multiple maneuvering targets with the presence of a hostile interceptor in the modern electronic warfare (EW) environment, how to maximize targets tracking performance while ensuring system’s anti-interception capability is of essential significance for the radar network consisting of multiple colocated MIMO radar (CMR) nodes. In this paper, we propose a novel low probability of intercept (LPI) constrained collaborative beampattern optimization and resource allocation (LPI-CBORA) strategy for multiple maneuvering targets tracking (MMTT) in the CMR network (CMRN). More precisely, the LPI-CBORA strategy is formulated as a nonconvex optimization problem, where the node-target assignment, beampattern design and bandwidth allocation are collaboratively considered for the first time. To tackle the nonconvex problem effectively, an efficient two-stage solution algorithm incorporating with the successive convex approximation (SCA) and the cyclic minimization algorithm (CMA) is developed. Numerical simulations are provided to verify the effectiveness and superiority of the proposed LPI-CBORA strategy.

Joint Resource and Detection Threshold Optimization for Maneuvering Targets Tracking in Colocated MIMO Radar Network

Compared with monostatic phased array radar, colocated MIMO radar network (CMRN) that owns richer controllable resources is able to offer superior tracking performance. In this paper, for targets tracking in cluttered environment with CMRN, a joint detection threshold optimization and transmit resource allocation (JDTO-TRA) algorithm is proposed to simultaneously maximize the tracking performance and minimize the resource consumption, whose key mechanism is to adaptively control the working parameters at both transmitters and detectors via optimization technique. To be more specific, we first formulate the JDTO-TRA strategy as a bi-objective optimization problem, where both the tracking performance and the resource consumption are considered in the objective function. The analytical expression for the posterior Cramér-Rao lower bound (PCRLB) incorporated with information reduction factor (IRF), which is caused by the measurement origin uncertainty (MOU), is given and adopted as the tracking performance metric, and the total transmit energy of CMRN is employed as the resource consumption metric. Hereafter, to tackle the formulated mixed-integer and non-convex problem effectively, an iterative and efficient two-step solution technique incorporating the simulated annealing (SA)-based hybrid particle swarm optimization (HPSO) and the cyclic minimization algorithm (CMA) is proposed, where the radar-target assignment, the sub-array number and the transmit energy of each activated colocated MIMO radar, and the false alarm rate (FAR) of the target can be controlled jointly and adaptively. Numerical simulation results are provided to demonstrate the effectiveness as well as the advantages of the JDTO-TRA algorithm compared with other popular existing algorithms.

Enumeration PCRLB-Based Power Allocation for Multitarget Tracking With Colocated MIMO Radar Systems in Clutter

An effective resource allocation strategy can maximize the remote sensing performance of radar systems, such as target detection and tracking. In this paper, two typical power allocation (PA) strategies are developed for the multi-target tracking (MTT) task in colocated MIMO (C-MIMO) radar systems with the consideration of the clutter. The multi-beam concept and the posterior PDF fusion are adopted by the C-MIMO radar system to obtain the global posterior distribution. Specifically, each radar generates multiple simultaneous beams with controllable power during each interval. To ensure that the limited system resources can be utilized effectively, the online PA scheme is implemented according to the prior knowledge predicted from the tracking cyclic recursive feedback results. The posterior Cramér-Rao lower bound (PCRLB) is derived by enumerating all possible target detection and false alarm occurrence cases, and is utilized as the tracking performance metric since it provides a more accurate lower bound on the target state estimation in clutter. Besides, to solve the computationally expensive problem of this PCRLB caused by enumeration operation, we propose a two-step approximate approach. Then, combined with the system resource configuration, two different types of resource optimization problems are designed, namely, performance maximization for a fixed power budget and direct resource minimization. These formulated PA problems are shown to be non-convex and non-linear. Therefore, we further propose a modified particle swarm optimization (MPSO) algorithm to solve these problems efficiently. Simulation results verify the superiority and effectiveness of the proposed PA strategies in terms of tracking performance in clutter.

Adaptive Design for Colocated MIMO Radar Beamspace Localization

Adaptive Design for Colocated MIMO Radar Beamspace Localization

Efficient Calibration of Colocated MIMO Radar

Efficient Calibration of Colocated MIMO Radar

Joint Design of Transmitting Waveform and Receiving Filter for Colocated MIMO Radar

Joint Design of Transmitting Waveform and Receiving Filter for Colocated MIMO Radar

MIMO waveform design for radar and communication integrated system in the presence of active interferences

In this paper, we propose a modified space-time coding scheme to design transmit waveform for the radar and communication integrated system in the presence of active interferences. Based on the shared co-located MIMO radar, the constraints of communication requirement, waveform orthogonality and active anti-interference are considered for the waveform design. To avoid the loss of information symbols for communication transmission and obtain sufficient anti-interference performance for radar detection, the transmit beam-pattern at each sub-pulse duration time is controlled to maintain the radiation powers unchanged towards communication direction and interference directions, which is distinct to the conventional transmit beamforming design. Furthermore, to tackle the coupling of the time-domain orthogonality and the space-domain beam-pattern, the constant envelope constraint of transmit waveform is relaxed to obtain the degree of freedom for space-time waveform design. Thus the peak-to-average power ratio (PAPR) reduction for transmit waveform is also discussed. Simulation results are finally presented to demonstrate the effectiveness of the proposed methods.

Signal Model and Parameter Estimation for Colocated Mimo Radar

In this paper, we consider the signal model and parameter estimation for multiple-input multiple-output (MIMO) radar with colocated antennas on stationary platforms. Considering internal clutter motion, a closed form of the covariance matrix of the clutter signal is derived. Based on the proposed closed form and low rank property of the clutter covariance matrix and by using the singular value decomposition, we have proposed a subspace model for the clutter signal. Following the proposed signal model, we have provided maximum likelihood (ML) estimation for its unknown parameters. Finally, the application of the proposed ML estimation in space time adaptive processing (STAP) is investigated in simulation results. Our ML estimation needs no secondary training data and it can be used in scenarios with nonhomogeneous clutter in range.

Joint Transceiver Design for Radar-Communication Spectral Sharing Systems by Minimizing Effective Interference Power

This brief investigates a spectrum sharing architecture for a colocated MIMO radar and a MIMO communication system. We develop an alternative formulation for joint resource allocation, aiming at minimizing the effective power of radar interference under the power and similarity constraints. The communication system mutual information (MI) is also simultaneously concerned in order to guarantee the communication performance. The degrees of freedom for system design are both the radar transceiver and the communication transmitter. For the joint designed original non-convex problem, an alternating iterative algorithm with multi-subproblems is derived based on the majorization-minimization algorithm, minimum variance distortionless response (MVDR) and semidefinite programming (SDP) algorithm. The convergence of the algorithm is guaranteed. Finally, we assess the effectiveness of the proposed approach via numerical results.

Constant modulus waveform design for colocated MIMO radar: A convex relaxation approach

This paper considers the waveform design for colocated MIMO radar to improve target detectability embedded in signal-dependent interferences, and maximize the SINR (signal-to-interference-plus-noise ratio). Due to constant modulus constraint, the problem is indeed non-convex and NP-hard. To address the challenge of this intractable problem, we propose a novel optimization framework for solving the problem based on convex relaxation. In the proposed algorithm, the original optimization problem is reformulated heuristically by introducing an auxiliary variable. Then it can be relaxed to a convex optimization problem and efficiently solved by alternative optimizing. Numerical results show that the proposed algorithm improves the beampattern performance significantly by generating much deeper notches at the interference directions, then obtains higher SINR over the state-of-the-art method.

Joint Design of Colocated MIMO Radar Constant Envelope Waveform and Receive Filter to Reduce SINR Loss.

In this paper, we aim at the problem that MIMO radar’s target detection performance is greatly reduced in the complex multi-signal-dependent interferences environment. We propose a joint design method based on semidefinite relaxation (SDR), fractional programming and randomization technique (JD-SFR) and a joint design method based on coordinate descent (JD-CD) to solve the actual transmit waveform and receive filter bank directly to reduce the loss of strong interference to the output signal-to-interference-plus-noise ratio (SINR) of the radar system. Therefore, the maximization of output SINR is taken as the criterion of the optimization problem. The designed waveforms take into account the radar transmitter’s hardware requirements for constant envelope waveforms and impose similarity constraints on the waveforms. JD-SFR uses SDR, fractional programming and randomization technique to deal with the non-convex optimization problems encountered in the solution process. JD-CD transforms the optimization problem into a function of the phase of the waveform and then solves the transmit waveform based on CD. Compared with other methods, the proposed method has lower output SINR loss under strong power interference and forms deep nulls on the direction beampattern of multiple interference sources, which indicates that it has better anti-interference performance.

Intelligent Reflecting Surface-Enhanced Target Detection in MIMO Radar

Motivated by the improved capacity and energy performance by intelligent reflecting surface (IRS) in multiinput-multioutput (MIMO) communication systems, we apply the IRS in the colocated MIMO radar systems to increase the received power and improve the target detection performance. To deal with this, we introduce an IRS-assisted target detection algorithm, in which the reflection optimization with practical IRS model is carried out, and then the optimal beamforming for target detection is proposed. Meanwhile, the Cramér-Rao bound (CRB) of the proposed method shows the effectiveness of utilizing the IRS in MIMO radar systems. Simulation results illustrate that the proposed method can achieve better target detection performance. With the increased number of elements in the IRS, the proposed method has better angle resolution and detection probability compared with a non-IRS-assisted MIMO radar system.

SINR Enhancement in Co-located MIMO RADAR with Multiple Targets

SINR Enhancement in Co-located MIMO RADAR with Multiple Targets

Machine Learning and Deep Learning Techniques for Colocated MIMO Radars: A Tutorial Overview

Radars are expected to become the main sensors in various civilian applications, ranging from health-care monitoring to autonomous driving. Their success is mainly due to the availability of both low cost integrated devices, equipped with compact antenna arrays, and computationally efficient signal processing techniques. An increasingly important role in the field of radar signal processing is played by machine learning and deep learning techniques. Their use has been first taken into consideration in human gesture and motion recognition, and in various healthcare applications. More recently, their exploitation in object detection and localization has been also investigated. The research work accomplished in these areas has raised various technical problems that need to be carefully addressed before adopting the above mentioned techniques in real world radar systems. In this manuscript, a comprehensive overview of the machine learning and deep learning techniques currently being considered for their use in radar systems is provided. Moreover, some relevant open problems and current trends in this research area are analysed. Finally, various numerical results, based on both synthetically generated and experimental datasets, and referring to two different applications are illustrated. These allow readers to assess the efficacy of specific methods and to compare them in terms of accuracy and computational effort.

Joint Radar Scheduling and Beampattern Design for Multitarget Tracking in Netted Colocated MIMO Radar Systems

In this letter, a joint radar scheduling and beampattern design (JRSBD) strategy is proposed to track multiple targets by a netted colocated multiple-input multiple-output (C-MIMO) radar system in clutter. The mechanism is to jointly optimize the radar scheduling and the waveform correlation matrix of each C-MIMO radar to maximize the tracking performance. First, we develop the deterministic covariance, as the performance metric, to quantify the actual target state estimate accuracy. Second, the JRSBD strategy is formulated as an optimization problem with some system constraints. The resulting optimization problem contains both binary and continuous variables and is nonconvex. Finally, we propose a sequential convex programming method to solve it. Numerical simulations demonstrate that the JRSBD strategy can effectively improve the target tracking accuracy.

Robust Power Allocation for Resource-Aware Multi-Target Tracking With Colocated MIMO Radars

Power allocation is an essential part of the design of colocated multiple-input and multiple-output (C-MIMO) radar systems for multi-target tracking (MTT). A widely adopted power allocation approach is to directly minimize the amount of allocated power while satisfying the desired performance for each tracked object so as to conserve the power resources. However, this approach may not lead to a satisfactory solution when the available power is not sufficient to achieve the desired performance for all the targets. To overcome the limitations of this approach, a robust power allocation (RPA) methodology is proposed in this paper based on the quality of service framework. At its core, the proposed RPA employs a task utility function that quantifies the tracking performance for different power allocations in a flexible manner. The Bayesian Cramér-Rao lower bound (BCRLB) is utilized to formulate the task utility function since it provides a lower bound on the accuracy of target state estimates. By using a set of weights to quantify the importance of different tracked objects, the objective function of RPA is modeled as the weighted sum of task utility functions. This formulation of the RPA problem is demonstrated to be a non-convex optimization problem in general. We show that the task utility function is unimodal. Based on the specific structure of the task utility function, we propose an iterative parallel search algorithm to find the solution. Numerical experiments involving scenarios with both sufficient and insufficient power resources demonstrate the robustness and efficiency of the proposed strategy.