Colocated Multiple-input Multiple-output Radar Research Articles

Performance Evaluation of Photonics-Based Coherent MIMO Radar Systems for Maritime Surveillance

Multiple-input multiple-output (MIMO) radars offer significant advantages over conventional standalone radars in terms of target detection and localization capabilities. However, to fully exploit their potential, such systems require excellent time and phase synchronization among the central unit and the radar heads. Only recently, thanks to microwave photonics (MWP) techniques, it has been possible to develop the first coherent MIMO radar demonstrators. In this paper, a simulation tool in MATLAB programming language is proposed to model coherent MIMO radars based on MWP techniques in maritime surveillance applications. Moreover, a novel approach for estimating the radar cross section of extended maritime targets is presented. The system performance in co-located and distributed MIMO configuration, as well as in single- and multi-band operations, is evaluated by means of relevant key performance indicators (KPIs). Simulations are carried out in two close-to-reality scenarios. In the first, a co-located MIMO radar is mounted on top of a patrolling vessel. In the second, a distributed MIMO radar is deployed inside a port. The proposed KPIs are analyzed in terms of both geometric and frequency diversities of the system, laying the foundations for general system optimization criteria valid in any given surveillance application.

Colocated MIMO Radar Waveform-Array Joint Optimization for Sparse Array.

Colocated multiple-input multiple-output (MIMO) radar can transmit a group of distinct waveforms via its colocated transmit antennas and the waveform diversity leads to several advantages in contrast to conventional phased-array radar. The performance depends highly on the degrees available, and element spacing can be deemed as another source of degrees of freedom. In this paper, we study the joint waveform and element spacing optimization problem. A joint waveform and array optimization criterion is proposed to match the transmit beampattern, the suppression range, and the angular sidelobes, under the constraints of minimal element spacing and total array aperture. Meanwhile, the effect of receive beamforming on suppressing mutual correlation between returns from different spatial directions is also incorporated into the optimization criterion. The optimization problem is solved by the sequential quadratic programming algorithm. Numerical results indicate that with more degrees of freedom from array spacings, colocated MIMO radar achieves a better transmit beampattern matching performance and a lower sidelobe level, compared with a fixed half-wavelength spaced array, but the benefits from additional degrees of freedom from array spacing optimization have a limit.

A Novel Approach for Direction of Arrival Estimation in Co-Located MIMO Radars by Exploiting Extended Array Manifold Vectors.

Multiple-input multiple-output (MIMO) radars enable better estimation accuracy with improved resolution in contrast to traditional radar systems; thus, this field has attracted attention in recent years from researchers, funding agencies, and practitioners. The objective of this work is to estimate the direction of arrival of targets for co-located MIMO radars by proposing a novel approach called flower pollination. This approach is simple in concept, easy to implement and has the capability of solving complex optimization problems. The received data from the far field located targets are initially passed through the matched filter to enhance the signal-to-noise ratio, and then the fitness function is optimized by incorporating the concept of virtual or extended array manifold vectors of the system. The proposed approach outperforms other algorithms mentioned in the literature by utilizing statistical tools for fitness, root mean square error, cumulative distribution function, histograms, and box plots.

Weak Signal Detection With Low-Bit Quantization in Colocated MIMO Radar

This paper addresses the weak signal detection problem in a massive colocated multiple-input multiple-output (MIMO) radar. To cope with the sheer amount of data produced by the large-scale antennas, a low-bit quantizer is introduced in the sampling process to enable both for hardware limitations and a high detection performance. The generalized likelihood ratio test (GLRT) detector is proposed for the quantized data, with the batch gradient descent algorithm being introduced to form an estimate of the unknown parameters. Furthermore, as a low-complexity alternative to the GLRT detector, we propose a multi-bit Rao detector, yielding a closed-form test statistic, whose theoretical distribution is also presented. Finally, we refine the design of the quantizer by optimizing the quantization thresholds, which are obtained using the particle swarm optimization algorithm. Results from simulation and experimental data demonstrate the performance of the detectors using both unquantized and quantized data. They corroborate the theoretical analyses and show that the performance with 3-bit quantization yields a performance that approaches the cases without quantization, while reducing the overall complexity of the system substantially.

A Joint Scheme of Antenna Placement and Power Allocation in a Compressive-Sensing-Based Colocated MIMO Radar

The spatial sparsity of targets in the radar scene is widely used in multiple-input multiple-output (MIMO) radar signal processing, either to improve the detection/estimation performance of the radar or to reduce the cost of the conventional MIMO radars (e.g., by reducing the number of antennas). While sparse target estimation is the main challenge in such an approach, here, we address the design of a compressive-sensing-based MIMO radar, which facilitates such estimations. In particular, we propose an efficient solution for the problem of joint power allocation and antenna placement based on minimizing the number of transmit antennas while constraining the coherence of the sensing matrix. Numerical results confirm the superiority of the proposed method over the existing ones.

Joint Waveform Control and Resource Optimization for Maneuvering Targets Tracking in Netted Colocated MIMO Radar Systems

In netted colocated multiple-input multiple-output (MIMO) radar system (NCMRS), constrained to the limited resource, one must decide when to run the NCMRS, which nodes are to be activated, how much energy should be consumed, and which waveform type as well as parameter should be transmitted by each activated node. To this end, in this article, we consider the problem of joint waveform control and space-time resource management for maneuvering targets tracking in NCMRS. First, the joint waveform type as well as parameter selection and space-time resource management optimization model is proposed, where both the network system resource consumption and the overall tracking performance of maneuvering targets are considered and employed as the network comprehensive cost. Then, to solve the established optimization problem efficiently, a modified particle swarm optimization (MPSO)-based joint waveform control and space-time resource optimization (JWCSTRO) algorithm is put forward. In the MPSO-based JWCSTRO algorithm, the system sampling period, the activated nodes, the node-target assignment, and the subarray number, transmit energy and transmit waveform type, as well as parameter of each activated node can be adjusted jointly and adaptively, where the former five implement the space-time resource optimization and the last one achieves the waveform control in NCMRS. Finally, numerical simulation results demonstrate the effectiveness of the proposed MPSO-based JWCSTRO algorithm. Furthermore, compared with the five other benchmark algorithms, the maximum and minimum improvement ratios of the network comprehensive cost of the proposed MPSO-based JWCSTRO algorithm are 44.89% and 21.68%, respectively.

Dynamic Antenna Selection for Colocated MIMO Radar

Antenna distribution plays an important role for the performance gain in multiple-input–multiple-output (MIMO) radar target tracking. Since to meet the requirements of the low probability of interception, especially in a hostile environment, only a finite number of antennas can be activated at each step. This naturally leads to a performance-driven resource management problem. In this paper, a dynamic antenna selection strategy is proposed for tracking targets in colocated MIMO radar. The derived posterior Cramér–Rao lower bound (PCRLB) of joint direction-of-arrival (DOA) and Doppler estimate were chosen as the optimization criteria. Furthermore, in the deviation, the target radar cross-section (RCS) as the determining variable and the random variable are both discussed. The objective function is related to the antenna allocation and non-convex, and an efficient fast discrete particle swarm optimization (FDPSO) algorithm is proposed for the solution exploration. Additionally, a closed-loop feedback system is established, where the main idea is that the tracking information from the current time epoch is utilized to predict the PCRLB and to guide the antenna adjustment for the next time epoch. The simulation results demonstrate the performance improvement compared with the three fixed-antenna configurations. Moreover, the FDPSO can provide close-to-optimal solutions while satisfying the real-time demand.

Mutual Coupling and Channel Imbalance Calibration of Colocated MIMO Radars

The Multiple-Input Multiple-Output (MIMO) radar combines the signals from multiple transmit and receive elements separately to form a virtual array. Consequently, system imperfections in the MIMO radar caused by channel imbalance and mutual coupling are repeatedly present in the virtual array, affecting the beamforming performance. In this paper, the beam pattern deviations caused by these imperfections are described mathematically in relation to a single point target. Furthermore, a nonparametric calibration technique to estimate and compensate for these effects at different angles is presented by exploiting that the same imperfections are present multiple times in the virtual array. The proposed calibration method is applied to measurements with an 8 <inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 8 colocated X-band MIMO radar, and the peak sidelobe level (PSL) and the integrated sidelobe level (ISL) are used as performance parameters. The calibration technique is found to improve both the PSL and the ISL by approximately 15 dB within the whole field-of-view (FOV) for antennas that are not minimum scatterers. For angles above &#x00B1;30&#x00B0; from the antenna boresight, the performance of the proposed calibration method is found to surpass other calibration methods. This is advantageous for MIMO radars as they typically cover a wide FOV.

Beampattern matching in colocated MIMO radar using transmit covariance matrix design

The common problem in the multiple-input multiple-output (MIMO) radar is that the beampattern matching realized by the optimization-based algorithms would lead to high computational complexity. Using the presented transmit covariance matrix design (TCMD) algorithm, the efficiency of MIMO radar beampattern matching can be improved drastically. In this paper, the designed covariance matrix (CM) is constructed as an iterative form, and the appropriate power factor is designed in closed-form to realize accurate spatial power control and guarantee the positive semidefiniteness of the design CM. Moreover, the beampattern matching is realized by successively adjusting the spatial power at the directions where the specifications are not satisfied. Numerical results of accurate spatial power control and beampattern matching are carried out to show the effectiveness and efficiency of the presented TCMD algorithm.

Joint Design of the Transmit Beampattern and Angular Waveform for Colocated MIMO Radar under a Constant Modulus Constraint

In this paper, we investigate the joint design of a transmit beampattern and angular waveform (AW) for colocated multiple-input multiple-output (MIMO) radars. The importance of the AW in the proposed signal processing strategy is first clarified, and then, two optimization models are established, which are aimed at either the power spectral density (PSD) design or the spectral compatibility and similarity design of the AW. There are two main differences between the proposed models and existing models. First, instead of matching a desired template or maximizing the transmit power on specific regions, the transmit beampattern in this paper is optimized to approach several key points, which guarantees the high transmit gain and the flexible adjustment of each beam gain. Second, instead of optimizing the performance of the transmit waveform, only the characteristics of the AW are examined, and they can be constrained quantitatively according to their relationship with the transmit gain. The two models can be unified into the same framework, and an efficient algorithm is proposed to solve the problem under a constant modulus constraint. The convergence of the proposed algorithm is demonstrated, and some improvements to reduce the computational complexity are proposed. Numerical simulations showed that compared to the existing methods, the proposed approach can be used to obtain a higher transmit gain, flexibly adjust each beam gain, and more accurately control the PSD, spectral compatibility, and similarity of the AW. Moreover, numerical simulations showed that, compared to the use of existing methods, the proposed algorithm has higher computational efficiency.

Co-Located MIMO Radar Target Detection in Cluttered and Noisy Environment Based on 2D Block Sparse Recovery

In the co-located multiple input multiple output (MIMO) radar, the matched filter receiver suffers from high range sidelobes, since the waveforms originating from different transmitters cannot be fully orthogonal. Traditional sparse recovery based receivers can suppress the sidelobes by assuming the sparsity of range profiles. However, in most practical situations, the range profile is not sparse due to the presence of clutter. In this paper, an effective co-located MIMO radar target detection algorithm is proposed based on modified smoothed-l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> norm sparse recovery, by exploiting the 2D block sparsity of the range-Doppler profiles of targets corresponding to different transmit-receive pairs. The proposed method can enhance the target detection performance in co-located MIMO radar system, especially for those weak targets which may be overwhelmed by high-level sidelobes of strong targets and clutters, without sacrificing computational efficiency. With a well-designed regularization approach and an adaptive regularization parameter update technique, the proposed method is also robust in noisy environment. Simulation and experimental results validate the effectiveness of the proposed method.

Novel Methodology to Assess RF Performance of Co-located MIMO Radar Systems Transmitting Binary Phased Coded Waveforms

In this work an experimental radar testbed and dual directional couplers (DDC) are used to collect measurements of the forward and reverse waves at each element, its mutual coupling, and beam-patterns. The collected measured data is used to validate a methodology for assessing radio frequency (RF) performance of a co-located multiple-input multipleoutput (MIMO) radar system transmitting binary phased coded (BP) waveforms. The estimated and measured active reflection coefficient (ARC) and beam-patterns are presented. The effect of different excitations and mutual coupling on the radiated fields is also presented.

Transmit beampattern synthesis for chirp space-time coding array by time delay design

In a multiple-input multiple-output (MIMO) radar, it is desired to maximize the power radiation in an angular region of interest, which, however, is hardly achievable because a set of orthogonal waveforms is employed at the engineering. Different from traditional colocated MIMO radar, space-time coding array (STCA) transmits an identical waveform with a tiny time delay circulating across array elements. It can provide a simple way to achieve the full angular coverage with a stable gain. In this paper, a new scheme referred to as chirp-STCA is proposed to synthesize the desired beampattern flexibly. With the quantitative relationship between the time delay and the span of beam spreading, the transmit beampattern synthesis issue is converted to the design of time delay value which can be readily realized based on the framework of chirp-STCA. As a result, the desired angular sector coverage of the radiation power can be realized by setting a proper time delay. The proposed chirp-STCA can provide many desired features including low range sidelobe level, low computational complexity, and constant modulus. In addition, the range-angle properties are examined by analyzing the transmit beampattern response and the ambiguity function. Numerical results are presented to demonstrate the effectiveness and simplicity of the chirp-STCA scheme for the transmit beampattern synthesis.

Adaptive resource management for multi-target tracking in co-located MIMO radar based on time-space joint allocation

Compared with the traditional phased array radar, the co-located multiple-input multiple-output (MIMO) radar is able to transmit orthogonal waveforms to form different illuminating modes, providing a larger freedom degree in radar resource management. In order to implement the effective resource management for the co-located MIMO radar in multi-target tracking, this paper proposes a resource management optimization model, where the system resource consumption and the tracking accuracy requirements are considered comprehensively. An adaptive resource management algorithm for the co-located MIMO radar is obtained based on the proposed model, where the sub-array number, sampling period, transmitting energy, beam direction and working mode are adaptively controlled to realize the time-space resource joint allocation. Simulation results demonstrate the superiority of the proposed algorithm. Furthermore, the co-located MIMO radar using the proposed algorithm can satisfy the predetermined tracking accuracy requirements with less comprehensive cost compared with the phased array radar.

Compressed-Domain Detection and Estimation for Colocated MIMO Radar

This article proposes a compressed-domain signal processing (CSP) multiple-input multiple-output (MIMO) radar, a MIMO radar approach that achieves substantial sample complexity reduction by exploiting the idea of CSP. CSP MIMO radar involves two levels of data compression followed by target detection at the compressed domain. First, compressive sensing is applied at the receive antennas, followed by a Capon beamformer, which is designed to suppress clutter. Exploiting the sparse nature of the beamformer output, a second compression is applied to the filtered data. Target detection is subsequently conducted by formulating and solving a hypothesis testing problem at each grid point of the discretized angle space. The proposed approach enables an eightfold reduction of the sample complexity in some settings as compared to a conventional compressed sensing (CS) MIMO radar, thus enabling faster target detection. Receiver operating characteristic curves of the proposed detector are provided. Simulation results show that the proposed approach outperforms recovery-based CS algorithms.

Adaptive simultaneous multibeam resource management for colocated MIMO radar in multiple targets tracking

Different from conventional phased array radar, the colocated multiple-input multiple-output (MIMO) radar can form multiple beams simultaneously through transmitting orthogonal waveforms by its sub-arrays. The illuminating mode of multiple beams can be changed with the sub-array number, providing greater flexibility in system resource management. In this paper, a novel resource management optimization model for the colocated MIMO radar in multiple targets tracking is proposed, where the average time and energy resource consumption of the system are minimized under the guarantee that the illuminated targets can be effectively detected. When solving the proposed optimization problem, the adaptive simultaneous multibeam resource management (ASMRM) algorithm for the colocated MIMO radar is obtained, where the sub-array number, the beam directions, the system sampling period, the transmit waveform energy and the working mode can be controlled adaptively. Simulation results demonstrate that the working parameters of the colocated MIMO radar can be changed effectively. Furthermore, in terms of the average system resource consumption, the proposed algorithm is superior to the algorithms with fixed parameters while realizing the effective multiple targets tracking.

Colocated MIMO radar waveform optimization with receive beamforming

Transmit waveform diversity brings some advantages to colocated Multiple-Input Multiple-Output (MIMO) radars. Current waveform design techniques do not sufficiently exploit the degrees of freedom from the receive end. In this paper, we devise waveform optimization criteria with the receive end beamforming operation. With mutual correlation sidelobes suppressed at the receive end, some degrees of freedom can be released for a lower global sidelobe level. For transmit beampatterns with unequal gains, two Peak Sidelobe Level (PSL) measures are discussed, one for identical range compression PSL and the other for the same distance to the noise level. Numerical results are given for beampatterns with mono-peak, two peaks with equal gains and two peaks with unequal gains, all indicating that the criteria result in better sidelobe suppression.

Constant-Modulus-Waveform Design for Multiple-Target Detection in Colocated MIMO Radar.

For improving the performance of multiple-target detection in a colocated multiple-input multiple-output (MIMO) radar system, a constant-modulus-waveform design method is presented in this paper. The proposed method consists of two steps: simultaneous multiple-transmit-beam design and constant-modulus-waveform design. In the first step, each transmit beam is controlled by an ideal orthogonal waveform and a weight vector. We optimized the weight vectors to maximize the detection probabilities of all targets or minimize the transmit power for the purpose of low intercept probability in the case of predefined worst detection probabilities. Various targets’ radar cross-section (RCS) fluctuation models were also considered in two optimization problems. Then, the optimal weight vectors multiplied by ideal orthogonal waveforms were a set of transmitted waveforms. However, those transmitted waveforms were not constant-modulus waveforms. In the second step, the transmitted waveforms obtained in the first step were mapped to constant-modulus waveforms by cyclic algorithm. Numerical examples are provided to show that the proposed constant-waveform design method could effectively achieve the desired transmit-beam pattern, and that the transmit-beam pattern could be adaptively adjusted according to prior information.

Repeat radar jammer suppression for a colocated MIMO radar

The digital radio frequency memory repeat jammer, which replicates transmit signals and transmits them back to a radar receiver to mimic real targets, is a notably efficient electronic countermeasure device. It has been pointed out that the colocated multiple-input multiple-output (MIMO) radar has an additional advantage in combatting with repeat jammers whose amplifiers operate in the saturation mode and thus can only transmit constant-modulus waveforms, because the amplitude modulation of angular waveforms is difficult to simulate for them. Two waveform design criteria are presented, and both suppress a mutual correlation between angular waveforms and corresponding constant-modulus jamming signals. The two approaches differ on whether the receive beamforming operation is considered. Numerical results validate that the two approaches can both efficiently suppress the mutual correlation and that the second approach outperforms the first approach in terms of the peak sidelobe level.

New Approach for DOA Estimation in MIMO Radar With Nonorthogonal Waveforms

It is desirable to emit orthogonal waveforms in multiple-input multiple-output (MIMO) radar systems. Unfortunately, the waveform orthogonality may not be always guaranteed in practice. Nonorthogonal transmit waveforms will incur two challenges i.e., spatially colored noise and nonideal virtual direction matrix, for direction-of-arrival (DOA) estimation in colocated MIMO radar. In this article, a temporal cross-correlation matrix-based algorithm is proposed to overcome the above drawbacks. Also, the stochastic Cramer-Rao bound (CRB) is derived. The proposed algorithm does not require the prior knowledge of the waveform correlation matrix. Moreover, it is attractive from the perspective of efficiency as well as accuracy. Numerical experiments demonstrate the effectiveness of our algorithm.