Target localization is one of the most important research topics in the field of radar signal processing. In this paper, the problem of multitarget enumeration and localization in the distributed multiple-input multiple-output (MIMO) radar with noncoherent processing mode is investigated. We first analyze the theoretical bound of the multitarget localization accuracy under the discrete time signal model and the Swerling 1 target model. It is determined by the Cram'r-Rao lower bound (CRLB) at low signal-to-noise ratio (SNR) and the sampling lower bound (SLB) when the SNR is high. Furthermore, an innovative multitarget enumeration and localization scheme is developed, which is based on the energy modeling of the multiple transmitter-receiver paths and the compressive sensing theory. To solve the sparse vector recovery issue, we design a lightweight iterative greedy pursuit algorithm including the similarity evaluation strategy. In addition, an iterative-based target position refinement process is designed to alleviate the off-grid problem caused by the spatial discretization. The proposal utilizes the samples of the raw signals and belongs to the category of the direct localization. Nevertheless, it has significantly higher computational efficiency and lower data communication burden than the conventional direct localization methods, while avoiding the complex data association encountered by the indirect localization methods. Finally, the simulation results validate the effectiveness and robustness of the proposed method.

This paper investigates the multi-target detection and localization problem for distributed multiple-input multiple-output (MIMO) radar. We first derive the optimal approach of this issue in the high-dimensional space according to the generalized likelihood ratio test (GLRT) and the maximum likelihood estimation (MLE). Moreover, the theoretical multi-target localization accuracy under the considered discrete time signal model is analyzed by invoking the Cramér-Rao lower bound (CRLB) and the sampling lower bound (SLB). To deal with the high complexity problem of the optimal solution, an energy-guided framework including two modules is proposed to detect and locate multiple targets. In the first module, the energy accumulation approach based on the GLRT is employed for multi-target detection, and a fast energy accumulation strategy in terms of the far-field case is developed to improve the computational efficiency. In the second module, an innovative iterative similarity evaluation (ISE) method is designed to determine the positions of multiple targets by exploiting the relationship between the energy accumulation characteristics and the multi-target existence conditions. The effectiveness of the proposed framework is verified via extensive simulations in both far-field and near-field cases.

Assessing the detection capability is crucial for the design and implementation of distributed MIMO radar systems. However, existing MIMO radar models have mainly been established under ideal conditions, assuming spatially uncorrelated target echoes, strictly orthogonal transmitted waveforms, and negligible system errors. Such idealized assumptions may not hold in practical scenarios, making it challenging to comprehensively and realistically evaluate radar detection performance. Motivated by this, our paper investigates the theoretical radar detection capability by removing these idealized assumptions. We develop a more general system model for distributed MIMO radar that considers the probable spatial correlations of target echoes, waveform non-orthogonality, and various system errors. These errors include synchronization errors in time, frequency, and phase, uncertainties in site position and beam pointing, as well as mismatch errors between the test-cell center and the target. Using this enhanced model, we derive an accurate and efficient equation to evaluate detection performance by employing a moment-matching approximation strategy. Additionally, we analyze the impact of non-ideal conditions through simulations. Our findings reveal several important insights. Firstly, channel-correlated target returns can be advantageous for target detection, particularly in low signal-to-noise ratio (SNR) scenarios. Secondly, non-orthogonal waveforms increase the sensitivity of radar detection performance to non-ideal factors. Lastly, the threshold at which system errors significantly degrade radar detection performance can be adjusted by radar parameters such as waveform characteristics and beamwidths. These results contribute to the design of radar systems in various aspects, including site placement, waveform optimization, beamforming, and test-cell creation.

Detection power is a significant performance indicator of a radar. The detection power of a traditional monostatic radar is a simple form described by the radar range equation and the exploited detector, while for a distributed MIMO radar, the description of its detection power becomes very complicated due to the complex geometry and the large number of transmit-receive channels. This paper systematically studies the detection power of a distributed MIMO radar system. First, we take advantage of a moment-matching approximation approach to deduce the basic equation for the detection power of a distributed MIMO radar system in the free space. On this basis, an exhaustive grid search (EGS) method and a fast boundary estimation (FBE) method are proposed to determine the detection power space (DPS) in the case that all transmit-receive beams focus on one point. The EGS method can obtain a more precise outline of the DPS but is time-consuming, while the FBE method can reduce the computational complexity with an acceptable accuracy loss. Further, to enhance the applicability of the DPS in radar design and resource scheduling, three DPS regularization schemes, i.e., the maximum inscribed sphere (MIS), the minimum circumscribed ellipsoid (MCE), and the maximum inscribed ellipsoid (MIE) are developed based on the convex optimization theory. Spheres and ellipsoids are easy to describe and can capture the size, position, and even orientation of the DPS to a certain extent. Finally, simulations are conducted to validate the established theories and methods.

This paper introduces a resource management approach for distributed multiple-input multiple-output (MIMO) radar systems equipped with phased array antennas. The approach focuses and adjusts narrow beams from all transmit and receive nodes to generate a regularly shaped observation area for reliable detection. Based on this, a structured early warning framework can be built by evenly arranging sufficient observation areas to cover the surveillance region and periodically scanning these areas for continuous monitoring. Observation area generation, a core technique for this framework, involves the joint optimization of beamforming weights for both transmit and receive nodes, as well as the beam dwell time. Our optimization strategy is designed to achieve two key objectives: minimizing beam dwell time to ensure rapid alerts for defense systems, and minimizing node transmit power to extend operational time while reducing the risk of intercept. To address the problem of observation area generation, we propose a two-stage method. The first stage uses the signal-to-clutter-plus-noise ratio (SCNR) as a new criterion to determine the transmit and receive beamforming weights. The second stage employs a power factor as an additional variable to scale the transmit beamforming weights under various beam dwell times, constructing a Pareto solution set for the problem. Numerical simulations validate the effectiveness of our method, demonstrating improved detection capabilities compared to monostatic phased array radar systems.

Evaluating target measurement performance is significant for the design and application of distributed multiple-input multiple-output (MIMO) radar. Existing research into the target measurement performance was mostly conducted under ideal conditions. In practice, however, distributed MIMO radar may inevitably suffer from the system errors in time sync, frequency sync, and beam pointing as well as the uncertainties in sensor positions. These non-ideal factors may lead to a degradation of the target estimation performance to some extent, and this joint impact remains unclear. Motivated by this, in this paper, we systematically explore the target measurement performance of distributed MIMO radar under non-ideal conditions. First, we formulate a general system model for distributed MIMO radar incorporating the errors in time sync, frequency sync, sensor position, and beam pointing. By treating these system errors as Gaussian random variables, we form the hybrid Cramér-Rao Bound (HCRB) for the joint estimation of the target parameters and these errors. The derived HCRB is shown to be effective in lower bounding the mean-square error (MSE) of the hybrid maximum likelihood/maximum a posteriori (ML/MAP) estimate. Therefore, this bound can be used as an asymptotic benchmark or metric of the radar performance in target measurement. Based on the HCRB, we also examine the effects of these non-ideal factors on the target measurement performance by simulations. The results offer some perspective on the relationships between the system tolerances for these system errors and the radar parameters; these findings will be helpful in the system design of distributed MIMO radar.