The target signal-to-noise ratio (SNR) can be notably improved by coherent-on-receive synthesis (CoRS) in distributed coherent aperture radar (DCAR). A core challenge of CoRS is to estimate the coherent parameters (CPs), including time, frequency, and phase, in order to cohere the multi-radar within DCAR. Conventional methods usually rely on the target’s own information to estimate the CPs, which is not available in highly dynamic environments. Additionally, the CPs of different targets, especially the phase, are unequal in high-frequency systems. This means that we cannot directly use the CPs of one target to compensate for others. To address these issues, an adaptive CoRS method using the dominant scatterer is proposed for millimeter-wave (MMW) DCAR in this paper. The basic idea is to correct the CPs of the dominant scatterer to compensate for other targets. The novelty lies in the adaptive phase compensation based on the estimated CPs. This phase compensation depends on a series of discrete phase values, which are derived from the limit of synthesis loss within a given configuration. Hence, this method avoids the requirement of prior information or massive searches for the possible locations of other targets. Moreover, the dominant scatterer in this work is an unknown target with strong scattering points in radar detection scenarios, and we focus on analyzing its selection criteria. To validate the proposed method, a prototype system has been fabricated and evaluated through experiments. It is demonstrated that the multi-target can realize CoRS effectively, thus enhancing the target SNR.

In this paper, a multi-aperture multiplexing multiple-input multiple-output (MAM-MIMO) sparse array is presented for cooperative automotive radars (CARs). The proposed sparse array composed of multiple subarrays can simultaneously cover a wide field-of-view (FOV) and achieve the required azimuth resolution at different ranges. To validate this idea, an optimization model for the MAM-MIMO sparse array is derived based on the example of CARs. This optimization model has been found by combining the peak-to-sidelobe ratio (PSLR) at all beams pointing within the constraints of different detection ranges. In addition, a hierarchical genetic algorithm based on the multi-objective decomposition method has been developed to obtain the optimized sparse array. The proposed method has been evaluated through both simulations and experiments. It is demonstrated that the optimized MAM-MIMO sparse array can effectively suppress sidelobes of its subarrays, yet with reasonably high azimuth resolutions and large FOVs.

Deep neural network (DNN) has shown great potential in direction-of-arrival (DOA) estimation. In high dynamic signal-to-noise (SNR) scenarios, the estimation accuracy of the weaker sources may degrade significantly due to insufficient training samples. This paper proposes a deep neural network framework with sliding window operation. The whole field-of-view (FOV) is divided into a series of sub-regions via sliding windows. Each sub-region is assumed to contain one source at most. Thus, the single-source data can be used to train all the networks, alleviating the need for the training samples and the prior information on the number of sources. A detector network and an estimator network are followed for each sub-region, enabling high estimation accuracy and the number of sources. Simulation and real data experiment results show that the proposed method can achieve excellent DOA and source number estimation performance. Specifically, in the real data experiment, the results show that the RMSE of the proposed method reaches 0.071, which is at least 0.03 lower than FFT, MUSIC, ESPRIT, and a deep learning method namely deep convolutional network (DCN), cannot estimate the lower SNR source in high dynamic SNR scenarios.

Low-altitude platforms, such as uncrewed aerial vehicles (UAVs), are increasingly employed for real-time sensing in edge-enabled Internet of Things (IoT) networks, where radar-based perception is critical for situational awareness. Multiple-input multiple-output (MIMO) radar systems offer high angular resolution and spatial diversity, making them well-suited for such applications. To fully exploit the benefits of MIMO, efficient waveform designs that support simultaneous multichannel transmission are essential. Doppler division multiple access (DDMA) is a promising waveform strategy that achieves transmit orthogonality through Doppler-domain encoding, offering strong inter-channel orthogonality and supporting low-complexity implementation. However, in multitarget environments, DDMA-MIMO radar suffers from mutual shadowing effects that degrade detection performance. To address this challenge, this article proposes a double-threshold detection framework tailored for DDMA-MIMO radar in edge sensing systems. The method employs a double-threshold strategy: an adaptive first threshold based on the Akaike information criterion (AIC) selects candidate targets, and a second threshold derived via maximum likelihood estimation produces the final detections. Theoretical analysis and simulation results demonstrate that the proposed double-threshold framework consistently outperforms conventional approaches, providing robust detection and effective false alarm regulation.

In this paper, we explore the problem of direction-of-arrival (DOA) estimation for a non-uniform linear array (NULA) under strong noise. The compressed sensing (CS)-based methods are widely used in NULA DOA estimations. However, these methods commonly rely on the tuning of parameters, which are hard to fine-tune. Additionally, these methods lack robustness under strong noise. To address these issues, this paper proposes a novel DOA estimation approach using a deep neural network (DNN) for a NULA in a low SNR. The proposed network is designed based on the denoising convolutional neural network (DnCNN) and the alternating direction method of multipliers (ADMM), which is dubbed as LDnADMM-Net. First, we construct an unfolded DNN architecture that mimics the behavior of the iterative processing of an ADMM. In this way, the parameters of an ADMM can be transformed into the network weights, and thus we can adaptively optimize these parameters through network training. Then, we employ the DnCNN to develop a denoising module (DnM) and integrate it into the unfolded DNN. Using this DnM, we can enhance the anti-noise ability of the proposed network and obtain a robust DOA estimation in a low SNR. The simulation and experimental results show that the proposed LDnADMM-Net can obtain high-accuracy and super-resolution DOA estimations for a NULA with strong robustness in a low signal-to-noise ratio (SNR).

Compared to parallel-fed antennas, series-fed microstrip antennas (SFMAs) exhibit strong coupling among the series-fed elements, leading to interdependent amplitude and phase distributions. This inherent coupling introduces additional design constraints and increases the computational complexity of SFMA synthesis. To address these challenges, a physics-informed graph convolutional network (PIGCN) framework is proposed to optimize the excitations of SFMAs for specific pattern objectives. By projecting the physical topology of the SFMA onto graph structures, mutual couplings are inherently embedded in the neural network. This projection allows information propagation along transmission paths, eliminating the need for extra learning of inter-element dependencies. Furthermore, an attention mechanism calculates the excitation adjustments based on correlation coefficients between pattern errors and element excitations. This integration improves sensitivity to dominant error sources and accelerates convergence. Two numerical experiments for different synthesis scenarios demonstrate that, for SFMAs, the proposed framework offers superior accuracy, efficiency, and flexibility compared to the PSO and the PINN-based method.