This paper proposes an improved multi-frame coherent integration algorithm to improve the detection performance of weak targets in heterogeneous radar. In the detection of weak targets, integration within a single frame may fail to provide sufficient signal-to-noise ratio (SNR) gain. In this case, multi-frame coherent integration is an effective solution. However, radar parameters may be different across frames (i.e., heterogeneous radar) in some practical situations, leading to a mismatch of Doppler frequencies and the fixed phases, which poses difficulties to multi-frame coherent integration. To calibrate the ranges and Doppler frequencies of heterogenous multi-frame echoes, this paper firstly employs an improved Keystone Transform (KT). Compared to conventional KT, the improved KT aligns inter-frame carrier frequencies by applying varying degrees of slow-time rescaling based on the carrier frequencies of each frame, and aligns inter-frame Pulse Repetition Frequencies (PRF) through a unified global slow-time resampling. Secondly, this paper derives the explicit expressions of the fixed-phase terms and adopts a method based on fractional range bins, thus achieving explicit compensation for mismatched phases. Finally, heterogenous multi-frame coherent integration is achieved through slow-time fast Fourier transform. The effectiveness of the proposed algorithm is validated by simulation analyses. Compared to existing entropy-based methods, the proposed algorithm demonstrates higher robustness and lower computational complexity, making it more effective in detecting weak targets under low SNR conditions.

Deploying Passive Multistatic Radar (PMR) on mobile platforms provides covert and cost-effective monitoring over a large area, offering certain advantages in countermeasure. However, mobile PMR faces significant challenges, such as Doppler distortion and phase deviations. A multi-parameter space target detection method is proposed for mobile PMR to achieve target detection in three-dimensional environments. By estimating the Doppler Frequency Rate (DFR), applying bistatic range phase compensation, and implementing azimuth time integration, frame division, and data fusion, the detection accuracy and the Signal-to-Noise Ratio (SNR) are improved. Simulation results indicate that the proposed method significantly enhances the SNR and produces accurate detection results, demonstrating its efficacy.

With the widespread application of multi-sensor systems, direction of arrival (DOA) estimation in complex electromagnetic environments is crucial for target detection and localization. Under non-ideal conditions, the received signals are easily affected by uncontrollable factors such as coherent signals and variations in signal power. In this letter, a novel DOA estimation method based on the complex-valued residual attention convolutional neural network (CRA-CNN) is proposed. A complex-valued residual network integrated with an attention mechanism is introduced to extract key features from the signal covariance matrix, significantly enhancing feature representation and discrimination. Notably, a novel loss function combining low-rank and sparse prior constraints is designed to enhance sensitivity to essential features while suppressing redundancy and noise. Simulation results demonstrate that CRA-CNN improves both the accuracy and robustness of DOA estimation.

With the extensive application of large-scale array antennas, the increasing number of array elements leads to the increasing dimension of received signals, making it difficult to meet the real-time requirement of direction of arrival (DOA) estimation due to the computational complexity of algorithms. Traditional subspace algorithms require estimation of the covariance matrix, which has high computational complexity and is prone to producing spurious peaks. In order to reduce the computational complexity of DOA estimation algorithms and improve their estimation accuracy under large array elements, this paper proposes a DOA estimation method based on Krylov subspace and weighted ℓ 1 -norm. The method uses the multistage Wiener filter (MSWF) iteration to solve the basis of the Krylov subspace as an estimate of the signal subspace, further uses the measurement matrix to reduce the dimensionality of the signal subspace observation, constructs a weighted matrix, and combines the sparse reconstruction to establish a convex optimization function based on the residual sum of squares and weighted ℓ 1 -norm to solve the target DOA. Simulation results show that the proposed method has high resolution under large array conditions, effectively suppresses spurious peaks, reduces computational complexity, and has good robustness for low signal to noise ratio (SNR) environment.

The sparse subband fusion techniques achieve range super-resolution and show potential for application in super-resolution detection of multiple moving targets. This application requires intersubband range and Doppler alignments for multiple moving targets. However, existing algorithms often rely on strict assumptions about target velocities, which significantly limits their applicability. To address this issue, this letter introduces a velocity-local-compensation improved Keystone transform (VLC-IKT) for intrasubband motion compensation and intersubband Doppler alignment, without imposing constraints on target velocities. Additionally, an improved range profiles cross-correlation algorithm (IRPCC) is proposed to align intersubband ranges. The simulation results confirm that the proposed algorithm effectively aligns intersubband range and Doppler for multiple moving targets with arbitrary velocities, significantly enhancing both the fused signal-to-noise ratio (SNR) and probability of detection (POD), especially in low SNR conditions, thereby establishing a foundation for applying sparse subband fusion to super-resolution detection of multiple moving targets.

Distributed arrays can achieve an extended effective aperture through sparse spatial deployment, significantly enhancing the resolution of parameter estimation. However, the incomplete array aperture induced by long baselines between subarrays inevitably introduces angle ambiguity. To address this issue, this article proposes a distributed nested array interpolation method that leverages multifrequency information and low-rank matrix reconstruction for unambiguous direction-of-arrival (DOA) estimation. For distributed arrays with nested subarrays, the physical array is first mapped into the virtual array domain by vectorizing the covariance matrix. Subsequently, multifrequency measurements are incorporated to extrapolate the virtual array, effectively expanding the aperture of sparse distributed subarrays. To recover residual missing elements, a low-rank covariance matrix completion model is established, employing the Schatten-p norm to enforce low-rank constraints and incorporating sparsity priors within an optimization framework. The resulting problem is efficiently solved via an iteratively reweighted least squares (IRLSs) algorithm. Finally, the completed covariance matrix enables high-resolution DOA estimation through subspace-based techniques. Extensive simulation and field experimental results demonstrate the proposed method’s effectiveness and robustness in achieving unambiguous, high-resolution DOA estimation in distributed array scenarios.