The performance of traditional direction of arrival (DOA) estimation methods always deteriorates at a low signal-to-noise ratio (SNR) or without sufficient observations. This paper investigates the Bayesian DOA estimation problem aided by the prior knowledge from the target tracker. The Bayesian Cramér–Rao lower bounds (CRLB) and the expected CRLB are first derived to evaluate the theoretical performance of Bayesian DOA estimation. Based on the maximum a posterior (MAP) estimator in the Bayesian framework, two methods are proposed. One is a two-step grid search method for a single target DOA case. The other is a gradient-based iterative solution for multiple targets DOA case, which extends the traditional Newton method by incorporating the prior knowledge. We also propose a minimum mean square error (MMSE) estimator using a Monte Carlo method, which requires trading off accuracy against computational complexity. By comparing with the maximum likelihood (ML) estimators and the MUSIC algorithm, the proposed three Bayesian estimators improve the DOA estimation performance in low SNR or with limited snapshots. Moreover, the performance is not affected by the correlation between sources.

Traditional radar target detectors, which are model-driven, often suffer remarkable performance degradation in complex clutter environments due to the weakness in modeling the unpredictable clutter. Deep learning (DL) methods, which are data-driven, have been introduced into the field of radar target detection (RTD) since their intrinsic non-linear feature extraction ability can enhance the separability between targets and the clutter. However, existing DL-based detectors are unattractive since they require a large amount of independent and identically distributed (i.i.d.) training samples of target tasks and fail to be generalized to the other new tasks. Given this issue, incorporating the strategy of meta-learning, we reformulate the RTD task as a few-shot classification problem and develop the Inter-frame Contrastive Learning-Based Meta Detector (IfCMD) to generalize to the new task efficiently with only a few samples. Moreover, to further separate targets from the clutter, we equip our model with Siamese architecture and introduce the supervised contrastive loss into the proposed model to explore hard negative samples, which have the targets overwhelmed by the clutter in the Doppler domain. Experimental results on simulated data demonstrate competitive detection performance for moving targets and superior generalization ability for new tasks of the proposed method.

Coherent LiDAR, which is based on triangular frequency modulated continuous wave (T-FMCW), is a promising technology for high-resolution imaging, because it can achieve long-range and high-precision measurements using low power. However, owing to the short wavelength of laser, the system is extremely sensitive to time-varying vibration. Most vibration compensation methods perform well in the scenes that contain strong scatterers using enough data. However, they lose validity in scenes without strong scatterers and sufficient observation time. To address this problem, we develop a second-order vibration error model for coherent LiDAR that divides the Doppler frequency shift caused by time-varying vibrations into quadratic and primary errors. Based on the vibration error model, we propose a two-stage time-varying vibration compensation method using the adaptive differential evolution (ADE) algorithm. In the first stage, we iteratively determine the optimal quadratic vibration coefficients that minimize the spectral entropy of the compensated dechirp signals using the ADE algorithm. Subsequently, we compensate for the quadratic errors using the estimated quadratic vibration coefficients. In the second stage, we use the frequency spectrum cross-correlation method to estimate the primary vibration coefficients and compensate for the primary errors. We perform experiments in the scene containing a point target and the scenes containing multi-component targets and distributed targets without strong scatterers. The results indicate that the proposed method can effectively compensate for time-varying vibration errors using only one-period T-FMCW data in different scenes. Further, the results of the proposed method are more accurate and stable than the Doppler frequency shift method.

The frequency diverse array (FDA) radar can provide an increased degree of freedom (DOF) in the range domain and offer benefits in range-dependent interference suppression. This paper proposes a vertical frequency diverse array (VFDA)-based approach to suppress deceptive jamming for forward-looking array radar under diving motion. Specifically, the effects of diving motion and frequency increment on the beampattern and clutter spectrum are analyzed. Diving motion introduces azimuth information into the elevation dimension echo, and VFDA offers extra DOF in the elevation dimension to distinguish the false-target jamming from the true target. On this basis, an elevation filter is designed using the linearly constrained minimum variance (LCMV) criterion to mitigate deceptive jamming. Then, the space–time adaptive processing (STAP) processor with clutter compensation is applied to suppress the remaining clutter. In addition, the design criterion for the frequency increment and computational complexity analysis are provided. Numerical simulations verify the effectiveness of the proposed method. The signal model is developed for the diving forward-looking array radar based on the VFDA. The effects of the diving motion and frequency increment on the beampattern and clutter spectrum are analyzed. A novel VFDA STAP approach is proposed to mitigate clutter and deceptive jamming. The design criterion for the frequency increment is provided and the computational complexities are analyzed. MSC: 94A12

When tracking maneuvering targets, model-driven approaches encounter difficulties in comprehensively delineating complex real-world scenarios and are prone to model mismatch when the targets maneuver. Meanwhile, contemporary data-driven methods have overlooked measurements' confidence, markedly escalating the challenge of fitting a mapping from measurement sequences to target state sequences. To address these issues, this paper presents a deep maneuvering target tracking methodology based on target state space projection. The proposed methodology initially establishes a projection from the target measurement sequence to the target state space by formulating the probability density function of measurement error and samples the distribution information of measurement noise within the target state space as a measurement representation. Under this representation, the sequential regression task of target state estimation can be transmuted into a task of detecting the target location in the state space. Subsequently, a deep detection network is devised to accomplish target location detection in the target state space. Finally, a loss function is designed to facilitate the network's training for attaining the desired network performance. Simulation experiments suggest that the proposed method can maintain satisfactory tracking performance even when the target maneuvers, and can rapidly converge and achieve higher estimation accuracy compared with existing methods after the target maneuvers.