Multi-station passive localization algorithms based on hybrid Time Difference of Arrival (TDOA) and Angle of Arrival (AOA) have been thoroughly studied. But it usually requires each station to obtain the AOA of the source and relies on the precise station position. These conditions are generally not satisfied in practical applications. In addition, the geometry between the source and these stations also affects the localization accuracy. This paper studies the passive source localization problem based on multi-station TDOA and single-station AOA measurements. First, we propose a closed-form solution source localization method based on multi-station TDOA and single-station AOA measurements, which requires only the reference station used to calculate TDOA to be able to observe the AOA measurements of the source. It solves the problem of hybrid localization of different numbers of TDOA and AOA measurements. Theoretical analysis proves that the performance of the proposed method can reach the Cramer-Rao Lower Bound (CRLB) under small noise conditions. Then, we model the relationship between the position of each station relative to the source and the localization accuracy. Through the D-optimality criterion, we obtain the optimal geometry between multiple stations and the source Simulation results confirm the validity of these theories.

Utilizing the optical characteristics of the target for detection and localization does not require actively emitting signals and has the advantage of strong concealment. Once the optoelectronic platform mounted on the unmanned aerial vehicle (UAV) detects the target, the vector pointing to the target in the camera coordinate system can estimate the angle of arrival (AOA) of the target relative to the UAV in the Earth-centered Earth-fixed (ECEF) coordinate system through a series of rotation transformations. By employing two UAVs and the corresponding AOA measurements, passive localization of an unknown target is possible. To achieve high-precision target localization, this paper investigates the following three aspects. Firstly, two error transfer models are established to estimate the noise distributions of the AOA and the UAV position in the ECEF coordinate system. Next, to reduce estimation errors, a weighted least squares (WLS) estimator is designed. Theoretical analysis proves that the mean squared error (MSE) of the target position estimation can reach the Cramér–Rao lower bound (CRLB) under the condition of small noise. Finally, we study the optimal placement problem of two coplanar UAVs relative to the target based on the D-optimality criterion and provide explicit conclusions. Simulation experiments validate the effectiveness of the localization method.

Infrared dim small target detection has received a lot of attention, because it is a crucial component of the IR search and track systems (IRST). The robust principal component analysis (RPCA) is a common detection framework, which works with poor performance with complex background edges and sparse clutters due to the inappropriate approximation of sparse items. A nonconvex constraint detection method based on the difference between the L1 and L2 (L1–L2) norm and total variation (TV) is presented. The L1–L2 norm is a more accurate sparse item approximation of L0 norm, which can achieve a better description of the sparse item to separate the target from the complex backgrounds. Then, the total variation norm is conducted on the target image to suppress the sparse clutters. The new model is solved using the alternating direction method of multipliers (ADMM) method. Then, the subproblems in the model are tackled by the difference of convex algorithm (DCA) and the Newton conjugate gradient (Newton-CG) solving L1–L2 norm and TV norm, respectively. In the experiment, we conducted experiments on multiple and single target datasets, and the proposed model outperforms the state-of-the-art (SOTA) methods in terms of background suppression and robustness to accurately detect the target. It can achieve a higher true position rate (TPR) with a low false position rate (FPR).