The unmanned aerial vehicle (UAV) industry has developed rapidly in recent years and is being applied in a wide range of fields. However, incidents involving unauthorized UAVs that threaten public safety have occurred frequently, highlighting the need for effective and accurate methods to detect and respond to illegal UAVs. This has led to the emergence of various UAV detection technologies, among which passive radar stands out due to its unique advantages. This review aims to offer insights that can support further research and development in the field of UAV detection using passive radar. We begin by exploring the origins of passive radar and then provide a comprehensive overview of its progress from multiple angles, particularly focusing on its application in UAV detection. Finally, we provide a forward-looking discussion on the future development trends and challenges faced by passive radar in UAV detection.

Accurate tracking and predicting unmanned aerial vehicle (UAV) trajectories are essential to ensure mission success, equipment safety, and data accuracy. Maneuverable UAVs exhibit complex and dynamic motion, and conventional tracking algorithms that rely on predefined models perform poorly when unknown parameters are used. To address this issue, this paper introduces a hybrid dual-scale neural network model based on the generalized regression multi-model and cubature information filter (GRMM-CIF) framework. We have established the GRMM-CIF filtering structure to differentiate motion modes and reduce measurement noise. Furthermore, considering trajectory datasets and rates of motion change, a neural network at different scales will be designed. We propose the dual-scale bidirectional long short-term memory (DS-Bi-LSTM) algorithm to address prediction delays in a multi-model context. Additionally, we employ scale sliding windows and threshold-based decision-making to achieve dual-scale trajectory reconstruction, ultimately enhancing tracking accuracy. Simulation results confirm the effectiveness of our approach in handling the uncertainty of UAV motion and achieving precise estimations.

While the uniform linear array (ULA) is a well‐known array configuration being widely used in various practical application scenarios, it also encounters numerous challenges in the field of direction‐of‐arrival (DOA) estimation. Specifically, it is difficult for the ULA to achieve a satisfactory DOA estimation accuracy, especially in the case of closely spaced sources. That is because that the resolvability of ULA is severely restricted by its limited DOA. To circumvent this issue and further improve the DOA estimation performance, a new array geometry is designed. Inspired by the fact that some linear sparse co‐arrays can yield higher DOFs and larger array apertures, the proposed array configuration is developed by nesting two or more minimum‐redundant arrays in a dedicated way. The simulation results indicate that the proposed array configuration can significantly increase the DOFs and the length of virtual array apertures, and an improved estimation accuracy can be accepted.

Direction of arrival (DOA) estimation plays a critical role in remote sensing, where it aids in identifying and tracking multiple targets across complex environments, from atmospheric monitoring to resource mapping. Leveraging difference covariance array (DCA) for DOA estimation has become prevalent, particularly with sparse arrays capable of resolving more targets than the number of sensors. This paper proposes a new hole-free sparse array configuration for remote sensing applications to achieve improved DOA estimation performance using DCA. By symmetrically placing a minimum redundancy array (MRA) and its complementary MRA on both sides of a sparse uniform linear array (ULA), this configuration maximizes degrees of freedom (DOFs) and minimizes mutual coupling effects. Expressions for calculating sensor positions and optimal element allocation methods to maximize DOFs are derived. Simulation experiments in various scenarios have shown the advantages of the proposed array in DOA estimation, including a strong ability to estimate multi-targets, high angular resolution, low estimation error, and strong robustness to mutual coupling.

In multi-static passive radar systems, the uncontrollability over the transmitters may cause adverse factors formulti-target surveillance, one of which is the absence of range information. In this situation, only the direction of arrival (DOA) and bistatic velocity information may be available. In this paper, to provide a stable state estimation of multi-target with low communication burden and limited frames in a two-dimensional space, a multi-target association method based on azimuth and bistatic velocity information is proposed for multi-static passive radar systems. In every single frame, an efficient two-stage method is proposed based on the principle that the true target’s azimuthal intersections possess high estimated vectorial velocity consistency; then the typical false associations that resemble the velocity consistency of true targets in a single frame (namely velocity ambiguity) are investigated, an inter-frame method utilizing inter-frame motion is proposed to eliminate these false associations. Simulations provide the evidence for the theoretical analysis and demonstrate the effectiveness and efficiency of the proposed method across various scenarios.

Direct localization methods offer significant advantages in modern high-frequency (HF) radar systems for maritime target localization. However, the presence of amplitude–phase errors poses severe challenges to their accuracy and reliability. Traditional active calibration methods for direct localization often face high costs and operational complexities, while self-calibration techniques are limited to addressing only single or, at most, two types of error models. To address these issues, the Multi-Station Azimuth-based Position Error Calibration Algorithm (MAPEC) is proposed in this paper, which is an active calibration algorithm to solve mixed amplitude–phase errors in direct positioning. The proposed algorithm converts the estimated amplitude–phase errors on each angle of stations into the corresponding position on the localization search grid and adjusts the steering vectors based on the converted errors. By integrating MAPEC with direct localization algorithms, the method enables precise and robust localization under complex amplitude–phase error conditions involving multiple error sources. Simulation results demonstrate that this approach significantly improves localization accuracy across various error conditions, validating its applicability to direct localization applications.