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.

High-frequency surface wave radar (HFSWR) is widely used for detecting sea surface or low-altitude targets due to its all-weather operation and over-the-horizon detection capability. To further enhance the maneuverability and detection range of HFSWR, shipborne HFSWR has been developed. However, compared to shore-based platforms, shipborne platforms face challenges such as a small array aperture and reduced Direction of Arrival (DOA) estimation performance due to their limited size. The traditional time–domain virtual aperture extension method, based on the principle of space-time equivalence, aims to improve the array aperture but has limitations when used for HFSWR background or multiple targets with different speeds. To address these issues, this paper proposes a range-Doppler domain (RD-domain) virtual aperture extension method for the uniform linear array, based on the uniform motion model. The contributions of this work include (1) a continuous motion model for shipborne HFSWR, (2) a virtual aperture processing flowchart for shipborne HFSWR, and (3) an RD-domain aperture extension method suitable for HFSWR background or multiple targets with varying speeds. Through simulation and experimental data, we validate the proposed method and analyze its performance.

To enhance the maneuverability and extend the detection range of high-frequency surface wave radar (HFSWR), shipborne systems have been developed as an alternative to shore-based platforms. However, the limited space on shipborne platforms results in a small radar array aperture, which consequently diminishes the radar’s Direction of Arrival (DOA) estimation performance. Additionally, the target echoes received by HFSWR are often accompanied by a large amount of strong clutter. Traditional extrapolation-based aperture extension methods fail because they cannot effectively distinguish between targets and clutter. Therefore, how to extend the aperture of shipborne HFSWR remains a problem to be addressed. To overcome these challenges, we improved conventional extrapolation-based aperture extension techniques by incorporating the signal processing workflow of HFSWR and proposed a novel aperture extension method for uniform linear arrays, based on range-Doppler domain spatiotemporal data block extrapolation (RDSDBE). Specifically, on the one hand, we extend the array aperture in the range-Doppler domain to address the failure of traditional aperture extension methods in the presence of strong clutter. On the other hand, we segment the target echoes in the time domain to tackle the issue of large aperture extension errors caused by the limited number of snapshots in shipborne scenarios. Through simulation and experimental data, we validated the proposed RDSDBE method and analyze its performance.

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.

Unmanned Surface Vehicles (USVs), as a novel type of shipborne platform system, are widely used for ocean monitoring. In a shore-transmit/ship-receive bistatic system, the detection range of shore-based high-frequency surface wave radar (HFSWR) can be greatly extended. However, the limited space on the ship restricts the antenna aperture, thus degrading the angular accuracy of the shipborne radar. An electromagnetic vector sensor (EMVS) estimates target angles by measuring the electromagnetic vector information of the incident wave, and its small physical size makes it well suited for shipborne platform. Nevertheless, a single EMVS offers limited angular resolution and may fail to separate closely spaced targets. To overcome this, we propose a coarse-to-fine range-Doppler domain aperture generation (CFRDAG) strategy that exploits USV motion to turn a single EMVS into a virtual EMVS array, thereby enlarging the effective aperture and improving USV-radar resolution.