Detection Characteristics and Technical Requirements of Anti-Drone Radars

Anti-drone radars are primarily designed for the precise monitoring of low-altitude airspace below 1,000 meters above the ground. By integrating dedicated signal processing modules and high-gain antennas, they efficiently capture clutter signals generated by ground objects, aerial targets, and various environmental interferences, providing high-quality foundational data for subsequent target identification, trajectory tracking, and countermeasure decision-making. According to general airspace classification standards in the aviation sector, airspace below 1,000 meters is explicitly defined as low-altitude airspace, with the area below 100 meters classified as ultra-low-altitude airspace. Affected by factors such as terrain obstruction and building reflections, environmental clutter in this region is more complex. Simultaneously, this area aligns with the endurance and operational requirements of small drones, making it the primary activity zone for consumer-grade aerial photography drones, industrial inspection drones, and even some malicious drones. Taking pulse-Doppler radar, the most widely applied and technologically mature radar in the anti-drone field, as an example, the typical low-speed, small-size (LSS) characteristics of drones significantly limit the detection accuracy, continuous stability, and anti-interference capabilities of radar systems across multiple dimensions, including signal strength, motion trajectory, radar cross-section (RCS), and flight attitude stability (as shown in Figure 3). This represents a core technical challenge that must be prioritized in the design, development, and performance optimization of anti-drone radars.

1. Multi-Scenario Adaptability and Target Identification Requirements

The core characteristic of drones—low-altitude flight—imposes stringent requirements on the multi-scenario adaptability and target identification capabilities of anti-drone radars. These radars must accurately identify various moving targets in the ground, low-altitude, and ultra-low-altitude zones across diverse and complex terrains and environments, such as urban buildings, mountainous regions, and open fields. These targets include pedestrians, ground vehicles, migrating bird flocks, and drones of different sizes and flight modes (e.g., multi-rotor, fixed-wing, vertical takeoff and landing). To mitigate interference from ground clutter (e.g., building wall reflections, terrain undulation disturbances, and ground vegetation scattering), some anti-drone radars employ an optimization strategy of dynamically adjusting the elevation angle. By real-time altering the illumination direction, coverage angle, and energy distribution of the radar beam, they actively avoid regions with concentrated ground clutter, improving the signal-to-noise ratio (SNR) of target signals. However, this passive avoidance method has notable technical limitations, often resulting in a high "missed detection rate" in drone detection. Since the (conventional) operational airspace of most consumer-grade and industrial-grade small drones is concentrated below 100 meters (ultra-low-altitude), it is challenging for the radar beam to achieve seamless coverage of this area after adjusting the elevation angle. Especially in complex terrains like high-density urban buildings and mountain valleys, blind spots caused by obstruction further expand, significantly increasing the risk of missed detections. Therefore, an efficient and reliable anti-drone radar system must possess mature automatic target recognition (ATR) capabilities. By utilizing deep learning algorithms to extract, classify, and validate captured signals, it can accurately distinguish drone targets from clutter, birds, and other interference sources, fundamentally reducing the risks of missed detections and false alarms while ensuring the reliability of detection results.

2. High Sensitivity Requirements for Weak Signal Detection

The inherent characteristic of drones—small size—results in an extremely low radar cross-section (RCS). Most small drones, particularly consumer-grade multi-rotor drones, have an RCS value ranging from 0.01 to 0.1 square meters, significantly lower than that of traditional aircraft like fighter jets and helicopters. The weak radar signals they reflect are easily masked by environmental clutter and electromagnetic interference, posing immense challenges for signal capture. This characteristic demands exceptionally high sensitivity from radar detectors, requiring robust capabilities for weak signal extraction, amplification, and filtering. While effectively filtering out electromagnetic interference and environmental clutter, the radar must also cover a wide detection range to achieve the dual performance objectives of "long-range detection and short-range precise positioning." The realization of this core performance objective must be based on high detection and identification reliability, necessitating the construction of a collaborative "hardware + algorithm" system through multidimensional technical optimization. At the hardware level, core components such as high-sensitivity antennas and low-noise receivers are upgraded to enhance signal reception and conversion efficiency. At the algorithmic level, advanced technologies like adaptive filtering, pulse compression, and constant false alarm rate (CFAR) detection are introduced to strengthen the identification capabilities for weak target signals. This ensures the accurate capture, feature recognition, and stable locking of weak target signals, preventing signal misjudgments and missed judgments from affecting the efficiency and accuracy of subsequent countermeasure operations, thereby meeting the demands of practical application scenarios.

3. Stable Tracking Requirements for Low-Speed Targets

The characteristic of drones—low-speed flight—also presents considerable challenges to the stable tracking function of radar systems. Most small drones fly at speeds ranging from 10 to 50 kilometers per hour, with some ultra-low-altitude hovering drones approaching zero speed. In such low-speed flight states, their motion characteristics are nearly indistinguishable from those of floating clutter, slow-flying birds, falling objects, and other interference targets. Traditional tracking algorithms struggle to differentiate them effectively based on speed differences, failing to maintain stable locking on drone targets and potentially misleading the judgments of auxiliary sensors like optical and infrared sensors. This leads to data deviations and decision errors in multi-sensor fusion systems. Such deviations are further propagated to the countermeasure units within the counter-unmanned aerial system (C-UAS) solution, such as directional jamming devices, physical interception mechanisms, and laser countermeasure systems, resulting in delayed and imprecise countermeasure actions. Consequently, they fail to intercept target drones in a timely and effective manner and may even cause interference to surrounding innocent targets. To address this issue, radar systems require high scan update rates and rapid target identification capabilities. By increasing beam scan frequency, optimizing dynamic tracking algorithms, and target trajectory prediction models, they can update target motion parameters (speed, trajectory, attitude, flight trend) in real time, swiftly distinguishing low-speed drones from various interference targets, and providing subsequent countermeasure units with real-time, accurate, and continuous target data support. This ensures the accuracy and timeliness of tracking and countermeasure operations, fully meeting the rapid response requirements of practical scenarios such as security, military, and event protection.

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