Analysis of the technical principles of passive detectors for aircraft

The core technical principle of an aircraft passive detector is that it does not actively emit electromagnetic signals. Instead, it passively receives electromagnetic signals radiated by the aircraft itself or reflected external environmental electromagnetic signals, and combines signal processing, multi-source data fusion, and other technologies to achieve the detection, positioning, identification, and tracking of aircraft. Its essence is to utilize "exogenous signals" or "target self-radiation signals" to complete the detection loop. Compared to active detection equipment (such as active radar), it has advantages such as strong concealment, resistance to anti-radiation weapons, and strong anti-interference capability. The core technology is divided into two major paths, corresponding to different signal sources and processing logics.

1. Core technology path 1: Based on the detection principle of the target's own electromagnetic radiation

During the operation of most aircraft (especially military fighter jets and civilian drones), the electronic equipment they carry will inevitably radiate electromagnetic signals, which become the "natural detection source" for passive detectors. The core of this approach is to accurately capture and analyze these target self-radiated signals, extracting key information such as location and motion state.

1. Signal source and acquisition

Detectable target self-radiated signals include: detection signals emitted by aircraft radar systems, communication signals between ground stations/remote controllers (such as 2.4GHz/5.8GHz control signals for drones), received and retransmitted signals from navigation devices (such as GPS), response signals from transponders, and jamming signals emitted by active jammers. Passive detectors, equipped with wideband and high-sensitivity RF receiving front-ends (such as ultra-wideband antennas and low-noise amplifiers), achieve full coverage capture of signals in mainstream frequency bands ranging from 25MHz to 6000MHz, effectively receiving even weak signals (with detection sensitivity up to -115dBm).

2. Signal processing and feature recognition

The captured signal needs to undergo complex digital signal processing to remove noise and interference: First, spatial filtering and frequency domain analysis are used to separate the target signal from environmental clutter (such as urban WiFi signals and broadcast signals). Then, the core features of the signal are extracted, including frequency band distribution, modulation method, signal strength, pulse parameters (such as pulse width and repetition frequency), etc., to form the "electronic fingerprint" of the target. By comparing the extracted features with a preset aircraft signal feature library (such as communication protocols of different aircraft models and radar signal parameters), preliminary identification of the target aircraft model can be completed, while distinguishing between legitimate flight targets and illegal intrusion targets (such as through a blacklist and whitelist mechanism).

3. Implementation of positioning and tracking

The core is achieved through a multi-station collaborative localization algorithm: deploying at least two or more passive detection devices, by measuring the time difference of arrival (TDOA), phase difference of arrival (PDOA), or direction of arrival (DOA) of the target signal at different devices, and combining with the known position coordinates of the devices, the three-dimensional spatial position of the target is calculated using the principle of triangulation. For moving targets, by continuously capturing the Doppler frequency shift changes of the signal, their flight speed and direction can be further calculated. Some systems also integrate acoustic signals (such as drone propeller noise) to assist in localization, improving localization accuracy in complex environments. The direction-finding accuracy of typical systems can reach about 2° root mean square (RMS), with a localization error of less than 100 meters, and they can track hundreds of targets simultaneously.

1. Core Technical Path 2: Detection Principle Based on Reflected Signals from External Environmental Radiation Sources

If the target aircraft is in a radio silence state (without self-radiation signals), passive detectors can utilize the existing "non-cooperative external radiation sources" (third-party radiation sources) in the environment to achieve detection by receiving the reflected/scattered signals generated after these radiation sources illuminate the target. This path is also known as "Passive Coherent Location" (PCL), which is the core operating mode of passive radar.

1. Type of external radiation source

The available external radiation sources cover both civilian and military scenarios, including commercial radio stations (FM/AM broadcasting), television transmitting stations, mobile communication base stations (GSM/5G), global positioning system (GPS) satellite signals, and even radiation signals from other active radars. These radiation sources are characterized by wide coverage and stable signals, among which low-frequency band signals (below 1GHz) can effectively weaken the effectiveness of radar-absorbing materials for stealth aircraft, becoming an important means of anti-stealth detection - for example, China's YLC-29 bistatic passive detection system utilizes civilian FM radio signals to detect stealth targets at a distance of up to 200 kilometers.

2. Signal reception and reference calibration

This path needs to simultaneously receive two types of signals: one is the "direct wave signal" (as the reference signal) that propagates directly from the external radiation source to the detector, and the other is the "reflected wave signal" (the target signal) that propagates to the detector after being reflected by the aircraft. The detector continuously monitors the direct wave signal through a dedicated "reference channel", dynamically samples and records its waveform characteristics (such as frequency, phase, and modulation method), providing a benchmark for subsequent signal comparison - this is a key step to compensate for the lack of an active transmission module in passive detectors and the inability to predict signal characteristics.

3. Signal comparison and target information extraction

By comparing and analyzing the direct wave (reference) and the reflected wave (target) through signal processing technology, we first calculate the time difference between the two. Combined with the location information of the external radiation source, the "bistatic distance" from the target to the radiation source and the detector can be obtained. Then, by analyzing the Doppler frequency shift of the reflected wave, we can obtain the motion speed and direction of the target. If equipped with a phased array antenna, the digital beamforming technology can also be used to determine the angle of arrival of the signal, achieving target direction positioning. For weak reflected signals (easily overwhelmed by noise), the system will employ techniques such as long-time coherent accumulation and element-level average constant false alarm rate (CFAR) to enhance signal energy and improve detection success rate.

4. Improving accuracy through multi-source collaboration

The detection accuracy of a single external radiation source is limited. In practical applications, a networking mode with multiple radiation sources and detectors is often adopted: by fusing multiple independent bistatic range, Doppler frequency shift, and angle of arrival measurement data, the positioning error can be significantly reduced, and the stability and continuity of target tracking can be improved. For example, the US "Silent Sentinel" passive radar utilizes TV transmitter signals to track targets with a radar reflective area of 10 square meters at a distance of up to 180 kilometers. After improvements, it can simultaneously track more than 200 targets.

1. Common key technologies: Weak signal detection and anti-interference guarantee

Regardless of the technical path, passive detectors face the core challenges of "weak target signals and complex environmental interference." To ensure performance, they rely on two key technologies: first, weak signal detection technology, which utilizes methods such as wavelet transform, deep learning (such as convolutional neural networks), and multi-feature fusion to separate target signals from strong noise and clutter, thereby improving detection probability and reducing false alarm rate; second, anti-interference technology, which employs spatial filtering, frequency agility, and multi-station data cross-validation to suppress industrial interference, artificial interference, and enemy electronic interference, ensuring the reliability of signal processing.

1. Principle Summary

The core logic of the aircraft passive detector lies in "passive perception and leveraging external sources for detection": either capturing the aircraft's own electromagnetic radiation signals and locking onto the target through multi-station positioning and feature analysis, or utilizing third-party radiation sources in the environment to achieve detection through the comparison of direct and reflected waves. The technical core encompasses high-sensitivity signal capture, precise signal feature extraction and comparison, multi-source data fusion for positioning, and the realization of strong anti-interference capabilities, ultimately accomplishing full-time and all-round monitoring of the aircraft without exposing itself.