Classification and recognition mainly distinguishes targets such as UAVs, birds, balloons, kites and ground clutter. Optimization is carried out from five dimensions: hardware configuration, multi‑sensor fusion, algorithm and AI models, business strategies, and on‑site engineering tuning:
1. Hardware Configuration Optimization (Fundamental Guarantee)
Sensor selection shall guarantee detection capability for low‑speed targets, so that hovering and slowly‑drifting UAVs can be tracked stably and continuously. Complete target features (e.g., radar micro‑Doppler signatures and RF fingerprints) shall be acquired to avoid feature loss caused by discontinuous tracks.
Adopt a multi‑sensor integrated detection scheme instead of relying on a single sensor for target judgment. Radars perform wide‑area search, radio‑frequency sensors obtain radio‑related features, and optoelectronic devices conduct image verification to complement each other’s strengths.
Optimize on‑site installation and deployment. Sensors shall avoid obstructions and strong electromagnetic interference sources as much as possible to reduce original‑signal distortion induced by ground clutter and multipath reflection. Complete geodetic coordinate calibration and GNSS time‑synchronization calibration for all sensors.
2. Multi‑source Data Fusion Optimization (Core for Reducing False Alarms)
Perform temporal‑spatial association and matching for data from radars, RF sensors and optoelectronic devices to judge whether outputs from different sensors correspond to one real target and avoid track mismatching.
Adopt a confidence‑weighted fusion mechanism. Outputs of each sensor are attached with recognition confidence values, and the system calculates the overall confidence comprehensively. Confirmation alarms shall not be generated directly based on outputs from a single sensor.
Implement track‑filtering strategies. Classification and recognition shall be performed only for continuously stable valid tracks. Transient clutter plots shall not participate in classification or trigger optoelectronic linkage to filter out massive transient false alarms.
Realize multi‑sensor handover tracking. When one sensor fails due to obstruction or weather conditions, remaining sensors maintain target information and re‑associate targets after conditions recover.
3. Algorithm and AI Model Optimization
Deploy dedicated AI recognition models oriented to low‑altitude UAV scenarios, instead of reusing algorithms designed for other scenarios such as bird detection, to improve discrimination capability for rotor‑based targets and various interferences.
Fully exploit multi‑dimensional target features, including radar micro‑Doppler spectral features, RF signal fingerprints, optoelectronic image features, together with target motion‑behavior features (hovering, straight‑line flight, circling, etc.) for joint judgment.
Carry out iterative training with on‑site samples. Collect real airspace samples from the project site, including local birds, kites, balloons and common UAVs in the region, and fine‑tune models to adapt to site‑specific environments.
Improve target libraries. Maintain UAV model libraries, whitelists, blacklists and interference‑sample libraries, and continuously input samples of new‑type UAVs and local interferences.
4. Business‑Strategy Optimization
Implement hierarchical alarm management to differentiate suspected targets from confirmed threats. Low‑confidence targets shall only trigger hints and be submitted for manual review. Confirmation alarms shall be issued only when high‑confidence conditions are satisfied.
Conduct threat assessment assisted by zoned defense strategies. Raise alarm‑judgment thresholds for areas closer to core protected zones and lower alarm levels for peripheral detection zones to reduce invalid alarms triggered by long‑range low‑confidence targets.
Strictly control automatic countermeasure logic. Automatic countermeasures shall not be executed for low‑confidence suspected targets to avoid mis‑disposal risks.
Fully store complete logs of alarms, false alarms and missed detections, review cases regularly, and tune system parameters continuously.
5. Engineering Operation and Maintenance Tuning
Dynamically adjust system parameters according to field environments. Raise track‑confidence thresholds appropriately for urban clutter‑prone environments, and relax thresholds moderately for open‑field environments.
Carry out regular system simulation tests by injecting simulated targets to verify recognition and alarm‑linkage logic.
Regularly upgrade firmware and algorithm models, and supplement samples of new‑type UAVs to adapt to evolving target types.