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\u2011sensor fusion, algorithm and AI models, business strategies, and on\u2011site engineering tuning:<\/p>\n
Sensor selection shall guarantee detection capability for low\u2011speed targets, so that hovering and slowly\u2011drifting UAVs can be tracked stably and continuously. Complete target features (e.g., radar micro\u2011Doppler signatures and RF fingerprints) shall be acquired to avoid feature loss caused by discontinuous tracks.<\/p>\n
Adopt a multi\u2011sensor integrated detection scheme instead of relying on a single sensor for target judgment. Radars perform wide\u2011area search, radio\u2011frequency sensors obtain radio\u2011related features, and optoelectronic devices conduct image verification to complement each other\u2019s strengths.<\/p>\n
Optimize on\u2011site installation and deployment. Sensors shall avoid obstructions and strong electromagnetic interference sources as much as possible to reduce original\u2011signal distortion induced by ground clutter and multipath reflection. Complete geodetic coordinate calibration and GNSS time\u2011synchronization calibration for all sensors.<\/p>\n
Perform temporal\u2011spatial 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.<\/p>\n
Adopt a confidence\u2011weighted 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.<\/p>\n
Implement track\u2011filtering 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.<\/p>\n
Realize multi\u2011sensor handover tracking. When one sensor fails due to obstruction or weather conditions, remaining sensors maintain target information and re\u2011associate targets after conditions recover.<\/p>\n
Deploy dedicated AI recognition models oriented to low\u2011altitude UAV scenarios, instead of reusing algorithms designed for other scenarios such as bird detection, to improve discrimination capability for rotor\u2011based targets and various interferences.<\/p>\n
Fully exploit multi\u2011dimensional target features, including radar micro\u2011Doppler spectral features, RF signal fingerprints, optoelectronic image features, together with target motion\u2011behavior features (hovering, straight\u2011line flight, circling, etc.) for joint judgment.<\/p>\n
Carry out iterative training with on\u2011site samples. Collect real airspace samples from the project site, including local birds, kites, balloons and common UAVs in the region, and fine\u2011tune models to adapt to site\u2011specific environments.<\/p>\n
Improve target libraries. Maintain UAV model libraries, whitelists, blacklists and interference\u2011sample libraries, and continuously input samples of new\u2011type UAVs and local interferences.<\/p>\n
Implement hierarchical alarm management to differentiate suspected targets from confirmed threats. Low\u2011confidence targets shall only trigger hints and be submitted for manual review. Confirmation alarms shall be issued only when high\u2011confidence conditions are satisfied.<\/p>\n
Conduct threat assessment assisted by zoned defense strategies. Raise alarm\u2011judgment thresholds for areas closer to core protected zones and lower alarm levels for peripheral detection zones to reduce invalid alarms triggered by long\u2011range low\u2011confidence targets.<\/p>\n
Strictly control automatic countermeasure logic. Automatic countermeasures shall not be executed for low\u2011confidence suspected targets to avoid mis\u2011disposal risks.<\/p>\n
Fully store complete logs of alarms, false alarms and missed detections, review cases regularly, and tune system parameters continuously.<\/p>\n
Dynamically adjust system parameters according to field environments. Raise track\u2011confidence thresholds appropriately for urban clutter\u2011prone environments, and relax thresholds moderately for open\u2011field environments.<\/p>\n
Carry out regular system simulation tests by injecting simulated targets to verify recognition and alarm\u2011linkage logic.<\/p>\n
Regularly upgrade firmware and algorithm models, and supplement samples of new\u2011type UAVs to adapt to evolving target types.<\/p>","protected":false},"excerpt":{"rendered":"
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\u2011sensor fusion, algorithm and AI models, business strategies, and on\u2011site engineering tuning: 1. Hardware Configuration Optimization (Fundamental Guarantee) Sensor selection shall guarantee detection capability for low\u2011speed targets, so that hovering and […]<\/p>","protected":false},"author":2,"featured_media":1350,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"rank_math_lock_modified_date":false,"footnotes":""},"categories":[3],"tags":[],"class_list":["post-1449","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news"],"_links":{"self":[{"href":"https:\/\/www.mskyeye.com\/de\/wp-json\/wp\/v2\/posts\/1449","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.mskyeye.com\/de\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.mskyeye.com\/de\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.mskyeye.com\/de\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.mskyeye.com\/de\/wp-json\/wp\/v2\/comments?post=1449"}],"version-history":[{"count":2,"href":"https:\/\/www.mskyeye.com\/de\/wp-json\/wp\/v2\/posts\/1449\/revisions"}],"predecessor-version":[{"id":1451,"href":"https:\/\/www.mskyeye.com\/de\/wp-json\/wp\/v2\/posts\/1449\/revisions\/1451"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.mskyeye.com\/de\/wp-json\/wp\/v2\/media\/1350"}],"wp:attachment":[{"href":"https:\/\/www.mskyeye.com\/de\/wp-json\/wp\/v2\/media?parent=1449"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.mskyeye.com\/de\/wp-json\/wp\/v2\/categories?post=1449"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.mskyeye.com\/de\/wp-json\/wp\/v2\/tags?post=1449"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}