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ARXIV:2603.11586 · UAV DETECTION AND TRACKING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.11586UAV DETECTION AND TRACKINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
An unsupervised LiDAR-based pipeline for detecting and tracking UAVs in sparse environments without labeled data.
Opportunity summary
Pain An unsupervised LiDAR-based pipeline for detecting and tracking UAVs in sparse environments without labeled data.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
An unsupervised LiDAR-based pipeline for detecting and tracking UAVs in sparse environments without labeled data. We introduce an unsupervised, LiDAR-only pipeline that addresses both detection and tracking without the need for labeled training data.
Non-repetitive solid-state LiDAR scanning leads to an extremely sparse measurement regime for detecting airborne UAVs: a small quadrotor at 10-25 m typically produces only 1-2 returns per scan, which is far below the point…
ScienceToStartup currently rates this 5.0/10 on the public viability pass. A two-environment evaluation strategy, combining real-world detection with RTK-GPS ground truth and simulation-based tracking with identity-annotated ground truth, overcomes the limitations of GNSS-only evaluation…
UAV Detection and Tracking moved forward this cycle; last verified April 2026. Public score 5.0/10.
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An unsupervised LiDAR-based pipeline for detecting and tracking UAVs in sparse environments without labeled data.
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10.48550/arXiv.2603.11586An unsupervised LiDAR-based pipeline for detecting and tracking UAVs in sparse environments without labeled data.
Abstract
Non-repetitive solid-state LiDAR scanning leads to an extremely sparse measurement regime for detecting airborne UAVs: a small quadrotor at 10-25 m typically produces only 1-2 returns per scan, which is far below the point densities assumed by most existing detection approaches and inadequate for robust multi-target data association. We introduce an unsupervised, LiDAR-only pipeline that addresses both detection and tracking without the need for labeled training data. The detector integrates range-adaptive DBSCAN clustering with a three-stage temporal consistency check and is benchmarked on real-world air-to-air flight data under eight different parameter configurations. The best setup attains 0.891 precision, 0.804 recall, and 0.63 m RMSE, and a systematic minPts sweep verifies that most scans contain at most 1-2 target points, directly quantifying the sparsity regime. For multi-target tracking, we compare deterministic Hungarian assignment with joint probabilistic data association (JPDA), each coupled with Interacting Multiple Model filtering, in four simulated scenarios with increasing levels of ambiguity. JPDA cuts identity switches by 64% with negligible impact on MOTA, demonstrating that probabilistic association is advantageous when UAV trajectories approach one another closely. A two-environment evaluation strategy, combining real-world detection with RTK-GPS ground truth and simulation-based tracking with identity-annotated ground truth, overcomes the limitations of GNSS-only evaluation at inter-UAV distances below 2 m.
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unverified0 refs; 0 sources; 17% coverage.
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Viability
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Dimensions overall score 5.0
PROBLEM
An unsupervised LiDAR-based pipeline for detecting and tracking UAVs in sparse environments without labeled data. We introduce an unsupervised, LiDAR-only pipeline that addresses both detection and tracking without the need for labeled training data.
METHOD
Non-repetitive solid-state LiDAR scanning leads to an extremely sparse measurement regime for detecting airborne UAVs: a small quadrotor at 10-25 m typically produces only 1-2 returns per scan, which is far below the point densities assumed by most existing detection approaches...
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. A two-environment evaluation strategy, combining real-world detection with RTK-GPS ground truth and simulation-based tracking with identity-annotated ground truth, overcomes the limitations of GNSS-only e...
WHY NOW
UAV Detection and Tracking moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed public claims while anchored extraction refreshes.
An unsupervised LiDAR-based pipeline for detecting and tracking UAVs in sparse environments without labeled data. We introduce an unsupervised, LiDAR-only pipeline that addresses both detection and tracking without the need for labeled training data.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Non-repetitive solid-state LiDAR scanning leads to an extremely sparse measurement regime for detecting airborne UAVs: a small quadrotor at 10-25 m typically produces only 1-2 returns per scan, which is far below the point densities assumed by most existing detection approaches and inadequate for robust multi-target data association. We introduce an unsupervised, LiDAR-only pipeline that addresses both detection and tracking without the need for labeled training data.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. A two-environment evaluation strategy, combining real-world detection with RTK-GPS ground truth and simulation-based tracking with identity-annotated ground truth, overcomes the limitations of GNSS-only evaluation at inter-UAV distances below 2 m.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
UAV Detection and Tracking moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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An unsupervised LiDAR-based pipeline for detecting and tracking UAVs in sparse environments without labeled data.
Segment
UAV Detection and Tracking
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Commercial read
5.0/10 public viability
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ARTIFACTS
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DEFENSIBILITY
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