Track association based on the empirical mode decomposition in passive localization

Kai Lu; Chundong Qi

doi:10.1117/12.2689576

10 August 2023 Track association based on the empirical mode decomposition in passive localization

Kai Lu, Chundong Qi

Proceedings Volume 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023); 1274809 (2023) https://doi.org/10.1117/12.2689576
Event: 5th International Conference on Information Science, Electrical and Automation Engineering (ISEAE 2023), 2023, Wuhan, China

Abstract

In distributed passive localization and tracking system, the track observed by the subsystem seems like Brownian motion track, because the tracked target is non-cooperative target and its maneuver is often complex, and the localization accuracy is poor. These track characteristics will seriously disturb track association between different subsystems. In order to solve this problem, the track to track association algorithm based on empirical mode decomposition (EMD) is proposed in this article. To lessen the impact of target placement and maneuvering mistakes, components that do not follow the track trend are removed from each dimension of the track recorded by each sub-system. The track motion trend vector is formed using the remaining low-frequency components as track characteristics, and the relevant correlation criteria are created. The track association between sub-systems is ultimately finished since the correlation threshold is self-adaptive and does not require the creation of a motion model. Results from simulations indicate that the suggested method is capable of successfully completing the track connection in passive systems

Citation Download Citation

Kai Lu and Chundong Qi "Track association based on the empirical mode decomposition in passive localization", Proc. SPIE 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023), 1274809 (10 August 2023); https://doi.org/10.1117/12.2689576

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KEYWORDS

Modal decomposition

Simulations

Feature extraction

Information fusion

Motion models

Satellites

Signal processing

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