Gaussian Mixture Probability Hypothesis Density Filter for Heterogeneous Multi-Sensor Registration

Yajun Zeng, Jun Wang, Shaoming Wei (), Chi Zhang, Xuan Zhou and Yingbin Lin
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Yajun Zeng: School of Electronics and Information Engineering, Beihang University, Beijing 100191, China
Jun Wang: School of Electronics and Information Engineering, Beihang University, Beijing 100191, China
Shaoming Wei: School of Electronics and Information Engineering, Beihang University, Beijing 100191, China
Chi Zhang: School of Aerospace Engineering, Tsinghua University, Beijing 100084, China
Xuan Zhou: School of Electronics and Information Engineering, Beihang University, Beijing 100191, China
Yingbin Lin: School of Electronics and Information Engineering, Beihang University, Beijing 100191, China

Mathematics, 2024, vol. 12, issue 6, 1-31

Abstract: Spatial registration is a prerequisite for data fusion. Existing methods primarily focus on similar sensor scenarios and rely on accurate data association assumptions. To address the heterogeneous sensor registration in complex data association scenarios, this paper proposes a Gaussian mixture probability hypothesis density (GM-PHD)-based algorithm for heterogeneous sensor bias registration, accompanied by an adaptive measurement iterative update algorithm. Firstly, by constructing augmented target state motion and measurement models, a closed-form expression for prediction is derived based on Gaussian mixture (GM). In the subsequent update, a two-level Kalman filter is used to achieve an approximate decoupled estimation of the target state and measurement bias, taking into account the coupling between them through pseudo-likelihood. Notably, for heterogeneous sensors that cannot directly use sequential update techniques, sequential updates are first performed on sensors that can obtain complete measurements, followed by filtering updates using extended Kalman filter (EKF) sequential update techniques for incomplete measurements. When there are differences in sensor quality, the GM-PHD fusion filter based on measurement iteration update is sequence-sensitive. Therefore, the optimal subpattern assignment (OSPA) metric is used to optimize the fusion order and enhance registration performance. The proposed algorithms extend the multi-target information-based spatial registration algorithm to heterogeneous sensor scenarios and address the impact of different sensor-filtering orders on registration performance. Our proposed algorithms significantly improve the accuracy of bias estimation compared to the registration algorithm based on significant targets. Under different detection probabilities and clutter intensities, the average root mean square error (RMSE) of distance and angular biases decreased by 11.8% and 8.6%, respectively.

Keywords: Gaussian mixture probability hypothesis density; heterogeneous sensor bias registration; sequential update techniques; complete measurements; incomplete measurements (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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