ArticleSensors (Basel, Switzerland)2024
A GM-JMNS-CPHD Filter for Different-Fields-of-View Stochastic Outlier Selection for Nonlinear Motion Tracking.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- A Study on Distributed Multi-Sensor Fusion for Nonlinear Systems Under Non-Overlapping Fields of View.Sensors (Basel, Switzerland) · 2025Article
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5 authors.
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Abstract
Most multi-target movements are nonlinear in the process of movement. The common multi-target tracking filtering methods directly act on the multi-target tracking system of nonlinear targets, and the fusion effect is worse under the influence of different perspectives. Aiming to determine the influence of different perspectives on the fusion accuracy of multi-sensor tracking in the process of target tracking, this paper studies the multi-target tracking fusion strategy of a nonlinear system with different perspectives. A GM-JMNS-CPHD fusion technique is introduced for random outlier selection in multi-target tracking, leveraging sensors with limited views. By employing boundary segmentation from distinct perspectives, the posterior intensity function undergoes decomposition into multiple sub-intensities through SOS clustering. The distribution of target numbers within the respective regions is then characterized by the multi-Bernoulli reconstruction cardinal distribution. Simulation outcomes demonstrate the robustness and efficacy of this approach. In comparison to other algorithms, this method exhibits enhanced robustness even amidst a decreased detection probability and heightened clutter rates.
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