Electrical & Electronics Engineering

Permanent URI for this collectionhttp://197.211.34.35:4000/handle/123456789/130

Electrical & Electronics Engineering

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Author: search.filters.author.Abdullahi DaniyanSubject: search.filters.subject.Sequential Monte Carlo

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    Robust Multi-Target Tracking with a Kalman-Gain CPHD Filter: Simulation and Experimental Validation
    (2025-04-19) Abdullahi Daniyan
    We introduce a novel cardinalized implementation of the Kalman-gain-aided particle probability hypothesis density (KG-SMC-PHD) filter, extending it to form the Kalman-Gain Particle Cardinalized Probability Hypothesis Density (KG- SMC-CPHD) filter. This new approach significantly enhances multi-target tracking by combining the particle-based state correction mechanism with the propagation of both the PHD and target cardinality distribution. Unlike conventional particle filters that require a large number of particles for acceptable performance, our method intelligently corrects selected particles during the weight update stage, resulting in a more accurate posterior with substantially fewer particles. Through comprehensive evaluations on both simulated and experimental datasets, the KG-SMC-CPHD filter demonstrates superior robustness and accuracy, particularly in high-clutter environments and nonlinear target dynamics. Notably, it offers improved cardinality estimation and maintains the computational efficiency and performance advantages of its predecessor, the KG-SMC-PHD filter, making it a powerful tool for advanced multi-target tracking applications.
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    Probability hypothesis density filter for parameter estimation of multiple hazardous sources
    (Elsevier, 2024-08-30) Abdullahi Daniyan; Cunjia Liu; Wen-Hua Chen
    This study introduces an advanced methodology for estimating the source term of multiple, variable-number biochemical hazard releases, where the exact count of sources is not predetermined. Focusing on environments monitored via a network of sensors, we tackle this challenge through a multi-source Bayesian filtering paradigm, employing the theory of random finite sets (RFS). Our novel approach leverages a modified particle filter-based probability hypothesis density (PHD) filter within the RFS framework, enabling simultaneous estimation of critical source characteristics (such as location, emission rate, and effective release height) and the quantification of source numbers. This method not only accurately estimates pertinent source parameters but is also adept at identifying the emergence of new sources and the cessation of existing ones within the monitored area. The efficacy of our approach is validated through extensive simulations, which mimic a range of scenarios with varying and unknown source counts, highlighting the proposed method’s robustness and precision.