Electrical & Electronics Engineering
Permanent URI for this collectionhttp://197.211.34.35:4000/handle/123456789/130
Electrical & Electronics Engineering
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Item Robust Multi-Target Tracking with a Kalman-Gain CPHD Filter: Simulation and Experimental Validation(2025-04-19) Abdullahi DaniyanWe 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.Item Bayesian data driven modelling of kinetochore dynamics: space-time organisation of the human metaphase plate(PLOS Biology, 2025-01-23) Constandina Koki; Alessio V Inchingolo; Abdullahi Daniyan; Enyu Li; Andrew D. McAinsh; Nigel J BurroughsMitosis is a complex self-organising process that achieves high fidelity separation of duplicated chromosomes into two daughter cells through capture and alignment of chromosomes to the spindle mid-plane. Chromosome movements are driven by kinetochores, multi-protein machines that attach chromosomes to microtubules (MTs), both controlling and generating directional forces. Using lattice light sheet microscopy imaging and automated near-complete tracking of kinetochores at fine spatio-temporal resolution, we produce a detailed atlas of kinetochore metaphase-anaphase dynamics in untransformed human cells (RPE1). We fitted 18 biophysical models of kinetochore metaphase-anaphase dynamics to experimental data using Bayesian inference, and determined support for the models with model selection methods, demonstrating substantial sister force asymmetry and time dependence of the mechanical parameters. Our analysis shows that K-fiber pulling and pushing strengths are inversely correlated and that there is substantial spatial organisation of KT dynamic properties both within, and transverse to the metaphase plate. Further, K-fiber forces are tuned over the last 5 mins of metaphase towards a set point, which we refer to as the anaphase ready state.Item Probability hypothesis density filter for parameter estimation of multiple hazardous sources(Elsevier, 2024-08-30) Abdullahi Daniyan; Cunjia Liu; Wen-Hua ChenThis 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.Item Secrecy Rate Optimizations for MIMO Communication Radar(IEEE, 2018-03-28) Anastasios Deligiannis; Abdullahi Daniyan; Sangarapillai Lambotharan; Jonathon A. ChambersIn this paper, we investigate transmit beampattern optimization techniques for a multiple-input multiple-output radar in the presence of a legitimate communications receiver and an eavesdropping target. The primary objectives of the radar are to satisfy a certain target-detection criterion and to simultaneously communicate safely with a legitimate receiver by maximizing the secrecy rate against the eavesdropping target. Therefore, we consider three optimization problems, namely target return signal-to-interference-plus-noise ratio maximization, secrecy rate maximization, and transmit power minimization. However, these problems are nonconvex due to the nonconcavity of the secrecy rate function, which appears in all three optimizations either as the objective function or as a constraint. To solve this issue, we use Taylor series approximation of the nonconvex elements through an iterative algorithm, which recasts the problem as a convex problem. Two transmit covariance matrices are designed to detect the target and convey the information safely to the communication receiver. Simulation results are presented to validate the efficiency of the aforementioned optimizations.Item Kalman-Gain Aided Particle PHD Filter for Multitarget Tracking(IEEE, 2017-04-05) Abdullahi Daniyan; Yu Gong; Sangarapillai Lambotharan; Pengming Feng; Jonathon ChambersWe propose an efficient sequential Monte Carlo probability hypothesis density (PHD) filter which employs the Kalman-gain approach during weight update to correct predicted particle states by minimizing the mean square error between the estimated measurement and the actual measurement received at a given time in order to arrive at a more accurate posterior. This technique identifies and selects those particles belonging to a particular target from a given PHD for state correction during weight computation. Besides the improved tracking accuracy, fewer particles are required in the proposed approach. Simulation results confirm the improved tracking performance when evaluated with different measures.Item Bayesian Multiple Extended Target Tracking Using Labeled Random Finite Sets and Splines(IEEE, 2018-10-04) Abdullahi Daniyan; Sangarapillai Lambotharan; Anastasios Deligiannis; Yu Gong; Wen-Hua ChenIn this paper, we propose a technique for the joint tracking and labeling of multiple extended targets. To achieve multiple extended target tracking using this technique, models for the target measurement rate, kinematic component, and target extension are defined and jointly propagated in time under the generalized labeled multi-Bernoulli filter framework. In particular, we developed a Poisson mixture variational Bayesian model to simultaneously estimate the measurement rate of multiple extended targets and extended target extension was modeled using B-splines. We evaluated our proposed method with various performance metrics. Results demonstrate the effectiveness of our approach.