Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The Probability Hypothesis Density (PHD) filter is the first-order momentum of Bayesian multi-target filter. The Gaussian Mixture PHD (GM-PHD) implementation is a closed form solution for the PHD filter. When targets are too close to each other, such as occlusion condition, the performance of the GM-PHD filter degrades significantly.  a novel algorithm is proposed to improve this drawback. Our method employs a renormalization scheme to re-manage the ights assigned to each target in the GM-PHD recursion. Simulation results show that our proposed approach significantly improves the overall estimation performance of GM-PHD filter.
['Mahdi Yazdian Dehkordi', 'Zohreh Azimifar', 'Mohammad Ali Masnadi-Shirazi']
I am writing a research paper on a idea An improvement on GM-PHD filter for occluded target tracking
[{"from": "human", "value": "I am writing a research paper on a idea An improvement on GM-PHD filter for occluded target tracking"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The Probability Hypothesis Density (PHD) filter is the first-order momentum of Bayesian multi-target filter. The Gaussian Mixture PHD (GM-PHD) implementation is a closed form solution for the PHD filter. When targets are too close to each other, such as occlusion condition, the performance of the GM-PHD filter degrades significantly.  a novel algorithm is proposed to improve this drawback. Our method employs a renormalization scheme to re-manage the ights assigned to each target in the GM-PHD recursion. Simulation results show that our proposed approach significantly improves the overall estimation performance of GM-PHD filter."}]