In this paper, a nonsimulation performance prediction-based PDAF with Bayesian detection (BD) is proposed where the parameter in detection is dynamically optimized in a tracker-aware manner. The theoretical analysis and simulation results show that the dynamic PDAF-BD always outperforms the PDAF-BD with fixed thresholds and can be better than the dynamic PDAF when the spatial density of detection sampling is large.


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    Title :

    Optimization and analysis of PDAF with Bayesian detection


    Contributors:
    Le Zheng (author) / Tao Zeng (author) / Quanhua Liu (author) / Teng Long (author) / Xiaodong Wang (author)


    Publication date :

    2016-08-01


    Size :

    748249 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English