Optimization and analysis of PDAF with Bayesian detection

Le Zheng; Tao Zeng; Quanhua Liu; Teng Long; Xiaodong Wang

doi:10.1109/TAES.2016.150176

Optimization and analysis of PDAF with Bayesian detection

Le Zheng, Tao Zeng, Quanhua Liu, Teng Long, Xiaodong Wang

信息与电子学院

Columbia University

科研成果: 期刊稿件 › 文章 › 同行评审

8 引用（Scopus）

摘要

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.

源语言	英语
文章编号	7738367
页（从-至）	1986-1995
页数	10
期刊	IEEE Transactions on Aerospace and Electronic Systems
卷	52
期	4
DOI	https://doi.org/10.1109/TAES.2016.150176
出版状态	已出版 - 8月 2016

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10.1109/TAES.2016.150176

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title = "Optimization and analysis of PDAF with Bayesian detection",

abstract = "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.",

author = "Le Zheng and Tao Zeng and Quanhua Liu and Teng Long and Xiaodong Wang",

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AU - Zheng, Le

AU - Zeng, Tao

AU - Liu, Quanhua

AU - Long, Teng

AU - Wang, Xiaodong

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AB - 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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ER -

Optimization and analysis of PDAF with Bayesian detection

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