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

School of Information and Electronics

Columbia University

Research output: Contribution to journal › Article › peer-review

8 Citations (Scopus)

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.

Original language	English
Article number	7738367
Pages (from-to)	1986-1995
Number of pages	10
Journal	IEEE Transactions on Aerospace and Electronic Systems
Volume	52
Issue number	4
DOIs	https://doi.org/10.1109/TAES.2016.150176
Publication status	Published - Aug 2016

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Zheng, L., Zeng, T., Liu, Q., Long, T., & Wang, X. (2016). Optimization and analysis of PDAF with Bayesian detection. IEEE Transactions on Aerospace and Electronic Systems, 52(4), 1986-1995. Article 7738367. https://doi.org/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.",

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AU - Liu, Quanhua

AU - Long, Teng

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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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Optimization and analysis of PDAF with Bayesian detection

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