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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10.1109/TAES.2016.150176

Cite this

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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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N2 - 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.

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.

UR - https://www.scopus.com/pages/publications/84997637265

U2 - 10.1109/TAES.2016.150176

DO - 10.1109/TAES.2016.150176

M3 - Article

AN - SCOPUS:84997637265

SN - 0018-9251

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SP - 1986

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JO - IEEE Transactions on Aerospace and Electronic Systems

JF - IEEE Transactions on Aerospace and Electronic Systems

IS - 4

M1 - 7738367

ER -

Optimization and analysis of PDAF with Bayesian detection

Abstract

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