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

Access to Document

10.1109/TAES.2016.150176

Cite this

@article{1b0bbfa496cc43c885146c7e76ed949a,

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",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.",

year = "2016",

month = aug,

doi = "10.1109/TAES.2016.150176",

language = "English",

volume = "52",

pages = "1986--1995",

journal = "IEEE Transactions on Aerospace and Electronic Systems",

issn = "0018-9251",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "4",

}

TY - JOUR

T1 - Optimization and analysis of PDAF with Bayesian detection

AU - Zheng, Le

AU - Zeng, Tao

AU - Liu, Quanhua

AU - Long, Teng

AU - Wang, Xiaodong

PY - 2016/8

Y1 - 2016/8

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 - http://www.scopus.com/inward/record.url?scp=84997637265&partnerID=8YFLogxK

U2 - 10.1109/TAES.2016.150176

DO - 10.1109/TAES.2016.150176

M3 - Article

AN - SCOPUS:84997637265

SN - 0018-9251

VL - 52

SP - 1986

EP - 1995

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

Access to Document

Other files and links

Fingerprint

Cite this