Paper Info
Title
Exact Fisher Information Matrix With State-Dependent Probability of Detection.
Abstract
In this paper, the exact Fisher information matrix (EFIM) is derived for target localization with range measurements under imperfect detection. We treat the probability of detection (PD) as a target state-dependent parameter, and take a partial derivative of PD with respect to the target state during the calculation of the EFIM. By introducing an additional information impact parameter (AIIP), we define a parameter to reveal the divergence between EFIM and the original Fisher information matrix. We analytically prove that the AIIP is bandwidth invariant, and subsequently we know that the divergence parameter is inversely linear with the square of the signal bandwidth. Moreover, we also analyze the connection of the divergence parameter with the signal-to-noise ratio (SNR) and the false alarm rate by simulation. The results suggest that approximating PD as a state-free parameter is feasible for most of the real radar applications with low false alarm rate, large bandwidth, and moderate SNR, except for some specific continuous wave radar systems, which operate with a small bandwidth and high false alarm rate.
Year
DOI
Venue
2017
10.1109/TAES.2017.2667418
IEEE Trans. Aerospace and Electronic Systems
Keywords
Field
DocType
Bandwidth,Signal to noise ratio,Radar applications,Probability density function,Probability,Analytical models
Control theory,Partial derivative,Artificial intelligence,Statistical power,Radar,Pattern recognition,Signal-to-noise ratio,Algorithm,Bandwidth (signal processing),Fisher information,Constant false alarm rate,Probability density function,Mathematics
Journal
Volume
Issue
ISSN
53
3
0018-9251
Citations 
PageRank 
References 
0
0.34
6
Authors
4
Name
Order
Citations
PageRank
Junkun Yan17911.13
Hongwei Liu241666.06
Wenqiang Pu3487.37
Zheng Bao41985155.03