NDLI: Joint estimation of target state and ionospheric height bias in over-the-horizon radar target tracking

Content Provider	IET Digital Library
Author	Geng, Hang Liang, Yan Yang, Feng Xu, Linfeng Pan, Quan
Abstract	Up to now, all over-the-horizon radar-based tracking methods have the prerequisite that the virtual ionospheric height, as the key ionosphere state, should be known, or should be estimated from the provided ionospheric measurements, or at least the prior statistical information of the ionosphere should be given. However, the ionospheric height is intermittently evolving and hence deviates from its nominal value. Therefore, no matter the virtual ionospheric height is directly given by ionosondes or estimated from the ionospheric measurements or prior statistical information, it may have ionospheric height bias. Here, the authors propose the problem of joint estimation of target state and ionospheric height bias in clutters. The problem is reformulated as the multi-rate state estimation with random coefficient matrices. Then, the linear minimum mean square error estimator with causality constraints is designed and extended to the non-linear measurement model via iterative optimisation. The proposed method is shown effective in the simulation about tracking one target in the case of four resolvable propagation modes.
Starting Page	1153
Ending Page	1167
Page Count	15
ISSN	17518784
Volume Number	10
e-ISSN	17518792
Issue Number	Issue 7, Aug (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/10/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2015.0318
Journal	IET Radar, Sonar & Navigation
Publisher Date	2016-03-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Atmospheric/ionospheric/magnetospheric Technique And Equipment Instrumentation And Technique For Aeronomy, Space Physics, And Cosmic Rays Interpolation And Function Approximation Ionospheric Technique Iterative Method Iterative Optimisation Least Mean Squares Method Linear Minimum Mean Square Error Estimator Multirate State Estimation Nonlinear Measurement Model Numerical Analysis Numerical Approximation And Analysis Optimisation Optimisation Technique Over-the-horizon Radar Target Tracking Radar Equipment Radar Tracking Random Coefficient Matrix Remote Sensing by Radar Signal Processing And Detection System And Application Target State Target Tracking Virtual Ionospheric Height
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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