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Cramer-Rao Lower Bound Evaluation for Linear Frequency Modulation Based Active Radar Networks Operating in a Rice Fading Environment

Shi, Chenguang; Salous, Sana; Wang, Fei; Zhou, Jianjiang

Cramer-Rao Lower Bound Evaluation for Linear Frequency Modulation Based Active Radar Networks Operating in a Rice Fading Environment Thumbnail


Authors

Chenguang Shi

Fei Wang

Jianjiang Zhou



Abstract

This paper investigates the joint target parameter (delay and Doppler) estimation performance of linear frequency modulation (LFM)-based radar networks in a Rice fading environment. The active radar networks are composed of multiple radar transmitters and multichannel receivers placed on moving platforms. First, the log-likelihood function of the received signal for a Rician target is derived, where the received signal scattered off the target comprises of dominant scatterer (DS) component and weak isotropic scatterers (WIS) components. Then, the analytically closed-form expressions of the Cramer-Rao lower bounds (CRLBs) on the Cartesian coordinates of target position and velocity are calculated, which can be adopted as a performance metric to access the target parameter estimation accuracy for LFM-based radar network systems in a Rice fading environment. It is found that the cumulative Fisher information matrix (FIM) is a linear combination of both DS component and WIS components, and it also demonstrates that the joint CRLB is a function of signal-to-noise ratio (SNR), target’s radar cross section (RCS) and transmitted waveform parameters, as well as the relative geometry between the target and the radar network architectures. Finally, numerical results are provided to indicate that the joint target parameter estimation performance of active radar networks can be significantly improved with the exploitation of DS component.

Citation

Shi, C., Salous, S., Wang, F., & Zhou, J. (2016). Cramer-Rao Lower Bound Evaluation for Linear Frequency Modulation Based Active Radar Networks Operating in a Rice Fading Environment. Sensors, 16(12), https://doi.org/10.3390/s16122072

Journal Article Type Article
Acceptance Date Nov 28, 2016
Online Publication Date Dec 6, 2016
Publication Date Dec 6, 2016
Deposit Date May 4, 2017
Publicly Available Date Mar 29, 2024
Journal Sensors
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 16
Issue 12
DOI https://doi.org/10.3390/s16122072

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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/

Copyright Statement
© 2016 by the authors; licensee MDPI, Basel, Switzerland. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).





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