IEICE TRANSACTIONS on Communications
Target Scattering Coefficients Estimation in Cognitive Radar under Temporally Correlated Target and Multiple Receive Antennas Scenario
Summary :
In cognitive radar systems (CRSs), target scattering coefficients (TSC) can be utilized to improve the performance of target identification and classification. This work considers the problem of TSC estimation for temporally correlated target. Multiple receive antennas are adopted to receive the echo waveforms, which are interfered by the signal-dependent clutter. Unlike existing estimation methods in time domain, a novel estimation method based on Kalman filtering (KF) is proposed in frequency domain to exploit the temporal TSC correlation, and reduce the complexity of subsequent waveform optimization. Additionally, to minimize the mean square error of estimated TSC at each KF iteration, in contrary to existing works, we directly model the design process as an optimization problem, which is non-convex and cannot be solved efficiently. Therefore, we propose a novel method, similar in some way to semi-definite programming (SDP), to convert the non-convex problem into a convex one. Simulation results demonstrate that the estimation performance can be significantly improved by the KF estimation with optimized waveform.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E98-B No.9 pp.1914-1923
- Publication Date
- 2015/09/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.E98.B.1914
- Type of Manuscript
- PAPER
- Category
- Sensing
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Peng CHEN, Lenan WU, "Target Scattering Coefficients Estimation in Cognitive Radar under Temporally Correlated Target and Multiple Receive Antennas Scenario" in IEICE TRANSACTIONS on Communications,
vol. E98-B, no. 9, pp. 1914-1923, September 2015, doi: 10.1587/transcom.E98.B.1914.
Abstract: In cognitive radar systems (CRSs), target scattering coefficients (TSC) can be utilized to improve the performance of target identification and classification. This work considers the problem of TSC estimation for temporally correlated target. Multiple receive antennas are adopted to receive the echo waveforms, which are interfered by the signal-dependent clutter. Unlike existing estimation methods in time domain, a novel estimation method based on Kalman filtering (KF) is proposed in frequency domain to exploit the temporal TSC correlation, and reduce the complexity of subsequent waveform optimization. Additionally, to minimize the mean square error of estimated TSC at each KF iteration, in contrary to existing works, we directly model the design process as an optimization problem, which is non-convex and cannot be solved efficiently. Therefore, we propose a novel method, similar in some way to semi-definite programming (SDP), to convert the non-convex problem into a convex one. Simulation results demonstrate that the estimation performance can be significantly improved by the KF estimation with optimized waveform.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E98.B.1914/_p
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@ARTICLE{e98-b_9_1914,
author={Peng CHEN, Lenan WU, },
journal={IEICE TRANSACTIONS on Communications},
title={Target Scattering Coefficients Estimation in Cognitive Radar under Temporally Correlated Target and Multiple Receive Antennas Scenario},
year={2015},
volume={E98-B},
number={9},
pages={1914-1923},
abstract={In cognitive radar systems (CRSs), target scattering coefficients (TSC) can be utilized to improve the performance of target identification and classification. This work considers the problem of TSC estimation for temporally correlated target. Multiple receive antennas are adopted to receive the echo waveforms, which are interfered by the signal-dependent clutter. Unlike existing estimation methods in time domain, a novel estimation method based on Kalman filtering (KF) is proposed in frequency domain to exploit the temporal TSC correlation, and reduce the complexity of subsequent waveform optimization. Additionally, to minimize the mean square error of estimated TSC at each KF iteration, in contrary to existing works, we directly model the design process as an optimization problem, which is non-convex and cannot be solved efficiently. Therefore, we propose a novel method, similar in some way to semi-definite programming (SDP), to convert the non-convex problem into a convex one. Simulation results demonstrate that the estimation performance can be significantly improved by the KF estimation with optimized waveform.},
keywords={},
doi={10.1587/transcom.E98.B.1914},
ISSN={1745-1345},
month={September},}
Copy
TY - JOUR
TI - Target Scattering Coefficients Estimation in Cognitive Radar under Temporally Correlated Target and Multiple Receive Antennas Scenario
T2 - IEICE TRANSACTIONS on Communications
SP - 1914
EP - 1923
AU - Peng CHEN
AU - Lenan WU
PY - 2015
DO - 10.1587/transcom.E98.B.1914
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E98-B
IS - 9
JA - IEICE TRANSACTIONS on Communications
Y1 - September 2015
AB - In cognitive radar systems (CRSs), target scattering coefficients (TSC) can be utilized to improve the performance of target identification and classification. This work considers the problem of TSC estimation for temporally correlated target. Multiple receive antennas are adopted to receive the echo waveforms, which are interfered by the signal-dependent clutter. Unlike existing estimation methods in time domain, a novel estimation method based on Kalman filtering (KF) is proposed in frequency domain to exploit the temporal TSC correlation, and reduce the complexity of subsequent waveform optimization. Additionally, to minimize the mean square error of estimated TSC at each KF iteration, in contrary to existing works, we directly model the design process as an optimization problem, which is non-convex and cannot be solved efficiently. Therefore, we propose a novel method, similar in some way to semi-definite programming (SDP), to convert the non-convex problem into a convex one. Simulation results demonstrate that the estimation performance can be significantly improved by the KF estimation with optimized waveform.
ER -
English
