TY - GEN
T1 - MIMO Radar Transmit Signal Optimization for Target Localization Exploiting Prior Information
AU - Xu, Chan
AU - Zhang, Shuowen
N1 - Funding Information:
This work was supported in part by the General Research Fund from the Hong Kong Research Grants Council under Grant 15230022, and in part by the National Natural Science Foundation of China under Grant 62101474.
Publisher Copyright:
© 2023 IEEE.
PY - 2023/8
Y1 - 2023/8
N2 - In this paper, we consider a multiple-input multiple-output (MIMO) radar system for localizing a target based on its reflected echo signals. Specifically, we aim to estimate the random and unknown angle information of the target, by exploiting its prior distribution information. First, we characterize the estimation performance by deriving the posterior Cramér-Rao bound (PCRB), which quantifies a lower bound of the estimation mean-squared error (MSE). Since the PCRB is in a complicated form, we derive a tight upper bound of it to approximate the estimation performance. Based on this, we analytically show that by exploiting the prior distribution information, the PCRB is always no larger than the Cramer-Rao bound (CRB) averaged over random angle realizations without prior information exploitation. Next, we formulate the transmit signal optimization problem to minimize the PCRB upper bound. We show that the optimal sample covariance matrix has a rank-one structure, and derive the optimal signal solution in closed form. Numerical results show that our proposed design achieves significantly improved PCRB performance compared to various benchmark schemes.
AB - In this paper, we consider a multiple-input multiple-output (MIMO) radar system for localizing a target based on its reflected echo signals. Specifically, we aim to estimate the random and unknown angle information of the target, by exploiting its prior distribution information. First, we characterize the estimation performance by deriving the posterior Cramér-Rao bound (PCRB), which quantifies a lower bound of the estimation mean-squared error (MSE). Since the PCRB is in a complicated form, we derive a tight upper bound of it to approximate the estimation performance. Based on this, we analytically show that by exploiting the prior distribution information, the PCRB is always no larger than the Cramer-Rao bound (CRB) averaged over random angle realizations without prior information exploitation. Next, we formulate the transmit signal optimization problem to minimize the PCRB upper bound. We show that the optimal sample covariance matrix has a rank-one structure, and derive the optimal signal solution in closed form. Numerical results show that our proposed design achieves significantly improved PCRB performance compared to various benchmark schemes.
UR - http://www.scopus.com/inward/record.url?scp=85171431809&partnerID=8YFLogxK
U2 - 10.1109/ISIT54713.2023.10206741
DO - 10.1109/ISIT54713.2023.10206741
M3 - Conference article published in proceeding or book
AN - SCOPUS:85171431809
T3 - IEEE International Symposium on Information Theory - Proceedings
SP - 310
EP - 315
BT - 2023 IEEE International Symposium on Information Theory, ISIT 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Symposium on Information Theory, ISIT 2023
Y2 - 25 June 2023 through 30 June 2023
ER -