TY - GEN
T1 - Using Coupled Multilinear Rank-(L, L, 1) Block Term Decomposition in Multi-Static-Multi-Pulse MIMO Radar to Localize Targets
AU - Yang, Jia Xing
AU - Gong, Xiao Feng
AU - Li, Hui
AU - Xu, You Gen
AU - Liu, Zhi Wen
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - We propose a tensorial method for target localization based on multi-static-multi-pulse MIMO radar, which consists of multiple widely separated transmitting and receiving arrays. We show how a set of tensors, which admits a coupled multilinear (ML) rank-(L, L, 1) block term decomposition (BTD), can be constructed from the output signals of different receiving arrays. As such, we compute the coupled ML rank-(L, L, 1) BTD of these tensors to obtain factor matrices. The target locations are then able to be determined from the latent DOA parameters in these factor matrices. The proposed method neither requires prior knowledge, nor assumes orthogonality between probing signals. In addition, by exploiting the coupling, different receiving array outputs are jointly processed, yielding improved performance than uncoupled BTD based methods. Simulation results are provided to illustrate the performance of the proposed method.
AB - We propose a tensorial method for target localization based on multi-static-multi-pulse MIMO radar, which consists of multiple widely separated transmitting and receiving arrays. We show how a set of tensors, which admits a coupled multilinear (ML) rank-(L, L, 1) block term decomposition (BTD), can be constructed from the output signals of different receiving arrays. As such, we compute the coupled ML rank-(L, L, 1) BTD of these tensors to obtain factor matrices. The target locations are then able to be determined from the latent DOA parameters in these factor matrices. The proposed method neither requires prior knowledge, nor assumes orthogonality between probing signals. In addition, by exploiting the coupling, different receiving array outputs are jointly processed, yielding improved performance than uncoupled BTD based methods. Simulation results are provided to illustrate the performance of the proposed method.
KW - Block Term Decomposition
KW - Direction of arrival of arrival
KW - MIMO radar
KW - Multilinear rank
KW - Tensor
UR - http://www.scopus.com/inward/record.url?scp=85068607804&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-22808-8_56
DO - 10.1007/978-3-030-22808-8_56
M3 - Conference contribution
AN - SCOPUS:85068607804
SN - 9783030228071
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 565
EP - 574
BT - Advances in Neural Networks – ISNN 2019 - 16th International Symposium on Neural Networks, ISNN 2019, Proceedings
A2 - Lu, Huchuan
A2 - Tang, Huajin
A2 - Wang, Zhanshan
PB - Springer Verlag
T2 - 16th International Symposium on Neural Networks, ISNN 2019
Y2 - 10 July 2019 through 12 July 2019
ER -