TY - JOUR
T1 - Low complexity MIMO sonar imaging using a virtual sparse linear array
AU - Liu, Xionghou
AU - Sun, Chao
AU - Yang, Yixin
AU - Zhuo, Jie
AU - Han, Yina
PY - 2016/4/20
Y1 - 2016/4/20
N2 - A multiple-input multiple-output (MIMO) sonar can synthesize a large-aperture virtual uniform linear array (ULA) from a small number of physical elements. However, the large aperture is obtained at the cost of a great number of matched filters with much heavy computation load. To reduce the computation load, a MIMO sonar imaging method using a virtual sparse linear array (SLA) is proposed, which contains the offline and online processing. In the offline processing, the virtual ULA of the MIMO sonar is thinned to a virtual SLA by the simulated annealing algorithm, and matched filters corresponding to inactive virtual elements are removed. In the online processing, outputs of matched filters corresponding to active elements are collected for further multibeam processing and hence, the number of matched filters in the echo processing procedure is effectively reduced. Numerical simulations show that the proposed method can reduce the computation load effectively while obtaining a similar imaging performance as the traditional method.
AB - A multiple-input multiple-output (MIMO) sonar can synthesize a large-aperture virtual uniform linear array (ULA) from a small number of physical elements. However, the large aperture is obtained at the cost of a great number of matched filters with much heavy computation load. To reduce the computation load, a MIMO sonar imaging method using a virtual sparse linear array (SLA) is proposed, which contains the offline and online processing. In the offline processing, the virtual ULA of the MIMO sonar is thinned to a virtual SLA by the simulated annealing algorithm, and matched filters corresponding to inactive virtual elements are removed. In the online processing, outputs of matched filters corresponding to active elements are collected for further multibeam processing and hence, the number of matched filters in the echo processing procedure is effectively reduced. Numerical simulations show that the proposed method can reduce the computation load effectively while obtaining a similar imaging performance as the traditional method.
KW - multiple-input multiple-output (MIMO) sonar
KW - simulated annealing
KW - sonar imaging
KW - sparse arrays
UR - http://www.scopus.com/inward/record.url?scp=84979674806&partnerID=8YFLogxK
U2 - 10.1109/JSEE.2016.00038
DO - 10.1109/JSEE.2016.00038
M3 - 文章
AN - SCOPUS:84979674806
SN - 1671-1793
VL - 27
SP - 370
EP - 378
JO - Journal of Systems Engineering and Electronics
JF - Journal of Systems Engineering and Electronics
IS - 2
M1 - 7514425
ER -