To reduce the computation burden of a large-aperture multiple-input multiple-output（MIMO） sonar imaging system,the phase-shift beamformer（PSBF） is used at the cost of bringing the intensity loss（IL）.The cause of...To reduce the computation burden of a large-aperture multiple-input multiple-output（MIMO） sonar imaging system,the phase-shift beamformer（PSBF） is used at the cost of bringing the intensity loss（IL）.The cause of the IL is analyzed in detail and a variable termed as IL factor is defined to quantify the loss amount.To compensate for the IL,two methods termed as intensity compensation for the PSBF（IC-PSBF） and the hybrid beamforming（HBF）,respectively,are proposed.The IC-PSBF uses previously estimated IL factors to compensate for output intensities of all PSBFs;and the HBF applies the IC-PSBF to the center beam region and the shifted-sideband beamformer（SSBF） to the side beam region,respectively.Numerical simulations demonstrate the effectiveness of the two proposed methods.展开更多
The maximum likelihood （ML） estimator demonstrates remarkable performance in direction of arrival （DOA） estimation for the multiple input multiple output （MIMO） sonar. However, this advantage comes with prohibit...The maximum likelihood （ML） estimator demonstrates remarkable performance in direction of arrival （DOA） estimation for the multiple input multiple output （MIMO） sonar. However, this advantage comes with prohibitive computational complexity. In order to solve this problem, an ant colony optimization （ACO） is incorporated into the MIMO ML DOA estimator. Based on the ACO, a novel MIMO ML DOA estimator named the MIMO ACO ML （ML DOA estimator based on ACO for MIMO sonar） with even lower computational complexity is proposed. By extending the pheromone remaining process to the pheromone Gaussian kernel probability distribution function in the continuous space, the pro- posed algorithm achieves the global optimum value of the MIMO ML DOA estimator. Simulations and experimental results show that the computational cost of MIMO ACO ML is only 1/6 of the MIMO ML algorithm, while maintaining similar performance with the MIMO ML method.展开更多
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 gre...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.展开更多
基金supported by the National Natural Science Foundation of China(51509204)the Opening Project of State Key Laboratory of Acoustics(SKLA201501)the Fundamental Research Funds for the Central Universities(3102015ZY011)
文摘To reduce the computation burden of a large-aperture multiple-input multiple-output（MIMO） sonar imaging system,the phase-shift beamformer（PSBF） is used at the cost of bringing the intensity loss（IL）.The cause of the IL is analyzed in detail and a variable termed as IL factor is defined to quantify the loss amount.To compensate for the IL,two methods termed as intensity compensation for the PSBF（IC-PSBF） and the hybrid beamforming（HBF）,respectively,are proposed.The IC-PSBF uses previously estimated IL factors to compensate for output intensities of all PSBFs;and the HBF applies the IC-PSBF to the center beam region and the shifted-sideband beamformer（SSBF） to the side beam region,respectively.Numerical simulations demonstrate the effectiveness of the two proposed methods.
基金supported by the National Natural Science Foundation of China (60972152)the National Laboratory Foundation of China (9140C2304080607)+1 种基金the Aviation Science Fund (2009ZC53031)the Doctoral Foundation of Northwestern Polytechnical University (CX201002)
文摘The maximum likelihood （ML） estimator demonstrates remarkable performance in direction of arrival （DOA） estimation for the multiple input multiple output （MIMO） sonar. However, this advantage comes with prohibitive computational complexity. In order to solve this problem, an ant colony optimization （ACO） is incorporated into the MIMO ML DOA estimator. Based on the ACO, a novel MIMO ML DOA estimator named the MIMO ACO ML （ML DOA estimator based on ACO for MIMO sonar） with even lower computational complexity is proposed. By extending the pheromone remaining process to the pheromone Gaussian kernel probability distribution function in the continuous space, the pro- posed algorithm achieves the global optimum value of the MIMO ML DOA estimator. Simulations and experimental results show that the computational cost of MIMO ACO ML is only 1/6 of the MIMO ML algorithm, while maintaining similar performance with the MIMO ML method.
基金supported by the National Natural Science Foundation of China(51509204)the Opening Project of State Key Laboratory of Acoustics(SKLA201501)the Fundamental Research Funds for the Central Universities(3102015ZY011)
文摘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.
