A broadband signal model is proposed for a distributed multiple-input multiple-output (MIMO) sonar system consisting of two transmitters and a receiving linear array. Transmitters are widely separated to illuminate the different aspects of an extended target of interest. The beamforming technique is utilized at the reception ends for enhancement of weak target echoes. A MIMO detector is designed with the estimated target position parameters within the general likelihood rate test (GLRT) framework. For the high signal-to-noise ratio case, the detection performance of the MIMO system is better than that of the phased-array system in the numerical simulations and the tank experiments. The robustness of the distributed phased-MIMO sonar system is further demonstrated in localization of a target in at-lake experiments.
A coherent-noncoherent joint processing framework is proposed for active sonar to combine diversity gain and beamforming gain for detection of a small target in shallow water environments. Sonar utilizes widely-spaced arrays to sense environments and illuminate a target of interest from multiple angles. Meanwhile, it exploits spatial diversity for time-reversal focusing to suppress reverberation, mainly strong bottom reverberation. For enhancement of robustness of time-reversal focusing, an adaptive iterative strategy is utilized in the processing framework. A probing signal is firstly transmitted and echoes of a likely target are utilized as steering vectors for the second transmission. With spatial diversity, target bearing and range are estimated using a broadband signal model. Numerical simulations show that the novel sonar outperforms the traditional phased-array sonar due to benefits of spatial diversity. The effectiveness of the proposed framework has been validated by localization of a small target in at-lake experiments.
Bistatic sonar or multistatic sonar system can collect more scattering information of targets than a monostatic sonar system. In this paper, sparse learning via iterative minimization method (SLIM) is introduced to distinguish wave components for time-domain (TD) back-propagation (BP) inverse scattering imaging improvement. Unlike the prevailing high central frequency (>100 kHz) and wideband imaging sonar systems, a relatively low-frequency band (1-10 kHz) is considered here. Due to the low sidelobe output of SLIM, the investigated object's surface in TD-BP image is much clearer in an ideal two-dimensional free field case. Furthermore, when the environmental information is known, this sparse reconstruction-based channel deconvolution method can be implemented to recognize, categorize the main propagating paths and then rectify their time of arrivals. Compared with the phase conjugation-based channel deconvolution method, the proposed approach's results have fewer sidelobes and higher signal-to-background ratio in the simulation.
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