A new optimization algorithm of sensor selection is proposed in this paper for decentralized large-scale multi-target tracking (MTT) network within a labeled random finite set (RFS) framework. The method is performed based on a marginalized δ-generalized labeled multi-Bernoulli RFS. The rule of weighted Kullback-Leibler average (KLA) is used to fuse local multi-target densities. A new metric, named as the label assignment (LA) metric, is proposed to measure the distance for two labeled sets. The lower bound of LA metric based mean square error between the labeled multi-target state set and its estimate is taken as the optimized objective function of sensor selection. The proposed bound is obtained by the information inequality to RFS measurement. Then, we present the sequential Monte Carlo and Gaussian mixture implementations for the bound. Another advantage of the bound is that it provides a basis for setting the weights of KLA. The coordinate descent method is proposed to compromise the computational cost of sensor selection and the accuracy of MTT. Simulations verify the effectiveness of our method under different signal-to- noise ratio scenarios.

No abstract

High-resolution imaging method is one of the researching focuses of underwater acoustic detection. Underwater small-target detection also requires detailed imaging technology. Multibeam echo sounders (MBESs) and synthetic aperture sonar (SAS) are the effective instruments widely researched to obtain underwater acoustic images. Constrained by the theory, the along-track resolution of MBES decreases with distance and the gaps problem of SAS always exists and both inevitably limit the quality of acoustic imaging. In this paper, a two dimensional multibeam synthetic aperture sonar (MBSAS) model is designed to overcome the shortcomings of conventional underwater imaging instruments. MBSAS can provide a three dimensional (3D) high-resolution acoustic image without a gap problem. An echo model and transducer array manifold are designed to meet the requirements of engineering applications. Imaging theory and target simulations prove the feasibility of the MBSAS model. The performance of the proposed model is demonstrated with a tank experiment. A detailed image is obtained through an experiment that can indicate the shapes of targets and has the ability to separate adjacent targets. The simulations and experimental results indicate that MBSAS can obtain a more detailed 3D full-scan image than conventional MBES and SAS system with a better energy focusing ability.

No abstract

To solve the false terrain problem caused by the tunnel effect in the conventional multibeam seafloor terrain detection, a detection method of multibeam seafloor terrain based on the ADOS-CFAR (Amplitude Dichotomy Order-Statistics Constant False Alarm Rate) is developed. This paper first discusses the principles and existing problems of the OS-CFAR and K-FINDER algorithms and then suggests the ADOS-CFAR algorithm and its fast calculation method for those problems. By adopting this algorithm into the detection method of multibeam seafloor terrain and carrying out the simulation study on the computational load of the ADOS-CFAR, it is shown that this method has a lower computational load on the premise of ensuring the detection performance. Finally, the proposed algorithm is analyzed and verified through the test data in Songhua Lake. The result indicates that this method can effectively avoid the false terrain caused by tunnel effect with higher terrain detection accuracy.