Multitarget tracking algorithms based on sonar usually run into detection uncertainty, complex channel and more clutters, which cause lower detection probability, single sonar sensors failing to measure when the target is in an acoustic shadow zone, and computational bottlenecks. This paper proposes a novel tracking algorithm based on multisensor data fusion to solve the above problems. Firstly, under more clutters and lower detection probability condition, a Gaussian Mixture Probability Hypothesis Density (GMPHD) filter with computational advantages was used to get local estimations. Secondly, this paper provided a maximum-detection capability multitarget track fusion algorithm to deal with the problems caused by low detection probability and the target being in acoustic shadow zones. Lastly, a novel feedback algorithm was proposed to improve the GMPHD filter tracking performance, which fed the global estimations as a random finite set (RFS). In the end, the statistical characteristics of OSPA were used as evaluation criteria in Monte Carlo simulations, which showed this algorithm’s performance against those sonar tracking problems. When the detection probability is 0.7, compared with the GMPHD filter, the OSPA mean of two sensor and three sensor fusion was decrease almost by 40% and 55%, respectively. Moreover, this algorithm successfully tracks targets in acoustic shadow zones.

Due to the advantage of distributed multisensor detection system makes sonar detect further and more accurate estimation of the target state, distributed multisensor data fusion algorithms are widely applied to sonar detection system. However, on the one hand, the most asynchronous algorithms focus on how to convert asynchronous data fusion into synchronous data fusion. On the other hand, sonar detection system suffers from more serious asynchronous data problems (such as more serious random delay and packet loss defaults) than radar and other fields. Therefore, the traditional asynchronous fusion method has some limitations. When the targets are sparse, this paper proposed a novel asynchronous multisonar data integration approach, in which the Gaussian Mixture Probability Hypothesis Density (GMPHD) filter is used to filter clutter for local sonar sensor. Then, the Gaussian Mixture Model (GMM) algorithm is used to model asynchronous data over a period of time. Finally, all local sonar detection data are integrated into a surveillance region image to help detecting target. Several simulation tests and a sea test are present in this paper to test this approach performance.

Robots working in an underwater dynamic environ-ment need complex visual perception to adapt to the environment, which requires emergency planning ability to deal with various visual pollution emergencies and improve the reliability of oper-ation. An emergency strategy is proposed for the visual pollution of the operating equipment, which used case-based reasoning (CBR). The correct detection and extraction of pollutant char-acteristic information are realized by similarity matching. Some general visual contaminated cases are used to build the case library of visual contamination. The image de noising strategy of case-based reasoning and the cleaning strategy of bionic cleaning equipment are analyzed and implemented. One bionic visual decontamination equipment is designed to clean the pollutants. Based on CBR for different pollution situations, the feasibility of several schemes is verified.