Multi-sensor Information Fusion And The Application Of The Guidance Of Torpedo

Multi-sensor information fusion means processing data from more than one sensor to get new efficient information, which is not available from any single sensor. Multi-sensor information fusion has been developed into an active research field. So it is significant to further get access to this field and apply this technology to the guidance of underwater weapon such as torpedo.In this dissertation, we mainly research into multi-sensor information fusion technology and apply it to line-guidance torpedo, and multi-sensor state estimation and track fusion algorithm are discussed. Research works is shown as follows:(1) Basic model of information fusion is studied in detail, and information fusion is classified in terms of function and structure. Four-level data processing function model is given.(2) On the basis of studying linear and nonlinear decentralized kalman filter algorithm, aiming at state estimation of linear decentralized multi-sensor data fusion system, state estimations of single sensor and centralized multi-sensor and decentralized multi-sensor system of multi-level system are studied respectively.(3) On the basis of kalman filer algorithm and on the premise of two assumptions, namely, state estimation error of each sensor is dependent or independent, kalman filter weight fusion algorithm and modified track-to-track fusion algorithm are proposed, then improved algorithm is applied to the simulation of line-guidance and terminal-self guidance of torpedo.(4) Applying measurement noise automatic weighting kalman filter to the guidance of line-guidance torpedo, we compromised target information and torpedo information, which in turn come from sonar of guidance station and inertial measurement units of torpedo. In the simulation, we added index weighting noise estimation of measurement noise to improve stability and adaptability of filter.