摘要
分析了标准kalman滤波(KF)和自适应kalman滤波(AKF)的原理,通过仿真实验,对两种滤波算法的性能进行比较。结合各种声纳的探测特性和水下目标的特点,建立了舰艇编队的数据融合方案。选用简单凸组合融合算法作为舰艇编队协同反潜的航迹融合算法,将多声纳融合系统的融合结果和简单交叉定位算法进行了比较研究。仿真实验结果表明:自适应kalman滤波比标准kalman滤波具有更好的目标跟踪性能,多声纳融合结果较简单交叉定位结果的性能有大幅度提高,所选用的融合算法航迹能够较好的与真实航迹吻合。
With analysis of Kalman Filter （KF） and Adaptive Kalman Filter （AKF）, the capability between them is compared by simulation experiments. Considering the measurement properties of different sonar and the underwater targets＇ characteristics, the data fusion method of naval fleet is proposed. Choose the simple convex-combination algorithm as the track fusion algorithm in naval fleet cooperative anti-submarine, comparing and researching the fusion result of multi-sonar and the simple cross-orientation. The simulation result presents that AKF is better at targets tracking than KF, meanwhile, multi-sonar data fusion is better than simple cross-orientation, and the track of data fusion is fit well for the real.
出处
《火力与指挥控制》
CSCD
北大核心
2007年第8期40-43,共4页
Fire Control & Command Control
基金
水下信息处理与控制国家重点实验室基金资助项目(51448080105ZS2601)
