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Improvement of memory fading filter applied in the underwater acoustic location algorithm
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摘要: 水下定位是自动化设备在水下建筑物安全监测工作过程中基础性、关键性的工作。滤波算法是水声定位算法的重要组成部分，有助于抑制噪声还原真实数据。衰减记忆滤波可以一定程度上处理由于模型不准确或模型变化及舍入误差累积等因素所引起的滤波发散问题。记忆衰减方式对算法的性能影响很大，对水声定位中的扩展卡尔曼滤波算法的记忆衰减方式进行了改进，主要改进在判断是否需要进行记忆衰减后，以观测值为标准进行记忆衰减。3种不同工况的水声定位仿真试验结果，验证了改进算法中记忆衰减方式的合理性和准确性；通过与现有记忆衰减算法仿真比较，改进算法可改善定位效果，提高连续定位精度。Abstract: Underwater positioning is the basic and critical work of automation equipment in the process of safety monitoring of underwater buildings. The filtering algorithm is an important part of underwater acoustic location algorithm, which helps to suppress noise and restore real data. Attenuated memory filtering can solve the problem of filtering divergence caused by inaccurate model, model change and rounding error accumulation to a certain extent. The memory of memory attenuation has a great impact on the performance of the algorithm. The current selection method has some problems, such as low flexibility, strong subjectivity and so on. In this paper, the memory attenuation mode of extended Kalman filter algorithm in underwater acoustic positioning is improved. The main idea is that the memory attenuation is carried out according to the observed value, after judging whether memory attenuation is needed. The underwater acoustic positioning simulation test results under three different working conditions verify the rationality and accuracy of the memory attenuation method in the improved algorithm. Compared with the existing memory attenuation algorithm simulation, the improved algorithm can improve the positioning effect and the continuous positioning accuracy.
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图 3 理想工况下3种算法的均方根误差比较
Figure 3. RMSE comparison of the three algorithms under ideal conditions
图 4 实际工况下3种算法的均方根误差比较
Figure 4. Comparison of RMSE of the three algorithms under actual working conditions
图 5 异常工况下3种算法观测野值出现前的均方根误差比较
Figure 5. Comparison of the RMSE of the three algorithms before the occurrence of outliers under abnormal conditions
图 6 异常工况下3种算法的均方根误差比较
Figure 6. RMSE comparison of the three algorithms under abnormal conditions
表 1 衰减因子相关分析
Table 1. Correlation analysis of attenuation factors
指标变量 预测时长 均方根误差 最优衰减因子 预测时长 1.000 −0.435* 0.275 均方根误差 −0.435* 1.000 −0.190 最优衰减因子 0.275 −0.190 1.000 注：*在0.05级别（双尾），相关性显著。 
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表 2 不同记忆衰减方法的累积定位均方根误差
Table 2. Cumulative root mean square error of different memory attenuation methods
记忆衰减方法 工况 累积位置均方根误差/m IFMKF 1 72.933 2 374.472 TFMKF 1 90.494 2 1730.732 EWFMKF 1 92.719 2 731.148
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