Zhang Yang, Wang Rong, Li Shouzhe, Qi Shengbo
The College of Engineering, Ocean University of China, Shandong 266100, China.
College of Letters and Science, University of Wisconsin-Madison, Madison, WI 53711, USA.
Sensors (Basel). 2020 Mar 31;20(7):1959. doi: 10.3390/s20071959.
One of the most important ocean water parameters in world ocean observations is temperature. In the application of high-precision ocean sensors, there are often various interferences and random noises. These noises will cause the linearity of the sensor to change, and it is difficult to estimate the statistical characteristics, and the results will deviate from the real temperature. Aiming at the problems in the application, this paper proposes a compound Kalman smoothing filter (CKSF) algorithm based on least square curve fitting. This algorithm first analyzes the system model of the sensor, uses the least square method to fit the theoretical data and eliminate the non-linear factors caused by system itself, then estimates the statistical characteristics of the noise required by modeling, using the wavelet transform method to track the change of noise in real time and to accurately estimate the noise variance. Finally, a compound filtering method including wavelet transform and Kalman smoothing filtering is used as the main denoising algorithm, which is more accurate than a single Kalman filtering result. The algorithm is applied to the temperature measurement process of the ocean temperature sensor. The results show that the accuracy and stability of the sensor are improved.
世界海洋观测中最重要的海水参数之一是温度。在高精度海洋传感器的应用中，常常存在各种干扰和随机噪声。这些噪声会导致传感器的线性度发生变化，难以估计其统计特性，结果会偏离真实温度。针对应用中存在的问题，本文提出一种基于最小二乘曲线拟合的复合卡尔曼平滑滤波器（CKSF）算法。该算法首先分析传感器的系统模型，用最小二乘法拟合理论数据并消除系统自身产生的非线性因素，然后对建模所需噪声的统计特性进行估计，利用小波变换方法实时跟踪噪声变化并准确估计噪声方差。最后，采用包括小波变换和卡尔曼平滑滤波的复合滤波方法作为主要去噪算法，其比单一卡尔曼滤波结果更准确。该算法应用于海洋温度传感器的温度测量过程。结果表明，传感器的精度和稳定性得到了提高。
