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An Improved Multisensor Self-Adaptive Weighted Fusion Algorithm Based on Discrete Kalman Filtering

机译：基于离散卡尔曼滤波的改进多传感器自适应加权融合算法

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When the multisensor self-adaptive weighted fusion algorithm fuses the data sources that were severely interfered by noise, its fusion precision, data smoothness, and algorithm stability will be reduced. To overcome this drawback, the idea was proposed with respect to an improved algorithm which optimized acquisition of fusion data sources with discrete Kalman filtering technique, thus reducing the negative impact on the fusion performance from noise. To verify the effectiveness of the improved algorithm, this paper simulated the fusion process of soil moisture data with fusion samples. The result proved that, under the same circumstance, the improved algorithm has a stronger restrain ability to noise and a better performance in fusion precision, data smoothness, and algorithm stability compared with the general algorithm.
机译：当多传感器自适应加权融合算法融合受噪声严重干扰的数据源时，其融合精度，数据平滑度和算法稳定性将减少。为了克服这一缺点，提出了一种关于改进算法的思想，该算法通过离散卡尔曼滤波技术优化了融合数据源的获取，从而降低了对来自噪声的融合性能的负面影响。为了验证改进算法的有效性，本文模拟了融合样品土壤湿度数据的融合过程。结果证明，在同样的情况下，与一般算法相比，改进的算法具有更强的抑制噪声能力和更好的融合精度，数据平滑度和算法稳定性。

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