基于小波分析和鲁棒最小二乘的信息融合估计算法

对于农田土壤的多传感器检测系统来说,其测量的信息具有非线性和时空变异等特性,因此对信息融合方法的基本要求是具有鲁棒性和并行处理能力.应用小波分析理论,对原始测量数据进行了降噪处理,使降噪后的数据更能反映土壤的本质及变化规律:应用鲁棒最小二乘估计技术可以对不同传感器数据进行综合处理,去除冗余,克服歧义,得到比任何单个传感器更全面、更准确、更可靠的信息.针对农业中的自然环境具有很强的不确定性和经验性,运用基于小波分析的降噪和现代信息融合思想,提出了一种基于小波降噪和鲁棒最小二乘的信息融合估计方法.通过实验分析,其结果表明该方法是可行和值得研究的.

Information Fusion Estimation Algorithm by Robust Least Squares and Wavelet Analysis

To the multi-sensor detection system,its measurement information is non-linear and spatiotemporal variation,etc.so the basic requirement of information fusion method is robust and parallel processing capabilities.In this paper,the wavelet analysis theory is applied to denoise the original data,which makes the data after denoising is better to reflect the nature of soil and its Variation Law.Robust least squares estimation technique can be integrated with different sensors data,to remove redundancy,to overcome the ambiguity,so it can obtain more comprehensive,more accurate and reliable information than any single sensor.Considering highly uncertain and empirical of the natural environment in farmland soil,the information fusion estimation method based on wavelet denoising and robust least squares is proposed.The analysis of experimental results shows that the method is feasible and worth studying.