基于参数估计的数据融合算法研究

Study on data fusion algorithms based on parameter-estimation

研究了有关分批估计、自适应加权和方差估计算法在多传感器数据融合中的有效性、准确度和实时性。通过实例在对几种算法进行仿真比较的基础上,说明了上述几种算法的有效性及其融合精度的差异,其结果表明:按测量方差值并采用自适应加权算法的融合效果最佳,有效地提高了融合精度,对考虑了环境噪声的多传感器数据采集系统较为适合。

The validity, accuracy and actual time of the algorithm for batched-estimation, self-adaptive weighting and variance-estimation are studied in multi-sensors data fusion. The validity and their difference of the precision in data fusion are illustrated based on PC simulations. The PC simulation shows that fusion effect is the best for self-adaptive weighting, and it can improve the precision more efficiency with measuring variances, and it is more reasonable for muhi-sensors data acquisition systems when consider environment noises.