Qiu Mingwei, Liu Bo
School of Information and Intelligent Science and Technology, Hunan Agricultural University, Changsha 410127, China.
Sensors (Basel). 2024 May 21;24(11):3275. doi: 10.3390/s24113275.
In order to avoid the loss of optimality of the optimal weighting factor in some cases and to further reduce the estimation error of an unbiased estimator, a multi-sensor adaptive weighted data fusion algorithm based on biased estimation is proposed. First, it is proven that an unbiased estimator can further optimize estimation error, and the reasons for the loss of optimality of the optimal weighting factor are analyzed. Second, the method of constructing a biased estimation value by using an unbiased estimation value and calculating the optimal weighting factor by using estimation error is proposed. Finally, the performance of least squares estimation data fusion, batch estimation data fusion, and biased estimation data fusion is compared through simulation tests, and test results show that biased estimation data fusion has a greater advantage in accuracy, stability, and noise resistance.
为了避免在某些情况下最优加权因子的最优性丧失，并进一步降低无偏估计器的估计误差，提出了一种基于有偏估计的多传感器自适应加权数据融合算法。首先，证明了无偏估计器可以进一步优化估计误差，并分析了最优加权因子最优性丧失的原因。其次，提出了利用无偏估计值构造有偏估计值并利用估计误差计算最优加权因子的方法。最后，通过仿真试验比较了最小二乘估计数据融合、批估计数据融合和有偏估计数据融合的性能，试验结果表明有偏估计数据融合在准确性、稳定性和抗噪声能力方面具有更大优势。
