测量噪声相关情况下的多传感器数据融合

对于测量噪声相关的多传感器测量模型，利用Cholesky分解和单位下三角阵的求逆方法，将其转化为测量噪声互不相关的等价的多传感器伪测量模型，然后基于Markov估计，提出了一种测量噪声相关情况下多传感器数据融合的新方法。与直接利用原始传感器测量值的Markov估计数据融合方法相比，两者的计算精度相同，但新方法的计算复杂度却大大降低。数值仿真实验进一步验证了新方法的有效性。

Multi-sensor Data Fusion with Correlated Measurement Noises

By using the Cholesky factorization and inverse method for unit lower triangular matrix, the multi-sensor measurement model with correlated measurement noises is transformed to an equivalent pseudo one with uncorrelated measurement noises; then based on the Markov estimation, a new multi-sensor data fusion method with correlated measurement noises is proposed. Compared with the Markov estimation data fusion method which uses the measurements of original sensors directly, they have the same computational precision, but the new method reduces the computational complexity greatly. Numerical simulation results are provided to demonstrate the validity of the new method.