An iterative algorithm based on data skewness criterion and its application in wireless location

The skewness of data samples reflects the degree to which the signal probability density function deviates from the symmetric distribution. The paper introduces a new iterative algorithm based on the data skewness return-to-zero criterion, which can eliminate the constantly positive non-Gaussian noise in data samples. It has high practical value for its good performance in mitigating constantly positive non-Gaussian noises in engineering applications. Taking for example the NLOS error mitigation in cellular wireless positioning, this paper tests and verifies the performance of iterative algorithm. The result shows that the algorithm can be carried out without identifying the existence of NLOS errors in positioning measurement data and its performance is better than that of the existing algorithms, for it can mitigate errors effectively and then improve the cellular positioning accuracy.