Abstract
The radio channel modeling and location system are the focus of many research fields. However, their performance is largely limited by line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. Currently, the most popular LOS/NLOS identification algorithm is based on the channel impulse response (CIR) feature. The decision threshold is applied to identify between LOS and NLOS conditions on this basis. However, the features around the threshold are usually very similar, which leads to low LOS/NLOS accuracy. In this letter, the feature extracted from the CIR is used to fit its distribution with the Gaussian mixture function. Then, a soft decision algorithm is used to classify the signal. In the experimental simulations, the algorithm is tested according to the measured data. The results reveal that by choosing different feature combinations, the algorithm effectively improves the accuracy of LOS and NLOS identification.
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