A Two-stage Neural-network Based Method for Cycle Slip Correction of GPS Measurements

摘要

To attain high accuracy results from GPS, the carrier phase observables have to be used to update the filter's states. However, a cycle slip that remains uncorrected will significantly degrade the filter's performance. In this paper, a novel method that can effectively detect and identify the small cycle slip is presented. First, the location of the cycle slip is detected by ascertaining the point of modulus maximal value of the wavelet coefficients since the cycle slip can be regarded as the singular point of the signal. Secondly, two kinds of prediction models based on artificial neural network (ANN) are established to correct the cycle slip. Experimental results with real data sets indicate that the method is effective and feasible.