Optimal linear neuron learning and Kalman Filter based backpropagation neural network for DGPS/INS integration

This paper introduces an optimal least mean square (LMS) rule for a linear neuron DGPS/INS integration method. The optimal LMS rule is based on an online calculation of the learning rate based on the minimum variance criteria. Then, using this rule, the neuron adaptively estimates scale factor and the bias INS error source values to optimally combine the DGPS with INS. A similar concept of optimality is used to derive a Kalman filter based backpropagation training rule for a neural network DGPS/INS integration method. This method facilitates the use of the extended Kalman filter trained backpropagation neural network training method, which achieves an optimal training criterion. The mathematical derivations for both methods are introduced in this work. The performance of these methods for the INS error sources estimation is also demonstrated using real DGPS/INS data.