A Mixed Optimization Method Based on Adaptive Kalman Filter and Wavelet Neural Network for INS/GPS During GPS Outages

Abstract

To improve the navigation performance of the navigation system combining inertial navigation system (INS) and global positioning system (GPS) under complicated environments, especially GPS outages, a navigation method -wavelet neural network based on random forest regression (RFR-WNN) to assist adaptive Kalman filter (AKF) -is proposed. AKF is employed to correct INS errors, the Kalman filter is improved by introducing adaptive factor, to suppress the influence of the complex environment and random errors on the filtering accuracy; RFR-WNN is used to construct a high-precision prediction model when GPS works well, and to provide the required observations for AKF update when GPS outages. To solve the problem that the single neural network structure is easy to cause the overfitting, unstable and low prediction accuracy due to the lack of comprehensive training samples, RFR is introduced to optimize the single WNN, which can improve the generalization ability and prediction accuracy. In order to verify the effectiveness and advancement of the proposed method, vehicle navigation experiments were carried out, the results indicate that the proposed method has better navigation accuracy and performance than compared methods during GPS outages, and this advantage is more obvious in the case that fewer samples are collected.INDEX TERMS Navigation technology, integrated navigation, wavelet neural network, adaptive Kalman filter, GPS outages, random forest regression.

Comparative study of reformed neural network based short‐term wind power forecasting models

“…Wavelet neural network is a neural network based on the topology of BP neural network, which uses wavelet basis function as the transfer function of hidden layer nodes, and the signal propagates forward while the error propagates backward. The topological structure of the wavelet neural network is shown [27] in Figure 3.…”

“…The adopted wavelet basis function is the Morlet mother wavelet basis function, and the mathematical formula [27]: $$\begin{equation}f(x) = \cos (1.75x){e^{ - {x^2}/2}}\end{equation}$$…”

UAV Swarm Navigation Using Dynamic Adaptive Kalman Filter and Network Navigation

“…This training model can compensate for divergent position errors during GNSS outage [ 14 ]. Wei et al trained a wavelet neural network based on a random forest regression to provide the observation information for the AKF during a GNSS outage [ 15 ]. Chen et al introduced a wavelet neural network (WNN) to establish the time-varying characteristic model of INS error [ 16 ].…”

“…Compared with the neural network-based algorithm mentioned in [ 14 , 15 , 16 , 17 , 18 ], this paper introduces the NNA to compensate the divergent position error of the UAV swarm during a GNSS outage. The proposed algorithm does not require a large number of network parameters and training resources, and the compensation result is not affected by historical data.…”

A Low-Cost MEMS Missile-Borne Compound Rotation Modulation Scheme

“…The single-axis MEMS rotation modulation error is analyzed and compensated [ 23 ]. However, special requirements in highly dynamic environments are not considered [ 24 ]. In a highly dynamic environment, a novel rotation scheme is designed to compensate for the modulation angular rate instability in the high spin state [ 25 ].…”