Multi-Sensor Fusion Adaptive Estimation for Nonlinear Under-Observed System with Multiplicative Noise

Abstract

The adaptive fusion estimation problem was studied for the multi-sensor nonlinear under-observed systems with multiplicative noise. A one-step predictor with state update equations was designed for the virtual state with virtual noise first of all. An extended incremental Kalman filter (EIKF) was then proposed for the nonlinear under-observed systems. Furthermore, an adaptive filtering method was given for optimization. The fusion adaptive incremental Kalman filter weighted by scalar was finally proposed. The comparison analysis was made to verify the optimization of the state estimation using adaptive filtering method in the filtering process.

Fusion of Land-Based and Satellite-Based Localization Using Constrained Weighted Least Squares

“…Based on Kalman filtering [23], the DOA-based localization results and TDOA-based localization results can be fused to obtain…”

“…Based on Kalman filtering [ 23 ], the DOA-based localization results and TDOA-based localization results can be fused to obtain where K denotes the Kalman gain, which is defined as [ 24 , 25 ] …”