Multi-sensor fusion approach based on nonlinear H∞ filter with interval type 2 fuzzy adaptive parameters tuning for unmanned vehicle localization

Abstract

In most applications of autonomous navigation, the state of a system must be estimated from noisy sensors. Accurate estimation of the true system state can be achieved using data fusion algorithms. Furthermore, the fusion scheme can be affected by many factors such as modeling errors and parameters uncertainties. The gaps and inconsistencies due to the sensors noise and modeling errors can be reached with robust nonlinear filtering. In this article, a new framework has been developed for data fusion algorithms based on nonlinear NH∞ filter with fuzzy adaptive bound and adaptive disturbances attenuations. Type-1 Fuzzy Adaptive NH∞ algorithm has been proposed and compared with the Interval Type 2 Fuzzy Adaptive NH∞, for unmanned vehicle localization. The proposed algorithms fuse data from low-cost sensors using inertial navigation system, Global Positioning System and monocular vision. Type-1 Fuzzy Adaptive NH∞ and Interval Type 2 Fuzzy Adaptive NH∞ algorithms, adaptively, handle the effects of noisy sensors, parameters uncertainties and modeling errors. Both algorithms use adaptive bounds [Formula: see text] and adaptive disturbance attenuation [Formula: see text] for higher-order terms of the Taylor development. The adaptive bounds consider issues associated to Gyro drift, image distortion and Global Positioning System dilution of precision which make the proposed algorithms more accurate than the classical NH∞. The validating experiment and the efficiency of Type-1 Fuzzy Adaptive NH∞ and Interval Type 2 Fuzzy Adaptive NH∞ have been conducted in real scenario and in unstructured outdoor environment without any a priori knowledge of the evolution of the vehicle. The experimental results have demonstrated the robustness and efficiency of these two filtering strategies against uncertainties and their online sensor switching capability. The Interval Type 2 Fuzzy Adaptive NH∞ algorithm provides more accuracy and robustness compared to the Type-1 Fuzzy Adaptive NH∞.

Dual-finite distributed H∞ filtering in sensor networks

“…To the end, it has been proved that inequalities can be satisfied if LMI are established. Moreover, the augmented system equation (7)…”

“…Such decentralized approach provides much flexibility as the SN topology can be adjusted for specific applications. The distributed filtering has been applied to a variety of practical systems, and some typical network-induced phenomena 6,7 -such as data packet dropouts, [8][9][10] communication link failures, 11 and external deception attacks 12,13 -have been taken into consideration.…”

Information-Based Georeferencing of an Unmanned Aerial Vehicle by Dual State Kalman Filter with Implicit Measurement Equations

“…As a general strategy, filtering techniques are usually used to encounter possible errors within various datasets and, hence, to derive the unknown parameters in the most accurate way possible. A number of strategies are proposed in the literature, among which the work by [20] could be mentioned, in which a nonlinear H∞ filter with fuzzy adaptive bound and adaptive disturbances attenuation is proposed. The main idea is to overcome data fusion problems, such as modeling errors, by means of a robust nonlinear filter.…”

A new filtering strategy for target tracking application using the second form of Smooth Variable Structure Filter

“…In the context of the untuned H- ∞ , this filter does not perform better than the correctly tuned KF, since it requires the selection of an additional parameter γ and it is not suitable if this parameter is too small. 21,22 Particle Filter (PF) uses a Monte Carlo method based on probability distribution over the state which used a finite set of particles. 23 Thus, for target tracking problems, a Gaussian Mixture Model (GMM)–based technique is presented, which is often conducted iteratively using the Expectation Maximization (EM) algorithm to identify the maximum a posteriori sequence of target maneuvers in a multilevel white noise model.…”