Fuzzy interacting multiple model H∞ particle filter algorithm based on current statistical model

In this paper, fuzzy theory and interacting multiple model are introduced into H∞ filter-based particle filter to propose a new fuzzy interacting multiple model H∞ particle filter based on current statistical model. Each model uses H∞ particle filter algorithm for filtering, in which the current statistical model can describe the maneuver of target accurately and H∞ filter can deal with the nonlinear system effectively. Aiming at the problem of large amount of probability calculation in interacting multiple model by using combination calculation method, our approach calculates each model matching probability through the fuzzy theory, which can not only reduce the calculation amount, but also improve the state estimation accuracy to some extent. The simulation results show that the proposed algorithm can be more accurate and robust to track maneuvering target

Practical Moving Target Detection in Maritime Environments Using Fuzzy Multi-sensor Data Fusion

As autonomous ships become the future trend for maritime transportation, it is of importance to develop intelligent autonomous navigation systems to ensure the navigation safety of ships. Among the three core components (sensing, planning and control modules) of the system, an accurate detection of target ships’ navigation information is critical. Within a typical maritime environment, the existence of sensor noises as well as the influences generated by varying environment conditions largely limit the reliability of using a single sensor for environment awareness. It is therefore vital to use multiple sensors together with a multi-sensor data fusion technology to improve the detection performance. In this paper, a fuzzy logic-based multi-sensor data fusion algorithm for moving target ships detection has been proposed and designed using both AIS and radar information. A two-stage fuzzy logic association method has been particularly developed and integrated with Kalman filtering to achieve a computationally efficient performance. The effectiveness of the proposed algorithm has been tested and validated in simulations where multiple target ships are transiting with complex movements

Robust Multi-sensor Data Fusion for Practical Unmanned Surface Vehicles (USVs) Navigation

The development of practical Unmanned Surface Vehicles (USVs) are attracting increasing attention driven by their assorted military and commercial application potential. However, addressing the uncertainties presented in practical navigational sensor measurements of an USV in maritime environment remain the main challenge of the development. This research aims to develop a multi-sensor data fusion system to autonomously provide an USV reliable navigational information on its own positions and headings as well as to detect dynamic target ships in the surrounding environment in a holistic fashion. A multi-sensor data fusion algorithm based on Unscented Kalman Filter (UKF) has been developed to generate more accurate estimations of USV’s navigational data considering practical environmental disturbances. A novel covariance matching adaptive estimation algorithm has been proposed to deal with the issues caused by unknown and varying sensor noise in practice to improve system robustness. Certain measures have been designed to determine the system reliability numerically, to recover USV trajectory during short term sensor signal loss, and to autonomously detect and discard permanently malfunctioned sensors, and thereby enabling potential sensor faults tolerance. The performance of the algorithms have been assessed by carrying out theoretical simulations as well as using experimental data collected from a real-world USV projected collaborated with Plymouth University. To increase the degree of autonomy of USVs in perceiving surrounding environments, target detection and prediction algorithms using an Automatic Identification System (AIS) in conjunction with a marine radar have been proposed to provide full detections of multiple dynamic targets in a wider coverage range, remedying the narrow detection range and sensor uncertainties of the AIS. The detection algorithms have been validated in simulations using practical environments with water current effects. The performance of developed multi-senor data fusion system in providing reliable navigational data and perceiving surrounding environment for USV navigation have been comprehensively demonstrated

GPS/INS Integration Accuracy Enhancement Using the Interacting Multiple Model Nonlinear Filters

In this paper, performance evaluation for various single model nonlinear filters and nonlinear filters with interactingmultiple model (IMM) framework is carried out. A high gain (high bandwidth) filter is needed to response fast enoughto the platform maneuvers while a low gain filter is necessary to reduce the estimation errors during the uniformmotion periods. Based on a soft-switching framework, the IMM algorithm allows the possibility of using highly dynamicmodels just when required, diminishing unrealistic noise considerations in non-maneuvering situations. The IMMestimator obtains its estimate as a weighted sum of the individual estimates from a number of parallel filters matchedto different motion modes of the platform. The use of an IMM allows exploiting the benefits of high dynamic models inthe problem of vehicle navigation. Simulation and experimental results presented in this paper confirm theeffectiveness of the method

Development of Fault Diagnosis and Fault Tolerant Control Algorithms with Application to Unmanned Systems

Unmanned vehicles have been increasingly employed in real life. They include unmanned air vehicles (UAVs), unmanned ground vehicles (UGVs), unmanned spacecrafts, and unmanned underwater vehicles (UUVs). Unmanned vehicles like any other autonomous systems need controllers to stabilize and control them. On the other hand unmanned systems might subject to different faults. Detecting a fault, finding the location and severity of it, are crucial for unmanned vehicles. Having enough information about a fault, it is needed to redesign controller based on post fault characteristics of the system. The obtained controlled system in this case can tolerate the fault and may have a better performance. The main focus of this thesis is to develop Fault Detection and Diagnosis (FDD) algorithms, and Fault Tolerant Controllers (FTC) to increase performance, safety and reliability of various missions using unmanned systems. In the field of unmanned ground vehicles, a new kinematical control method has been proposed for the trajectory tracking of nonholonomic Wheeled Mobile Robots (MWRs). It has been experimentally tested on an UGV, called Qbot. A stable leader-follower formation controller for time-varying formation configuration of multiple nonholonomic wheeled mobile robots has also been presented and is examined through computer simulation. In the field of unmanned aerial vehicles, Two-Stage Kalman Filter (TSKF), Adaptive Two-Stage Kalman Filter (ATSKF), and Interacting Multiple Model (IMM) filter were proposed for FDD of the quadrotor helicopter testbed in the presence of actuator faults. As for space missions, an FDD algorithm for the attitude control system of the Japan Canada Joint Collaboration Satellite - Formation Flying (JC2Sat-FF) mission has been developed. The FDD scheme was achieved using an IMM-based FDD algorithm. The efficiency of the FDD algorithm has been shown through simulation results in a nonlinear simulator of the JC2Sat-FF. A fault tolerant fuzzy gain-scheduled PID controller has also been designed for a quadrotor unmanned helicopter in the presence of actuator faults. The developed FDD algorithms and fuzzy controller were evaluated through experimental application to a quadrotor helicopter testbed called Qball-X4

Discrete Time Systems

Discrete-Time Systems comprehend an important and broad research field. The consolidation of digital-based computational means in the present, pushes a technological tool into the field with a tremendous impact in areas like Control, Signal Processing, Communications, System Modelling and related Applications. This book attempts to give a scope in the wide area of Discrete-Time Systems. Their contents are grouped conveniently in sections according to significant areas, namely Filtering, Fixed and Adaptive Control Systems, Stability Problems and Miscellaneous Applications. We think that the contribution of the book enlarges the field of the Discrete-Time Systems with signification in the present state-of-the-art. Despite the vertiginous advance in the field, we also believe that the topics described here allow us also to look through some main tendencies in the next years in the research area