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A full-order multi-objective anti-disturbance robust filter for SINS/GPS navigation systems with multiple disturbances is designed. Generally, the unmodeled dynamics, the external environmental disturbance and the inertial apparatus random drift may exist simultaneously in an integrated navigation system, which can be classified into three type of disturbances, that is, the Gaussian noise, the norm bounded noise and the time correlated noise. In most classical studies, the disturbances in integrated navigation systems are classified as Gaussian noises or norm bounded noises, where the Kalman filtering or robust filtering can be employed, respectively. While it is not true actually, such assumptions may lead to conservative results. The paper aims to discuss these issues.

The Gaussian noises, the norm bounded noises and the time correlated noises in the integrated navigation system are considered simultaneously in this contribution. As a result, the time correlated noises are augmented as a part of system state of the integrated navigation system error model, the relative integrated navigation problem can be transformed into a full-order multi-objective robust filter design problem for systems with Gaussian noises and norm bounded disturbances. Certainly, the errors of the time correlated noises are estimated and compensated for high precision navigation purpose. Sufficient conditions for the existence of the proposed filter are presented in terms of linear matrix inequalities (LMIs) such that the system stability is guaranteed and the disturbance attenuation performance is achieved.

Simulations for SINS/GPS integrated navigation system given show that the proposed full-order multi-objective anti-disturbance filter, has stronger robustness and better precision when multiple disturbances exist, that is, the present algorithm not only can suppression the effect of white noises and norm bounded disturbance but also can estimate and compensate the modeled disturbance.

The proposed algorithm has stronger anti-disturbance ability for integrated navigation with multiple disturbances. In fact, there exist multiple disturbances in integrated navigation system, so the proposed scheme has important significance in applications.

The federated filter created by Carlson has been widely used in multi-sensor integrated navigation. Compared with no-reset federated filter, the reset mode has greater sub-filters’ performance, but faults of any subsystem would affect other healthy subsystems via global fusion and the sub-optimality of sub-filters’ estimation has influence on fault detection sensitivity. It’s a challenge to design a robust reset federated filter.

The time-varying observation noise is designed to reduce proportions of observation information in faulty sub-filters. A new dynamic information distribution algorithm based on optimal residual chi-square detection function is presented to reduce proportions of faulty sub-filters’ estimation in information fusion filter.

The robust filtering algorithm represents a filtering strategy for reset federated filter. Compared with fault isolation, the navigation result is smoother by using this algorithm. It has significant benefits in avoiding faulty sensors’ contamination and the performance of federated filter is greatly improved.

The approach described in this paper provides a new method to deal with federated reset filter’s faulty problems. This new robust federated filter algorithm possesses a great potential for various applications.

The approach described in this paper can be used in multi-sensor integrated navigation with no fewer than three sensors.

Compared with conventional approach of fault isolation, the proposed algorithm does not destroy the continuity and integrity of the filtering process. It improves the performance of the federated filter by reducing proportions of faulty observation information. It also reduces the influence of sub-optimality on fault detection sensitivity.

Considers control systems operating under potentially faulty conditions. Discusses the problem of designing a single unit which not only handles the required control action but also identifies faults occurring in actuators and sensors. In common practice, units for control and for diagnosis are designed separately. Attempts to identify situations in which this is a reasonable approach and cases in which the design of each unit should take the other into consideration. Presents a complete characterization for each case and gives systematic design procedures for both the integrated and non‐integrated design of control and diagnosis units. Shows how a combined module for control and diagnosis can be designed which is able to follow references and reject disturbances robustly, control the system so that undetected faults do not have disastrous effects, reduce the number of false alarms and identify which faults have occurred.

The purpose of the paper is to present an approach to detect and isolate the aircraft sensor and control surface/actuator failures affecting the mean of the Kalman filter innovation sequence.

The extended Kalman filter (EKF) is developed for nonlinear flight dynamic estimation of an F‐16 fighter and the effects of the sensor and control surface/actuator failures in the innovation sequence of the designed EKF are investigated. A robust Kalman filter (RKF) is very useful to isolate the control surface/actuator failures and sensor failures. The technique for control surface detection and identification is applied to an unstable multi‐input multi‐output model of a nonlinear AFTI/F‐16 fighter. The fighter is stabilized by means of a linear quadratic optimal controller. The control gain brings all the eigenvalues that are outside the unit circle, inside the unit circle. It also keeps the mechanical limits on the deflections of control surfaces. The fighter has nine state variables and six control inputs.

In the simulations, the longitudinal and lateral dynamics of an F‐16 aircraft dynamic model are considered, and the sensor and control surface/actuator failures are detected and isolated.

A real‐time detection of sensor and control surface/actuator failures affecting the mean of the innovation process applied to the linearized F‐16 fighter flight dynamic is examined and an effective approach to isolate the sensor and control surface/actuator failures is proposed. The nonlinear F‐16 model is linearized. Failures affecting the covariance of the innovation sequence is not considered in the paper.

An approach has been proposed to detect and isolate the aircraft sensor and control surface/actuator failures occurred in the aircraft control system. An extended Kalman filter has been developed for the nonlinear flight dynamic estimation of an F‐16 fighter. Failures in the sensors and control surfaces/actuators affect the characteristics of the innovation sequence of the EKF. The failures that affect the mean of the innovation sequence have been considered. When the EKF is used, the decision statistics changes regardless the fault is in the sensors or in the control surfaces/actuators, while a RKF is used, it is easy to distinguish the sensor and control surface/actuator faults.

The purpose of this paper is to consider the process of design and implementation of an enhanced fuzzy H_∞ (EFH_∞) estimation algorithm to determine the attitude and heading angles of ground vehicles, which are frequently affected by considerable exogenous disturbances. To detect the changes of disturbances, a fuzzy system is designed based on expert knowledge and experiences of a navigation engineer. In the EFH_∞ estimator, the intensity bounds of disturbances affecting the measurements are updated using a heuristic combination of three change‐detection indices. Performance of the proposed estimator is evaluated by Monte‐Carlo simulations and field tests of three kinds of vehicles using a manufactured attitude‐heading reference system (AHRS). In both simulations and real tests, the proposed estimator results in a superior performance compared to those of the recently developed and standard H_∞ estimators.

Design, implementation and real tests of the EFH_∞ estimator are considered for an AHRS specialized for vehicular applications. In the AHRS, three‐axis accelerometers (TAA) and three‐axis magnetometers (TAM) may be affected by large disturbances due to non‐gravitational accelerations and local magnetic fields. Therefore, the design parameters of EFH_∞ estimator including the theoretic bound of disturbance intensity and the attenuation level are adaptively tuned using a fuzzy combination of three change‐detection indices. Once a sensor is affected by an exogenous disturbance, the fuzzy system will increase the scale factor of the corresponding measurement disturbance to place more confidence on the data of the AHRS dynamics including measurements of gyros with respect to the data coming from the TAA and TAM.

An intelligent fault detector is proposed for considering changes of disturbances to adjust the upper bounds of the estimator's disturbances and the length of data to update the fuzzy system inputs. The EFH_∞ estimator is suitable to attenuate the effects of disturbances changes on accurate estimation of the attitude and heading angles, intelligently.

The paper provides a fuzzy state estimator for adaptively adjusting the theoretic disturbance matrices according to the actual intensity of the disturbances affecting the AHRS dynamics and the measurement sensors.

The purpose of this paper is to propose a localizability-based particle filtering localization algorithm for mobile robots to maintain localization accuracy in the high-occluded and dynamic environments with moving people.

First, the localizability of mobile robots is defined to evaluate the influences of both the dynamic obstacles and prior-map on localization. Second, based on the classical two-sensor track fusion algorithm, the odometer-based proposal distribution function (PDF) is corrected, taking account of the localizability. Then, the corrected PDF is introduced into the classical PF with “roulette” re-sampling. Finally, the robot pose is estimated according to all the particles.

The experimental results show that, first, it is necessary to consider the influence of the prior-map during the localization in the high-occluded and dynamic environments. Second, the proposed algorithm can maintain an accurate and robust robot pose in the high-occluded and dynamic environments. Third, its real timing is acceptable.

When the odometer error and occlusion caused by the dynamic obstacles are both serious, the proposed algorithm also has a probability evolving into the kidnap problem. But fortunately, such serious situations are not common in practice.

To check the ability of real application, we have implemented the proposed algorithm in the campus cafeteria and metro station using an intelligent wheelchair. To better help the elderly and disabled people during their daily lives, the proposed algorithm will be tested in a social welfare home in the future.

The localizability of mobile robots is defined to evaluate the influences of both the dynamic obstacles and prior-map on localization. Based on the localizability, the odometer-based PDF is corrected properly.

The purpose of this paper is to focus on two major areas of concern for the Photovoltaic (PV) system, i.e. power quality and maximum power point tracking (MPPT). Novel control strategies have been proposed for both these issues, and their respective superiorities over the existing techniques have been established. On the other hand, as far as MPPT is concerned, two limitations are found in the available techniques. One is the inability of effective MPPT in dynamic conditions where the environmental parameters changes very rapidly. Second one is the ineffective tracking of global maxima under partial shading conditions.

Here, modified Kalman filtering approach has been applied for estimating the reference current of active power filter, incorporated for power quality improvement. The proposed Kalman algorithm introduces a weighted matrix, which advances the estimated values of state variables. This paper presents a simple and enhanced model-based (MB) MPPT algorithm that has the capability of tracking MPPT effectively in both these working conditions. The proposed MB algorithm uses the mathematical modelling, and based on precised estimation of parameters, it pre-determines the MPP analytically.

It has been tested successfully for dynamic variations of insolation, temperature and partial shading, where all these three parameters are rigorously varied over the full scale of practical values. The results have been also investigated experimentally and compared with the simulated one. A close matching of both the results has been shown through the plots, which validates the effectiveness of proposed algorithms.

This research paper is part of the original research work carried out in Lab. Simulated results are obtained in MATLAB/Simulink platform, whereas these are further validated experimentally on 2-KW panel constituted with all types of commercial products, namely, mono, poly and thin-film.

At present, smartphones are embedded with accelerometers, gyroscopes, magnetometers and WiFi sensors. Most researchers have delved into the use of these sensors for localization. However, there are still many problems in reducing fingerprint mismatching and fusing these positioning data. The purpose of this paper is to improve positioning accuracy by reducing fingerprint mismatching and designing a weighted fusion algorithm.

For the problem of magnetic mismatching caused by singularity fingerprint, derivative Euclidean distance uses adjacent fingerprints to eliminate the influence of singularity fingerprint. To improve the positioning accuracy and robustness of the indoor navigation system, a weighted extended Kalman filter uses a weighted factor to fuse multisensor data.

The scenes of the teaching building, study room and office building are selected to collect data to test the algorithm’s performance. Experiments show that the average positioning accuracies of the teaching building, study room and office building are 1.41 m, 1.17 m, and 1.77 m, respectively.

The algorithm proposed in this paper effectively reduces fingerprint mismatching and improve positioning accuracy by adding a weighted factor. It provides a feasible solution for indoor positioning.

This paper aims to target tracking in the marine environment is typically obtained by considering the measurement parameters like frequency, elevation and bearing. Marine environmental surveillance provides critical information and assistance for the exploitation and maintenance of marine resources.

With the use of intelligent sensor techniques like Hull-mounted and towed array sensors, convenient, precise and dependable three-dimensional (3D) underwater target tracking is introduced.

This research investigates a method to develop a reliable Unscented Kalman Filter (UKF) algorithm for enhanced underwater target tracking in a 3D scenario by using bearing, frequency and elevation measurements. In applications for underwater target tracking, uncertainty and inaccuracies are typically described by using Gaussian additive noise.

The proposed UKF algorithm is tested and analyzed using 100 Monte Carlo simulations with the Gaussian generated noise.

A novel vision-based relative navigation system (VBRNS) plays an important role in aeronautics and astronautics fields, and the filter is the core of VBRNS. However, most of the existing filtering algorithms used in VBRNS are derived based on Gaussian assumption and disregard the non-Gaussianity of VBRNS. Therefore, a novel robust filtering named as cubature Huber-based filtering (CHF) is proposed and applied to VBRNS to improve the navigation accuracy in non-Gaussian noise case.

Under the Bayesian filter framework, the third-degree cubature rule is used to compute the cubature points which are propagated through state equation, and then the predicted mean and the associated covariance are taken. A combined minimum l₁ and l₂-norm estimation method referred as Huber’s criterion is used to design the measurement update. After that, the vision-based relative navigation model is presented and the CHF is used to integrate the line-of-sight measurements from vision camera with inertial measurement of the follower to estimate the precise relative position, velocity and attitude between two unmanned aerial vehicles. During the design of relative navigation filter, the quaternions are used to represent the attitude and the generalized Rodrigues parameters are used to represent the attitude error. The simulation is conducted to demonstrate the effectiveness of the algorithm.

By this means, the VBRNS could perform better than traditional VBRNS whose filter is designed by Gaussian filtering algorithms. And the simulation results demonstrate that the CHF could exhibit robustness when the system is non-Gaussian. Moreover, the CHF has more accurate estimation and faster rate of convergence than extended Kalman Filtering (EKF) in face of inaccurate initial conditions.

A novel robust nonlinear filtering algorithm named as CHF is proposed and applied to VBRNS based on cubature Kalman filtering (CKF) and Huber’s technique. The CHF could adapt to the non-Gaussian system effectively and perform better than traditional Gaussian filtering such as EKF.