Nonlinear robust adaptively SDRE filtering method for SINS/SAR integrated Navigation System

Performance evaluation of Cubature Kalman filter in a GPS/IMU tightly-coupled navigation system

We present a new optimal control approach to robust control of robot manipulators in the framework of state dependent Riccati equation (SDRE) technique. To treat this highly nonlinear control system, we formulate it as a nonlinear optimal regulator problem. SDRE technique was used to synthesize an optimal controller to this class of robot control problem. We also synthesize a neural network based extra controller to achieve the robustness in the presence of the parameter uncertainties. A typical two-link robot position control problem was studied to show the effectiveness of SDRE approach and robust extra control design to robotic application.

This paper presents a new robust adaptive unscented particle filtering algorithm by adopting the concept of the adaptive robust filtering to the unscented particle filter. This algorithm adaptively determines the equivalent weight function and adaptively adjusts the adaptive factor constructed from predicted residuals to resist the disturbances of singular observations and the kinematic model noise, thus preventing particles from degeneracy. It also uses the unscented transformation to improve the accuracy of particle filtering, thus providing the reliable state estimation for improving the performance of adaptive robust filtering. Experiments and comparison analysis demonstrate that the proposed filtering algorithm can effectively resist disturbances due to system state noise and observation noise, and the filtering accuracy is much higher than the extended Kalman filter, unscented Kalman filter, standard particle filter and unscented particle filter.

For land-based navigation, Euler angles are often used in INS/GPS integrated navigation systems. However, the trigonometric operations required in the updates and forming of the rotation matrices for transforming the INS measurements from the body frame to the navigation frame turns the system model to be highly nonlinear. Besides, using low-cost MEMS-based IMUs, the gyroscope bias errors must be correctly estimated and compensated, which makes the nonlinearity problem a critical one. In this contribution, three Kalman filtering methods (i.e., Extended Kalman filter with simplified system model, Extended Kalman filter with linearized system model and Unscented Kalman filter with nonlinear system model) are utilized in INS/GPS tightly-coupled integration. Simulations and field experiments are conducted. Numerical results are compared in terms of both estimation accuracy and processing time.

The integration of position and velocity is commonly used for observation model in the GPS/INS integrated navigation system, but correlation between the system’s measurements of this observation model decrease the navigation accuracy. So the integration of pseudo-range was adopted for observation model to eliminate the influence of correlation. As the observation model is nonlinear, the unscented particle filter (UPF) was introduced for states estimation to eliminate the linearization error. The simulation was carried out under the condition of less than four GPS satellites data and the filtering results of UPF method were compared with the results of extended Kalman filter (EKF) method. The simulation results show that the estimated values with both UPF and EKF method are close to the real values but the estimation error of UPF algorithm is smaller than that of EKF method.

Navigation includes the integration of methodologies and systems for estimating time-varying position, velocity and attitude of moving objects. Navigation incorporating the integrated inertial navigation system (INS) and global positioning system (GPS) generally requires extensive evaluations of nonlinear equations involving double integration. Currently, integrated navigation systems are commonly implemented using the extended Kalman filter (EKF). The EKF assumes a linearized process, measurement models and Gaussian noise distributions. These assumptions are unrealistic for highly nonlinear systems like land vehicle navigation and may cause filter divergence. A particle filter (PF) is developed to enhance integrated INS/GPS system performance as it can easily deal with nonlinearity and non-Gaussian noises. In this paper, a hybrid extended particle filter (HEPF) is developed as an alternative to the well-known EKF to achieve better navigation data accuracy for low-cost microelectromechanical system sensors. The results show that the HEPF performs better than the EKF during GPS outages, especially when simulated outages are located in periods with high vehicle dynamics.

The extended Kalman filter (EKF) as a primary integration scheme has been applied in the Global Positioning System (GPS) and inertial navigation system (INS) integrated system. Nevertheless, the inherent drawbacks of EKF contain not only instability caused by linearization, but also massive calculation of Jacobian matrix. To cope with this problem, the adaptive interacting multiple model (AIMM) filter method is proposed to enhance navigation performance. The soft-switching characteristic, which is provided by interacting multiple model algorithm, permits process noise to be converted between upper and lower limits, and the measurement covariance is regulated by Sage adaptive filtering on-line Moreover, since the pseudo-range and Doppler observations need to be updated, an updating policy for classified measurement is considered. Finally, the performance of the GPS/INS integration method on the basis of AIMM is evaluated by a real ship, and comparison results demonstrate that AIMM could achieve a more position accuracy.

The extended Kalman filter (EKF) has been used widely in Global Position System (GPS) and Strapdown Inertial Navigation Systems (SINS) integrated navigation systems,which simply linearizes all nonlinear models.However, careful treatment of the nonlinearity of the system models is particularly critical when the integrated systems use low cost micro-electro-mechanical (MEMS) inertial sensors. Hence application of the EKF in MEMS inertial sensor-based integrated navigation systems will likely lead to inaccurate results. To overcome the shortcomings of the EKF, the unscented Kalman filter (UKF) has been proposed.The UKF does not require the linearization of the system models. Alternatively it uses a set of deterministically selected "sigma-points",which completely capture the true mean and covariance of the original random vector. Then these sigma-points are propagated through the nonlinear models.This captures the mean and covariance to second order accuracy for arbitrary nonlinear functions. In this paper the EKF and UKF are applied in GPS/SINS integrated systems respectively. Simulation result demonstrates that the performance of the UKF is better than the EKF in MEMS inertial sensor-based SINS/GPS integrated systems.

In this paper, we investigate the problem of autonomous integrated navigation of strapdown inertial navigation system (SINS)/ celestial navigation system (CNS) for a ballistic missile. To handle the nonlinear missile dynamic model, three filtering methods including extended Kalman filter (EKF), extended Kalman particle filter (EKPF) and unscented particle filter (UPF) are utilized to fusion the integrated navigation system and improve the navigation accuracy. The performance of the navigation system based on the three methods is analyzed and compared. Simulation results indicate that the UPF can provide the highest navigation accuracy with the price of highest computation burden.

A new integrated navigation system designed for small size Unmanned Aerial Vehicles (UAVs) is presented. The proposed system is based on a number of low-cost avionics sensors, including Global Navigation Satellite Systems (GNSS), Micro-Electro-Mechanical System (MEMS) based Inertial Measurement Unit (IMU) and Vision Based Sensors (VBS). The use of an Aircraft Dynamics Models (ADMs) to provide additional information to compensate for the shortcomings of Vision Based Navigation (VBN) and MEMS-IMU sensors in high-dynamics attitude determination tasks is also considered. Additionally, the research concentrates on the potential of carrier-phase GNSS for Attitude Determination (GAD) using interferometric techniques. The main objective is to design a compact, light and relatively inexpensive system capable of providing the required navigation performance (position and attitude data) in all phases of flight of small UAVs, with a special focus on precision approach and landing, where VBN techniques can be fully exploited in a multi-sensor data fusion architecture. An Extended Kalman Filter (EKF) is developed to integrate the information provided by the different sensors and to provide estimates of position, velocity and attitude of the UAV platform in real-time. Three different integrated navigation system architectures are implemented. The first architecture uses VBN at 20 Hz and GNSS at 1 Hz to augment the MEMS-IMU running at 100 Hz. The second mode also includes the ADM (computations performed at 100 Hz) to provide augmentation of the attitude channel. The third fusion architecture uses GNSS based attitude values. The simulations are carried out on the AEROSONDE UAV performing high-dynamics manoeuvres repre-sentative of the UAV operational flight envelope. Simulation of the VBN-IMU-GNSS (VIG) integrated navigation system shows that the system can attain position, velocity and attitude accuracies complying with Category Two (CAT II) precision approach requirements. Simulation of the VBN-IMU-GNSS-ADM (VIGA) system also shows promising results, since the achieved attitude accuracy is higher using the ADM-VBN-IMU than using VBN-IMU only. However, due to rapid divergence of the ADM virtual sensor, there is a need for frequent re-initialisation of the ADM data module, which is strongly dependent on the UAV flight dynamics and the specific manoeuvring transitions performed. In the simulation of the third integrated navigation system, the VIG system is augmented by employing the GAD, forming the VIG-GAD (VIGGA) system architecture. The performances achieved with the VIG, VIGA and VIGGA integrated Navigation and Guidance System (NGS) are presented and are in line with the International Civil Aviation Organization (ICAO) precision approach requirements.

In connection with the increasing complexity of aircraft navigation support, with growing demands placed on them, it is increasingly necessary to develop integrated navigation data processing system by solving synthesis problems based on optimal filtering methods. One of the promising ways to improve aircraft navigation support is to create an integrated navigation system (INS). In this work the triadic structure of a promising integrated navigation system based on inertial and satellite technologies is proposed. Combining nautical meters as part of the triad concept allows the flexible use of various navigation data processing algorithms and trajectory optimization ones at different stages of flight. Based on optimal filtering theory methods the integrated navigation data processing algorithm, which is an extended Kalman filter, is synthesized. A distinctive feature of the algorithm is that the variables of the state vector which are to be measured, are the errors in the determination of appropriate navigation and timing parameters. Priori information about the dynamics of errors is more reliably known than the one about aircraft motion variables. It is shown that the use of the proposed navigation system as a navigation aid for problem solving of highly accurate aircraft position indication satisfies the requirements for navigation and timing data accuracy. The imitating statistical modeling of optimal filtering algorithm of the triad integrated system is carried out. The accuracy characteristics of subsystems for different operating configurations of the constellation are given. Analysis of the results of the accuracy characteristics study of INS in various operating conditions has showed that the positioning accuracy in the navigation computer is up to date satellite navigation receivers.

Bearing systems and harmonic drives in robots introduce complex kinematic errors which result in joint kinematic errors that reduce the accuracy of their manipulators. Typical calibration methods do not consider these complex errors, thus, limiting post calibration performance. In this paper, a method of modeling and calibrating robot kinematic errors by building a joint-dependent kinematic error model is presented. Measurements are collected by a laser tracker and Active Target mounted on the end of the last robot link. A joint-dependent kinematic error model is constructed and the model parameters are identified with a mathematical algorithm based on maximum likelihood estimation. The kinematic error model is used to modify joint commands offline. Experiments are implemented on a FANUC LR Mate 200/ robot. Using 250 measurements to construct the kinematic error model, the mean residual between the measured and modeled positions is reduced from 3.379 to 0.105 mm, a 96.9% reduction. Compensation is applied to an independent set of 100 measurements, and the mean residual is reduced from 3.614 to 0.131 mm, a 96.4% reduction.

This paper proposes an improved initial alignment method for a strap-down inertial navigation system/global navigation satellite system (SINS/GNSS) integrated navigation system with large misalignment angles. Its methodology is based on the three-dimensional special Euclidean group and extended Kalman filter (SE2(3)/EKF) and aims to overcome the challenges of achieving fast alignment under large misalignment angles using traditional methods. To accurately characterize the state errors of attitude, velocity, and position, these elements are constructed as elements of a Lie group. The nonlinear error on the Lie group can then be well quantified. Additionally, a group vector mixed error model is developed, taking into account the zero bias errors of gyroscopes and accelerometers. Using this new error definition, a GNSS-assisted SINS dynamic initial alignment algorithm is derived, which is based on the invariance of velocity and position measurements. Simulation experiments demonstrate that the alignment method based on SE2(3)/EKF can achieve a higher accuracy in various scenarios with large misalignment angles, while the attitude error can be rapidly reduced to a lower level.

The GPS/INS Integrated Navigation System is nonlinear in nature. To deal with the accuracy of GPS/INS navigation under nonlinear, strong tracking cubature Kalman flter (STCKF) is applied to the system. The heart of the CKF is a cubature rule, which makes it possible to numerically compute multivariate moment integrals encountered in the nonlinear Bayesian filter. STCKF is presented for simulation. Simulation results show the superior performance of this approach when compared with clasaical suboptimal techniques such as extended Kalman filter (EKF). The results of simulation demonstrate the improved performance of the STCKF over conventional nonlinear filters. The research provides theoretical support for engineering design and modification. STCKF has the advantages of high reliability, low sensitivity, strong robustness, strong stability and convergence.

A new quaternion-based method for the SINS/SAR (strap-down inertial navigation system/synthetic aperture radar) integrated navigation system is presented. This method overcomes the shortcomings due to the linear SINS error model used in the currently existing SINS/SAR integrated navigation systems. A quaternion-based matrix is derived for describing the attitude of SINS. Quaternion-based nonlinear error and observation models are established for the SINS/SAR integrated navigation system. An adaptive unscented particle filtering (UPF) algorithm is developed based on the quaternion-based nonlinear models for optimal data fusion in the SINS/SAR integrated navigation system. Experimental results demonstrate that the proposed quaternion based method can effectively reduce the navigation error and improve the navigational positioning precision.