A Quaternion-Based Method for SINS/SAR Integrated Navigation System

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.

Aiming at the integrated navigation system with model errors, the model errors is assumed as a process noise for Gaussian white noise to process in Kalman filtering, thus causing larger state estimation error of nonlinear filtering system and even divergent. This paper presents a nonlinear model predictive particle filtering method considers the model error of real-time estimation, and then corrects the nonlinear and non-Gaussian system model with model error. Compared and simulated the new method with the predictive filtering and the particle filtering in Strap-down Inertial Navigation System/Synthetic Aperture Radar integrated navigation system. Experiment and comparison results demonstrate that the proposed method can improve the filter performance and integrated navigation calculation accuracy significantly.

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.

To solve the problem of information fusion in the strapdown inertial navigation system (SINS)/celestial navigation system (CNS)/global positioning system (GPS) integrated navigation system described by the nonlinear/non-Gaussian error models, a new algorithm called the federated unscented particle filtering (FUPF) algorithm was introduced. In this algorithm, the unscented particle filter (UPF) served as the local filter, the federated filter was used to fuse outputs of all local filters, and the global filter result was obtained. Because the algorithm was not confined to the assumption of Gaussian noise, it was of great significance to integrated navigation systems described by the non-Gaussian noise. The proposed algorithm was tested in a vehicle’s maneuvering trajectory, which included six flight phases: climbing, level flight, left turning, level flight, right turning and level flight. Simulation results are presented to demonstrate the improved performance of the FUPF over conventional federated unscented Kalman filter (FUKF). For instance, the mean of position-error decreases from (0.640×10−6 rad, 0.667×10−6 rad, 4.25 m) of FUKF to (0.403×10−6 rad, 0.251×10−6 rad, 1.36 m) of FUPF. In comparison of the FUKF, the FUPF performs more accurate in the SINS/CNS/GPS system described by the nonlinear/non-Gaussian error models.

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.

Recognition that measurement error in the independent (predictor) variable of data used for regression modeling necessitates special methods of model fitting dates back to the late 1800s. In the ensuing 100 years attention was devoted almost entirely to measurement error in linear regression models, at which time advances in computing opened the door to the nonlinear statistical modeling generally, and to the study of measurement error in nonlinear regression modeling in particular. This article contains an introduction to the problem of measurement error in regression using linear regression measurement error models to illustrate key ideas. It includes a discussion of the types of measurement error commonly studied (classical and Berkson, differential and nondifferential) and how those types arise in practice. A summary of the consequences of ignoring measurement error when analyzing data measured with error is presented, with detailed results given for simple and multiple linear measurement error models. Pointers to the literature on both linear and nonlinear measurement error models are provided.

The tightly coupled GPS/SINS integrated navigation system is studied in this paper. In order to meet the requirement of precision and stability, a nonlinear error model of SINS based on additive quaternion is established and a kind of SRUKF(Square-Root Unscented Kalman Filter) algorithm is designed. Compared whith EKF, The SRUKF not only improves the location precision greatly but also avoids the calculating burden of Jacobin matrices. Simulation results show the feasibility and effectiveness of the proposed algorithm in tightly coupled GPS/SINS integrated navigation system.

This paper proposes a twice rapid transfer alignment algorithm based on dual models in order to solve the problems such as long convergence time, poor accuracy, and heavy computation burden resulting from the traditional nonlinear error models. The quaternion matching method based on quaternion error model along with the extended Kalman filter (EKF) is applied to deal with the large misalignment in the first phase. Then in the second transfer alignment phase, velocity plus attitude matching method as well as classical Kalman filter is adopted. The simulation and the results of vehicle tests demonstrate that this method combines the advantages of both nonlinear and linear error models with the guarantee of accuracy and fastness.

A novel laser Doppler velocimeter and its integrated navigation system with strapdown inertial navigation

Integrated navigation systems have evolved in response to the increasing requirements for more accurate navigation of marine survey vessels. Some systems have been better than others in terms of choice of devices for combination, or in mathematical handling of data from the respective sensors, but all have been inflexible in terms of interchange of sensor types or adaptation of new devices. This paper studies the basic philosophy of navigation system design with primary consideration given to system objectives and control structure. The result is a sensor-independent approach to integrated navigation system design. INTRODUCTION At this point in time, there is no universal aid to navigation which can provide continuous position fixes and velocities on a worldwide, 24-hour per day basis, and still deliver the accuracies required by all users. There are several systems which offer accuracy and continuity, but operate over a limited range; e.g., doppler satellites yield worldwide coverage but give only periodic fix updates; while VLF radio systems yield nearly continuous, worldwide coverage but lack the accuracy required for precision navigation. The Global Positioning System (GPS), promises to give simultaneous solutions to many of these problems, but commercial availability is not likely to be realized before the late 1980's. The approach taken to meet user requirements in precise navigation has been the "integrated navigation system" a combination of two or more different navigation subsystems. This paper deals with the philosophy of integrated system design, reviews the deficiencies in some past approaches to system integration, and describes a design structure which permits flexibility in the choice of sensor subsystems and provides for longer product lifetime. CATEGORIES OF NAVIGATION AIDS Since the earliest days of navigation, mariners have used tools to assist in determining their position (position fix) and in setting and keeping a course over a period of time to reach a desired new position (dead reckoning). Ocean voyages depended upon natural phenomena, with celestial observations for position reference and magnetic compass directions to maintain headings. Local navigation (piloting) has always used line-of-sight reference to coastline features or reference markers to maintain a desired course from successive triangulation bearings. Twentieth century techniques have considerably improved life for the navigator, extending operation on a 24-hour per day, all-weather basis while increasing the accuracy of measurement. Three techniques of measurement form the basis for most of the navigation aids currently in use: radio wave systems, which use broadcast wavelength as a "yardstick" for distance or velocity measurements between the navigator and known reference points; acoustic systems, which perform range or rangerate measurements based on propogation time of sound through water; and gyroscopic systems, which measure orientation and angular velocities within the Earth's gravitational field. The following paragraphs describe systems from all of these categories, giving some specific strengths and weaknesses of each system type.

The robust adaptive filtering method based on the State Dependent Riccati Equation technique for SINS/SAR integrated navigation system. This method adopts the state dependent coefficient form to convert nonlinear system model into linear system for avoiding the errors caused by the traditional numerical linearization process. It also adopts the concepts of robust estimation and adaptive factor to construct reasonably the discriminate statistics and adaptive factor for resisting the disturbances of singular observations and kinematic model error. Experimental results and comparison analysis demonstrate that the proposed filtering method can not only effectively resist disturbances from nonlinear system state noise and observation noise, but it also can achieve higher accuracy than the extended kalman filtering and SDRE filtering methods.

The constraints of weight, volume and power for Small unmanned air vehicle (UAV) restrict the application of sensors with heavy and good performance and powerful processors. This paper presents a real-time solution of autonomous flight navigation and its results for small UAV by applying small, cheap, low precision and low-power integrated navigation system, which includes Strap-down Inertial Navigation System (SINS) based on Micro-electro-mechanical system (MEMS) inertial sensors, Global Positioning System (GPS) receiver and magnetometer. The Square-Root Unscented Kalman filter (SR-UKF) for data fusion using in this MEMS-SINS/GPS/ magnetometer integrated navigation system provides continuous and reliable navigation results for the loops of guidance and control for the small UAV with autonomous flight. The whole integrated navigation system algorithm is implemented within low-power embedded microprocessors. The real-time flight test results show that the MEMS-SINS/GPS/magnetometer integrated navigation system is effective and accurate.

Robust UKF orbit determination method with time-varying forgetting factor for angle/range-based integrated navigation system

For the long-range voyage and light Autonomous Underwater Vehicle (AUV), the high-precision navigation and positioning is the key point in the completion of the task. Therefore, this paper designs an underwater integrated navigation system, including the strapdown inertial navigation system (SINS), Doppler velocity loci (DVL), magnetometers and depth sensor. Based on the principle of each navigation sensor and the corresponding error model, the mathematic model of each sub-filter of the integrated navigation system is firstly established. Secondly, combining the combination filter and federated filtering theory, the integrated navigation system based on federated filtering is designed. Finally, to eliminate the influence of unknown or time-varying statistical characteristics on the Kalman filter (KF), a new federated filtering algorithm based on Sage-Husa adaptive KF is proposed to estimate each local filter. And the corresponding simulation results will show the performance of the research.

There are the scale factor error of LDV (laser Doppler velocimeter) and the misalignment between the SINS (Strapdown inertial navigation system) and the vehicle in a SINS/LDV integrated navigation system. In this paper, the effects of these errors on the attitude, velocity and position of dead reckoning are derived, and a new online calibration method aiming to calibrate the scale factor of LDV and the misalignment between the SINS and the vehicle for the integrated system is put forward. This method, which is utilize the velocity and position of the Global Position System (GPS) as references, use the velocity error and position error of dead reckoning to estimate these errors. Through simulation and experiment, the validity and feasibility of the method are verified. The results show that the scale factor and the misalignment can be calibrated with satisfying accuracy, and the related research can provide technical support for high precision navigation of SINS/LDV integrated navigation systems.