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To solve the problem of Kalman filter (KF) performance degradation in unmanned aerial vehicle (UAV) applications, a novel interactive robust filter algorithm for GNSS/SINS integrated navigation is proposed in this paper. The strong tracking Kalman filter (STKF) is robust to uncertain system noise but is ineffective to abnormal measurement information. Based on the same performance index function with STKF, a measurement noise covariance matrix adaptive Kalman filter algorithm (MAKF) is presented, but it is ineffective under uncertain system noise. Furthermore, the interactive robust filter algorithm based on STKF and MAKF (IF-STKF-MAKF) is proposed, given the complementary characteristics of the above two filter algorithms. The STKF and MAKF operate in parallel based on the same system model. The filter probability of each filter is updated according to the likelihood function to perform output fusion and input interaction. The simulation and experiment results demonstrate that the IF-STKF-MAKF is effective and can achieve high estimation accuracy under both system noise anomalies and measurement information anomalies. In the vehicle experiment, the position accuracy of the proposed IF-STKF-MAKF method has been improved by more than 30% compared with KF, STKF, and MAKF. This method can also be extended to land vehicles, mobile robots, etc. [ABSTRACT FROM AUTHOR]

For aircrafts equipped with BeiDou Navigation Satellite System/Strapdown Inertial Navigation System integrated navigation system, BeiDou Navigation Satellite System information can be used to achieve autonomous alignment. However, due to the complex polar environment and multipath effect, BeiDou Navigation Satellite System measurement noise often exhibits a non-Gaussian distribution that will severely degrade the estimation accuracy of standard Kalman filter. To address this problem, a new polar alignment algorithm based on the Huber estimation filter is proposed in this article. Considering the special geographical conditions in the polar regions, the dynamic model and the measurement model of BeiDou Navigation Satellite System/Strapdown Inertial Navigation System integrated alignment system in the grid frame are derived in this article. The BeiDou Navigation Satellite System measurement noise characteristics in the polar regions are analyzed and heavy-tailed characteristics are simulated, respectively. Since the estimation accuracy of standard Kalman filter can be severely degraded under non-Gaussian noise, a Kalman filter based on the Huber estimation is designed combining grid navigation system and generalized maximum likelihood estimation. The simulation and experiment results demonstrate that the proposed algorithm has better robustness under non-Gaussian noise, and it is effective in the polar regions. By employing the proposed algorithm, the rapidity and accuracy of the alignment process can be improved. [ABSTRACT FROM AUTHOR]

A fault-detection method for relative navigation based on Kullback–Leibler divergence (KLD) is proposed. Different from the traditional χ 2-based approaches, the KLD for a filter is following a hybrid distribution that combines χ 2 distribution and F -distribution. Using extended Kalman filter (EKF) as the estimator, the distance between the priori and posteriori data of EKF is calculated to detect the abnormal measurements. After fault detection step, a fault exclusion method is applied to remove the error observations from the fusion procedure. The proposed method is suitable for the Kalman filter-based multisensor relative navigation system. Simulation and experimental results show that the proposed method can detect the abnormal measurement successfully, and its positioning accuracy after fault detection and exclusion outperforms the traditional χ 2-based method. [ABSTRACT FROM AUTHOR]

Considering that traditional visual navigation cannot be utilised in low illumination and sparse feature environments, a novel visual-inertial integrated navigation method using a Structured Light Visual (SLV) sensor for Micro Aerial Vehicles (MAVs) is proposed in this paper. First, the measurement model based on an SLV sensor is studied and built. Then, using the state model based on error equations of an Inertial Navigation System (INS), the measurement model based on the error of the relative motion measured by INS and SLV is built. Considering that the measurements in this paper are mainly related to the position and attitude information of the present moment, the state error accumulation in traditional visual-inertial navigation can be avoided. An Adaptive Sage-Husa Kalman Filter (ASHKF) based on multiple weighting factors is proposed and designed to make full use of the SLV measurements. The results of the simulation and the experiment based on real flight data indicate that high accuracy position and attitude estimations can be obtained with the help of the algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]

The navigation for hypersonic cruise vehicle (HCV) is a challenging task because of the complex vehicle dynamic and sensor measurement noise it suffered. This paper proposes a kind of adaptive robust Kalman filter using Mahalanobis distance for HCV navigation. The innovation-based adaptive estimation is discussed first. Based on Mahalanobis distance theory, a kind of robust covariance matrix estimation method is used to modify the innovation-based adaptive Kalman filter. Considering that the vehicle maneuver characteristics and noise statistics parameters varies during different periods, dual-frequency tuning for unknown noise statistics is designed based on this. The algorithm proposed by this paper is applied to hypersonic cruise vehicle navigation. Simulation has been made to verify the performance of the new algorithm according to HCV flight profile and characteristics; both Gaussian and non-Gaussian simulation are included. [ABSTRACT FROM AUTHOR]

The integrity and usability of the navigation system are important safeguards to small unmanned aerial vehicles' autonomous flight. A quadrotor aircraft with ARM microprocessor is designed. A multi-information fusion navigation scheme is proposed, which consists of micro-electro mechanical system (MEMS) inertial components, magnetometers, GPS, optic-flow imaging sensor and barometer. And an asynchronous centralized filter method is designed for separating the time update period and the measurement update period. According to the nonlinear characteristics of the navigation scheme, a nonlinear Kalman filter based on Sigma-point Kalman filter(SPKF) is proposed. In all, a micro multi-information navigation system of the quadrotor aircraft is built. Experiments are carried out and the results show that the designed navigation system can supply good attitude, velocity and position reference both in a static-state test and in a dynamic flight test. The navigation system can meet the accuracy and reliability requirements of the quadrotor aircraft under different flight tasks. [ABSTRACT FROM AUTHOR]

Electronic appliances and ferromagnetic materials can be easily found in any building in urban environment. A steady magnetic environment and a pure value of geomagnetic field for calculating the heading of the smartphone in case of pedestrian walking indoors is hard to obtain. Therefore, an independent inertial heading correction algorithm without involving magnetic field but only making full use of the embedded Micro-Electro-Mechanical System (MEMS) Inertial measurement unit (IMU) device in the smartphone is presented in this paper. Aiming at the strict navigation requirements of pedestrian smartphone positioning, the algorithm focused in this paper consists of Gravity Assisted (GA) and Middle Time Simulated-Zero Velocity Update (MTS-ZUPT) methods. With the help of GA method, the different using-mode of the smartphone can be judged based on the data from the gravity sensor of smartphone. Since there is no zero-velocity status for handheld smartphone, the MTS-ZUPT algorithm is proposed based on the idea of Zero Velocity Update (ZUPT) algorithm. A Kalman Filtering algorithm is used to restrain the heading divergence at the middle moment of two steps. The walking experimental results indicate that the MTS-ZUPT algorithm can effectively restrain the heading error diffusion without the assistance of geomagnetic heading. When the MTS-ZUPT method was integrated with GA method, the smartphone navigation system can autonomously judge the using-mode and compensate the heading errors. The pedestrian positioning accuracy is significantly improved and the walking error is only 1.4% to 2.0% of the walking distance in using-mode experiments of the smartphone. [ABSTRACT FROM AUTHOR]

The process of the transfer alignment for the shipborne inertial navigation system is greatly affected by its external environment. Under the entire influence of its external environment, the structure of the ship will be deformated. First of all, the flexural deformation is analyzed in the environment of the engine vibration and the impact of waves, taking the lever arm effect into consideration. Secondly, the specific force integration matching scheme which is suitable to the moving of carriers in the environment of the low speed is adopted in order to accomplish transfer alignment between master and slave inertial navigation system. Thirdly, a method has been well designed to improve the Kalman filter techniques based on the extended state observer. The usage of the method makes it possible to estimate the disturbance of the uncertain model and realizes dynamic feedback linearization in the inertial navigation system for ship during the process of transfer alignment. Finally, the above theoretical analyses are verified through simulation. [ABSTRACT FROM AUTHOR]

The state model and measurement accuracy of the system can be changed according to the dynamic condition, and different filtering methods are proposed according to the different rate of the signal. It is proposed to build switching system, combing such methods as time series analysis model, the least square, and adaptive moving average algorithm together to form a switching noise reduction method. Adaptive Kalman filter based on state correction is studied to improve the filtering accuracy of the system and tracking performance. The method is finally testified by simulation results, and it shows that the system noise can be reduced, the system changes be tracked dynamically, and the dynamic noise reduction effect of the system be improved. [ABSTRACT FROM AUTHOR]

• Only EOSTP is needed without installing laser rangefinder. • A nonlinear 3-D location model with clear physical meaning is established. • CKF and CRTS are combined to realize high precision target location of UAV. • Approaching DEM by calling the target location solution model repeatedly. Target location is a major function of unmanned aerial vehicles (UAV). In aerial photogrammetry, due to the large object distance, various slight measurement errors will be magnified, resulting in a significant decrease in target location precision. In order to improve the precision of target location, an innovative method of UAV target location is designed. This method utilizes the feature of the airborne electro-optical stabilization and tracking platform (EOSTP) which can track a ground target and keep it in the center of the camera's field of view to make multiple observations of one target point. The nonlinear system model of the target space position is built according to the geometric relationship between the line-of-sight (LOS) pointing vector and the target point, and then the Cubature Kalman Filter (CKF) algorithm is used to estimate the target position. Considering that CKF algorithm is easily affected by the initial value errors, the initial target position estimation method based on digital elevation model (DEM) approximation is proposed. The validity of the location algorithm is verified via simulation experiments and the results of actual flight test show that this algorithm can improve the location accuracy of ground target in rough terrain area obviously. [ABSTRACT FROM AUTHOR]

In ordinary aircrafts, the geographic coordinate is usually taken as reference coordinate to build navigation systems. Changes of gravity field and radius caused by the non-perfect sphere and uneven mass distribution of the earth are ignored or simplified in navigation algorithm, which have little effect on the accuracy of the navigation system in a short-time and short-distance flight. As the Aerospace Vehicles (ASVs) have to complete long-term on-orbit maneuver over wide flight enveloping, ignoring changes of gravity field and radius will bring about cumulative navigation errors and result in the declined accuracy of the navigation system. Hence, this paper first proposes an inertial navigation algorithm in launch inertial coordinate for ASVs aimed at reducing the effects caused by the inaccurate earth model on the navigation system. Then, the model of INS/CNS/GPS integrated navigation system in launch inertial coordinate is built. Considering the uncertain noise in space, the performance of Kalman filter may be poor, a robust filtering algorithm based on extended H ∞ filter is designed to realize multi-sensors information fusion; The state model and measurement model in launch inertial coordinate are built. The simulation results show that the proposed method and algorithm can improve the accuracy and reliability of the integrated navigation system for ASVs. [ABSTRACT FROM AUTHOR]

Aimed at the problem of nonlinear and time-varying noise characteristics in inertial and land-based integrated navigation system, a cubature Kalman filter algorithm based on maximum a posterior estimation and fading factor has been proposed, and the fuzzy control theory is used to make it better to track the time-varying noise characteristics. Nonlinear measurement model of the land-based navigation system has been established. Online identification and adaptive adjustment of the measurement noise features has been realized by means of the designed noise estimator, which can effectively improve the estimation precision and inhibit filtering divergence. The simulation results show that the method proposed by the paper has a higher filtering accuracy compared with the traditional cubature Kalman filter. The horizontal positioning accuracy is improved by about 40%, and the horizontal velocity accuracy is improved by about 60%. The new algorithm can enhance the applicability of the land-based navigation system in required navigation performance. [ABSTRACT FROM AUTHOR]