Federated unscented particle filtering algorithm for SINS/CNS/GPS system

Abstract

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.

Keywords

navigation system / integrated navigation / unscented Kalman filter / unscented particle filter

References

[1] KalmanR. E.. A new approach to linear filtering and prediction problems [J]. Journal of Basic Engineering, 1960, 82: 35-46

[2] FossenT. I., PerezT.. Kalman filtering for positioning and heading control of ships and offshore rigs [J]. IEEE Transactions on Control Systems, 2009, 29(6): 32-46

[3] SchneebeliM., MatzlerC.. A calibration scheme for microwave radiometers using tipping curves and Kalman filtering [J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(12): 4201-4209

[4] YiJ. G., WangH. P., ZhangJ. J., SongD. Z., JayasuriyaS., LiuJ. T.. Kinematic modeling and analysis of skid-steered mobile robots with applications to low-cost inertial-measurement-unit-based motion estimation [J]. IEEE Transactions on Robotics, 2009, 25(5): 1087-1097

[5] SayadiO., ShamsollahiM. B.. ECG denoising and compression using a modified extended Kalman filter structure [J]. IEEE Transactions on Biomedical Engineering, 2008, 55(9): 2240-2248

[6] BoussakM.. Implementation and experimental investigation of sensorless speed control with initial rotor position estimation for interior permanent magnet synchronous motor drive [J]. IEEE Transactions on Power Electronics, 2005, 20(6): 1413-1422

[7] LightcapC. A., BanksS. A.. An extended Kalman filter for real-time estimation and control of a rigid-link flexible-joint manipulator [J]. IEEE Transactions on Control Systems Technology, 2010, 18(1): 91-103

[8] AlanisA. Y., SanchezE. N., LoukianovA. G., Perez-Cisne-RosM. A.. Real-time discrete neural block control using sliding modes for electric induction motors [J]. IEEE Transactions on Control Systems Technology, 2010, 18(1): 11-21

[9] JULIER S J, UHLMANN J K, DURRANT-WHYTEN H F. A new approach for filtering nonlinear system [C]// Processing of the American Control Conference. Washington D.C., 1995: 1628–1632.

[10] ZhanR.-h., WanJ.-wei.. Neural network-aided adaptive unscented Kalman filter for nonlinear state estimation [J]. Signal Processing Letters, 2006, 13(7): 445-448

[11] BellottoN., HuH. S.. Multisensor-based human detection and tracking for mobile service robots [J]. Systems, Man, and Cybernetics, 2009, 39(1): 167-181

[12] LevenW. F., LantermanA. D.. Unscented Kalman filters for multiple target tracking with symmetric measurement equations [J]. IEEE Transactions on Automatic Control, 2009, 54(2): 370-375

[13] CarlsonN. A.. Federated square root filter for decentralized parallel processes [J]. IEEE Transactions on Aerospace and Electronic Systems, 1990, 26(3): 517-525

[14] DoucetA.. On sequential simulation-based methods for Bayesian filtering [J]. Statistics and Computing, 2000, 10(3): 197-208

[15] LiY., AiH. Z., YamashitaT., LaoS. H., KawadeM.. Tracking in low frame rate video: A cascade particle filter with discriminative observers of different life spans [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(10): 1728-1740

[16] Martinez-EsplaJ. J., Martinez-MarinT., Lopez-SanchezJ. M.. A particle filter approach for INSAR phase filtering and unwrapping [J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(4): 1197-1211

[17] OzdemirO., NiuR., VarshneyP. K.. Tracking in wireless sensor networks using particle filtering: Physical layer considerations [J]. IEEE Transactions on Signal Processing, 2009, 57(5): 1987-1999

[18] NordlundP., GustafssonF.. Marginalized particle filter for accurate and reliable terrain-aided navigation [J]. IEEE Transactions on Aerospace and Electronic Systems, 2009, 45(4): 1385-1399

[19] HoffmannG. M., TomlinC. J.. Mobile sensor network control using mutual information methods and particle filters [J]. IEEE Transactions on Automatic Control, 2010, 55(1): 32-47

[20] MustiereF., BolicM., BouchardM.. Speech enhancement based on nonlinear models using particle filters [J]. IEEE Transactions on Neural Networks, 2009, 20(12): 1923-1937

[21] MerweR. V. D., DoucetA., FreitasN. D., EricW.The unscented particle filter [R], 2000, Cambridge, Cambridge University