A Novel Hybrid Unscented Particle Filter based on Firefly Algorithm for Tightly-Coupled Stereo Visual-Inertial Vehicle Positioning

2019 ◽  
Vol 73 (3) ◽  
pp. 613-627
Author(s):  
Xiuyuan Li ◽  
Wenxue Gao ◽  
Jiashu Zhang
Keyword(s):  
Kalman Filter ◽  
Computational Complexity ◽  
Particle Filter ◽  
Firefly Algorithm ◽  
Unscented Kalman Filter ◽  
Estimation Accuracy ◽  
Positioning Systems ◽  
Unscented Particle Filter ◽  
Tightly Coupled ◽  
Vehicle Positioning

This paper presents a hybrid unscented particle filter (UPF) based on the firefly algorithm for tightly-coupled stereo visual-inertial vehicle positioning systems (VIVPS). Compared with standard UPF, this novel approach can achieve similar estimation accuracy with much less computational complexity. To reduce the computational complexity, the time updating of the hybrid unscented Kalman filter is conducted via the formula of standard linear Kalman filter on the basis of the constructed linear/nonlinear mixed filter model. The particle updating of the particle filter is optimised by modified firefly algorithm to reduce the number of particles needed by means of moving particles towards high likelihood regions via the attraction and movement of fireflies, leading to a significant reduction of computational complexity. Experimental results show the average execution time of the proposed approach is 23·8% that of the standard UPF with similar accuracy, indicating the designed method for tightly-coupled stereo VIVPS can better satisfy the real-time requirement of the system.

INS/GPS Tightly-coupled Integration using Adaptive Unscented Particle Filter

2010 ◽  
Vol 63 (3) ◽  
pp. 491-511 ◽  
Author(s):  
Junchuan Zhou ◽  
Stefan Knedlik ◽  
Otmar Loffeld
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Unscented Kalman Filter ◽  
Estimation Accuracy ◽  
Proposal Distribution ◽  
Sampling Step ◽  
Computational Burden ◽  
Monte Carlo Experiments ◽  
Unscented Particle Filter ◽  
Tightly Coupled

With the rapid developments in computer technology, the particle filter (PF) is becoming more attractive in navigation applications. However, its large computational burden still limits its widespread use. One approach for reducing the computational burden without degrading the system estimation accuracy is to combine the PF with other filters, i.e., the extended Kalman filter (EKF) or the unscented Kalman filter (UKF). In this paper, the a posteriori estimates from an adaptive unscented Kalman filter (AUKF) are used to specify the PF importance density function for generating particles. Unlike the sequential importance sampling re-sampling (SISR) PF, the re-sampling step is not required in the algorithm, because the filter does not reuse the particles. Hence, the filter computational complexity can be reduced. Besides, the latest measurements are used to improve the proposal distribution for generating particles more intelligently. Simulations are conducted on the basis of a field-collected 3D UAV trajectory. GPS and IMU data are simulated under the assumption that a NovAtel DL-4plus GPS receiver and a Landmark™ 20 MEMS-based IMU are used. Navigation under benign and highly reflective signal environments are considered. Monte Carlo experiments are made. Numerical results show that the AUPF with 100 particles can present improved system estimation accuracy with an affordable computational burden when compared with the AEKF and AUKF algorithms.

scholarly journals Integration of GPS Precise Point Positioning and MEMS-Based INS Using Unscented Particle Filter

2021 ◽  
Author(s):  
Mahmoud Abd Rabbou ◽  
Ahmed El-Rabbany
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Navigation System ◽  
Precise Point Positioning ◽  
Unscented Kalman Filter ◽  
Inertial Sensor ◽  
Measurement Models ◽  
Unscented Particle Filter ◽  
Tightly Coupled ◽  
Point Positioning

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) integrated system involves nonlinear motion state and measurement models. However, the extended Kalman filter (EKF) is commonly used as the estimation filter, which might lead to solution divergence. This is usually encountered during GPS outages, when low-cost micro-electro-mechanical sensors (MEMS) inertial sensors are used. To enhance the navigation system performance, alternatives to the standard EKF should be considered. Particle filtering (PF) is commonly considered as a nonlinear estimation technique to accommodate severe MEMS inertial sensor biases and noise behavior. However, the computation burden of PF limits its use. In this study, an improved version of PF, the unscented particle filter (UPF), is utilized, which combines the unscented Kalman filter (UKF) and PF for the integration of GPS precise point positioning and MEMS-based inertial systems. The proposed filter is examined and compared with traditional estimation filters, namely EKF, UKF and PF. Tightly coupled mechanization is adopted, which is developed in the raw GPS and INS measurement domain. Un-differenced ionosphere-free linear combinations of pseudorange and carrier-phase measurements are used for PPP. The performance of the UPF is analyzed using a real test scenario in downtown Kingston, Ontario. It is shown that the use of UPF reduces the number of samples needed to produce an accurate solution, in comparison with the traditional PF, which in turn reduces the processing time. In addition, UPF enhances the positioning accuracy by up to 15% during GPS outages, in comparison with EKF. However, all filters produce comparable results when the GPS measurement updates are available. Keywords: GPS; PPP; INS; EKF; UKF; UPF; tightly coupled

scholarly journals Integration of GPS Precise Point Positioning and MEMS-Based INS Using Unscented Particle Filter

2021 ◽  
Author(s):  
Mahmoud Abd Rabbou ◽  
Ahmed El-Rabbany
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Navigation System ◽  
Precise Point Positioning ◽  
Unscented Kalman Filter ◽  
Inertial Sensor ◽  
Measurement Models ◽  
Unscented Particle Filter ◽  
Tightly Coupled ◽  
Point Positioning

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) integrated system involves nonlinear motion state and measurement models. However, the extended Kalman filter (EKF) is commonly used as the estimation filter, which might lead to solution divergence. This is usually encountered during GPS outages, when low-cost micro-electro-mechanical sensors (MEMS) inertial sensors are used. To enhance the navigation system performance, alternatives to the standard EKF should be considered. Particle filtering (PF) is commonly considered as a nonlinear estimation technique to accommodate severe MEMS inertial sensor biases and noise behavior. However, the computation burden of PF limits its use. In this study, an improved version of PF, the unscented particle filter (UPF), is utilized, which combines the unscented Kalman filter (UKF) and PF for the integration of GPS precise point positioning and MEMS-based inertial systems. The proposed filter is examined and compared with traditional estimation filters, namely EKF, UKF and PF. Tightly coupled mechanization is adopted, which is developed in the raw GPS and INS measurement domain. Un-differenced ionosphere-free linear combinations of pseudorange and carrier-phase measurements are used for PPP. The performance of the UPF is analyzed using a real test scenario in downtown Kingston, Ontario. It is shown that the use of UPF reduces the number of samples needed to produce an accurate solution, in comparison with the traditional PF, which in turn reduces the processing time. In addition, UPF enhances the positioning accuracy by up to 15% during GPS outages, in comparison with EKF. However, all filters produce comparable results when the GPS measurement updates are available. Keywords: GPS; PPP; INS; EKF; UKF; UPF; tightly coupled

scholarly journals Iterative Truncated Unscented Particle Filter

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 214
Author(s):  
Yanbo Wang ◽  
Fasheng Wang ◽  
Jianjun He ◽  
Fuming Sun
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
State Estimation ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Process Models ◽  
Filter Method ◽  
Proposal Distribution ◽  
Truncation Method ◽  
Unscented Particle Filter

The particle filter method is a basic tool for inference on nonlinear partially observed Markov process models. Recently, it has been applied to solve constrained nonlinear filtering problems. Incorporating constraints could improve the state estimation performance compared to unconstrained state estimation. This paper introduces an iterative truncated unscented particle filter, which provides a state estimation method with inequality constraints. In this method, the proposal distribution is generated by an iterative unscented Kalman filter that is supplemented with a designed truncation method to satisfy the constraints. The detailed iterative unscented Kalman filter and truncation method is provided and incorporated into the particle filter framework. Experimental results show that the proposed algorithm is superior to other similar algorithms.

Fast Kalman SLAM

2010 ◽  
Vol 44-47 ◽  
pp. 3174-3179
Author(s):  
Wu Zhou ◽  
Chun Xia Zhao ◽  
Mian Hao Zhang
Keyword(s):  
Kalman Filter ◽  
Computational Complexity ◽  
Extended Kalman Filter ◽  
Simulation Analysis ◽  
Unscented Kalman Filter ◽  
Estimation Accuracy ◽  
Map Building ◽  
Complex Environments ◽  
Path Component ◽  
High Computational Complexity

When Simultaneous Localization and Map Building is carried out in complex environments, reduction of computational complexity is a key problem. With a view to the high computational complexity of particle filter, a SLAM solution named ‘Fast Kalman SLAM’ is introduced. Adopting the ‘decomposition’ idea in the FastSLAM algorithm, Fast Kalman SLAM factors the joint SLAM state into a path component and a conditional map component. The robot pose is estimated recursively with Mean Extended Kalman Filter (MEKF) or Unscented Kalman Filter (UKF), while the map with Extended Kalman Filter (EKF). Simulative experiments are carried out to evaluate the performance of the presented algorithm. And Simulation analysis is made for the presented algorithm. The experimental results indicate that the new algorithm reduces computational complexity greatly and ensures estimation accuracy at the same time.

Improved Filter Strategies for Precise Geolocation of Unexploded Ordnance using IMU/GPS Integration

2009 ◽  
Vol 62 (3) ◽  
pp. 365-382 ◽  
Author(s):  
Jong Ki Lee ◽  
Christopher Jekeli
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Extended Kalman Filter ◽  
Inertial Sensors ◽  
Unscented Kalman Filter ◽  
Detection Systems ◽  
Filter Methods ◽  
Unscented Particle Filter ◽  
Measurement Units ◽  
Non Gaussian

Efficient and precise geolocation can be achieved by integrating a ranging system, such as GPS, with inertial sensors in order to bridge short outages, enhance accuracy degradation, and increase the temporal resolution in the ranging system. Optimal integration depends on appropriate filter methods that can accommodate the particular short-term dynamics experienced by platforms, such as UXO ground-based detection systems. The traditional extended Kalman filter was designed to integrate data from a linearized system excited by Gaussian noise. We compared this filter to modern filters that obviate these prerequisites, including the unscented Kalman filter, the particle filter, and adaptive variations thereof, using simulated IMU/ranging systems that follow a typical trajectory with both straight and curved segments. The unscented filter performed significantly better than the extended Kalman filter, particularly over the curved segments, yielding up to 50% improvement in the position accuracy using medium-grade inertial measurement units. Similar improvement was obtained for the unscented particle filter, and its adaptive variant, over the unscented Kalman filter (which performed comparably to the extended Kalman filter) when the statistical distribution of the IMU noise was non-symmetric (i.e., essentially non-Gaussian). While the few-centimetre geolocation accuracy goal for highly dynamic UXO characterization applications remains a challenge if tactical grade IMUs are integrated with a significantly degraded ranging system, using filters appropriate to the inherent nonlinear dynamics and potential non-Gaussian nature of the sensor noise tend to reduce overall errors compared to the traditional filter.

scholarly journals Evaluation of Multi-Sensor Fusion Methods for Ultrasonic Indoor Positioning

2021 ◽  
Vol 11 (15) ◽  
pp. 6805
Author(s):  
Khaoula Mannay ◽  
Jesús Ureña ◽  
Álvaro Hernández ◽  
José M. Villadangos ◽  
Mohsen Machhout ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Experimental Tests ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Smart Devices ◽  
Final Position ◽  
Positioning Systems ◽  
Local Positioning System ◽  
Tightly Coupled ◽  
Position Estimate

Indoor positioning systems have become a feasible solution for the current development of multiple location-based services and applications. They often consist of deploying a certain set of beacons in the environment to create a coverage volume, wherein some receivers, such as robots, drones or smart devices, can move while estimating their own position. Their final accuracy and performance mainly depend on several factors: the workspace size and its nature, the technologies involved (Wi-Fi, ultrasound, light, RF), etc. This work evaluates a 3D ultrasonic local positioning system (3D-ULPS) based on three independent ULPSs installed at specific positions to cover almost all the workspace and position mobile ultrasonic receivers in the environment. Because the proposal deals with numerous ultrasonic emitters, it is possible to determine different time differences of arrival (TDOA) between them and the receiver. In that context, the selection of a suitable fusion method to merge all this information into a final position estimate is a key aspect of the proposal. A linear Kalman filter (LKF) and an adaptive Kalman filter (AKF) are proposed in that regard for a loosely coupled approach, where the positions obtained from each ULPS are merged together. On the other hand, as a tightly coupled method, an extended Kalman filter (EKF) is also applied to merge the raw measurements from all the ULPSs into a final position estimate. Simulations and experimental tests were carried out and validated both approaches, thus providing average errors in the centimetre range for the EKF version, in contrast to errors up to the meter range from the independent (not merged) ULPSs.

Sign in / Sign up

Export Citation Format

Share Document