Adaptive Federal Kalman Filtering for SINS/GPS Integrated System

A new adaptive federal Kalman filter for a strapdown integrated navigation system/global positioning system (SINS/GPS) is given. The developed federal Kalman filter is based on the trace operation of parameters estimation's error covariance matrix and the spectral radius of update measurement noise variance-covariance matrix for the proper choice of the filter weight and hence the filter gain factors. Theoretical analysis and results from simulation in which the SINS/GPS was compared to conventional Kalman filter are presented. Results show that the algorithm of this adaptive federal Kalman filter is simpler than that of the conventional one. Furthermore, it outperforms the conventional Kalman filter when the system is undertaken measurement malfunctions because of its possession of adaptive ability. This filter can be used in the vehicle integrated navigation system.

Bridging GPS outages of tightly-coupled GPS/SINS using GMDH neural network

A tightly coupled GPS （ global positioning system ）/SINS （ strap down inertial navigation system） based on a GMDH （ group method of data handling） neural network was presented to solve the problem of degraded accuracy for less than four visible GPS satellites with poor signal quality. Positions and velocities of the satellites were predicted by a GMDH neural network, and the pseudo ranges and pseudo range rates received by the GPS receiver were simulated to ensure the regular op eration of the GPS/SINS Kalman filter during outages. In the mathematical simulation a tightly cou pled navigation system with a proposed approach has better navigation accuracy during GPS outages, and the anti jamming ability is strengthened for the tightly coupled navigation system.

A GPS/BDS dual-mode positioning algorithm for a train based on CIPSO_EKF

When using global positioning system/BeiDou navigation satellite(GPS/BDS)dual-mode navigation system to locate a train,Kalman filter that is used to calculate train position has to be adjusted according to the features of the dual-mode observation.Due to multipath effect,positioning accuracy of present Kalman filter algorithm is really low.To solve this problem,a chaotic immune-vaccine particle swarm optimization_extended Kalman filter(CIPSO_EKF)algorithm is proposed to improve the output accuracy of the Kalman filter.By chaotic mapping and immunization,the particle swarm algorithm is first optimized,and then the optimized particle swarm algorithm is used to optimize the observation error covariance matrix.The optimal parameters are provided to the EKF,which can effectively reduce the impact of the observation value oscillation caused by multipath effect on positioning accuracy.At the same time,the train positioning results of EKF and CIPSO_EKF algorithms are compared.The eastward position errors and velocity errors show that CIPSO_EKF algorithm has faster convergence speed and higher real-time performance,which can effectively suppress interference and improve positioning accuracy.

Adaptive SINS/GPS Outlier Detection and Accommodation Using Innovation Orthogonal

The outlier detection and accommodation of integration navigation of strapdown inertial navigation systems and global position system（SINS/GPS） were studied.Based on analyzing the innovation orthogonal property in Kalman filter,an outlier adaptive detection approach was first presented,which included the determination of evaluation function and threshold and the logic decision of outlier occurrence.To effectively attenuate the influence on estimation accuracy,a modified Kalman filter algorithm was proposed by accommodation of the dynamic data with outlier.Results of data processing from vehicle-test SINS/GPS integration navigation show the effectiveness of the proposed method.

The Optimal Path Algorithm Analysis of GPS Navigation in Taxi Management System Based on A＊ Algorithm

The optimal path algorithm analysis of GPS navigation in taxi management system based on A＊ algorithm was introduced in this paper. Through improving the traditional Dijkstra algorithm and avoiding problems such as ＂time-consuming and low efficiency＂ in Dijkstra algorithm with traversal search for each node, A＊ algorithm could help the taxi find the optimal path and bring convenience for traffic management.

Cubature Kalman Filter Under Minimum Error Entropy With Fiducial Points for INS/GPS Integration

Traditional cubature Kalman filter(CKF)is a preferable tool for the inertial navigation system(INS)/global positioning system(GPS)integration under Gaussian noises.The CKF,however,may provide a significantly biased estimate when the INS/GPS system suffers from complex non-Gaussian disturbances.To address this issue,a robust nonlinear Kalman filter referred to as cubature Kalman filter under minimum error entropy with fiducial points(MEEF-CKF)is proposed.The MEEF-CKF behaves a strong robustness against complex nonGaussian noises by operating several major steps,i.e.,regression model construction,robust state estimation and free parameters optimization.More concretely,a regression model is constructed with the consideration of residual error caused by linearizing a nonlinear function at the first step.The MEEF-CKF is then developed by solving an optimization problem based on minimum error entropy with fiducial points(MEEF)under the framework of the regression model.In the MEEF-CKF,a novel optimization approach is provided for the purpose of determining free parameters adaptively.In addition,the computational complexity and convergence analyses of the MEEF-CKF are conducted for demonstrating the calculational burden and convergence characteristic.The enhanced robustness of the MEEF-CKF is demonstrated by Monte Carlo simulations on the application of a target tracking with INS/GPS integration under complex nonGaussian noises.

Evaluation of driving behavior based on massive vehicle trajectory data

Based on the driver surveillance video data and controller area network(CAN)data,the methods of studying commercial vehicles’driving behavior is relatively advanced.However,these methods have difficulty in covering private vehicles.Naturalistic driving studies have disadvantages of small sample size and high cost,one new driving behavior evaluation method using massive vehicle trajectory data is put forward.An automatic encoding machine is used to reduce the noise of raw data,and then driving dynamics and self-organizing mapping(SOM)classification are used to give thresholds or the judgement method of overspeed,rapid speed change,rapid turning and rapid lane changing.The proportion of different driving behaviors and typical dangerous driving behaviors is calculated,then the temporal and spatial distribution of drivers’driving behavior and the driving behavior characteristics on typical roads are analyzed.Driving behaviors on accident-prone road sections and normal road sections are compared.Results show that in Shenzhen,frequent lane changing and overspeed are the most common unsafe driving behaviors;16.1%drivers have relatively aggressive driving behavior;the proportion of dangerous driving behavior is higher outside the original economic special zone;dangerous driving behavior is highly correlated with traffic accident frequency.

Performance assisted enhancement based on change point detection and Kalman filtering

A performance assisted enhancement Kalman filtering algorithm(PAE-KF) for GPS/INS integration navigation in urban areas was presented in this work. The aim of this PAE-KF algorithm was to prevent "deep contamination" caused by error GPS data. This filtering algorithm effectively combined fault estimation of raw GPS data and nonholonomic constraint of vehicle. In fault estimation, a change point detection algorithm based on abrupt change model was proposed. Statistical tool was then used to infer the future bound of GPS data, which can detect faults in GPS raw data. If any kinds of faults were detected, dead reckoning mechanism begins to compute current position. Nonholonomic constraint condition of vehicle was used to estimate velocity of vehicle and change point detection was added into classic Kalman filtering structure. Experiment on vehicle shows that even when the GPS signals are unavailable for a period of time, this method can also output high accuracy data.

TEC and Instrumental Bias Estimation of GAGAN Station Using Kalman Filter and SCORE Algorithm

The standalone Global Positioning System (GPS) does not meet the higher accuracy requirements needed for approach and landing phase of an aircraft. To meet the Category-I Precision Approach (CAT-I PA) requirements of civil aviation, satellite based augmentation system (SBAS) has been planned by various countries including USA, Europe, Japan and India. The Indian SBAS is named as GPS Aided Geo Augmented Navigation (GAGAN). The GAGAN network consists of several dual frequency GPS receivers located at various airports around the Indian subcontinent. The ionospheric delay, which is a function of the total electron content (TEC), is one of the main sources of error affecting GPS/SBAS accuracy. A dual frequency GPS receiver can be used to estimate the TEC. However, line-of-sight TEC derived from dual frequency GPS data is corrupted by the instrumental biases of the GPS receiver and satellites. The estimation of receiver instrumental bias is particularly important for obtaining accurate estimates of ionospheric delay. In this paper, two prominent techniques based on Kalman filter and Self-Calibration Of pseudo Range Error (SCORE) algorithm are used for estimation of instrumental biases. The estimated instrumental bias and TEC results for the GPS Aided Geo Augmented Navigation (GAGAN) station at Hyderabad (78.47°E, 17.45°N), India are presented.

风云三号C卫星星载GPS/BDS实时定轨分析

随着北斗卫星导航系统(BeiDou navigation satellite system, BDS)的建设与运行,低轨卫星开始搭载GPS/BDS双系统接收机以实现卫星轨道确定。利用风云三号C(FengYun-3C,FY3C)卫星星载GPS/BDS双频伪距与载波相位观测数据,设置4种仿真试验方案,分别进行星载GPS/BDS在轨实时定轨数据处理,重点进行BDS观测数据对伪距实时定轨和载波相位实时定轨的精度影响分析和算法耗时分析。结果表明,采用伪距观测值,可获得1.0 m的位置精度和1.0 mm/s的速度精度;采用载波相位观测值,可获得0.3 m的位置精度和0.3 mm/s的速度精度,且引入BDS观测值后,伪距实时定轨精度降低,相位实时定轨精度有所改善。

Evaluation of navigation performances of GPS devices near interchange area pertaining to wrong-way driving

Based on past studies, exit ramp terminals are the common locations for drivers to enter a physically separated highway in the wrong direction. Currently, many drivers, especially nonlocal drivers, often rely on voice-guided navigation apps and Global Positioning System （GPS） devices to navigate their routes on and off freeways. A few studies have reported that GPS devices sometimes give drivers wrong information and cause wrong-way entry into a freeway, especially at some confusing interchanges, such as partial cloverleaf and compressed diamond interchanges. The access points located close to exit ramps may also cause a problem for GPS devices in sending accurate voice-guidance. It is unknown if current GPS devices are capable of properly informing drivers regarding turning movements in advance of exit ramp terminals at some common interchanges. The objective of this study is to evaluate the most commonly used GPS devices/navigation apps to identify existing problems and their potential for reducing wrong-way driving （WWD） incidents at interchange terminals. Field experiments were conducted at 10 common freeway interchanges or interchanges with nearby access driveways in the state of Alabama. Results show that most GPS devices have difficulty in providing correct guidance when the spacing between an access point and an exit ramp is less than 300 feet. Our comparison of five different GPS devices used on the same routes reveals that navigation apps have more limitations in guiding drivers than stand-alone GPS devices. Recommendations are offered to help GPS mapping companies improve their devices or add new features to reduce the occurrence of WWD.

Efficient fastest-path computations for road maps

In the age of real-time online traffic information and GPS-enabled devices,fastest-path computations between two points in a road network modeled as a directed graph,where each directed edge is weighted by a“travel time”value,are becoming a standard feature of many navigation-related applications.To support this,very efficient computation of these paths in very large road networks is critical.Fastest paths may be computed as minimal-cost paths in a weighted directed graph,but traditional minimal-cost path algorithms based on variants of the classical Dijkstra algorithm do not scale well,as in the worst case they may traverse the entire graph.A common improvement,which can dramatically reduce the number of graph vertices traversed,is the A*algorithm,which requires a good heuristic lower bound on the minimal cost.We introduce a simple,but very effective,heuristic function based on a small number of values assigned to each graph vertex.The values are based on graph separators and are computed efficiently in a preprocessing stage.We present experimental results demonstrating that our heuristic provides estimates of the minimal cost superior to those of other heuristics.Our experiments show that when used in the A*algorithm,this heuristic can reduce the number of vertices traversed by an order of magnitude compared to other heuristics.