Car Navigation Research Articles

A Multi-Sensor Tight Fusion Method Designed for Vehicle Navigation.

Using the Global Navigation Satellite System (GNSS), it is difficult to provide continuous and reliable position service for vehicle navigation in complex urban environments, due to the natural vulnerability of the GNSS signal. With the rapid development of the sensor technology and the reduction in their costs, the positioning performance of GNSS is expected to be significantly improved by fusing multi-sensors. In order to improve the continuity and reliability of the vehicle navigation system, we proposed a multi-sensor tight fusion (MTF) method by combining the inertial navigation system (INS), odometer, and barometric altimeter with the GNSS technique. Different fusion strategies were presented in the open-sky, insufficient satellite, and satellite outage environments to check the performance improvement of the proposed method. The simulation and real-device tests demonstrate that in the open-sky context, the error of sensors can be estimated correctly. This is useful for sensor noise compensation and position accuracy improvement, when GNSS is unavailable. In the insufficient satellite context (6 min), with the help of the barometric altimeter and a clock model, the accuracy of the method can be close to that in the open-sky context. In the satellite outage context, the error divergence of the MTF is obviously slower than the traditional GNSS/INS tightly coupled integration, as seen by odometer and barometric altimeter assisting.

Correction Algorithm for the Navigation System of an Autonomous Unmanned Underwater Vehicle.

More accurate navigation systems are always required for autonomous unmanned underwater vehicles (AUUV)s under various circumstances. In this paper, a measuring complex of a heavy unmanned underwater vehicle (UUV) was investigated. The measuring complex consists of an inertial navigation platform system, a Doppler lag (DL) and an estimation algorithm. During a relatively long-term voyage of an UUV without surfacing and correction from buoys and stationary stations, errors of the measuring complex will increase over time. The increase in errors is caused by an increase in the deviation angles of the gyro platform relative to the accompanying trihedron of the selected coordinate system. To reduce these angles, correction is used in the structure of the inertial navigation system (INS) using a linear regulator. To increase accuracy, it is proposed to take into account the nonlinear features of INS errors; an adaptive nonlinear Kalman filter and a nonlinear controller were used in the correction scheme. Considering that, a modified nonlinear Kalman filter and a regulator in the measuring complex are proposed to improve the accuracy of the measurement information, and modification of the nonlinear Kalman filter was performed through a genetic algorithm, in which the regulator was developed by the State Dependent Coefficient (SDC) method of the formulated model. Modeling combined with a semi-natural experiment with a real inertial navigation system for the UUV demonstrated the efficiency and effectiveness of the proposed algorithms.

Speech Enhancement Parameter Adjustment to Maximize Accuracy of Automatic Speech Recognition

Consumer electronics equipped with a microphone array, such as car navigation devices and headsets commonly implement speech enhancement techniques based on the gradient method to cope with additive noise. However, while these techniques had been originally developed for voice communication and can maximize the signal-to-distortion ratio (SDR), they cannot always maximize automatic speech recognition (ASR) accuracy. For this reason, the front-end speech enhancement parameters have been adjusted by human experts to each environment and acoustic model. In this study, we developed a novel system for maximizing the accuracy of a given ASR engine by automatically adjusting the front-end speech enhancement. The proposed method allows consumers to use ASR through the consumer electronics with less stress when ambient noise varies. A genetic algorithm (GA) is used to generate parameter values of the front-end speech enhancement for particular environments. The generated values can be dynamically assigned to input speech signals by preliminarily clustering the environments based on noise features. In evaluations, parameter values determined by our method outperformed one adjusted by a human expert.

A Fast Robust In-Motion Alignment Method for SINS With DVL Aided

In this paper, a fast robust in-motion alignment method is proposed for the strapdown inertial navigation system (SINS) with DVL aided. The proposed method is divided into two procedures, which are coarse alignment procedure and fine alignment procedure. In the coarse alignment procedure, an apparent velocity modeling method is investigated. Building upon this model, a robust Kalman filter (RKF) is designed for parameter estimation, then an optimal observation vector, in which the outliers of the Doppler Velocity Log (DVL) outputs are eliminated, is reconstructed. As compared with the existing method, in the proposed method, the robustness of coarse alignment procedure is enhanced. To improve the accuracy of the coarse alignment method, a fine alignment method is designed. Different from the traditional fine alignment method, the error model of the proposed fine alignment method is constructed in n 0-frame. With this advantage, the time-varying error attitude estimation for the traditional method has transformed into a time-invariant error attitude estimation. Thus, the raw data and intermediate data, which are collected in the coarse alignment procedure, can be reused in the fine alignment procedure. Take advantage of the high performance of the navigational computer, the forward-forward data processing can be carried on repeatedly, the one cycle of the forward-forward procedure consumes about 0.3 s in the real-time system. Simulation and field tests showed that the proposed method can obtain a high-accuracy initial alignment results and suppress the interference of the outliers of the DVL outputs.

Vehicle Navigation Systems Involving Inertial Sensors and Odometry Data from On-Board Diagnostics in Non-Gps Applications

This paper explores the applicability of on-board diagnostics data for minimizing inertial navigation errors in vehicles. The results of driving tests were presented and discussed. Knowledge of a vehicle’s exact initial position and orientation was crucial in the navigation process. Orientation errors at the beginning of navigation contributed to positioning errors. GPS data were not processed by the algorithm during navigation.

The Synthesis of the Reference Image and Algorithms for Vehicle Navigation Systems

The Synthesis of the Reference Image and Algorithms for Vehicle Navigation Systems

A Formation Autonomous Navigation System for Unmanned Surface Vehicles With Distributed Control Strategy

By deploying unmanned surface vehicles (USVs), the efficiency and reliability of mission execution can be improved. This paper is concerned with the problem of formation autonomous navigation system (FANS) for USVs. The decision layer of the system is composed of path planning subsystem and navigation control subsystem to realize fast and effective autonomous navigation system. The FANS is constructed by leader-follower structure and distributed control strategy, which make the individuals in the formation have certain autonomy. The dynamic domain tunable fast marching square algorithm proposed in the path planning subsystem can not only adjust the safety and length of the path planned, but also continuously re-plan the path according to the motion information of the USV formation and target-vessels. Navigation control subsystem based on finite control set model predictive control can quickly and safely guide and control formation in local sea environment. The simulation tests are carried out in static and dynamic harbor environments respectively, which verifies the ability of the FANS to achieve stable formation tracking and autonomous safe navigation.

Adaptive real-time optimal energy management strategy for extender range electric vehicle

The extender range electric vehicle (EREV) is an effective way to solve the “mileage anxiety” of pure electric vehicles, and the fuel economy of EREV is the key point of energy optimization. This paper designed an adaptive real-time optimal energy management strategy for EREV. Firstly, an improved shooting algorithm is proposed, which can determine the range of the equivalent factor (EF) according to the power configuration parameters of the vehicle, and then the secant method is used to quickly calculate the initial value of the EF. Secondly, from the perspective of energy flow, the intrinsic operation mechanism of equivalent consumption minimization strategy (ECMS) control strategy is revealed, and the working relationship between the five working modes of EREV is clarified. Thirdly, based on the car navigation and geographic location information system, the EF is periodically updated to achieve effective maintenance of the battery state of charge (SOC), so as to obtain the optimal power allocation. Finally, The fuel economy and real-time performance of the proposed energy management strategy are simulated and compared. To verify fuel economy, the rule-based control strategy and the power following control strategy were used as comparison. The results show that the proposed control strategy has better fuel economy and adaptability. To verify real-time performance, the proportional integral derivative ECMS (PID-ECMS) and shooting method ECMS (S-ECMS) were used as comparison. The results show that the proposed strategy is better in both fuel economy and real-time performance.

Mitigation of Multipath Effects Based on a Robust Fractional Order Bidirectional Least Mean Square (FOBLMS) Beamforming Algorithm for GPS Receivers

Consideration on positioning and location services among the public has been increasing in the recent years with their applications in most of the anticipating milieus such as automobile navigation system etc. This insists for a development of high recitation global navigation satellite system such as global positioning system (GPS). Multipath effects, interference, signal jamming etc. are the major sources of error influencing the performance of the GPS receiver. Literature presents many of the multipath mitigation techniques. Among them, adaptive processing technology based beamforming algorithms appears a viable solution for multipath mitigation. The least mean square (LMS) beamforming algorithms were sensitive to dynamic environments thus affecting the accuracy of GPS. In this paper, an adaptive beamforming algorithm called fractional order bidirectional least mean square (FOBLMS) algorithm is proposed to mitigate the multipath effects and to conceal the jammer signal in a GPS receiver. The FOBLMS is an integration of the fractional calculus and bidirectional least mean square algorithm. The effectiveness of the proposed algorithm is validated using the bit error rate and experimentation gain results over the existing beamforming algorithms. Experimental results demonstrated that the performance of the proposed beamforming algorithm is better than LMS algorithm with maximal relative antenna gain of 28.92 dB, 32.84 dB for two and four element antenna arrays at − 60° and 10°, direction of arrivals respectively. The outcome of this work would be useful for developing a robust technique for multipath mitigation in GPS receivers.

Design and Signal Delay Analysis of Spinning Missile Electric Rudder Control System

In order to meet the requirements of ultra-small special-purpose missiles for miniaturization, high dynamic characteristics and low signal delay of the missile-mounted rudder, and to reduce the working pressure of the navigation computer control system, a digital electric rudder control system based on MCU and H-bridge integrated power drive chip was designed and implemented. The system uses the Texas Instruments C2000 high-performance MCU as the core control unit to process the single-channel rudder position feedback signal. After the fuzzy position PID algorithm calculation, the control signal is output in the form of pulse width modulation (PWM) waveform, and the rudder control is completed by the DRV8872 power drive chip. Aiming at the problem of phase delay in missile attitude control system, the reason of delay is analyzed from the perspective of system composition. The phase delay formula is derived through theoretical calculation and sine sweep test. A method based on navigation computer deviation compensation is proposed. It has been proved by experiments that the rudder control system has good dynamic response capability and has good compensation effect on system signal delay.

상용 MEMS IMU와 차량 CAN 데이터를 이용한 지상차량 항법용 GPS/INS/DR 통합항법시스템 구현 및 실측결과

상용 MEMS IMU와 차량 CAN 데이터를 이용한 지상차량 항법용 GPS/INS/DR 통합항법시스템 구현 및 실측결과

Vehicular Navigation Based on the Fusion of 3D-RISS and Machine Learning Enhanced Visual Data in Challenging Environments

Based on the 3D Reduced Inertial Sensor System (3D-RISS) and the Machine Learning Enhanced Visual Data (MLEVD), an integrated vehicle navigation system is proposed in this paper. In demanding conditions such as outdoor satellite signal interference and indoor navigation, this work incorporates vehicle smooth navigation. Firstly, a landmark is set up and both of its size and position are accurately measured. Secondly, the image with the landmark information is captured quickly by using the machine learning. Thirdly, the template matching method and the Extended Kalman Filter (EKF) are then used to correct the errors of the Inertial Navigation System (INS), which employs the 3D-RISS to reduce the overall cost and ensuring the vehicular positioning accuracy simultaneously. Finally, both outdoor and indoor experiments are conducted to verify the performance of the 3D-RISS/MLEVD integrated navigation technology. Results reveal that the proposed method can effectively reduce the accumulated error of the INS with time while maintaining the positioning error within a few meters.

A High-Integrity and Low-Cost Navigation System for Autonomous Vehicles

The introduction of autonomous vehicles can potentially lead to enhanced situational awareness and safety for road transport. However, the performance required for autonomous operations places stringent requirements on the vehicle navigation systems design. Relevant performance measures include not only the accuracy of the system but also its ability to detect sensor faults within a specified Time-to-Alert (TTA) and without generating an excessive number of false alarms. A new integrated navigation system architecture is proposed which utilizes Global Navigation Satellite Systems (GNSS), low-cost Inertial Navigation Systems (INS), visual odometry and Vehicle Dynamic Models (VDM). The system design is based on various navigation modes, each with independent failure mechanisms and fault-detection capabilities. A two-step data fusion approach is adopted to optimize the system accuracy and integrity performance. This includes a Knowledge-Based Module (KBM) performing a detailed sensor integrity analysis followed by a conventional Extended Kalman Filer (EKF). CAV navigation integrity requirement (i.e., alert limits and time-to-alert) are considered in the KBM where fault detection probabilities are calculated for each mode and translated to protection levels. A simulation case study is executed to verify the performance of each navigation mode in the presence of faults affecting the individual navigation sensors. Results confirm that the required CAV integrity performances are met, while the inclusion of visual odometry and VDM data provides significant performance enhancements both in terms of accuracy and integrity over existing INS/GNSS systems.

Enhancement of Heading Accuracy for GPS/INS by Employing Average Velocity in Low Dynamic Situations

In this paper, we proposed an algorithm that improves the heading accuracy of the global positioning system (GPS)/inertial navigation system (INS) integrated system used in automobiles by manipulating INS velocity and GPS velocity measurements. Two velocities are provided by the GPS receiver: the velocity calculated using the position difference and the velocity calculated using the Doppler shift. The velocity obtained using the position difference is an average velocity for a certain time period, which is inaccurate under dynamic conditions because of its time delay. In contrast, the velocity from the Doppler shift is an instantaneous velocity and has no time delay. However, it also relies on pseudo-range error and noise, which degrades the heading estimation accuracy in low dynamic situations. Thus, although the velocity measurements can improve heading accuracy, the navigation performance is often degraded when the velocity measurements are used for GPS/INS integration. To improve the heading accuracy by solving the aforementioned problems, we proposed a heading accuracy improvement algorithm that employs the average velocity measurements obtained using the averaged GPS velocity and the average velocity of the INS. Since the proposed average velocity measurements are calculated using long baseline, the proposed algorithm can improve the heading accuracy without using other sensors, especially in the case of low dynamic situations. It can be easily applied to the existing GPS/INS integrated system, making it suitable for use in automotive navigation systems. In this research, it is verified that the average velocity measurements can be substituted for Kalman filter measurements, and the performance improvements are confirmed through simulations and experiments.

Study of the route correction system for the on-board navigation system of an unmanned aerial vehicle on the grounds of radar images of the terrain

The system of route correction of an unmanned aerial vehicle (UAV) is considered. For the route correction the on-board radar complex is used. In conditions of active interference, it is impossible to use radar images for the route correction so it is proposed to use the on-board navigation system with algorithmic correction. An error compensation scheme of the navigation system in the output signal using the algorithm for constructing a predictive model of the system errors is applied. The predictive model is building using the genetic algorithm and the method of group accounting of arguments. The quality comparison of the algorithms for constructing predictive models is carried out using mathematical modeling.

Robust autonomous flight in cluttered environment using a depth sensor

This paper presents a novel collision-free navigation system for the unmanned aerial vehicle based on point clouds that outperform compared to baseline methods, enabling high-speed flights in cluttered environments, such as forests or many indoor industrial plants. The algorithm takes the point cloud information from physical sensors (e.g. lidar, depth camera) and then converts it to an occupied map using Voxblox, which is then used by a rapid-exploring random tree to generate finite path candidates. A modified Covariant Hamiltonian Optimization for Motion Planning objective function is used to select the best candidate and update it. Finally, the best candidate trajectory is generated and sent to a Model Predictive Control controller. The proposed navigation strategy is evaluated in four different simulation environments; the results show that the proposed method has a better success rate and a shorter goal-reaching distance than the baseline method.

Attitude anti-interference federal filtering algorithm for MEMS-SINS/GPS/magnetometer/SV integrated navigation system

The attitude measurement systems composed of magnetic, angular rate and gravity sensors in the navigation system of an unmanned aerial vehicle are vulnerable to disturbed magnetic field observations. In this paper, an anti-interference integrated navigation federal Kalman filter is designed with fusing the information of microelectromechanical-system-based strapdown inertial navigation system, gravity field, geomagnetic field and solar vector (direction of sun). The filter works in the mode of fusion reset or no reset according to environmental conditions or manual setting. In addition, the characteristic is discovered that disturbed magnetic field observations can interfere with the state estimation covariance matrixes of quaternions, which in turn affect the fusion update of estimated quaternions in no reset mode of the federal filter. In order to eliminate the effect of magnetic field observations on the attitude information of pitch and roll angles, a method of correction update for a yaw angle component in quaternions using the magnetometer is designed, and an Euler angle information fusion method is developed by constructing a pseudo-covariance matrix of Euler angels in fusion update of the filter. Thus, when there exist disturbed magnetic field observations and the filter is in no reset mode, the estimated pitch and roll angles of the main filter after fusion update are not affected, and the yaw angle estimation errors of the main filter decrease relative to the estimation errors of the sub-filter which is corrected by a magnetometer. The effectiveness of the algorithm is illustrated by a numerical simulation and a semi-physical simulation.

Study on the observability degree of integrated inertial navigation system of autonomous underwater vehicle

Strapdown inertial navigation system (SINS) initial alignment error model is presented under moving base. SINS navigation error models and the integrated navigation error models are proposed. The state observability during the initial alignment under moving base was thoroughly studied according to the piece-wise constant system method and singular value decomposition method. Simulation experiments were carried out under different vehicle movements with same integration and different integration with same vehicle movements respectively. Simulation experiments show that the observability degree of states variables can be improved under linear or rotation movement, furthermore, the outer measurements aided SINS can also improve the observability degree of system state variables accordingly. The observability degree increases of state variables improve the accuracy of estimation and the speed of convergence of Kalman filter applied in the integrated inertial navigation system.

Study on the observability degree of integrated inertial navigation system of autonomous underwater vehicle

Strapdown inertial navigation system (SINS) initial alignment error model is presented under moving base. SINS navigation error models and the integrated navigation error models are proposed. The state observability during the initial alignment under moving base was thoroughly studied according to the piece-wise constant system method and singular value decomposition method. Simulation experiments were carried out under different vehicle movements with same integration and different integration with same vehicle movements respectively. Simulation experiments show that the observability degree of states variables can be improved under linear or rotation movement, furthermore, the outer measurements aided SINS can also improve the observability degree of system state variables accordingly. The observability degree increases of state variables improve the accuracy of estimation and the speed of convergence of Kalman filter applied in the integrated inertial navigation system.

THE POSITIONING OF STREET NAMES ON EGOCENTRICALLY REFERENCED MAPS: ASSESSMENT OF READING TIME

Abstract: The main objective of this research is to evaluate the reading time of street names on dynamic egocentrically referenced maps for In-Car Route Guidance and Navigation Systems. Two different road network patterns (grid-like or non-grid-like) were considered and tasks were devised to search for street names in two different route segments (where the driver was, or where the driver would have to manoeuvre). Considering a non-grid-like road network pattern, for the task of searching for the name of a road where the driver would manoeuvre, street names aligned horizontally and next to the route were read significantly faster than street names positioned along the route. Street names positioned obliquely required more reading time than street names aligned horizontally. Dynamic maps egocentrically referenced should present street names horizontally aligned and next to the route for the segment related to where the driver will manoeuvre, for both road network patterns. Also, Street names should be positioned next to the car symbol.