Showing papers on "GPS/INS published in 2014"

An Integrated Vehicle Navigation System Utilizing Lane-Detection and Lateral Position Estimation Systems in Difficult Environments for GPS

Abstract: A navigation filter combines measurements from sensors currently available on vehicles - Global Positioning System (GPS), inertial measurement unit, inertial measurement unit (IMU), camera, and light detection and ranging (lidar) - for achieving lane-level positioning in environments where stand-alone GPS can suffer or fail. Measurements from the camera and lidar are used in two lane-detection systems, and the calculated lateral distance (to the lane markings) estimates of both lane-detection systems are compared with centimeter-level truth to show decimeter-level accuracy. The navigation filter uses the lateral distance measurements from the lidar- and camera-based systems with a known waypoint-based map to provide global measurements for use in a GPS/Inertial Navigation System (INS) system. Experimental results show that the inclusion of lateral distance measurements and a height constraint from the map creates a fully observable system even with only two satellite observations and, as such, greatly enhances the robustness of the integrated system over GPS/INS alone. Various scenarios are presented, which affect the navigation filter, including satellite geometry, number of satellites, and loss of lateral distance measurements from the camera and lidar systems.

A novel hybrid fusion algorithm to bridge the period of GPS outages using low-cost INS

Abstract: Land Vehicle Navigation (LVN) mostly relies on integrated system consisting of Inertial Navigation System (INS) and Global Positioning System (GPS). The combined system provides continuous and accurate navigation solution when compared to standalone INS or GPS. Different fusion methodology such as those based on Kalman filtering and particle filtering has been proposed that estimates and models the INS error during the GPS signal availability. In the case of outages, the developed model provides an INS error estimates, thereby improving its accuracy. However, these fusion approaches possess several inadequacies related to sensor error model, immunity to noise and computational load. Alternatively, Neural Network (NN) based approaches has been proposed. In the case of low-cost INS, the NN suffers from poor generalization capability due to the presence of high amount of noises. The paper thus introduces a novel and hybrid fusion methodology utilizing Dempster-Shafer (DS) theory augmented by Support Vector Machines (SVM), known as DS-SVM. The INS and GPS data fusion is carried using DS fusion whereas SVM models the INS error. During GPS availability, DS provides accurate solution; whereas during outages, the trained SVM model corrects the INS error thereby improving the positioning accuracy. The proposed methodology is evaluated against the existing Artificial Neural Network (ANN) and the Random Forest Regression (RFR) methodology. A total of 20-87% improvement in the positional accuracy was found against ANN and RFR.

A New Technique for INS/GNSS Attitude and Parameter Estimation Using Online Optimization

Abstract: Integration of inertial navigation system (INS) and global navigation satellite system (GNSS) is usually implemented in engineering applications by way of Kalman-like filtering. This form of INS/GNSS integration is prone to attitude initialization failure, especially when the host vehicle is moving freely. This paper proposes an online constrained-optimization method to simultaneously estimate the attitude and other related parameters including GNSS antenna's lever arm and inertial sensor biases. This new technique benefits from self-initialization in which no prior attitude or sensor measurement noise information is required. Numerical results are reported to validate its effectiveness and prospect in high accurate INS/GNSS applications.

A Hybrid Prediction Method for Bridging GPS Outages in High-Precision POS Application

Abstract: Position and orientation system (POS) is a key technology widely used in remote sensing applications, which integrates inertial navigation system (INS) and GPS using a Kalman filter (KF) to provide high-accuracy position, velocity, and attitude information for remote sensing motion compensation. However, when GPS signal is blocked, the POS accuracy will decrease owing to the unbounded INS error accumulation. To improve the reliability and accuracy of POS, this paper proposes a hybrid prediction method for bridging GPS outages. This method uses radial basis function (RBF) neural network coupled with time series analysis to forecast the measurement update of KF, resulting in reliable performance during GPS outages. In verifying the proposed hybrid prediction method, a flight experiment was conducted in 2011, based on a high-precision Laser POS. Experimental results show that the proposed hybrid prediction method is more effective than two other methods (KF and RBF neural network).

GPS/INS/Odometer Integrated System Using Fuzzy Neural Network for Land Vehicle Navigation Applications

Abstract: The integration of Global Positioning Systems (GPS) with Inertial Navigation Systems (INS) has been very actively studied and widely applied for many years. Some sensors and artificial intelligence methods have been applied to handle GPS outages in GPS/INS integrated navigation. However, the integrated system using the above method still results in seriously degraded navigation solutions over long GPS outages. To deal with the problem, this paper presents a GPS/INS/odometer integrated system using a fuzzy neural network (FNN) for land vehicle navigation applications. Provided that the measurement type of GPS and odometer is the same, the topology of a FNN used in a GPS/INS/odometer integrated system is constructed. The information from GPS, odometer and IMU is input into a FNN system for network training during signal availability, while the FNN model receives the observations from IMU and odometer to generate odometer velocity correction to enhance resolution accuracy over long GPS outages. An actual experiment was performed to validate the new algorithm. The results indicate that the proposed method can improve the position, velocity and attitude accuracy of the integrated system, especially the position parameters, over long GPS outages.

A Cost-Effective Sideslip Estimation Method Using Velocity Measurements From Two GPS Receivers

Abstract: This paper demonstrates that the vehicle sideslip can be estimated through the kinematic relationship of velocity measurements from two low-cost GPS receivers. To compensate for the low update rate of low-cost GPS receivers, acceleration/angular rate measurements from an inertial measurement unit (IMU) are merged with the GPS measurements using an extended Kalman filter (EKF). Two technical challenges were addressed: 1) unsynchronized updates of the two GPS receivers and 2) significant delays in GPS velocity measurement. A stochastic observability analysis reveals that the proposed method guarantees the observability when a vehicle has nonzero yaw rates. Experimental verification shows that the vehicle sideslip is estimated regardless of surface friction levels under several maneuvers.

Enhanced, Delay Dependent, Intelligent Fusion for INS/GPS Navigation System

Abstract: Low-cost navigation systems, deployed for ground vehicles' applications, are designed based on the loosely coupled fusion between the global positioning system (GPS) and the inertial measurement unit (IMU). However, low-cost GPS receivers provide the position and velocity of the vehicle at a lower sampling rate than the IMU-sampled vehicle dynamics. In addition, the GPS measurements might be missed or delayed due to the receiver's inability to lock on the signal or due to obstruction from neighboring vehicles or infrastructures. In this paper, an architecture based on an adaptive neuro-fuzzy inference system is proposed for fusing the GPS/IMU measurements. This integration incorporates the variable delay between the IMU and GPS signals as an additional input to the fusion system. In addition, once the GPS signal becomes available, the measurement is used as a correction reference value to provide an enhancement to the estimation accuracy. The performance of the proposed method is initially demonstrated using a GPS/IMU simulation environment. Subsequently, an experimental test is also conducted to validate the performance of the method.

Reducing Low-Cost INS Error Accumulation in Distance Estimation Using Self-Resetting

Abstract: Error accumulation is one of the critical factors that limit the effective use of low-cost inertial navigation systems (INS) in tracking applications, especially during prolonged time intervals. This paper proposes an improved method to reduce the error accumulation by automatically resetting it in the INS-based systems. This method does not require any additional external sensors and reference systems and therefore offers various ad-vantages. Necessary calibration has been done to accurately evaluate the parameters used within the method. The perform-ance of this proposed resetting method has been experimentally evaluated by tracking the moving vehicles both in indoor and outdoor environments. Results of the experiments demonstrate that the proposed method can significantly reduce the INS errors and successfully recover the trajectories of the moving vehicles with an adequate accuracy.

Selective Integration of GNSS, Vision Sensor, and INS Using Weighted DOP Under GNSS-Challenged Environments

Abstract: Accurate and precise navigation solution can be obtained by integrating multiple sensors such as global navigation satellite system (GNSS), vision sensor, and inertial navigation system (INS). However, accuracy of position solutions under GNSS-challenged environment occasionally degrades due to poor distributions of GNSS satellites and feature points from vision sensors. This paper proposes a selective integration method, which improves positioning accuracy under GNSS-challenged environments when applied to the multiple navigation sensors such as GNSS, a vision sensor, and INS. A performance index is introduced to recognize poor environments where navigation errors increase when measurements are added. The weighted least squares method was applied to derive the performance index, which measures the goodness of geometrical distributions of the satellites and feature points. It was also used to predict the position errors and the effects of the integration, and as a criterion to select the navigation sensors to be integrated. The feasibility of the proposed method was verified through a simulation and an experimental test. The performance index was examined by checking its correlation with the positional error covariance, and the performance of the selective navigation was verified by comparing its solution with the reference position. The results show that the selective integration of multiple sensors improves the positioning accuracy compared with nonselective integration when applied under GNSS-challenged environments. It is especially effective when satellites and feature points are posed in certain directions and have poor geometry.

Loosely Coupled INS/GPS Integration with Constant Lever Arm using Marginal Unscented Kalman Filter

Abstract: A loosely coupled Inertial Navigation System (INS) and Global Positioning System (GPS) are studied, particularly considering the constant lever arm effect. A five-element vector, comprising a craft's horizontal velocities in the navigation frame and its position in the earth-centred and earth-fixed frame, is observed by GPS, and in the presence of lever arm effect, the nonlinear observation equation from the state vector to the observation vector is established and addressed by the correction stage of an unscented Kalman filter (UKF). The conditionally linear substructure in the nonlinear observation equation is exploited, and a computationally efficient refinement of the UKF called marginalized UKF (MUKF) is investigated to incorporate this substructure where fewer sigma points are needed, and the computational expense is cut down while the high accuracy and good applicability of the UKF are retained. A performance comparison between UKF and MUKF demonstrates that the MUKF can achieve, if not better, at least a comparable performance to the UKF, but at a lower computational expense.

High-precision globally-referenced position and attitude via a fusion of visual SLAM, carrier-phase-based GPS, and inertial measurements

Abstract: A novel navigation system for obtaining high-precision globally-referenced position and attitude is presented and analyzed. The system is centered on a bundle-adjustment-based visual simultaneous localization and mapping (SLAM) algorithm which incorporates carrier-phase differential GPS (CDGPS) position measurements into the bundle adjustment in addition to measurements of point features identified in a subset of the camera images, referred to as keyframes. To track the motion of the camera in real-time, a navigation filter is employed which utilizes the point feature measurements from all non-keyframes, the point feature positions estimated by bundle adjustment, and inertial measurements. Simulations have shown that the system obtains centimeter-level or better absolute positioning accuracy and sub-degree-level absolute attitude accuracy in open outdoor areas. Moreover, the position and attitude solution only drifts slightly with the distance traveled when the system transitions to a GPS-denied environment (e.g., when the navigation system is carried indoors). A novel technique for initializing the globally-referenced bundle adjustment algorithm is also presented which solves the problem of relating the coordinate systems for position estimates based on two disparate sensors while accounting for the distance between the sensors. Simulation results are presented for the globally-referenced bundle adjustment algorithm which demonstrate its performance in the challenging scenario of walking through a hallway where GPS signals are unavailable.

Optimization of Intelligent Approach for Low-Cost INS/GPS Navigation System

Abstract: Due to the inherent highly nonlinear vehicle state error dynamics obtained from low-cost inertial navigation system (INS) and Global Positioning System (GPS) along with the unknown statistical properties of these sensors, the optimality/accuracy of the classical Kalman filter for sensor fusion is not guaranteed. Therefore, in this paper, low-cost INS/GPS measurement integration is optimized based on different artificial intelligence (AI) techniques: Neural Networks (NN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) architectures. The proposed approaches are aimed at achieving high-accuracy vehicle state estimates. The architectures utilize overlapping windows for delayed input signals. Both the NN approaches and the ANFIS approaches are used once with overlapping position windows as the input and once with overlapping position and velocity windows as the input. Experimental tests are conducted to evaluate the performance of the proposed AI approaches. The achieved accuracy is presented and discussed. The study finds that using ANFIS, with both position and velocity as input, provides the best estimates of position and velocity in the navigation system. Therefore, the dynamic input delayed ANFIS approach is further analyzed at the end of the paper. The effect of the input window size on the accuracy of state estimation is also discussed.

Adaptive robust ultra-tightly coupled global navigation satellite system/inertial navigation system based on global positioning system/BeiDou vector tracking loops

Abstract: With the development of global navigation satellite system (GNSS), the GNSS/inertial navigation system (INS) integrated system offers the users better positioning or navigation performance. This paper proposes an adaptive robust ultra-tightly coupled GNSS/INS system based on a novel vector tracking strategy for combining both global positioning system (GPS) L1 and BeiDou B1 signals' tracking together. The inherent mechanism of the vector tracking approach has been analysed to describe the relationship between the replica signals and user's dynamic state. Then, an adaptive robust filter is used to gain the accurate estimates of vehicle states when the vehicle is under a weak-signal or large manoeuvring environment. Finally, the experimental platform is set up using a GPS/BeiDou signal simulator and an inertial measurement unit simulator and the test results show that the proposed ultra-tightly coupled system can keep the tracking loops from the high dynamic perturbations, which saves the cost time of signal reacquisition. Moreover, the presented adaptive robust ultra-tightly coupled system can obtain a higher accuracy than Kalman filtering in a simultaneous weak-signal and large manoeuvring environment.

Vehicle positioning by map matching as feedback for ins/gps navigation system during gps signal loss

Abstract: A method and apparatus of vehicle positioning uses map matching as feedback for an integrated navigation system where a map and navigation system is coupled with an inertial navigation system (INS) using low-precision vehicle sensors when the vehicle passes through a tunnel or other area suffering from GPS signal loss. The method and apparatus operates to detect whether the vehicle has reached an entry point of a tunnel, and if so, immediately starts a map matching operation to match the current vehicle position with a road link of the tunnel. The current position determined by the map matching operation is feedbacked to an integration Kalman filter thereby correcting errors caused by the vehicle sensors. The method and apparatus resumes the normal navigation operation including GPS navigation as soon as it detects that the vehicle is out of the tunnel.

Kalman Filter Realization for Orientation and Position Estimation on Dedicated Processor

Abstract: This paper presents Kalman filter design which has been programmed and evaluated in dedicated STM32 platform. The main aim of the work performed was to achieve proper estimation of attitude and position signals which could be further used in unmanned aerial vehicle autopilots. Inertial measurement unit and GPS receiver have been used as measurement devices in order to achieve needed raw sensor data. Results of Kalman filter estimation were recorded for signals measurements and compared with raw data. Position actualization frequency was increased from 1 Hz which is characteristic to GPS receivers, to values close to 50 Hz. Furthermore it is shown how Kalman filter deals with GPS accuracy decreases and magnetometer measurement noise.

IM-filter for INS/GPS-integrated navigation system containing low-cost gyros

Abstract: This paper proposes a new interacting multiple (IM) filter containing simplified unscented Kalman filter (UKF) -based subfilters with different heading initializations for a low-performance inertial sensors-based inertial navigation system (INS)/global positioning system (GPS) -integrated navigation system tolerant toward large initial heading error. Since each individual subfilter of the IM filter is updated adaptively using the combined information of the estimates from the subfilters, it can converge into a true steady state irrespective of the initial heading of a vehicle containing the navigation system. Thereby the IM filter can provide a stable navigation solution. For the subfilters of the IM filter, a simplified UKF is presented. This has a mixed structure of the extended Kalman filter and UKF and has a lighter computational load than the UKF in the multirate INS/GPS integration. Also, simplified UKF-based subfilters for the INS/GPS-integrated navigation system are designed. Monte Carlo simulations are performed to validate the performance of the proposed IM filter, and an experiment is carried out to confirm the simulation results.

Integrated navigation fusion strategy of INS/UWB for indoor carrier attitude angle and position synchronous tracking.

Abstract: In some GPS failure conditions, positioning for mobile target is difficult. This paper proposed a new method based on INS/UWB for attitude angle and position synchronous tracking of indoor carrier. Firstly, error model of INS/UWB integrated system is built, including error equation of INS and UWB. And combined filtering model of INS/UWB is researched. Simulation results show that the two subsystems are complementary. Secondly, integrated navigation data fusion strategy of INS/UWB based on Kalman filtering theory is proposed. Simulation results show that FAKF method is better than the conventional Kalman filtering. Finally, an indoor experiment platform is established to verify the integrated navigation theory of INS/UWB, which is geared to the needs of coal mine working environment. Static and dynamic positioning results show that the INS/UWB integrated navigation system is stable and real-time, positioning precision meets the requirements of working condition and is better than any independent subsystem.

Accurate real-time relative localization using single-frequency GPS

Abstract: For outdoor navigation, GPS provides the most widely-used means of node localization; however, the level of accuracy provided by low-cost receivers is typically insufficient for use in high-precision applications. Additionally, many of these applications do not require precise absolute Earth coordinates, but rather rely on relative positioning to infer information about the geometric configuration of the constituent nodes in a system. This paper presents a novel approach that uses GPS to derive relative location information for a scalable network of single-frequency receivers. Networked nodes share their raw satellite observations, enabling each node to localize its neighbors in a pairwise fashion as opposed to computing its own standalone position. Random and systematic errors are mitigated in novel ways, challenging long-standing beliefs that precision GPS systems require extensive stationary calibration times or complex equipment configurations. In addition to presenting the mathematical basis for our technique, a working prototype is developed, enabling experimental evaluation of several real-world test scenarios. The results of these experiments indicate sub-meter relative positioning accuracy under various conditions and in varying environments. This represents up to order of magnitude increase in precision over existing absolute positioning techniques or other unimodal GPS-based solutions.

Observability Analysis of a MEMS INS/GPS Integration System with Gyroscope G-Sensitivity Errors

Abstract: Gyroscopes based on micro-electromechanical system (MEMS) technology suffer in high-dynamic applications due to obvious g-sensitivity errors. These errors can induce large biases in the gyroscope, which can directly affect the accuracy of attitude estimation in the integration of the inertial navigation system (INS) and the Global Positioning System (GPS). The observability determines the existence of solutions for compensating them. In this paper, we investigate the observability of the INS/GPS system with consideration of the g-sensitivity errors. In terms of two types of g-sensitivity coefficients matrix, we add them as estimated states to the Kalman filter and analyze the observability of three or nine elements of the coefficient matrix respectively. A global observable condition of the system is presented and validated. Experimental results indicate that all the estimated states, which include position, velocity, attitude, gyro and accelerometer bias, and g-sensitivity coefficients, could be made observable by maneuvering based on the conditions. Compared with the integration system without compensation for the g-sensitivity errors, the attitude accuracy is raised obviously.

An exploration of low-cost sensor and vehicle model Solutions for ground vehicle navigation

Abstract: This paper discusses an exploratory analyses of the benefits of using Vehicle Odometry/Steer Angle and an accurate vehicle model (VM) to replace/assist a low-cost Inertial Measurement Unit (IMU) for blended ground vehicle navigation. In this research, multiple variations of the tightly coupled Extended Kalman Filter (EKF) algorithm are performed using multiple sensor sets to find the optimal solution, factoring in sensor cost and pose accuracy. Many automotive precision navigation solutions have been developed based on sensor fusion in recent years; however, as autonomous navigation technology becomes more prevalent on consumer vehicles, the need for a high-accuracy, low-cost pose solution is increasing. One widely used solution to this problem is the combination of a Micro-Electro-Mechanical (MEMS) IMU with Global Positioning System (GPS); however, this may not be the optimal solution due to the high noise characteristics of lower cost IMU's. Measurements from GPS, IMU/Inertial Navigation System (INS), and VM are used in this research. The different algorithm setups being investigated include: GPS/VM sensor fusion with accurate vehicle model constraints, GPS/INS with low-cost commercially available IMU, and GPS/INS/VM with the IMU. The determination of the level of IMU necessary for GPS/INS fusion to exceed the pose solution accuracy achievable using GPS/VM sensor fusion with accurate vehicle model constraints is a priority for this research. Another goal of this research is the quantitative and qualitative analysis of the benefits of using VM to assist normal GPS/INS EKF and whether the inclusion of VM in either the time update or the measurement update results in a more accurate pose solution. Direct experimental comparison of tightly coupled EKF Fault Detection and Exclusion (FDE) algorithms based on vehicle wheel speed and steering angle versus the IMU measurements to determine if either sensor set yields a distinct advantage over the other is also investigated. All analysis will be based on real world experimental data.

A new coarse-time GPS positioning algorithm using combined Doppler and code-phase measurements

Abstract: A new coarse-time Global Positioning System (GPS) positioning algorithm based on the use of Doppler and code-phase measurements is proposed and described. The proposed method was demonstrated to be essential for reducing the time to first fix and the power consumption in a GPS receiver. Only 1 ms of data is required to obtain a positioning fix with accuracy comparable to that of the traditional GPS navigation algorithm. The algorithm is divided into two parts. In the first part, the Doppler measurement of the GPS signal is used to determine the coarse user position. With proper constraints, the required time accuracy for the Doppler measurements can be relaxed to be as long as 12 h. In the second part of the algorithm, the accurate user position is calculated by means of the 1 ms code-phase data. The traditional tracking process is no longer necessary in the proposed algorithm. Using the acquired 1-ms code-phase measurement, the positioning accuracy was determined to be approximately a few tens of meters in our experimental results. However, if the data length is extended to 10 ms, the positioning accuracy can be improved to within 10---20 m, which is similar to that of the traditional GPS positioning method. Various experiments were conducted to verify the usefulness of the proposed algorithm.

Cooperative localization using a foot-mounted inertial navigation system and ultrawideband ranging

Abstract: This paper aims to evaluate the performance gains that can be obtained by introducing cooperative localization in an indoor firefighter localization system, through the use of scenario-based simulations. Robust and accurate indoor localization for firefighters is a problem that is not yet resolved. Foot-mounted inertial navigation systems (INS) have been examined for first responder localization, but they have an accumulating position error that grows over time. By using ultrawideband (UWB) ranging between the firefighters and combining range measurements with position and uncertainty estimates from the foot-mounted INS via a cooperative localization approach it is possible to reduce the position error significantly. An error model for the position estimates received from single and dual foot-mounted INS is proposed based on experimental results, and it contains a scaling error which depends on the distance travelled and a heading error which grows linearly over time. The position error for dead-reckoning systems depends on the type of movement. Similarly, an error model for the UWB range measurements was designed where the range measurements experience a bias and variance, which is determined by the number of walls between the transmitter and receiver. By implementing these error models in a scenario-based simulation environment it is possible to evaluate the performance gain of different cooperative localization algorithms. A centralized extended Kalman Filter (EKF) algorithm has been implemented, and the position accuracy and heading improvements are evaluated over a smoke diving operation scenario. The cooperative localization scheme reduces the position errors by up to 70% in a scenario where a three-person smoke diver team performs a search and rescue operation.

Amplified wavelet-ANFIS-based model for GPS/INS integration to enhance vehicular navigation system

Abstract: Inertial navigation system (INS) relying on gyroscopes and accelerometers has been recently utilized in land vehicles. These INS sensors are integrated with Global Positioning System (GPS) to provide reliable positioning solutions in case of GPS outages that commonly occur in urban canyons. The major inadequacies of INS navigation sensors are the high noise level and the large bias instabilities that are stochastic in nature. The effects of these inadequacies manifest themselves as large position errors during GPS outages. Wavelet analysis is a signal processing method which is recently auspicious by many researchers due to its advantageous adaptation to non-stationary signals and able to perform analysis in both time and frequency domain over other signal processing methods such as the fast Fourier transform in some fields. This research proposes the utilization of wavelet de-nosing to improve the signal-to-noise ratio of each of the INS sensors. In addition, a neuro-fuzzy module is used to provide a reliable prediction of the vehicle position during GPS outages. The results from a road test experiment show the effectiveness of the proposed wavelet--neuro-fuzzy module.

Kalman filtered GPS accelerometerbased accident detection and location system: A low-cost approach

Abstract: A low-cost accident detection system utilizing cheap ADXL345 accelerometers and GPS receiver is proposed in this communication. The accident detection algorithm was developed based on sudden deceleration. The double integration of the acceleration and heading from the tilt angles of accelerometers were used to determine the location. Kalman filter was utilized to correct the accumulated double integration errors with the trusted GPS data. The field tests demonstrated the correct functioning of the accident detection algorithm and location. The proposed lowcost system can save many lives by the automated accident detection and accurate location even during GPS outage.

An Autonomous Waist-Mounted Pedestrian Dead Reckoning System by Coupling Low-Cost MEMS Inertial Sensors and GPS Receiver for 3D Urban Navigation

Abstract: Global positioning system (GPS) offers a perfect solution to the 3-dimension(3D) navigation. However, the GPS-only solution can’t provide continuous and accurate position information in the unfavourable environments, such as urban canyons, indoor buildings, dense foliages due to signal blockage, interference, or jamming etc. A pedestrian dead reckoning (PDR) system integrating the self-contained inertial sensors with GPS receiver is proposed to provide a seamless outdoor/indoor 3D pedestrian navigation. The MEM sensor module attached to the user’s waist is composed of a 3-axis accelerometer, a 3-axis gyroscope, a 3-axis digital compass and a barometric pressure sensor, which doesn’t rely on any infrastructure. The positioning algorithm implements a loosely coupled GPS/PDR integration. The sensor data are fused via a complementary filter to reduce the integral drift and magnetic disturbance for accurate heading. The four key components of the PDR algorithm: step detection, stride length estimation, heading and position determination are described in detail and implemented by the microcontroller. The step is detected using the accelerometer signals by the combination of three approaches: sliding window, peak detection and zero-crossing. The step length is estimated using a simple linear relationship with the step frequency. By coupling the step length, azimuth and height, 3D navigation is achieved. The performance of the proposed system is carefully verified through several field outdoor and indoor walking tests. The positioning errors are below 3% of the total traveled distance. The main error source comes from the orientation estimation. The results indicate that the proposed system is effective in accurate tracking.

Low-end MEMS IMU can contribute in GPS/INS deep integration

Abstract: In a deeply-coupled GPS/INS integrated system, the use of the inertial aiding information can improve the tracking loop performance and make the system more robust. To meet this requirement, the inertial aiding information should have sufficient accuracy in short-term (such as the sampling interval of GPS, e.g. 1sec). The MEMS (Micro-Electro Mechanical System) IMU (Inertial Measurement Unit) can be a promising candidate due to its small size and low cost. There should be no doubt that MEMS INS (Inertial Navigation System) can aid the GPS receiver tracking loop by eliminating the dominant part of the motion dynamic stress, considering that the INS errors induced by the receiver motion dynamics is much less than the motion dynamic itself, when the receiver manoeuvres. So the only concern the side effect caused by MEMS INS, which determine whether MEMS IMU is qualified for deep integration, is its navigation error independent with the motion dynamics (i.e. manoeuvre-independent error). This paper assesses this side effect of MEMS INS in terms of providing Doppler aiding data in to the GPS carrier tracking loop through a thorough error propagation analysis. The Laplace transform analysis is applied to the simplified INS error dynamic equations under stationary condition and find out the transfer relation between the error sources and the velocity estimation errors. Then the velocity error is converted to Doppler aiding error and substitute into the GPS tracking loop to analyze the corresponding carrier phase error. Results show that the largest velocity error caused by maneuver-independent errors is less than 0.1m/s during the typical GPS update interval (e.g. 1 sec), which meets the real road test results. The consequent carrier phase tracking error caused by the maneuver-independent error of MEMS INS is below 1.2 degree, which is much less than receiver inherent errors (e.g. the oscillator error and thermal noise). Conclusion can be reached that even the low-end MEMS IMUs have the ability of aiding the GPS receiver signal tracking although it induces some additional errors.

An Enhanced 3D Multi-Sensor Integrated Navigation System for Land-Vehicles

Abstract: In urban areas, Global Positioning System (GPS) accuracy deteriorates due to signal degradation and multipath effects. To provide accurate and robust navigation in such GPS-denied environments, multi-sensor integrated navigation systems are developed. This paper introduces a 3D multi-sensor navigation system that integrates inertial sensors, odometry and GPS for land-vehicle navigation. A new error model is developed and an efficient loosely coupled closed-loop Kalman Filter (Extended KF or EKF) integration scheme is proposed. In this EKF-based integration scheme, the inertial/odometry navigation output is continuously corrected by EKF-estimated errors, which keeps the errors within acceptable linearization ranges which improves the prediction accuracy of the linearized dynamic error model. Consequently, the overall performance of the integrated system is improved. Real road experiments and comparison with earlier works have demonstrated a more reliable performance during GPS signal degradation and accurate estimation of inertial sensor errors (biases) have led to a more sustainable performance reliability during long GPS complete outages.

An Investigation of tightly-coupled UWB/low-cost GPS for vehicle-to-infrastructure relative positioning

Abstract: A method for tightly-coupling carrier-phase differential GPS with ultra-wideband (UWB) ranging for vehicle-to-infrastructure relative navigation is proposed. Relative position, velocity, clock errors, GPS phase ambiguities and UWB systematic errors are estimated using an extended Kalman filter. The method is tested with real data and evaluated in terms of position accuracy, GPS float ambiguity convergence, time to fix ambiguities, and correctness of the ambiguity solution. Performance with geodetic and consumer grade GPS receivers is compared. The effect of UWB operational range and the number of available UWB ranging sources is also evaluated.