Reduced Inertial Sensor System Research Articles

A Hybrid RISS/GNSS Method During GNSS Outage in the Land Vehicle Navigation System

The integrated navigation system of an inertial navigation system (INS) and a global navigation satellite system (GNSS) is a standard solution in land vehicle navigation applications. Considering a low-cost solution, the reduced inertial sensor system (RISS) is adopted in place of INS to provide better navigation performance for land vehicles with fewer inertial sensors and lower computations. However, low-cost sensors can quickly deteriorate the navigation solution during GNSS outage. Hence, a novel RISS /GNSS method with the assistance of the long short-term memory (LSTM) neural network (NN), which has the ability of adaptive memorizing, is proposed to bridge GNSS outage by means of data fusion. In addition, zero-velocity detection is applied to advance the navigation performance provided by the LSTM algorithm during GNSS outage. We examined the performance of this method by using real road test experiments in a land vehicle equipped with GNSS receivers and inertial sensors in addition to a high-end GNSS /INS to provide the reference solution. During 300-s GNSS outage, the experimental results illustrate that this hybrid method based on the LSTM algorithm can enhance the navigation accuracy by 50% when compared with the standalone RISS algorithm, and provide 30% improvements in comparison with the nonlinear autoregressive with exogenous input (NARX) algorithm.

Enhanced Map-Aided GPS/3D RISS Combined Positioning Strategy in Urban Canyons

To realize the effective positioning in urban canyons, an enhanced map-aided Global Positioning System (GPS)/three-dimensional (3D) reduced inertial sensor system (RISS) tightly combined positioning strategy is proposed. First, the 3D RISS is only based on the built-in controller area network (CAN). CAN bus sensor without additional sensors is first constructed to lower the cost. Then, a simple but effective enhanced map is created to assist positioning. Based on the map, a Kalman filtering (KF) tightly coupled method is proposed to fuse the 3D RISS with GPS information and to achieve the preliminary positioning. In KF-based preliminary positioning method, a simply observation noise variance optimization algorithm based on 2D enhanced map is proposed to improve KF method. In this algorithm, the value of the observation noise variance matrix is determined only according to the building plane information which is contained in the enhanced map. Further, a multiweight map matching algorithm is proposed for optimizing the initial positioning results. In this algorithm, factors such as distance, direction, road network topology, and lane change are considered and applied to map matching to further increase the positioning performance and form the final positioning results. Finally, the effectiveness of the strategy is proved by field test. The results show that this method has better accuracy and reliability than the conventional method.

Implementation of Parallel Cascade Identification at Various Phases for Integrated Navigation System

Global navigation satellite systems (GNSS) are widely used for the navigation of land vehicles. However, the positioning accuracy of GNSS, such as the global positioning system (GPS), deteriorates in urban areas due to signal blockage and multipath effects. GNSS can be integrated with a micro-electro-mechanical system (MEMS)–based inertial navigation system (INS), such as a reduced inertial sensor system (RISS) using a Kalman filter (KF) to enhance the performance of the integrated navigation solution in GNSS challenging environments. The linearized KF cannot model the low-cost and small-size sensors due to relatively high noise levels and compound error characteristics. This paper reviews two approaches to employing parallel cascade identification (PCI), a non-linear system identification technique, augmented with KF to enhance the navigational solution. First, PCI models azimuth errors for a loosely coupled 2D RISS integrated system with GNSS to obtain a navigation solution. The experimental results demonstrated that PCI improved the integrated 2D RISS/GNSS performance by modeling linear, non-linear, and other residual azimuth errors. For the second scenario, PCI is utilized for modeling residual pseudorange correlated errors of a KF-based tightly coupled RISS/GNSS navigation solution. Experimental results have shown that PCI enhances the performance of the tightly coupled KF by modeling the non-linear pseudorange errors to provide an enhanced and more reliable solution. For the first algorithm, the results demonstrated that PCI can enhance the performance by 77% as compared to the KF solution during the GNSS outages. For the second algorithm, the performance improvement for the proposed PCI technique during the availability of three satellites was 39% compared to the KF solution.

Integration of GNSS Precise Point Positioning and Reduced Inertial Sensor System for Lane-Level Car Navigation

The last decade has witnessed a growing demand for precise positioning in many applications, including autonomous car navigation. The safety features in autonomous driving and Advanced Driver Assistance Systems (ADAS) require lane-level positioning accuracy. Such accuracy can be obtained from the Global Navigation Satellite Systems (GNSS) through either differential techniques or Precise Point Positioning (PPP). PPP is currently favored over differential GNSS because it provides a global solution without the need for local reference stations. Nevertheless, employing PPP for land vehicles would be challenging due to frequent signal degradation and blockage. Integrating PPP with an Inertial Navigation System (INS) can solve the solution continuity problem; however, the INS solution drifts over time, resulting in losing the desired accuracy. Implementing a reliable PPP/INS system that can preserve the required accuracy is not trivial, especially with financial and computational cost constraints. This article proposes the integration of PPP with the Reduced Inertial Sensor System (RISS) for lane-level car navigation. The high-precision needed in lane-level positioning can be achieved by integrating PPP with high-end INS. Since high-end INS are expensive, this work proposes the use of RISS instead of the traditional INS. RISS uses only one gyroscope and two accelerometers, which can save more than half the high-end INS cost. The proposed PPP/RISS system was tested through three road tests that included highway driving under several overpasses. The system was able to maintain horizontal position errors of less than 50 cm.

INVESTIGATION OF DIFFERENT LOW-COST LAND VEHICLE NAVIGATION SYSTEMS BASED ON CPD SENSORS AND VEHICLE INFORMATION

Abstract. Recently, many companies and research centres have been working on research and development of navigation technologies for self-driving cars. Many navigation technologies were developed based on the fusion of various sensors. However, most of these techniques used expensive sensors and consequently increase the overall cost of such cars. Therefore, low-cost sensors are now a rich research topic in land vehicle navigation. Consumer Portable Devices (CPDs) such as smartphones and tablets are being widely used and contain many sensors (e.g. cameras, barometers, magnetometers, accelerometers, gyroscopes, and GNSS receivers) that can be used in the land vehicle navigation applications.This paper investigates various land vehicle navigation systems based on low-cost self-contained inertial sensors in CPD, vehicle information and on-board sensors with a focus on GNSS denied environment. Vehicle motion information such as forward speed is acquired from On-Board Diagnosis II (OBD-II) while the land vehicle heading change is estimated using CPD attached to the steering wheel. Additionally, a low-cost on-board GNSS/inertial integrated system is also employed. The paper investigates many navigation schemes such as different Dead Reckoning (DR) systems, Reduced Inertial Sensor System (RISS) based systems, and aided loosely coupled GNSS/inertial integrated system.An experimental road test is performed, and different simulated GNSS signal outages were applied to the data. The results show that the modified RISS system based on OBD-II velocity, onboard gyroscopes, accelerometers, and CPD-based heading change provides a better navigation estimation than the typical RISS system for 90s GNSS signal outage. On the other hand, typical inertial aided with CPD heading change, OBD-II velocity updates, and Non-Holonomic Constraint (NHC) provide the best navigation result.

An Efficient Ultra-Tight GPS/RISS Integrated System for Challenging Navigation Environments

The Global Positioning System (GPS) provides an accurate navigation solution in the open sky. However, in some environments such as urban areas or in the presence of signal jamming, GPS signals cannot be easily tracked since they could be harshly attenuated or entirely blocked. This often requires the GPS receiver to go into a signal re-acquisition phase for the corresponding satellite. To avoid the intensive computations necessary for the signal re-lock in a GPS receiver, a robust signal-tracking mechanism that can hold and/or rapidly re-lock on the signals and keep track of their dynamics becomes a necessity. This paper augments a vector-based GPS signal tracking system with a Reduced Inertial Sensor System (RISS) to produce a new ultra-tight GPS/INS integrated system that enhances receivers’ tracking robustness and sensitivity in challenging navigation environments. The introduced system is simple, efficient, reliable, yet inexpensive. To challenge the proposed method with real jamming conditions, real experiment work was conducted inside the Anechoic Chamber room at the Royal Military College of Canada (RMC). The Spirent GSS6700 signal simulator was used to generate GPS signals, and an INS Simulator is used for simulating the inertial measurement unit (IMU) to generate the corresponding trajectory raw data. The NEAT jammer, by NovAtel, was used to generate real jamming signals. Results show a good performance of the proposed method under real signal jamming conditions.

A Novel Multi-Level Integrated Navigation System for Challenging GNSS Environments

Global Navigation Satellite Systems (GNSS) is utilized to provide route guidance information to land and autonomous vehicles. The GNSS-based positioning and navigation (POS/NAV) usually suffer from satellite signal blockage, interference, and multipath in urban areas. Autonomous land vehicles are equipped with cameras, radars, and laser ranging devices. The availability of these systems provides an attractive opportunity to increase the POS/NAV system accuracy. This research focuses on the development of an integrated multi-sensor POS/NAV system capable of offering seamless positioning for autonomous land vehicles. A new multi-sensor POS/NAV module integrating both adaptive cruise control frequency modulated continuous wave (ACC-FMCW) radar (RAD), and magnetometer measurements with the reduced inertial sensor system (RISS) was designed to update the navigation system during GNSS outages. Augmenting RAD/RISS system with the magnetometer measurements produces a robust solution. The designed system is further improved by utilizing fast orthogonal search (FOS) to provide nonlinear error modeling of the residual errors associated with the RAD/RISS positioning solution in order to reduce the error growth overextended and frequent GNSS outages.The proposed systems were evaluated on several real road test trajectories involving different types of land vehicles experiencing different motion dynamics. GNSS outages of up to 10 minutes were intentionally introduced to examine the performance. The results show that the proposed methods have resulted in a significant performance improvement in the positioning accuracy that can reach more than 80% if compared to the present techniques that rely only on integrating the inertial sensor technology with GNSS.

Inertial Navigation System of Pipeline Inspection Gauge

In this brief, a high-accuracy inertial navigation system (INS) for a pipeline inspection gauge (PIG) is proposed. Two mechanization approaches are investigated. First, a full INS dynamic model is used, and second, a 3-D reduced inertial sensor system (RISS) is utilized. The INS uses the full inertial measurement unit (IMU) data to calculate the navigation solution, whereas the RISS uses an encoder, one single-axis gyroscope, and two accelerometers. The RISS model is proposed in this brief for its better accuracy and less computational complexity. Due to the accumulated error in the INS or RISS solution, an extended Kalman filter is proposed to fuse the IMU data with PIG measurements. For the full INS model, these measurements are the encoder’s derived velocity constraint and the detected pipe length measurement. On the other hand, for the RISS model, it only refers to the detected pipe length measurement. An experimental setup, with a prototype of the in-pipe robot, is designed and built to test and validate our algorithms in a real pipe environment. Subsequently, the accuracy of the proposed algorithms is verified experimentally.

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 Low Cost Civil Vehicular Seamless Navigation Technology Based on Enhanced RISS/GPS between the Outdoors and an Underground Garage

Vehicles have to rely on satellite navigation in an open environment. However, satellite navigation cannot obtain accurate positioning information for vehicles in the interior of underground parking lots, as they comprise a semi-enclosed navigation space. Therefore, vehicular navigation needs to take into consideration both outdoor and indoor environments. Actually, outdoor navigation and indoor navigation require different positioning methods, and it is of great importance to choose a reasonable navigation and positioning algorithm solution for vehicles. Fortunately, the integrated navigation of the Global Positioning System (GPS) and the Micro-Electro-Mechanical System (MEMS) inertial navigation system could solve the problem of switching navigation algorithms in the entrance and exit of underground parking lots. This paper proposes a low cost vehicular seamless navigation technology based on the reduced inertial sensor system (RISS)/GPS between the outdoors and an underground garage. Specifically, the enhanced RISS is a positioning algorithm based on three inertial sensors and one odometer, which could achieve a similar location effect as the full model integrated navigation, reduce the costs greatly, and improve the efficiency of each sensor.

Low-Cost Reduced Navigation System for Mobile Robot in Indoor/Outdoor Environments

This paper presents a low-cost based approach for solving the navigation problem of wheeled mobile robots to perform required tasks within indoor and outdoor environments. The presented solution is based on probabilistic approaches for multiple sensor fusion utilizing low-cost visual/inertial sensors. For the outdoor environment, the Extended Kalman Filter (EKF) is used to estimate the robot position and orientation, the system consists of wheel encoders, a reduced inertial sensor system (RISS), and a Global Positioning System (GPS). For the indoor environment, where GPS signals are blocked, another EKF algorithm is proposed using low cost depth sensor (Microsoft Kinect stream). EKF indoor localization is based on landmarks extracted from the depth measurements. A hybrid low-cost reduced navigation system (HLRNS) for indoor and outdoor environments is proposed and validated in both simulation and experimental environments. Additionally, an input-output state feedback linearization (I-O SFL) technique is used to control the robot to track the desired trajectory in such an environment. According to the conducted validation simulation and experimental testing, the proposed HLRNS provides an acceptable performance to be deployed for real-time applications.

Drilling trajectory survey technology based on 3D RISS with a single fiber optic gyroscope

Recently, a complete inertial measurement unit (CIMU) with three-axis Fiber optic gyroscopes (FOG) has replaced the three-axis magnetometers in Measurement While Drilling system (MWDs) to survey the drilling trajectory. However, installing three FOGs into MWDs meets lots of challenges. Firstly, because of the limited space in the downhole, CIMU is too big to accommodate all kinds of well. Secondly, the capacity of the power battery supporting the MWD is limited while CIMU has a high-power consumption. Finally, because of the high cost of FOGs, CIMU increases the drilling tool expense. Thus, on the basis of implementing the survey drilling trajectory function, it is critical to reduce the number of FOGs. Targeting a low-power, low-cost and high-accuracy navigation solution for MWD, this paper proposed a three-dimension (3D) navigation solution using a reduced inertial sensor system (RISS) for MWD. The RISS consists of only one single-axis FOG and three-axis accelerometers. In order to improve the measurement accuracy, the drilling piper length information obtained from a recursive algorithm named as Average Angle Method (AAM) was fused to RISS using the Kalman filter method. To validate the availability and utility of this surveying method, three experiments simulating different drilling process were performed using three-axis rate table under laboratory conditions. The experiment results show that the proposed solution could provide accurate directional and orientation information of the wellbore trajectory while drilling.

Odometer Velocity and Acceleration Estimation Based on Tracking Differentiator Filter for 3D-Reduced Inertial Sensor System.

Velocity information from the odometer is the key information in a reduced inertial sensor system (RISS), and is prone to noise corruption. In order to improve the navigation accuracy and reliability of a 3D RISS, a method based on a tracking differentiator (TD) filter was proposed to track odometer velocity and acceleration. With the TD filter, an input signal and its differential signal are estimated fast and accurately to avoid the noise amplification that is brought by the conventional differential method. The TD filter does not depend on an object model, and has less computational complexity. Moreover, the filter phase lag is decreased by the prediction process with the differential signal of the TD filter. In this study, the numerical simulation experiments indicate that the TD filter can achieve a better performance on random noises and outliers than traditional numerical differentiation. The effectiveness of the TD filter on a 3D RISS is demonstrated using a group of offline data that were obtained from an actual vehicle experiment. We conclude that the TD filter can not only quickly and correctly filter velocity and estimate acceleration from the odometer velocity for a 3D RISS, but can also improve the reliability of the 3D RISS.

Improving the RISS/GNSS Land-Vehicles Integrated Navigation System Using Magnetic Azimuth Updates

Navigation of land or self-driving vehicles is essential for safe and accurate travel. The global navigation satellite systems (GNSSs), such as global positioning system (GPS) are the primary sources of navigation information for such purpose. However, high-rise buildings in urban canyons block the GPS satellites signals. Alternatively, inertial navigation system (INS) is typically working as a backup. A reduced inertial sensor system (RISS) is used instead of the full INS to achieve the same purpose in land vehicles navigation with fewer sensors and computations. Unfortunately, the RISS solution drifts over time, but this can be mitigated when integrated with the GPS. However, the integration solution drifts in the case of GPS signal loss (outages). Therefore, the position errors grow especially during extended periods of GPS outages. Azimuth/heading angle is critical to keep the vehicle on the route. In this paper, an azimuth update estimated from a calibrated magnetometer is introduced to improve the accuracy of the overall system. A new approach is proposed for pre-processing the magnetometer data utilizing a discrete-cosine-transform (DCT)-based pre-filtering stage. The obtained azimuth is utilized in updating the RISS system during the whole trajectory and mainly during GPS outage periods. The proposed approach significantly decreases both the azimuth error and the position error growth rate when driving in urban canyons where the GPS signals are blocked. Finally, the proposed system was tested on a real road trajectory data for a metropolitan area. The results demonstrate that the accuracy of the whole system improved, especially during the GPS outage periods.

A Reduced Inertial Sensor System Based on MEMS for Wellbore Continuous Surveying While Horizontal Drilling

This research introduces a low cost, small size directional solution for the rotary steerable system installed directly behind the drilling bit. The proposed solution employs a reduced inertial sensor system (RISS) to provide the 3-D navigation solution for wellbore continuous surveying while horizontal drilling. The RISS consists of one micro-electromechanical systems (MEMS) gyroscope and three-axis MEMS accelerometers. The azimuth angle is obtained using a single gyro. The inclination and the toolface angle are derived from the measurements of the three-axis accelerometers together with an accurate earth gravity model. With the attitude information, velocities of the drilling bit are determined by the penetration rate of the drill bit, which is continuously available by using the information of the drill pipe length and drilling time. To limit the long-term growth of the surveying errors, a Kalman filter relying on the minimum curvature method position was developed to estimate the position errors. The proposed method is examined by conducting a drilling simulation experiment in a laboratory using the three-axis rate table. Two different MEMS systems were tested during the experiment. Results showed that the proposed technique could significantly limit the long-term growth of the position errors. The experiments were performed for almost 2 h and demonstrated no significant divergence of the RISS algorithm.

Integrated cooperative localization for Vehicular networks with partial GPS access in Urban Canyons

Accurate and ubiquitous localization is the driving force for location-based services in Vehicular Ad-hoc NETworks (VANETs). Most of present vehicle localization systems rely on Global Positioning Sytems (GPS). In urban canyons, GPS suffer from prolonged outages. Other vehicle motion sensors (e.g. gyroscopes, accelerometers and odometers) suffer from unsustainable error accumulation. This research presents a novel Cooperative Localization scheme that utilizes Round Trip Time (RTT) for inter-vehicle distance calculation, integrated with Reduced Inertial Sensor System (RISS) measurements to update the position of not only the vehicle to be localized, but its neighbors as well. We adopted the Extended Kalman Filter (EKF), to limit the effect of errors in both the sensors and the neighbors' positions, in computing the new location. Our scheme is also extended to account for the scenarios where some of the vehicles might experience GPS availability for a short duration. The ultimate aim of this work is to efficiently manage and coordinate this heterogeneous set of sensors/technologies into a consistent, accurate, and robust navigation system. Different scenarios using different velocities and neighbors' densities were implemented. GPS updates with different percentages and error variances were also introduced to test the robustness of the proposed scheme. The scheme is implemented and tested using the standard compliant network simulator 3 (ns-3), vehicle traces were generated using Simulation of Urban MObility (SUMO) and error models were introduced to the sensors, the initial and the updated positions. Results show that our RISS-based scheme outperforms the non-cooperative RISS typically used in challenging GPS environments. GPS updates with low error variance can enhance the accuracy of the proposed cooperative scheme and share this enhancement among the network. Moreover, we compare our proposed scheme to a GPS cooperative scheme and demonstrate the reliability of a RISS-based cooperative scheme for relatively long time duration.

A Highly Reliable and Cost-Efficient Multi-Sensor System for Land Vehicle Positioning.

In this paper, we propose a novel positioning solution for land vehicles which is highly reliable and cost-efficient. The proposed positioning system fuses information from the MEMS-based reduced inertial sensor system (RISS) which consists of one vertical gyroscope and two horizontal accelerometers, low-cost GPS, and supplementary sensors and sources. First, pitch and roll angle are accurately estimated based on a vehicle kinematic model. Meanwhile, the negative effect of the uncertain nonlinear drift of MEMS inertial sensors is eliminated by an H∞ filter. Further, a distributed-dual-H∞ filtering (DDHF) mechanism is adopted to address the uncertain nonlinear drift of the MEMS-RISS and make full use of the supplementary sensors and sources. The DDHF is composed of a main H∞ filter (MHF) and an auxiliary H∞ filter (AHF). Finally, a generalized regression neural network (GRNN) module with good approximation capability is specially designed for the MEMS-RISS. A hybrid methodology which combines the GRNN module and the AHF is utilized to compensate for RISS position errors during GPS outages. To verify the effectiveness of the proposed solution, road-test experiments with various scenarios were performed. The experimental results illustrate that the proposed system can achieve accurate and reliable positioning for land vehicles.

Visual-LiDAR Odometry Aided by Reduced IMU

This paper proposes a method for combining stereo visual odometry, Light Detection And Ranging (LiDAR) odometry and reduced Inertial Measurement Unit (IMU) including two horizontal accelerometers and one vertical gyro. The proposed method starts with stereo visual odometry to estimate six Degree of Freedom (DoF) ego motion to register the point clouds from previous epoch to the current epoch. Then, Generalized Iterative Closest Point (GICP) algorithm refines the motion estimation. Afterwards, forward velocity and Azimuth obtained by visual-LiDAR odometer are integrated with reduced IMU outputs in an Extended Kalman Filter (EKF) to provide final navigation solution. In this paper, datasets from KITTI (Karlsruhe Institute of Technology and Toyota technological Institute) were used to compare stereo visual odometry, integrated stereo visual odometry and reduced IMU, stereo visual-LiDAR odometry and integrated stereo visual-LiDAR odometry and reduced IMU. Integrated stereo visual-LiDAR odometry and reduced IMU outperforms other methods in urban areas with buildings around. Moreover, this method outperforms simulated Reduced Inertial Sensor System (RISS), which uses simulated wheel odometer and reduced IMU. KITTI datasets do not include wheel odometry data. Integrated RTK (Real Time Kinematic) GPS (Global Positioning System) and IMU was replaced by wheel odometer to simulate the response of RISS method. Visual Odometry (VO)-LiDAR is not only more accurate than wheel odometer, but it also provides azimuth aiding to vertical gyro resulting in a more reliable and accurate system. To develop low-cost systems, it would be a good option to use two cameras plus reduced IMU. The cost of such a system will be reduced than using full tactical MEMS (Micro-Electro-Mechanical Sensor) based IMUs because two cameras are cheaper than full tactical MEMS based IMUs. The results indicate that integrated stereo visual-LiDAR odometry and reduced IMU can achieve accuracy at the level of state of art.

Pseudoranges Error Correction in Partial GPS Outages for a Nonlinear Tightly Coupled Integrated System

Integrated navigation systems based on a tightly coupled integration scheme utilize pseudoranges and pseudorange rates from Global Positioning System (GPS) satellites measured by the receiver. The positioning accuracy is highly dependent on the accuracy of the pseudoranges whose residual errors can deteriorate the overall positioning accuracy. The integrated system can be improved by the provision of more accurate pseudoranges through modeling the residual correlated errors. This paper utilizes parallel cascade identification (PCI), which is a nonlinear system identification technique, to model these correlated errors. To address the nonlinear error characteristics in the whole integrated navigation system, a nonlinear filter, i.e., mixture particle filter (M-PF), is employed to perform tightly coupled integration of a 3-D reduced inertial sensor system (RISS) with a GPS. The M-PF can accommodate the PCI models of the pseudorange errors in the measurement model. The results demonstrate the advantages of using M-PF-PCI for correcting the pseudoranges and enhancing the positioning solution as compared with M-PF-only, Kalman filter (KF)-PCI, and KF-only solutions.

ENHANCING KALMAN FILTERING–BASED TIGHTLY COUPLED NAVIGATION SOLUTION THROUGH REMEDIAL ESTIMATES FOR PSEUDORANGE MEASUREMENTS USING PARALLEL CASCADE IDENTIFICATION

Integrated global positioning system (GPS) solutions that utilize micro-electro-mechanical systems (MEMS)-based inertial sensors provide a more accurate navigation solution than stand-alone GPS in challenging scenarios. To keep the integrated solution less affected by sensor errors and to decrease the cost, a reduced inertial sensor system (RISS), which consists of only one gyroscope and two accelerometers, together with an odometer and integrated with GPS, is proposed. Tightly coupled integration is a better choice in demanding scenarios, as it can provide GPS aiding even when the number of visible satellites is three or less. However, inaccuracies of pseudoranges measured by the GPS receiver and used as aiding in the RISS/odometer/GPS integration solution will affect the overall positioning accuracy. This article explores the benefits of using parallel cascade identification (PCI), a nonlinear system identification technique that improves the overall navigation solution by modeling residual pseudorange correlated errors to be used by a Kalman filter (KF)–based tightly coupled RISS/odometer/GPS navigational solution. When less than four satellites are visible, the identified parallel cascade model for the still visible satellites is used to predict the residual pseudorange errors for these respective satellites, and the corrected pseudorange value is provided to KF. The performance of PCI for correcting the pseudoranges is examined and verified using road test trajectories and compared to a traditional tightly coupled RISS/odometer/GPS KF solution. The results demonstrate the advantages of this technique in correcting the pseudoranges and enhancing the positional solution.