In-car positioning and navigation has been a killer application for Global Positioning System (GPS) receivers, and a variety of electronics for consumers and professionals have been launched on a large scale. Positioning technologies based on stand-alone GPS receivers are vulnerable and, thus, have to be supported by additional information sources to obtain the desired accuracy, integrity, availability, and continuity of service. A survey of the information sources and information fusion technologies used in current in-car navigation systems is presented. The pros and cons of the four commonly used information sources, namely, 1) receivers for radio-based positioning using satellites, 2) vehicle motion sensors, 3) vehicle models, and 4) digital map information, are described. Common filters to combine the information from the various sources are discussed. The expansion of the number of satellites and the number of satellite systems, with their usage of available radio spectrum, is an enabler for further development, in combination with the rapid development of microelectromechanical inertial sensors and refined digital maps.

In-Car Positioning and Navigation Technologies—A Survey

The Global Positioning System (GPS) is a worldwide navigation system that requires a clear line of sight to the orbiting satellites For land vehicle navigation, a clear line of sight cannot be maintained all the time as the vehicle can travel through tunnels, under bridges, forest canopies or within urban canyons In such situations, the augmentation of GPS with other systems is necessary for continuous navigation Inertial sensors can determine the motion of a body with respect to an inertial frame of reference Traditionally, inertial systems are bulky, expensive and controlled by government regulations Micro-electro mechanical systems (MEMS) inertial sensors are compact, small, inexpensive and most importantly, not controlled by governmental agencies due to their large error characteristics Consequently, these sensors are the perfect candidate for integrated civilian navigation applications with GPS However, these sensors need to be calibrated to remove the major part of the deterministic sensor errors before they can be used to accurately and reliably bridge GPS signal gaps A new multi-position calibration method was designed for MEMS of high to medium quality The method does not require special aligned mounting and has been adapted to compensate for the primary sensor errors, including the important scale factor and non-orthogonality errors of the gyroscopes A turntable was used to provide a strong rotation rate signal as reference for the estimation of these errors Two different quality MEMS IMUs were tested in the study The calibration results were first compared directly to those from traditional calibration methods, eg six-position and rate test Then the calibrated parameters were applied in three datasets of GPS/INS field tests to evaluate their accuracy indirectly by comparing the position drifts during short-term GPS signal outages

https://iopscience.iop.org/article/10.1088/0957-0233/18/7/016/pdf

A new multi-position calibration method for MEMS inertial navigation systems

Recent technological developments have led to the production of inexpensive, non-invasive, miniature magneto-inertial sensors, ideal for obtaining sport performance measures during training or competition. This systematic review evaluates current evidence and the future potential of their use in sport performance evaluation. Articles published in English (April 2017) were searched in Web-of-Science, Scopus, Pubmed, and Sport-Discus databases. A keyword search of titles, abstracts and keywords which included studies using accelerometers, gyroscopes and/or magnetometers to analyse sport motor-tasks performed by athletes (excluding risk of injury, physical activity, and energy expenditure) resulted in 2040 papers. Papers and reference list screening led to the selection of 286 studies and 23 reviews. Information on sport, motor-tasks, participants, device characteristics, sensor position and fixing, experimental setting and performance indicators was extracted. The selected papers dealt with motor capacity assessment (51 papers), technique analysis (163), activity classification (19), and physical demands assessment (61). Focus was placed mainly on elite and sub-elite athletes (59%) performing their sport in-field during training (62%) and competition (7%). Measuring movement outdoors created opportunities in winter sports (8%), water sports (16%), team sports (25%), and other outdoor activities (27%). Indications on the reliability of sensor-based performance indicators are provided, together with critical considerations and future trends.

Trends Supporting the In-Field Use of Wearable Inertial Sensors for Sport Performance Evaluation: A Systematic Review

This paper evaluates the performance of a tightly coupled GPS/INS integrated system based on low cost MEMS IMUs in dense urban areas, and investigates two different methods to improve its performance. The first method used is to derive observations from two different constraint equations reflecting the behavior of a typical land vehicle. The first constraint equation is derived assuming that the vehicle does not slip and always remains in contact with the ground. If these assumptions are true the velocity of the vehicle in the plane perpendicular to the forward direction should be zero. The second constraint equation is derived from the fact that the height does not change much in a short time interval in a land vehicular environment. Thus, when a GPS outage occurs (partial/complete), the integrated system combines the INS and constraints-derived virtual measurements to keep the position and velocity errors bounded. This method is suitable for use in real-time applications. The second method is specifically suitable for a post-mission application and involves the use of Rauch-Tung-Striebel (RTS) smoother. The designed system performance is evaluated using two data sets collected in dense urban areas. The obtained results demonstrate the effectiveness of different algorithms considered, in controlling the INS error growth, and indicates the potential of MEMS IMUs for use in land vehicle navigation applications.

GPS/MEMS INS integrated system for navigation in urban areas

This paper presents methods to calibrate and compensate for non-zero biases, non-unit scale factors, axis misalignments and cross-axis sensitivities of both the tri-axial accelerometer and gyroscopic setups in a micro-electro-mechanical systems (MEMS) based inertial measurement unit (IMU). These methods depend on the Earth's gravity as a stable physical calibration standard. Specifically, the calibration of gyroscopes is significantly improved by comparing the outputs of the accelerometer and the IMU orientation integration algorithm, after arbitrary motions. The derived property and proposed cost function allow the gyroscopes to be calibrated without external equipment, such as a turntable, or requiring precise maneuvers. Both factors allow the IMU to be easily calibrated by the user in the field so that it can function as an accurate orientation sensor. A custom-made prototype IMU is used to demonstrate the effectiveness of the proposed methods, with data that are carefully obtained using prescribed motions, as well as those less rigorously collected from the IMU when it is mounted on the head of a user or held in hands with brief random movements. With calibration, the observed average static angular error is less than a quarter of a degree and the dynamic angular error is reduced by a factor of 2 to 5.

https://iopscience.iop.org/article/10.1088/0957-0233/19/8/085202/pdf

Methods for in-field user calibration of an inertial measurement unit without external equipment

Navigation involves the integration of methodologies and systems for estimating the time varying position and attitude of moving objects. Inertial Navigation Systems (INS) and the Global Positioning System (GPS) are among the most widely used navigation systems. The use of cost effective MEMS based inertial sensors has made GPS/INS integrated navigation systems more affordable. However MEMS sensors suffer from various errors that have to be calibrated and compensated to get acceptable navigation results. Moreover the performance characteristics of these sensors are highly dependent on the environmental conditions such as temperature variations. Hence there is a need for the development of accurate, reliable and efficient thermal models to reduce the effect of these errors that can potentially degrade the system performance. In this paper, the Allan variance method is used to characterize the noise in the MEMS sensors. A six-position calibration method is applied to estimate the deterministic sensor errors such as bias, scale factor, and non-orthogonality. An efficient thermal variation model is proposed and the effectiveness of the proposed calibration methods is investigated through a kinematic van test using integrated GPS and MEMS-based inertial measurement unit (IMU).

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0373463307004560

A Standard Testing and Calibration Procedure for Low Cost MEMS Inertial Sensors and Units

In the last decade, the Land-Vehicle Navigation (LVN) market has grown rapidly. For most LVN systems, GPS is used for positioning. However, GPS has poor accuracy in urban areas due to signal blockages. Therefore, the LVN market has targeted the integration of other sensors with GPS. In this case, sensors' cost and size are major issues. Recent advances in MEMS inertial sensors made it possible to develop low-cost and compact IMUs. However, MEMS provide poor accuracy when used without updates (e.g., during GPS outages). In such periods, other updates are required for better performance. Vehicle full-stops, i.e., Zero-Velocity-Updates (ZUPTs), are usually applied for this purpose. However, this is not practical especially when GPS blockages are frequent. In this paper, 3D Auxiliary Velocity Updates (AVUs) are used, namely, non-holonomic constraints and odometer-derived velocity. Using kinematic MEMS/GPS data with several GPS signal blockages, the results showed a significant accuracy improvement after applying AVUs.

An Accurate Land-Vehicle MEMS IMU/GPS Navigation System Using 3D Auxiliary Velocity Updates

A vital necessity for any kind of inertial navigation system (INS) is the alignment of its axis with the vehicle body frame (VBF). Civilian vehicle navigation has strict requirements with respect to cost, size, reliability, and ease of implementation of the system. Microelectromechanical system (MEMS) inertial sensors have satisfied the cost and size requirements for civilian vehicle navigation; however, reliability and ease of implementation of these low-cost and miniaturized navigation systems are still parts of major research and investigation. This paper focuses on an important aspect of the ease of implementation for inertial sensors. From a civilian user perspective, accurately aligning the inertial system with respect to the vehicle, before every use, is not a desirable quality for a portable navigation system. In addition, it is not realistic to assume that even a careful user can achieve good alignment accuracy of the system. The purpose of this paper is to investigate the effects of misalignment errors that will produce errors in initial alignment and affect the navigation accuracy for two different inertial systems. The inertial systems are classified according to the number of sensors used in the system. The first system consists of three gyros and three accelerometers [full inertial measurement unit (IMU)], whereas the second system only has one gyro and two horizontal accelerometers (partial IMU).

Civilian Vehicle Navigation: Required Alignment of the Inertial Sensors for Acceptable Navigation Accuracies

Recent navigation systems integrating GPS with Micro-Electro-Mechanical Systems (MEMS) Inertial Measuring Units (IMUs) have shown promising results for several applications based on low-cost devices such as vehicular and personal navigation. However, as a trend in the navigation market, some applications require further reductions in size and cost. To meet such requirements, a MEMS full IMU configuration (three gyros and three accelerometers) may be simplified. In this context, different partial IMU configurations such as one gyro plus three accelerometers or one gyro plus two accelerometers could be investigated. The main challenge in this case is to develop a specific navigation algorithm for each configuration since this is a time-consuming and costly task. In this paper, a universal approach for processing any MEMS sensor configuration for land vehicular navigation is introduced. The proposed method is based on the assumption that the omitted sensors provide relatively less navigation information and hence, their output can be replaced by pseudo constant signals plus noise. Using standard IMU/GPS navigation algorithms, signals from existing sensors and pseudo signals for the omitted sensors are processed as a full IMU. The proposed approach is tested using land-vehicle MEMS/GPS data and implemented with different sensor configurations. Compared to the full IMU case, the results indicate the differences are within the expected levels and that the accuracy obtained meets the requirements of several land-vehicle applications.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0373463307004213

Xiaoji Niu

Papers

A new multi-position calibration method for MEMS inertial navigation systems

A Standard Testing and Calibration Procedure for Low Cost MEMS Inertial Sensors and Units

An Accurate Land-Vehicle MEMS IMU/GPS Navigation System Using 3D Auxiliary Velocity Updates

Civilian Vehicle Navigation: Required Alignment of the Inertial Sensors for Acceptable Navigation Accuracies

A universal approach for processing any MEMS inertial sensor configuration for land-vehicle navigation