Information Aided Navigation: A Review

The performance of inertial navigation systems is largely dependent on the stable flow of external measurements and information to guarantee continuous filter updates and bind the inertial solution drift. Platforms in different operational environments may be prevented at some point from receiving external measurements, thus exposing their navigation solution to drift. Over the years, a wide variety of works have been proposed to overcome this shortcoming, by exploiting knowledge of the system current conditions and turning it into an applicable source of information to update the navigation filter. This paper aims to provide an extensive survey of information aided navigation, broadly classified into direct, indirect, and model aiding. Each approach is described by the notable works that implemented its concept, use cases, relevant state updates, and their corresponding measurement models. By matching the appropriate constraint to a given scenario, one will be able to improve the navigation solution accuracy, compensate for the lost information, and uncover certain internal states, that would otherwise remain unobservable.Comment: 8 figures, 3 table

Accurate pedestrian indoor navigation by tightly coupling foot-mounted IMU and RFID measurements

We present a new method to accurately locate persons indoors by fusing inertial navigation system (INS) techniques with active RFID technology. A foot-mounted inertial measuring units (IMUs)-based position estimation method, is aided by the received signal strengths (RSSs) obtained from several active RFID tags placed at known locations in a building. In contrast to other authors that integrate IMUs and RSS with a loose Kalman filter (KF)-based coupling (by using the residuals of inertial- and RSS-calculated positions), we present a tight KF-based INS/RFID integration, using the residuals between the INS-predicted reader-to-tag ranges and the ranges derived from a generic RSS path-loss model. Our approach also includes other drift reduction methods such as zero velocity updates (ZUPTs) at foot stance detections, zero angular-rate updates (ZARUs) when the user is motionless, and heading corrections using magnetometers. A complementary extended Kalman filter (EKF), throughout its 15-element error state vector, compensates the position, velocity and attitude errors of the INS solution, as well as IMU biases. This methodology is valid for any kind of motion (forward, lateral or backward walk, at different speeds), and does not require an offline calibration for the user gait. The integrated INS+RFID methodology eliminates the typical drift of IMU-alone solutions (approximately 1% of the total traveled distance), resulting in typical positioning errors along the walking path (no matter its length) of approximately 1.5 m

Heading drift mitigation for low-cost inertial pedestrian navigation

The concept of autonomous pedestrian navigation is often adopted for indoor pedestrian navigation. For outdoors, a Global Positioning System (GPS) is often used for navigation by utilizing GPS signals for position computation but indoors, its signals are often unavailable. Therefore, autonomous pedestrian navigation for indoors can be realized with the use of independent sensors, such as low-cost inertial sensors, and these sensors are often known as Inertial Measurement Unit (IMU) where they do not rely on the reception of external information such as GPS signals. Using these sensors, a relative positioning concept from initialized position and attitude is used for navigation. The sensors sense the change in velocity and after integration, it is added to the previous position to obtain the current position. Such low-cost systems, however, are prone to errors that can result in a large position drift. This problem can be minimized by mounting the sensors on the pedestrian’s foot. During walking, the foot is briefly stationary while it is on the ground, sometimes called the zero-velocity period. If a non-zero velocity is then measured by the inertial sensors during this period, it is considered as an error and thus can be corrected. These repeated corrections to the inertial sensor’s velocity measurements can, therefore, be used to control the error growth and minimize the position drift. Nonetheless, it is still inadequate, mainly due to the remaining errors on the inertial sensor’s heading when the velocity corrections are used alone. Apart from the initialization issue, therefore, the heading drift problem still remains in such low-cost systems. In this research, two novel methods are developed and investigated to mitigate the heading drift problem when used with the velocity updates. The first method is termed Cardinal Heading Aided Inertial Navigation (CHAIN), where an algorithm is developed to use building ‘heading’ to aid the heading measurement in the Kalman Filter. The second method is termed the Rotated IMU (RIMU), where the foot-mounted inertial sensor is rotated about a single axis to increase the observability of the sensor’s heading. For the CHAIN, the method proposed has been investigated with real field trials using the low-cost Microstrain 3DM-GX3-25 inertial sensor. It shows a clear improvement in mitigating the heading drift error. It offers significant improvement in navigation accuracy for a long period, allowing autonomous pedestrian navigation for as long as 40 minutes with below 5 meters position error between start and end position. It does not require any extra heading sensors, such as a magnetometer or visual sensors such as a camera nor an extensive position or map database, and thus offers a cost-effective solution. Furthermore, its simplicity makes it feasible for it to be implemented in real-time, as very little computing capability is needed. For the RIMU, the method was tested with Nottingham Geospatial Institute (NGI) inertial data simulation software. Field trials were also undertaken using the same low-cost inertial sensor, mounted on a rotated platform prototype. This method improves the observability of the inertial sensor’s errors, resulting also in a decrease in the heading drift error at the expense of requiring extra components

Algorithms for Autonomous Personal Navigation Systems

Personal positioning is a challenging topic in the area of navigation mainly because of the cost, size and power consumption constraints imposed on the hardware. Satellite based positioning techniques can meet the requirements for many applications, but cover well only outdoor environment. Problems like weak satellite signals make the positioning impossible indoors. Urban canyons are also difficult areas for GNSS based navigation because of large multipath errors and satellite signal outages. Many applications require seamless positioning in all environments. However, there is no overall solution for navigation in GNSS denied environment, which is reliable, accurate, cost effective and quickly installed. Recently developed systems for indoor positioning often require pre-installed infrastructure. Another approach is to use fully autonomous navigation systems based on self-contained sensors and street or indoor maps. This thesis is concerned with autonomous personal navigation devices, which do not rely on the reception of external information, like satellite or terrestrial signals. The three proposed algorithms can be integrated into personal navigation systems. The first algorithm computes positioning for a map aided navigation system designed for land vehicles traveling on road network. The novelty is in application of particle filtering to vehicle navigation using road network database. The second algorithm is aimed at map aided vehicle navigation indoors. The novelty is in the method for correction of position and heading. The third algorithm computes solution for pedestrian navigation system, which is based on body mounted inertial measurement unit and models of human gait

Context-aided Inertial Navigation via Belief Condensation

Inertial navigation systems suffer from drift errors that degrade their performance. The main existing techniques to mitigate such impairment are based on the detection of stance phases under the specific situational context of pedestrian walking with a foot-mounted inertial measurement unit (IMU). Existing approaches achieve acceptable performances only under simple circumstances, such as smooth movements and short periods of time. In addition, they lack a principled unifying methodology to exploit contextual information. In this paper, we establish a general framework for context-aided inertial navigation and present efficient algorithms for its implementation based on the inference technique called belief condensation (BC). We evaluate the proposed techniques against the state of the art through the experimental case study of pedestrian walking with a foot-mounted IMU. Our results show that the proposed techniques can remarkably improve the navigation accuracy while keeping moderate complexities

Heading drift mitigation for low-cost inertial pedestrian navigation

The concept of autonomous pedestrian navigation is often adopted for indoor pedestrian navigation. For outdoors, a Global Positioning System (GPS) is often used for navigation by utilizing GPS signals for position computation but indoors, its signals are often unavailable. Therefore, autonomous pedestrian navigation for indoors can be realized with the use of independent sensors, such as low-cost inertial sensors, and these sensors are often known as Inertial Measurement Unit (IMU) where they do not rely on the reception of external information such as GPS signals. Using these sensors, a relative positioning concept from initialized position and attitude is used for navigation. The sensors sense the change in velocity and after integration, it is added to the previous position to obtain the current position. Such low-cost systems, however, are prone to errors that can result in a large position drift. This problem can be minimized by mounting the sensors on the pedestrian’s foot. During walking, the foot is briefly stationary while it is on the ground, sometimes called the zero-velocity period. If a non-zero velocity is then measured by the inertial sensors during this period, it is considered as an error and thus can be corrected. These repeated corrections to the inertial sensor’s velocity measurements can, therefore, be used to control the error growth and minimize the position drift. Nonetheless, it is still inadequate, mainly due to the remaining errors on the inertial sensor’s heading when the velocity corrections are used alone. Apart from the initialization issue, therefore, the heading drift problem still remains in such low-cost systems. In this research, two novel methods are developed and investigated to mitigate the heading drift problem when used with the velocity updates. The first method is termed Cardinal Heading Aided Inertial Navigation (CHAIN), where an algorithm is developed to use building ‘heading’ to aid the heading measurement in the Kalman Filter. The second method is termed the Rotated IMU (RIMU), where the foot-mounted inertial sensor is rotated about a single axis to increase the observability of the sensor’s heading. For the CHAIN, the method proposed has been investigated with real field trials using the low-cost Microstrain 3DM-GX3-25 inertial sensor. It shows a clear improvement in mitigating the heading drift error. It offers significant improvement in navigation accuracy for a long period, allowing autonomous pedestrian navigation for as long as 40 minutes with below 5 meters position error between start and end position. It does not require any extra heading sensors, such as a magnetometer or visual sensors such as a camera nor an extensive position or map database, and thus offers a cost-effective solution. Furthermore, its simplicity makes it feasible for it to be implemented in real-time, as very little computing capability is needed. For the RIMU, the method was tested with Nottingham Geospatial Institute (NGI) inertial data simulation software. Field trials were also undertaken using the same low-cost inertial sensor, mounted on a rotated platform prototype. This method improves the observability of the inertial sensor’s errors, resulting also in a decrease in the heading drift error at the expense of requiring extra components

On Fault Detection and Exclusion in Snapshot and Recursive Positioning Algorithms for Maritime Applications

Resilient provision of Position, Navigation and Timing (PNT) data can be considered as a key element of the e-Navigation strategy developed by the International Maritime Organization (IMO). An indication of reliability has been identified as a high level user need with respect to PNT data to be supplied by electronic navigation means. The paper concentrates on the Fault Detection and Exclusion (FDE) component of the Integrity Monitoring (IM) for navigation systems based both on pure GNSS (Global Navigation Satellite Systems) as well as on hybrid GNSS/inertial measurements. Here a PNT-data processing Unit will be responsible for both the integration of data provided by all available on-board sensors as well as for the IM functionality. The IM mechanism can be seen as an instantaneous decision criterion for using or not using the system and, therefore, constitutes a key component within a process of provision of reliable navigational data in future navigation systems. The performance of the FDE functionality is demonstrated for a pure GNSS-based snapshot weighted iterative least-square (WLS) solution, a GNSS-based Extended Kalman Filter (EKF) as well as for a classical error-state tightly-coupled EKF for the hybrid GNSS/inertial system. Pure GNSS approaches are evaluated by combining true measurement data collected in port operation scenario with artificially induced measurement faults, while for the hybrid navigation system the measurement data in an open sea scenario with native GNSS measurement faults have been employed. The work confirms the general superiority of the recursive Bayesian scheme with FDE over the snapshot algorithms in terms of fault detection performance even for the case of GNSS-only navigation. Finally, the work demonstrates a clear improvement of the FDE schemes over non-FDE approaches when the FDE functionality is implemented within a hybrid integrated navigation system

Multi-Sensor Methods for Mobile Radar Motion Capture and Compensation.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

Novel Methods for Personal Indoor Positioning

Currently, people are used to getting accurate GNSS based positioning services. However, in indoor environments, the GNSS cannot provide the accuracy and availability comparable to open outdoor environments. Therefore, alternatives to GNSS are needed for indoor positioning. In this thesis, methods for pedestrian indoor positioning are proposed. With these novel methods, the mobile unit performs all the required positioning measurements and no dedicated positioning infrastructure is required.This thesis proposes novel radio map configuration methods for WLAN fingerprinting based on received signal strength measurements. These methods with different model parameters were studied in field tests to identify the best models with reasonable positioning accuracy and moderate memory requirements. A histogram based WLAN fingerprinting model is proposed to aid IMU based pedestrian dead reckoning that is obtained using a gyro and a 3-axis accelerometer, both based on MEMS technology. The sensor data is used to detect the steps taken by a person on foot and to estimate the step length and the heading change during each step.For the aiding of the PDR with WLAN positioning, this thesis proposes two different configurations of complementary extended Kalman filters. The field tests show that these configurations produce equivalent position estimates. Two particle filters are proposed to implement the map aided PDR: one filter uses only the PDR and map information, while the other uses also the WLAN positioning. Based on the field tests, map aiding improves the positioning accuracy more than WLAN positioning.Novel map checking algorithms based on the sequential re-selection of obstacle lines are proposed to decrease the computation time required by the indoor map matching. To present the map information, both unstructured and structured obstacle maps are used. The feasibility of the proposed particle filter algorithms to real time navigation were demonstrated in field tests

Applications of MEMS Gyroscope for Human Gait Analysis

After decades of development, quantitative instruments for human gait analysis have become an important tool for revealing underlying pathologies manifested by gait abnormalities. However, the gold standard instruments (e.g., optical motion capture systems) are commonly expensive and complex while needing expert operation and maintenance and thereby be limited to a small number of specialized gait laboratories. Therefore, in current clinical settings, gait analysis still mainly relies on visual observation and assessment. Due to recent developments in microelectromechanical systems (MEMS) technology, the cost and size of gyroscopes are decreasing, while the accuracy is being improved, which provides an effective way for qualifying gait features. This chapter aims to give a close examination of human gait patterns (normal and abnormal) using gyroscope-based wearable technology. Both healthy subjects and hemiparesis patients participated in the experiment, and experimental results show that foot-mounted gyroscopes could assess gait abnormalities in both temporal and spatial domains. Gait analysis systems constructed of wearable gyroscopes can be more easily used in both clinical and home environments than their gold standard counterparts, which have few requirements for operation, maintenance, and working environment, thereby suggesting a promising future for gait analysis