Chris Goodall

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

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, e.g. 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.

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

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.

Vehicle navigator using a mixture particle filter for inertial sensors/odometer/map data/GPS integration

The market for vehicular navigators boomed over the last few years. These navigators rely mainly on satellite based navigation systems such as the Global Positioning System (GPS) to assist drivers. Due to interruption or degradation in such systems in dense urban scenarios, they have to be augmented with other systems to achieve continuous and accurate vehicular navigation. GPS is integrated with low-cost micro-electro mechanical system (MEMS)-based inertial sensors. However, these sensors provide inadequate performance in degraded GPS environments because of their complex error characteristics that often lead to large position drift errors. This paper proposes a continuous and accurate solution integrating low-cost MEMS-based inertial sensors, the vehicle odometer, GPS, and map data from road networks. Despite the traditional inadequate performance of MEMS-based sensors in this problem, the performance is enhanced through: (i) a special combination of inertial sensors and odometer that has better performance for land vehicles than traditional solutions; (ii) The use of map information from road networks to constrain the positioning solution; (iii) The use of an advanced particle filtering (PF) technique to perform the integration, which work with nonlinear models and better modeling of inertial sensor errors, in addition to better integration with the map data. The performance of the proposed positioning system has been verified extensively on real road tests in downtown trajectories with degraded or totally denied GPS for long durations.

An Efficient Method for Evaluating the Performance of MEMS IMUs

Advances in MEMS technology combined with the miniaturization of electronics, have made it possible to produce chip-based inertial sensor for use in measuring angular velocity and acceleration. These chips are small, lightweight, consume very little power and are extremely reliable. They have therefore found a wide spectrum of applications in the automotive and other industrial applications. Currently, new MEMS inertial sensors or IMUs developed by various manufacturers continue to emerge on the market. However, such sensors should be evaluated in terms of navigation performance. Common testing in the lab can provide parameters such as sensor noise density and bias instability but cannot predict the corresponding performance of a full navigation system. IMU/GPS field testing is the only way to evaluate the performance of MEMS IMUs especially when GPS signals are temporarily blocked. However, testing every MEMS sensor (or IMU) in the field is not practical since it is a time- consuming and costly task. Therefore, the main objective of this paper is the development of an efficient method for evaluating the navigation performance of any MEMS IMU using lab testing only. The developed method is based on using MEMS sensors static data signals to estimate the MEMS sensor errors. Hence, by grafting these errors into the signals of a high quality IMU (gyro drift of 0.005 deg/h), collected in a previously conducted typical field test, a quasi field dataset of the MEMS is obtained since the high quality IMU signals can be considered as the true inertial sensor. Such emulated MEMS IMU field data can then be processed with the corresponding GPS data collected in the same test to evaluate the MEMS IMU navigation performance. To test the efficiency of the proposed method, several land-vehicle kinematic datasets with GPS, a high-quality IMU and different MEMS IMUs were used. Static data of the same MEMS IMUs was collected and then the proposed method was applied. The performance of the MEMS IMU actual and emulated datasets is compared during several GPS signal blockage periods. The results show that both solutions have a similar behavior with an average difference of only 20% in terms of accumulated position drifts. This illustrates the usefulness of the proposed technique in addition to the cost and time savings.

Nonlinear filtering for tightly coupled RISS/GPS integration

The integration of Global Positioning System (GPS), inertial sensors and other motion sensors inside land vehicles enable reliable positioning in challenging GPS environments. GPS signals may suffer from blockage in urban canyons and tunnels resulting in interrupted positioning information. Inertial sensors are standalone sensors that can be integrated with GPS and can bridge the blockage periods as they do not rely on any external signals. Recently, miniaturized Micro-Electro-Mechanical Systems (MEMS)-based inertial sensors are abundantly used for vehicle safety applications such as air-bag deployment, roll-over detection, etc. These sensors can be used as inertial navigation system (INS) after integrating with GPS for reliable navigation solution even in denied GPS signal environments. The traditional technique for this integration is based on Kalman filter (KF) with a dedicated inertial sensor module consisting of three orthogonal gyros and three orthogonal accelerometers. This research targets a low cost navigation solution for land vehicles and hence it utilizes a reduced inertial sensor system (RISS) consisting of MEMS-based single axis gyro and a dual axis accelerometer. Additionally, the vehicles odometer is used and an integrated 3D navigation solution is achieved. To improve the positioning accuracy a nonlinear filtering technique, particle filter (PF) is used to avoid linearization errors. Because of PF ability to deal directly with nonlinear models, it can accommodate arbitrary sensor characteristics and motion dynamics. Consequently, tightly coupled integration which has a nonlinear measurement model can be directly used in PF without introducing any errors. An enhanced version of PF is implemented known as Mixture PF and the performance of this method is examined by actual road tests in a land vehicle and compared with KF.

Improving INS/GPS Navigation Accuracy through Compensation of Kalman Filter Errors

The Kalman filter is often used to integrate satellite navigation systems with inertial navigation systems. Such integrated systems are especially useful for navigation of vehicles in urban environments where satellite signals are frequently blocked by tall buildings. The filter weights the measurements of both navigation systems to provide an overall optimal solution. Unfortunately, an optimal solution is only achieved when the filter has been supplied with ideal a priori information such as proper measurement noise characteristics and system dynamics. If such parameters are not perfect they can be detected and compensated for using an intelligent navigation scheme which is adaptable to different sensors. As dynamics are encountered, satellite signal blockages are simulated to test the optimality of the filter. A neural network is then trained to learn any residual deterministic errors which are then removed from future system drifts during signal blockages.

A game-theoretic approach for calibration of low-cost magnetometers under noise uncertainty

Pedestrian heading estimation is a fundamental challenge in Global Navigation Satellite System (GNSS)-denied environments. Additionally, the heading observability considerably degrades in low-speed mode of operation (e.g. walking), making this problem even more challenging. The goal of this work is to improve the heading solution when hand-held personal/portable devices, such as cell phones, are used for positioning and to improve the heading estimation in GNSS-denied signal environments. Most smart phones are now equipped with self-contained, low cost, small size and power-efficient sensors, such as magnetometers, gyroscopes and accelerometers. A magnetometer needs calibration before it can be properly employed for navigation purposes. Magnetometers play an important role in absolute heading estimation and are embedded in many smart phones. Before the users navigate with the phone, a calibration is invoked to ensure an improved signal quality. This signal is used later in the heading estimation. In most of the magnetometer-calibration approaches, the motion modes are seldom described to achieve a robust calibration. Also, suitable calibration approaches fail to discuss the stopping criteria for calibration. In this paper, the following three topics are discussed in detail that are important to achieve proper magnetometer-calibration results and in turn the most robust heading solution for the user while taking care of the device misalignment with respect to the user: (a) game-theoretic concepts to attain better filter parameter tuning and robustness in noise uncertainty, (b) best maneuvers with focus on 3D and 2D motion modes and related challenges and (c) investigation of the calibration termination criteria leveraging the calibration robustness and efficiency.

Optimal Signal Sampling Configuration for MEMS INS/GPS Navigation

For vehicle navigation, Global Positioning System (GPS) provides long term accurate measurements, but only when a direct line of sight to four or more satellites exists. Inertial navigation systems (INS), on the other hand, are self contained sensors that can provide short term measurements. The integration of the two systems can effectively provide continuous navigation data even during GPS signal outages. Traditional INSs are bulky and expensive, and therefore, can not be used for daily civilian applications. With the evolution of MEMS technology, MEMS-based INS sensors are evolving into more accurate, compact and inexpensive units. Hence, there is a growing interest in exploring the capabilities of these sensors in the field of vehicle navigation. Most of the research is targeted towards finding the best error models and integration techniques that can reduce the high drift and errors associated with these sensors. One of the important aspects of this integration is the optimal configuration for sampling frequency, number of bits and time delay during recording of the various sensor outputs. The very low cost of the MEMS sensors makes the cost of the signal sampling, i.e. analog to digital conversion (ADC), an issue. These parameters will reduce the on-board memory requirement, speed up the computation and hence, significantly reduce the final cost to the consumers.