Zainab Syed

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

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).

Coarse alignment for marine SINS using gravity in the inertial frame as a reference

Marine strapdown inertial navigation systems (SINS) inevitably experience disturbing motion, even if the carrier ship is moored. The method of ground coarse alignment, which is based on the assumption that SINS is on a stationary carrier with limited vibration, therefore cannot be used to perform the marine SINS coarse alignment. In this paper, a novel method using the gravity in the inertial frame as a reference is investigated for marine SINS alignment. Its algorithmic principle is described in details. The results obtained from both simulation and turntable-test data show that the attitude determined by this novel method can meet the accuracy requirement of coarse alignment and it can be used as input for fine alignment.

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

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).

Hybrid extended particle filter (HEPF) for integrated inertial navigation and global positioning systems

Navigation includes the integration of methodologies and systems for estimating time-varying position, velocity and attitude of moving objects. Navigation incorporating the integrated inertial navigation system (INS) and global positioning system (GPS) generally requires extensive evaluations of nonlinear equations involving double integration. Currently, integrated navigation systems are commonly implemented using the extended Kalman filter (EKF). The EKF assumes a linearized process, measurement models and Gaussian noise distributions. These assumptions are unrealistic for highly nonlinear systems like land vehicle navigation and may cause filter divergence. A particle filter (PF) is developed to enhance integrated INS/GPS system performance as it can easily deal with nonlinearity and non-Gaussian noises. In this paper, a hybrid extended particle filter (HEPF) is developed as an alternative to the well-known EKF to achieve better navigation data accuracy for low-cost microelectromechanical system sensors. The results show that the HEPF performs better than the EKF during GPS outages, especially when simulated outages are located in periods with high vehicle dynamics.

Thermal Calibration of Low Cost MEMS Sensors for Land Vehicle Navigation System

For vehicle navigation, Global Positioning System (GPS) provides long term accurate positions, but only when direct lines of sight to four or more satellites exist. Inertial Navigation Systems (INS), on the other hand, are self contained sensors that can provide short term accurate navigation information. The integration of the two systems can effectively provide continuous navigation data even during GPS signal outages. Traditional inertial systems were heavy, bulky and costly. In the past two decades, the use and development of light weight, compact and cost effective Micro-Electro-Mechanical Systems (MEMS) based inertial sensors has made the civilian integrated vehicle navigation systems more affordable. However, these sensors still have to make their way in the field due to their significant error sources such as turn-on biases or scale factors variations. 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 degrade the system performance.

Hybrid Extended Particle Filter (HEPF) for integrated civilian navigation system

Integration of complementary systems like inertial navigation system (INS) and Global Positioning System (GPS), improves navigation parameters accuracy. Currently, integrated navigation systems are commonly implemented using extended Kalman filter (EKF) and unscented Kalman filter (UKF). The EKF assumes linear process and measurement models while UKF generates sigma points using the real mean and standard deviation of data. However, both EKF and UKF assume the noise to be Gaussian, which is unrealistic for highly nonlinear systems. To overcome these limitations, particle filter (PF) was proposed lately which is a non-parametric filter and hence can easily deal with non-linearity and non-Gaussian noises. In this paper, hybrid extended particle filter (HEPF) is developed as an alternative to the EKF to achieve better navigation accuracy for low-cost micro electro mechanical systems (MEMS) sensors. Experimental GPS/INS datasets consisting of GPS carrier phase data and inertial measurements from low-cost MEMS-grade inertial measurement unit (IMU) is used to evaluate the proposed HEPF. The HEPF performance is compared to that of other estimation techniques such as the EKF. The results show that both HEPF and EKF provide comparable navigation results during periods without GPS outages. However in cases when GPS outages are simulated, HEPF performs much better than the EKF, especially when simulated outages are located during periods with high vehicle dynamics.

Improved Vehicle Navigation Using Aiding with Tightly Coupled Integration

Vehicle navigation poses difficulties as it requires the uninterrupted availability of accurate positioning information, even in circumstances without an ideal condition. Global Positioning System (GPS) provides consistently accurate positioning solutions if four or more GPS satellites can be observed. Unfortunately, this condition is usually not satisfied if a vehicle is going through urban canyon, tunnel or forest canopy. Even with High Sensitivity and Assisted GPS receivers, reliable positioning using GPS alone in difficult urban situations is still a challenge. Inertial Navigation System (INS), consists of self- contained sensors that can continuously provide accurate short term positioning solutions. The integration of GPS and INS can overcome the GPS drawback and provide continuous navigation solutions even during GPS signal outages. Though newly developed MEMS-based INS sensors have relatively low accuracy, they are compact and inexpensive, which is very suitable for vehicle navigation. Hence, there is a growing interest in exploring the capabilities of these sensors in the field of vehicle navigation. This paper presents the integration of GPS with MEMS-based INS in a tightly coupled scheme. Tightly coupled integration can make use of GPS signals even if less than four GPS satellites are observed. Thus it offers better integration options. To further improve the GPS/INS integration results, non- holonomic constraints and heading observations were used in this study to improve the online positioning accuracies. The results showed the drift errors could be significantly reduced when non- holonomic constraints and/or heading information were used, during periods with GPS signal outage. In addition, a backward smoother called Rauch-Tung-Striebel (RTS) was also implemented for offline processing needs purpose. The integration results showed that the RTS smoother can significantly reduce the drift errors even if neither non-holonomic constraints nor heading information were used.

Swarm Optimization-Based Magnetometer Calibration for Personal Handheld Devices

Inertial Navigation Systems (INS) consist of accelerometers, gyroscopes and a processor that generates position and orientation solutions by integrating the specific forces and rotation rates. In addition to the accelerometers and gyroscopes, magnetometers can be used to derive the user heading based on Earths magnetic field. Unfortunately, the measurements of the magnetic field obtained with low cost sensors are usually corrupted by several errors, including manufacturing defects and external electro-magnetic fields. Consequently, proper calibration of the magnetometer is required to achieve high accuracy heading measurements. In this paper, a Particle Swarm Optimization (PSO)-based calibration algorithm is presented to estimate the values of the bias and scale factor of low cost magnetometers. The main advantage of this technique is the use of the artificial intelligence which does not need any error modeling or awareness of the nonlinearity. Furthermore, the proposed algorithm can help in the development of Pedestrian Navigation Devices (PNDs) when combined with inertial sensors and GPS/Wi-Fi for indoor navigation and Location Based Services (LBS) applications.

Wireless Access Point Localization Using Nonlinear Least Squares and Multi-Level Quality Control

Locations of WiFi access points (APs) are important for WiFi positioning when a propagation model is used. The pre-surveyed propagation parameters, such as the path-loss exponent, are usually not available when localizing the APs in a new environment. This letter introduces a novel method that estimates the AP locations and the parameters of the received signal strength (RSS) propagation model simultaneously using the weighted nonlinear least squares (NLLS) method. This method can run on consumer portable devices autonomously in real time without any a-priori information, and eliminate the need of pre-survey. Another contribution of this letter is to introduce a multi-level quality control mechanism, and utilize the statistical testing method in AP localization and propagation parameters (PPs) determination for the first time. Indoor experiments show that the proposed method provided more promising results than previous methods.