Ezzaldeen Edwan

Constrained Angular Motion Estimation in a Gyro-Free IMU

In this paper, we present an extended Kalman filter (EKF)-based solution for the estimation of the angular motion using a gyro-free inertial measurement unit (GF-IMU) built of twelve separate mono-axial accelerometers. Using such a GF-IMU produces a vector, which we call the angular information vector (AIV) that consists of 3D angular acceleration terms and six quadratic terms of angular velocities. We consider the multiple distributed orthogonal triads of accelerometers that consist of three nonplanar distributed triads equally spaced from a central triad as a specific case to solve. During research for the possible filter schemes, we derived equality constraints. Hence we incorporate the constraints in the filter to improve the accuracy of the angular motion estimation, which in turn improves the attitude accuracy (direction cosine matrix (DCM) or quaternion vector).

INS/Wi-Fi based indoor navigation using adaptive Kalman filtering and vehicle constraints

Due to the complementary nature of inertial navigation system (INS) and Wi-Fi positioning principles, an INS/Wi-Fi integrated system is expected to form a low-cost and continuous indoor navigation solution with better performance than using the standalone systems. In this paper, we explore the integration of Wi-Fi measurements with data from microelectromechanical systems (MEMS) based inertial measurement unit (IMU) for indoor vehicle navigation. Two enhancements, which employ adaptive Kalman filtering (AKF) and vehicle constraints, for supporting the integrated system are presented. One field experiment has been conducted for estimating the trajectory of a mobile robot vehicle. The numerical results show that the enhanced integrated system provides higher navigation accuracy, compared to using standalone Wi-Fi positioning and conventional INS/Wi-Fi integration.

2D/3D indoor navigation based on multi-sensor assisted pedestrian navigation in Wi-Fi environments

Because of the complementary nature of inertial measurement unit based pedestrian dead reckoning (PDR) and Wi-Fi positioning, the combination of both systems yields a synergetic effect resulting in higher navigation performance. Barometric sensors can provide height information for 2D/3D indoor navigation applications in multi-floor environments. In this paper, we explore the multi-sensor assisted pedestrian navigation. A PDR/Wi-Fi/barometer integrated system is presented. The adaptive Kalman filter is employed for sensor fusion, which can adapt dynamic noise statistics. One field experiment has been conducted in a multi-floor building. The numerical results are presented to show the navigation performance of the integrated system.

Low-cost INS/GPS with nonlinear filtering methods

For land-based navigation, Euler angles are often used in INS/GPS integrated navigation systems. However, the trigonometric operations required in the updates and forming of the rotation matrices for transforming the INS measurements from the body frame to the navigation frame turns the system model to be highly nonlinear. Besides, using low-cost MEMS-based IMUs, the gyroscope bias errors must be correctly estimated and compensated, which makes the nonlinearity problem a critical one. In this contribution, three Kalman filtering methods (i.e., Extended Kalman filter with simplified system model, Extended Kalman filter with linearized system model and Unscented Kalman filter with nonlinear system model) are utilized in INS/GPS tightly-coupled integration. Simulations and field experiments are conducted. Numerical results are compared in terms of both estimation accuracy and processing time.

Reduced DCM based attitude estimation using low-cost IMU and magnetometer triad

In this paper, we describe an attitude estimation algorithm based on the direction cosine matrix (DCM) attitude representation and analyze its performance. This algorithm is appropriate for the implementation of a low-cost attitude and heading reference system (AHRS) which is composed of micro electrical mechanical system (MEMS) inertial measurement unit (IMU) and magnetometer triad. To reduce the computational burden, we estimate only six elements of the nine elements of the DCM. Kalman filtering is used to fuse the angular rate, specific force and magnetometer triad measurements. The DCM model has an advantage over other attitude representations because it has linear measurement equations of accelerometer and magnetometer triads. For the DCM orthogonalization, we recommend a low computational burden algorithm within the integration filter. Finally experimental data is used to verify the efficiency of the algorithm.

Tightly-coupled INS/GPS using Quaternion-based Unscented Kalman filter

PS receiver has dominated the field of positioning and navigation for decades [1]. However, its performance depends on the signal environments. It provides a continuous navigation solution only when more than four satellites are in view. In order to solve this problem, other navigation systems, e.g., inertial navigation system, are often employed to integrate with GPS for having a robust and continuous navigation solution. Historically, due to the high cost in manufacturing the inertial sensors, the INS/GPS integration system are mostly employed in military and aerospace industry [2, 3]. Recently, the advent of micro-electromechanical systems (MEMS) technology drives the discrete, heavy and inflexible inertial sensor system to small, cost-effective, light-weight, portable and lowpower silicon-based inertial devices. Although the cheap MEMS-based inertial sensors do not exhibit high accurate navigation performance, they can meet the requirements of many land-based navigation applications when aided with GPS devices. An INS/GPS system combines the advantages of both sides and provides accurate and uninterrupted navigation results, working in all environments, and constituting a potential and powerful alternative to the GPS alone navigation devices. Nowadays, the INS and GPS integrated solutions are the back-bones of many modern navigation systems, which are employed in industrial and military applications. Substantial research effort has been devoted to extensive algorithmic developments and performance analysis. The objective is mainly at the promotion of system estimation accuracy with low-cost sensor systems, putting a focus of interest onto powerful sensor fusion algorithms. The so-called tightly-coupled integration is one of the approaches to fuse the INS and GPS measurements. However, when modeling the underlying problem, the system propagation and observation models are nonlinear. The most common application of the Kalman filter (KF) on nonlinear systems is the extended (or linearized) Kalman filter (EKF) [4-6], which is based on a first-order linearization of the nonlinear stochastic system models with the assumption of Gaussian distributed noises. Although the EKF maintains the elegant and computationally efficient update form of the KF, it suffers from a number of drawbacks. That is, the linearized transformations are reliable, only if the error propagation can be well approximated by a linear function, because the small error

Performance investigation of barometer aided GPS/MEMS-IMU integration

The integration of inertial sensors is a widely-adopted method for covering GPS outages. However, the position accuracy in vertical direction is often challenged in comparison with the one in the horizontal plane. This paper explores the possibility of using MEMS barometer as an augmentation in the GPS/INS integration in land application. A prototype system, composed of a commercial GPS receiver with low-cost MEMS IMU and barometer on a DSP platform, is implemented and described. In addition, the initial investigation of the MEMS barometer is addressed. With the field experiment data collected from a car-driving test in a hilly region, the benefit of barometer aiding is demonstrated.

GPS/INS integration for GF-IMU of twelve mono-axial accelerometers configurations

In this paper we present an integration scheme and performance analysis for the integration of a gyro-free inertial measurement unit (GF-IMU) with a GPS receiver. The GF-IMU is composed of twelve separate mono-axial accelerometers. Using such a GF-IMU produces a vector, which we call the angular information vector that consists of 3D angular acceleration terms and six quadratic terms of angular velocities. Traditional GPS/INS integration using error state space model has been modified to fit this special IMU. Simulation results are presented.

Constrained-trajectory based GPS/INS integration for reliable position and attitude determination

In this paper, we propose a constrained method to incorporate a priori flight trajectory information into the GPS derived navigation solutions which will ensure the availability and reliability of the GPS solutions even when the number of satellites reduces to less than four. Moreover, we fuse the navigation solutions derived from the proposed constrained method with those obtained from the inertial navigation system (INS) in order to fully gain their complementary features of both systems. The simulation results show that the proposed scheme provides the complete 6 degree-of-freedom (DoF) state at high data rate with only two satellites required.

GPS/INS Integration for Footprint Chasing in Bistatic SAR Experiments

The paper starts with a brief consideration of a novel bistatic SAR experiment and the requirements on attitude and position determination in SAR. GPS/INS integration strategies are summarized. To obtain an affordable system for the antenna pointing for footprint chasing, low-cost inertial measurement units are in the focus. By an observability analysis it is proven that additional redundant attitude information as it can be derived from a multi-antenna GPS receiver can remarkably aid the integration.