Junchuan Zhou

INS/GPS Tightly-coupled Integration using Adaptive Unscented Particle Filter

With the rapid developments in computer technology, the particle filter (PF) is becomingmore attractive in navigation applications. However, its large computational burden stilllimits its widespread use. One approach for reducing the computational burden withoutdegrading the system estimation accuracy is to combine the PF with other filters, i.e., theextended Kalman filter (EKF) or the unscented Kalman filter (UKF). In this paper, thea posteriori estimates from an adaptive unscented Kalman filter (AUKF) are used to specifythe PF importance density function for generating particles. Unlike the sequential import-ance sampling re-sampling (SISR) PF, the re-sampling step is not required in the algorithm,because the filter does not reuse the particles. Hence, the filter computational complexity canbe reduced. Besides, the latest measurements are used to improve the proposal distributionfor generating particles more intelligently. Simulations are conducted on the basis of a field-collected 3D UAV trajectory. GPS and IMU data are simulated under the assumption thata NovAtel DL-4plus GPS receiver and a Landmark

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.

INS/GPS for High-Dynamic UAV-Based Applications

The carrier-phase-derived delta pseudorange measurements are often used for velocity determination. However, it is a type of integrated measurements with errors strongly related to pseudorange errors at the start and end of the integration interval. Conventional methods circumvent these errors with approximations, which may lead to large velocity estimation errors in high-dynamic applications. In this paper, we employ the extra states to “remember” the pseudorange errors at the start point of the integration interval. Sequential processing is employed for reducing the processing load. Simulations are performed based on a field-collected UAV trajectory. Numerical results show that the correct handling of errors involved in the delta pseudorange measurements is critical for high-dynamic applications. Besides, sequential processing can update different types of measurements without degrading the system estimation accuracy, if certain conditions are met.

Sequential Processing of Integrated Measurements in Tightly-coupled INS/GPS Integrated Navigation System

In INS/GPS tightly-coupled integration, the carrier-phase derived delta pseudorange measurements are often used for velocity determination. However, it is a type of integrated measurements with errors strongly related to the pseudorange errors at the start and end of the integration interval. Conventional methods circumvent these errors with approximations, which may lead to large velocity estimation errors in high dynamic applications. In order to tackle this problem, two methods are proposed and compared in this paper. Sequential processing is used in both methods for measurement updates. Simulations are performed based on a field collected UAV trajectory. Numerical results show that the correct handling of the errors involved in the delta pseudorange measurements is critical for high dynamic applications, and that sequential processing can be used to update different types of measurements without degrading the system estimation accuracy if certain conditions are met.

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.

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.

Integrated GPS navigation for civilian vehicles in challenging environment

The performance of the GPS based vehicle navigation will be degraded in the attenuated signal environments and reflected conditions. In this paper two types of sensor fusions are proposed to improve the reliability of GPS navigation in such environments: (1) the integration of GPS with other GNSS, including GLONASS and GALILEO, and (2) the integration of GPS with low-cost INS. Numerical results demonstrate that both methods will significantly improve the robustness and accuracy when the number of tracked GPS satellite is insufficient to generate reliable results.