GPS/INS

About: GPS/INS is a research topic. Over the lifetime, 3554 publications have been published within this topic receiving 62784 citations.

A theoretical performance analysis of the modernized GPS signals

Abstract: The frequency and signal design of the American global positioning system (GPS) is currently in a modernization period. The baseline configuration of GPS only consists of two signals on two carrier frequencies (C/A and P(Y) code). At present the existing satellites (IIA, IIR) are replaced by new modernized satellites (IIR-M, IIF) which are capable of transmitting additional signals. The modernization of GPS includes the extension of the frequency plan by a third frequency band (L5). We will evaluate the theoretical potential performance of the baseline signals and the new modernized signals of GPS in terms of positioning accuracy. This includes a discussion of the main error sources and the signal characteristics (signal modulation, carrier frequency and receiver bandwidth). Based on the contributions of the individual error sources we will give estimations of the typical performance of GPS for single-frequency and double-frequency receivers.

Algorithm for Pedestrian Navigation Combining IMU Measurements and Gait Models

Abstract: This paper presents a novel approach to INS velocity aiding in autonomous pedestrian navigation systems with body-mounted IMU The proposed solution uses a kinetic model of human gait as a virtual velocity sensor In this paper we show how an understanding of INS error dynamics and knowledge of human motion help to curb the divergence of INS computed horizontal velocity and tilt errors Heading and heading gyro drift cannot be corrected with this method and require some additional procedures This algorithm is based on Kalman filter and can be adapted for implementation on real-time pedestrian navigation systems equipped with 6 DOF IMU The algorithm accuracy performance was investigated using data from indoor walking tests

An Enhanced 3D Multi-Sensor Integrated Navigation System for Land-Vehicles

Abstract: In urban areas, Global Positioning System (GPS) accuracy deteriorates due to signal degradation and multipath effects. To provide accurate and robust navigation in such GPS-denied environments, multi-sensor integrated navigation systems are developed. This paper introduces a 3D multi-sensor navigation system that integrates inertial sensors, odometry and GPS for land-vehicle navigation. A new error model is developed and an efficient loosely coupled closed-loop Kalman Filter (Extended KF or EKF) integration scheme is proposed. In this EKF-based integration scheme, the inertial/odometry navigation output is continuously corrected by EKF-estimated errors, which keeps the errors within acceptable linearization ranges which improves the prediction accuracy of the linearized dynamic error model. Consequently, the overall performance of the integrated system is improved. Real road experiments and comparison with earlier works have demonstrated a more reliable performance during GPS signal degradation and accurate estimation of inertial sensor errors (biases) have led to a more sustainable performance reliability during long GPS complete outages.

Precise RTK Positioning with GPS/ INS Tight Coupling and Multipath Estimation

Abstract: Real-Time Kinematic (RTK) positioning is attractive for numerous applications including autonomous driving of vehicles. However, multipath remains a challenge for RTK positioning especially in urban environments. Choke-ring antennas which suppress code multipath cannot be used due to restrictions on the size, weight and costs. The used low-cost patch antennas cannot suppress the multipath, which can be of several tens of meters. Therefore, we will include a code multipath parameter for each satellite in our RTK positioning to prevent a mapping of the multipath into other state parameters and to exploit the temporal correlation of the multipath. We also enhance the ambiguity fixing by introducing two fixing phases: A candidate determination phase and a candidate tracking phase. In the first phase, we determine sets of integer candidate vectors using LAMBDAs integer decorrelation and tree search [1]. As the float solution and/ or its statistics might be biased, we determine integer candidate vectors at multiple epochs with different float solutions and merge these integer candidate vectors. Thereby, we increase the likelihood of including the correct candidate vector in the set of candidate vectors. In the second phase, a conditional least-squares phase-only baseline estimate is determined for each candidate vector at every epoch. The sum of squared measurement residuals is accumulated over time for every candidate vector. This second phase has two important advantages over an instantaneous decision: First, the accumulation of the residuals improves the discrimination between candidates. Secondly, the used single epoch least-squares phase-only solutions are not affected by the float solution. This is helpful since any temporal correlation in the phase measurements (e.g. due to phase multipath) could lead to erroneous statistics of the float solution. We fix the RTK and attitude ambiguities sequentially: First, the attitude ambiguities are fixed in a tree search using soft a priori information on the baseline length. Subsequently, the RTK ambiguities are fixed in another tree search using both the measurements of the RTK baseline and the fixed measurements of the attitude baseline. We select the final candidate based on the accumulated sum of squared phase residuals and the baseline stability. The fixed attitude enables a precise estimation of the accelerometer and angular rate biases. A precise RTK position and attitude is then be obtained by tracking the fixed solution with GPS/ INS tight coupling.We show the RTK performance for both static and kinematic measurements: We obtained a millimeter-level positioning accuracy for static conditions and a centimeter-level positioning accuracy for kinematic conditions with multipath errors of up to 50 m.