GPS/INS

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

A new coarse-time GPS positioning algorithm using combined Doppler and code-phase measurements

Abstract: A new coarse-time Global Positioning System (GPS) positioning algorithm based on the use of Doppler and code-phase measurements is proposed and described. The proposed method was demonstrated to be essential for reducing the time to first fix and the power consumption in a GPS receiver. Only 1 ms of data is required to obtain a positioning fix with accuracy comparable to that of the traditional GPS navigation algorithm. The algorithm is divided into two parts. In the first part, the Doppler measurement of the GPS signal is used to determine the coarse user position. With proper constraints, the required time accuracy for the Doppler measurements can be relaxed to be as long as 12 h. In the second part of the algorithm, the accurate user position is calculated by means of the 1 ms code-phase data. The traditional tracking process is no longer necessary in the proposed algorithm. Using the acquired 1-ms code-phase measurement, the positioning accuracy was determined to be approximately a few tens of meters in our experimental results. However, if the data length is extended to 10 ms, the positioning accuracy can be improved to within 10---20 m, which is similar to that of the traditional GPS positioning method. Various experiments were conducted to verify the usefulness of the proposed algorithm.

Real-Time Implementation of GPS Aided Low-Cost Strapdown Inertial Navigation System

Abstract: This work details the study, development, and experimental implementation of GPS aided strapdown inertial navigation system (INS) using commercial off-the-shelf low-cost inertial measurement unit (IMU). The data provided by the inertial navigation mechanization is fused with GPS measurements using loosely-coupled linear Kalman filter implemented with the aid of MPC555 microcontroller. The accuracy of the estimation when utilizing a low-cost inertial navigation system (INS) is limited by the accuracy of the sensors used and the mathematical modeling of INS and the aiding sensors' errors. Therefore, the IMU data is fused with the GPS data to increase the accuracy of the integrated GPS/IMU system. The equations required for the local geographic frame mechanization are derived. The direction cosine matrix approach is selected to compute orientation angles and the unified mathematical framework is chosen for position/velocity algorithm computations. This selection resulted in significant reduction in mechanization errors. It is shown that the constructed GPS/IMU system is successfully implemented with an accurate and reliable performance.

A topological approach with MEMS in smartphones based on routing-graph

Abstract: GNSS (Global Navigation Satellite System) supported navigation with smartphones is an established technique. Solutions for position estimation for GNSS-shaded areas are developed more and more in the research field of the indoor navigation. The MEMS (MicroElectroMechanical Systems) sensors integrated into smartphones will be used increasingly for this. The aim of the research group at the HCU is the autonomous navigation only with MEMS sensors, favors are accelerometer, gyroscope and barometer. The quality of MEMS inertial sensors based position estimate decreases with time. Additional information should be used as support which is a prerequisite to realize a navigation application. A support is therefore possible with the available map data or the routing graph. In most approaches, the low-cost sensors are fused with the help of particle filter and Kalman filter at present. This serves the simple integration of external support, such as maps. In this work an approach without these filters is presented. A position estimate based on the routing nodes and edges shall be realized, only with the integrated MEMS inertial sensors in the smartphone. A fusion with further supports is not provided. The position estimate is calculated on the path network which is normally the basis for the routing to calculate the path. The position on a routing edge is derived from the acceleration sensors and the gyroscopes with step detection and orientation comparison. At a routing node, the sensor data is used to choose the probably nearest routing edge. For comparison purposes, approaches with Kalman filter and particle filter are applied to the same data set.

Three-dimension indoor positioning algorithms using an integrated RFID/INS system in multi-storey buildings

Abstract: Location based services (LBS) require a reliable, accurate and continuous position determination of mobile users. This is particularly true in indoor environments where the widely used Global Positioning System ( GPS) is not available due to its signal outages. One solution is to integrate different techniques in a multi-sensor positioning system to overcome the limitations of a single sensor. In this chapter an approach is described using a three-dimensional Radio Frequency Identifi cation (3D RFID) location fi ngerprinting probabilistic approach with map-based constraints in order to provide reliable positions in indoor 3D environments. An Extended Kalman Filter (EKF) is used to integrate 3D RFID positioning method with an Inertial Navigation System (INS) in order to produce an accurate and continuous positioning estimation.