GPS/INS

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

GPS/INS Based Lateral and Longitudinal Control Demonstration: Final Report

Abstract: This report describes the results of a study focusing on implementing and analyzing the performance of a Differential Global Positioning System (DGPS) aided Inertial Navigation System (INS) for possible future application in Advanced Vehicle Control Systems (AVCS) The DGPS/INS system provided estimates of vehicle position, linear velocities, and angular rates Position accuracy at the centimeter level was achieved and demonstrated through two experiments

Improving the energy consumption in mobile phones by filtering noisy GPS fixes with modified Kalman filters

Abstract: Real-time location-based tracking applications require of continuous GPS calculations and transmissions, which consume a considerable amount of the phone's battery. As a result, methods have been devised to reduce the amount of GPS calculations and transmissions without sacrificing the tracking capabilities of the applications. One of these methods is based on a state machine that dynamically changes the frequency of GPS updates according to the user direction, speed, received signal strength, and other factors. However, the state machine, although efficient in terms of energy savings, still presents one major problem: it does not take into account the presence of noise in GPS data. In order to distinguish between actual GPS data and noise, three versions of the Kalman filter have been implemented within the state machine. These modified Kalman filters remove noisy GPS fixes with little to no input from the user in a very efficient manner. The filters are discussed in detail and tested against one another to determine which one removes GPS noise better and which one reduces the energy consumption in the cellular phone more with no loss of valuable tracking data. Experiments conducted show the Adaptive Kalman Filter as the best performer. No loss of valuable tracking data is seen while it introduces a significant decrease in the number of “asleep” fixes. The Adaptive Robust Kalman Filter is the second best performer of the three filters. It shows no loss of tracking data, while a slightly less decrease in “sleep” fixes. Testing shows that the Robust Kalman Filter is the worst performer of the three. This is because the Robust Kalman Filter is the slowest version to “wake up” and make transitions to a “sleep” state.

Inertial navigation accuracy enhancement

Abstract: Apparatus for the improvement of inertial navigation system (INS) position/velocity comprises frame stores for storing sequential images from a video camera and a comparator for comparing pixels in predicted images, derived from earlier stored images as modified by INS linear and angular velocity outputs, with pixels in actual later stored images, to derive a velocity error correction signal which is used to correct the INS output. The video processing correction system described is passive and hence does not reveal the aircraft's whereabouts as would be the case with other positional correction systems using radar, nor does it rely on systems outside the control of the INS, e.g., GPS, nor is it reliant on a vast terrain feature database.

GPS/INS Integration Based on Dynamic ANFIS Network

Abstract: This article presents a new structure for solving global positioning system (GPS) outages for long periods without requiring any prior information about the characteristics of the inertial navigation system (INS) and GPS. Kalman filter (KF) is widely used in INS and GPS integration to present a forceful navigation solution by overcoming the GPS outage problems. However, KF is usually criticized for working under predefined models and for its observability problem of hidden state variables, sensor dependency, and linearization dependency. Therefore, this article proposes a dynamic adaptive neuro-fuzzy inference system (DANFIS) to predict the INS error during GPS outages based on the current and previous raw INS data. The proposed integrated system is evaluated using a real field test data. The performance of the proposed technique is also compared with the traditional artificial intelligence (AI) technique and KF. The results showed great improvements in positioning and especially in velocity for MEMS grade IMU and for different length of GPS outages.