Inertial sensors comprise of two primary sensor units: accelerometers and gyroscopes. An inertial measurement unit (IMU) combines multiple accelerometers and gyros, usually three of each, to produce a three-dimensional measurement of specific force and angular rate (Weston and Titterton 2000). New designs of INS employ a strap down architecture, whereby the inertial sensors are fixed with respe...

The paper deals with the study of the influence of the inertial sensors errors on the position, speed and attitude of a bidimensional strap-down inertial navigation system (SDINS) in a vertical plane. To test this influence we put up MATLAB/SIMULINK models for the acceleration and rotation sensors based on the sensors data sheets and on the IEEE equivalent models for the inertial sensors. The m...

This paper focuses on accuracy and other technology trends for inertial sensors, Global Positioning Systems (GPS), and integrated Inertial Navigation System (INS)/GPS systems, including considerations of interference, that will lead to better than 1 meter accuracy navigation systems of the future. For inertial sensors, trend-setting sensor technologies will be described. A vision of the inertia...

An Inertial Navigation System (INS) can easily track position, velocity and orientation of any moving vehicle. Generally, deterministic errors are present in an uncalibrated Inertial Measurement Unit (IMU) which leads to the requirement of an accurate estimation of navigation solution. These inertial sensors, thus, needs to be calibrated to reduce the error inherent in these systems. By mathema...

with the continuous development of the industrialization process, the countries all over the world gradually appeared lack of agricultural labor force and aging phenomenon, which was especially prominent in developed countries. however the agricultural robot with high operating efficiency, high qualities of work will play an increasingly important role in future agricultural production. robot n...

New approaches to the development of data fusion algorithms for inertial network systems are described. The aim of this development is to increase the accuracy of estimates of inertial state vectors in all the network nodes, including the navigation states, and also to improve the fault tolerance of inertial network systems. An analysis of distributed inertial sensing models is presented and ne...

Determination of (in)activity periods when monitoring human body motion is a mandatory preprocessing step in all human inertial navigation and position analysis applications. Distinction of (in)activity needs to be established in order to allow the system to recompute the calibration parameters of the inertial sensors as well as the Zero Velocity Updates (ZUPT) of inertial navigation. The perio...

We introduce the inertial-DNF (iDNF) model, an expansion of the dynamic neural filter (DNF) model, a model generating spatiotemporal patterns similar to those observed in the locust antennal lobes (ALs). The DNF model, which was described in previous works, includes one temporal scale defining the discrete dynamics inherent to the model. It lacks a second, slow, temporal scale that exists in th...