A Rapid and Adaptive Alignment under Mooring Condition Using Adaptive EKF and CNN-Based Learning.

Alignment of the inertial navigation system (INS) in the mooring environment should take into account the movements of the waves or wind. The alignment of the INS is performed through an extended Kalman filter (EKF) using zero velocity as a measurement. However, in the mooring condition, this is not perfect stationary, thus the measurement error covariance matrix should be adjusted.

Enhanced Pedestrian Navigation Based on Course Angle Error Estimation Using Cascaded Kalman Filters.

An enhanced pedestrian dead reckoning (PDR) based navigation algorithm, which uses two cascaded Kalman filters (TCKF) for the estimation of course angle and navigation errors, is proposed. The proposed algorithm uses a foot-mounted inertial measurement unit (IMU), waist-mounted magnetic sensors, and a zero velocity update (ZUPT) based inertial navigation technique with TCKF. The first stage filter estimates the course angle error of a human, which is closely related to the heading error of the IMU.

Kinematic Model-Based Pedestrian Dead Reckoning for Heading Correction and Lower Body Motion Tracking.

In this paper, we present a method for finding the enhanced heading and position of pedestrians by fusing the Zero velocity UPdaTe (ZUPT)-based pedestrian dead reckoning (PDR) and the kinematic constraints of the lower human body. ZUPT is a well known algorithm for PDR, and provides a sufficiently accurate position solution for short term periods, but it cannot guarantee a stable and reliable heading because it suffers from magnetic disturbance in determining heading angles, which degrades the overall position accuracy as time passes. The basic idea of the proposed algorithm is integrating the left and right foot positions obtained by ZUPTs with the heading and position information from an IMU mounted on the waist.

Design of a MEMS piezoresistive differential pressure sensor with small thermal hysteresis for air data modules.

The design, fabrication, and evaluation results of a MEMS piezoresistive differential pressure sensor fabricated by the dry etching process are described in this paper. The proposed sensor is designed to have optimal performances in mid-pressure range from 0 psi to 20 psi suitable for a precision air data module. The piezoresistors with a Wheatstone bridge structure are implanted where the thermal effects are minimized subject to sustainment of the sensitivity.

Design of a base station for MEMS CCR localization in an optical sensor network.

This paper introduces a design and implementation of a base station, capable of positioning sensor nodes using an optical scheme. The base station consists of a pulse laser module, optical detectors and beam splitter, which are mounted on a rotation-stage, and a Time to Digital Converter (TDC). The optical pulse signal transmitted to the sensor node with a Corner Cube Retro-reflector (CCR) is reflected to the base station, and the Time of Flight (ToF) data can be obtained from the two detectors.

Double fault detection of cone-shaped redundant IMUs using wavelet transformation and EPSA.

A model-free hybrid fault diagnosis technique is proposed to improve the performance of single and double fault detection and isolation. This is a model-free hybrid method which combines the extended parity space approach (EPSA) with a multi-resolution signal decomposition by using a discrete wavelet transform (DWT). Conventional EPSA can detect and isolate single and double faults.

Target Localization Using Ensemble Support Vector Regression in Wireless Sensor Networks.

Target localization, whose goal is to estimate the location of an unknown target, is one of the key issues in applications of wireless sensor networks (WSNs). With recent advances in fabrication technology, deployments of large-scale WSNs have become economically feasible. However, there exist issues such as limited communication and the curse of dimensionality in applying machine-learning algorithms such as support vector regression (SVR) on large-scale WSNs.

Adaptive step length estimation algorithm using optimal parameters and movement status awareness.

An adaptive step length estimation algorithm operating with optimal parameters and a movement status awareness algorithm are proposed. The proposed algorithm was developed as a means for increasing the accuracy in estimating walking distances. The algorithm was applied to pedestrian navigation system, ubiquitous health monitoring systems, and so forth.