Multi-sensor personal navigator supported by adaptive knowledge based system: Performance assessment

Abstract

The prototype of a personal navigator, which integrates Global Positioning System (GPS), tactical grade inertial measurement unit (IMU), digital barometer, magnetometer, and human pedometry to support navigation and tracking of military and rescue ground personnel has been developed at The Ohio State University Satellite Positioning and Inertial Navigation (SPIN) Laboratory. This paper discusses the design, implementation and performance assessment of the prototype, with a special emphasis on dead-reckoning (DR) navigation supported by a human locomotion model. The primary components of the human locomotion model are step frequency (SF), extracted from GPS-timed impact micro-switches placed on the shoe soles of the operator, step length (SL), and step direction (SD), both determined by predictive models derived by the adaptive knowledge based system (KBS). SL KBS is based on Artificial Neural Networks (ANN) and Fuzzy Logic (FL), and is trained a priori using sensory data collected by various operators in various environments during GPS signal reception. An additional KBS module, in the form of a Kalman Filter (KF), isused to improve the heading information (SD) available from the magnetometer and gyroscope under GPS-denied conditions, as well as to integrate the DR parameters to reconstruct the trajectory based on SL and SD. The current target accuracy of the system is 3-5 m CEP (circular error probable, 50%). This paper provides a performance analysis in the indoor and outdoor environments for two different operators. The system's navigation limitation in DR mode is tested in terms of time and trajectory length to determine the upper limit of indoor operation before the need for re-calibration.

Image Navigation Using A Tracking-Based Approach

“…To capture the landmark of opportunity a tracking solution should also be used because of the uncertainty in the image sensor and the platform location. An EKF will smooth the error estimation noise than the single cross-fixes described in (5) - (8). To measure the observability of both the navigation solution and the landmark of opportunity position, the observability gramian…”

“…Many incorporate inexpensive INS [2], [8]. It is well known, though, that these inertial systems have drifts and biases that without corrections will grow [8], [9].…”

Indoor Localization Algorithms for an Ambulatory Human Operated 3D Mobile Mapping System

“…While a direct comparison can-not be made, the authors rely on a correlation-like technique to validate loop closure transformations. To demonstrate the performance of a correlation only scheme in indoor environments, we perform the loop closure vetting again using only the correlation metric c of Equation (12). Figure 15 shows the resulting ROC curves.…”

“…Foot mounted systems have also been considered for indoor navigation [10][11][12]. In [10], a single IMU is mounted to a shoe and used to localize the operator without any additional external sensors.…”

“…The system utilizes the constraint that the shoe is stationary when it is in contact with the ground to estimate sensor bias and reduce accumulated drift. Other systems, including the works of Angermann et al [11] and Moafipoor et al [12], extend the idea by incorporating a barometer, magnetometer, and GPS sensor information to obtain an accurate position estimate that is globally referenced by occasional GPS fixes. While these types of systems are lightweight and inexpensive, the complex motion of the human gait makes it difficult to fuse the position estimate with data from sensors not mounted to the operator's shoe.…”