3,709 research outputs found
Improving Foot-Mounted Inertial Navigation Through Real-Time Motion Classification
We present a method to improve the accuracy of a foot-mounted,
zero-velocity-aided inertial navigation system (INS) by varying estimator
parameters based on a real-time classification of motion type. We train a
support vector machine (SVM) classifier using inertial data recorded by a
single foot-mounted sensor to differentiate between six motion types (walking,
jogging, running, sprinting, crouch-walking, and ladder-climbing) and report
mean test classification accuracy of over 90% on a dataset with five different
subjects. From these motion types, we select two of the most common (walking
and running), and describe a method to compute optimal zero-velocity detection
parameters tailored to both a specific user and motion type by maximizing the
detector F-score. By combining the motion classifier with a set of optimal
detection parameters, we show how we can reduce INS position error during mixed
walking and running motion. We evaluate our adaptive system on a total of 5.9
km of indoor pedestrian navigation performed by five different subjects moving
along a 130 m path with surveyed ground truth markers.Comment: In Proceedings of the International Conference on Indoor Positioning
and Indoor Navigation (IPIN'17), Sapporo, Japan, Sep. 18-21, 201
Optimizing Indoor Location Based Tracking through Proper Filter Selection and Wireless Sensor Network Design
Indoor positioning system (IPS) is a topic that is coming up more and more for various reasons, such as allowing companies to track important objects using radio frequency identification (RFID) and employees with Bluetooth devices inside a facility. Geofencing is one of the biggest topics with IPS and is meant to limit access to a network in specified areas. Devices that incorporate indoor tracking are not initially precise when objects and employees are on the move. This movement requires devices to have a reliable filter for noise and package lose. For this paper, the comparison between extended Kalman filters and unscented Kalman filter in a controlled environment will help indicate which is ideal for IPS tracking. Both filters will be applied and compared on location accuracy metrics. The proper design of the wireless network is also crucial for having an effective IPS method. This will show the difference in wireless networks and how the initial design will lead to greater chance of success for IPS
UWB-INS Fusion Positioning Based on a Two-Stage Optimization Algorithm
Ultra-wideband (UWB) is a carrier-less communication technology that transmits data using narrow pulses of non-sine waves on the nanosecond scale. The UWB positioning system uses the multi-lateral positioning algorithm to accurately locate the target, and the positioning accuracy is seriously affected by the non-line-of-sight (NLOS) error. The existing non-line-of-sight error compensation methods lack multidimensional consideration. To combine the advantages of various methods, a two-stage UWB-INS fusion localization algorithm is proposed. In the first stage, an NLOS signal filter is designed based on support vector machines (SVM). In the second stage, the results of UWB and Inertial Navigation System (INS) are fused based on Kalman filter algorithm. The two-stage fusion localization algorithm achieves a great improvement on positioning system, it can improve the localization accuracy by 79.8% in the NLOS environment and by 36% in the (line-of-sight) LOS environment
Cooperative localization by dual foot-mounted inertial sensors and inter-agent ranging
The implementation challenges of cooperative localization by dual
foot-mounted inertial sensors and inter-agent ranging are discussed and work on
the subject is reviewed. System architecture and sensor fusion are identified
as key challenges. A partially decentralized system architecture based on
step-wise inertial navigation and step-wise dead reckoning is presented. This
architecture is argued to reduce the computational cost and required
communication bandwidth by around two orders of magnitude while only giving
negligible information loss in comparison with a naive centralized
implementation. This makes a joint global state estimation feasible for up to a
platoon-sized group of agents. Furthermore, robust and low-cost sensor fusion
for the considered setup, based on state space transformation and
marginalization, is presented. The transformation and marginalization are used
to give the necessary flexibility for presented sampling based updates for the
inter-agent ranging and ranging free fusion of the two feet of an individual
agent. Finally, characteristics of the suggested implementation are
demonstrated with simulations and a real-time system implementation.Comment: 14 page
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