Adaptive Cardinal Heading Aided for Low Cost Foot-Mounted Inertial Pedestrian Navigation

The use of a low-cost MEMS-based Inertial Measurement Unit (IMU) provides a cost-effective approach for navigation purposes. Foot-mounted IMU is a popular option for indoor inertial pedestrian navigation, as a small and light MEMS-based inertial sensor can be tied to a pedestrian's foot or shoe. Without relying on GNSS or other external sensors to enhance navigation, the foot-mounted pedestrian navigation system can autonomously navigate, relying solely on the IMU. This is typically performed with the standard strapdown navigation algorithm in a Kalman filter, where Zero Velocity Updates (ZVU) are used together to restrict the error growth of the low-cost inertial sensors. ZVU is applied every time the user takes a step since there exists a zero velocity condition during stance phase. While velocity and correlated attitude errors can be estimated correctly using ZVUs, heading error is not because it is unobservable. In this paper, we extend our previous work to correct the heading error by aiding it using Multiple Polygon Areas (MPA) with adaptive weighting factor. We termed the approach as Adaptive Cardinal Heading Aided Inertial Navigation (A-CHAIN). We formulated an adaptive weighting factor applied to measurement noise to enhance measurement confidence. We then incorporated MPA heading into the algorithm, whereas multiple buildings with the same orientation are grouped together and assigned a specific heading information as a priori. Results shown that against the original CHAIN, the proposed Adaptive-CHAIN improved the position accuracy by more than five-fold

Packet Loss Rate Differentiation in slotted Optical Packet Switching OCDM/WDM

We propose a multi-class mechanism for Optical Code Division Multiplexing (OCDM), Wavelength Division Multiplexing (WDM) Optical Packet Switch (OPS) architecture capable of supporting Quality of Service (QoS) transmission. OCDM/WDM has been proposed as a competitive hybrid switching technology to support the next generation optical Internet. This paper addresses performance issues in the slotted OPS networks and proposed four differentiation schemes to support Quality of Service. In addition, we present a comparison between the proposed schemes as well as, a simulation scheduler design which can be suitable for the core switch node in OPS networks. Using software simulations the performance of our algorithm in terms of losing probability, the packet delay, and scalability is evaluated

Understanding the performance of zero velocity updates in MEMS-based pedestrian navigation

Zero Velocity Update (ZUPT) is an important update to aid an autonomous inertial pedestrian navigation. The objectives of this paper are to briefly revisit the concept of ZUPT and its importance, testing it on real walking pedestrian and comparing its performance when used with either conventional ‘Dead Reckoning approach (DR)’ or with ‘Kalman Filter approach (KF)’ as either one of these approaches is commonly used in literature. Performances were analyzed further with the inclusion of two correction modes (Linearly Weighted Interpolation and Residual Velocity). Experiments were performed using a low cost Inerital Measurement Unit (IMU) from MicroStrain (3DM-GX1). It was shown that the KF approach outperformed DRonly approach, but comparable performance with KF was noticed when DR is combined with correction mode. Finally, a combination of RV correction mode with forward KF solution was shown to improve the position output

Increased error observability of an inertial pedestrian navigation system by rotating IMU

Indoor pedestrian navigation suffers from the unavailability of useful GNSS signals for navigation. Often a low-cost non-GNSS inertial sensor is used to navigate indoors. However, using only a low-cost inertial sensor for the system degrades its performance due to the low observability of errors affecting such low-cost sensors. Of particular concern is the heading drift error, caused primarily by the unobservability of z-axis gyro bias errors, which results in a huge positioning error when navigating for more than a few seconds. In this paper, the observability of this error is increased by proposing a method of rotating the inertial sensor on its y-axis. The results from a field trial for the proposed innovative method are presented. The method was performed by rotating the sensor mechanically–mounted on a shoe–on a single axis. The method was shown to increase the observability of z-axis gyro bias errors of a low-cost sensor. This is very significant because no other integrated measurements from other sensors are required to increase error observability. This should potentially be very useful for autonomous low-cost inertial pedestrian navigation systems that require a long period of navigation time

Automatic Urban Road Users' Tracking System

This paper presents a Dynamic Gradient Pattern (DGP) based on Kalman filtering technique for urban road users tracking.  DGP technique is proposed to enhance rigid object descriptive ability for improved verification. DGP descriptor along with weighted centroid was integrated with a Kalman filtering framework to enhance data association robustness and tracking accuracy. To handle multiple objects tracking, a DGP verification approach is addressed based on normalized Bhattacharyya distance. The proposed technique achieves a closer trajectory for rigid body movement. The DGP descriptor can discriminate the objects correctly, and it overcomes the partial occlusion and misdetection by verifying object location using the normalized Bhattacharyya distance between DGP features. Experimental evaluation is performed on urban videos that include a slow-motion temporary stop and partial occlusion.  The experimental results demonstrate that the detecting and tracking accuracy are above 98.08% and 97.70% respectively

Rotating a mens inertial measurement unit for a foot-mounted pedestrian navigation

Pedestrian navigation especially indoors suffers from the unavailability of useful GNSS signals for positioning. Alternatively, a low-cost Inertial Measurement Unit (IMU) positioning system that does not depend on the GNSS signal can be used for indoor navigation. However its performance is still compromised because of the fast-accumulating heading drift error affecting such a low-cost IMU sensor. This results in a huge positioning error when navigating more than a few seconds using only the low-cost sensor. In this study, real field trials results are presented when a foot-mounted IMU is rotated on a single axis. Two promising results have been obtained. First, it mitigates the heading drift error significantly and second, it increases the observability of IMU z-axis gyro bias error. This has resulted in a greatly reduced error in position for the low-cost pedestrian navigation system