A Wireless Local Positioning System Concept and 6D Localization Approach for Cooperative Robot Swarms Based on Distance and Angle Measurements

Abstract

In wireless sensor networks, spatially distributed nodes provide location-dependent sensor information. Therefore, knowledge about the 3D position of all nodes is crucial for the numerous applications that require autonomous mobility. Furthermore, to acquire the nodes' poses and the complete 6D network constellation, the 3D orientation of each node is also required. While many theoretical localization concepts exist for wireless sensor networks, there is still a lack of reliable system and localization concepts which enable robust real-time tracking in real-world scenarios. Therefore, we present a system approach based on an advanced 24 GHz wireless local positioning system, providing distance and angle measurements between pairs of nodes. Furthermore, an extended Kalman filter based localization algorithm is proposed, which evaluates these measurements to track the time varying 6D poses of all nodes in the network. Because only relative measurements are available, one node is chosen to define a joint navigation system. Hence, the proposed system works without any previously installed infrastructure or prior information of the network. The system and localization algorithm are validated by measurements performed in a mobile wireless sensor network comprising six nodes in an indoor scenario with strong multipath propagation. However, despite the challenging environment, the system allows for a stable and accurate 6D pose estimation of all robots in the network with 3D positioning root mean square errors of 6 to 15 cm.

Exchanging Bandwidth With Aperture Size in Wireless Indoor Localization - Or Why 5G/6G Systems With Antenna Arrays Can Outperform UWB Solutions

“…As discussed in Section III, these large arrays also enable the suppression of the effects of multipath propagation, which is commonly considered the main problem for indoor localization systems [2]. This effect is similarly valid for moving receivers or transmitters, thereby creating a synthetic aperture, which can is examined in [51]. Note that the same effect also enables the in-situ calibration of antenna arrays in indoor localization systems, as in [52].…”

“…To combine the information of several receivers, typically recursive filters are used for sensor fusion and the incorporation of the beacon movement statistics [7], [16], [38], [51], [53], [54]. For this purpose, the measurement error statistics must be modeled as precisely as possible.…”

“…In contrast, the multipath propagation can impinge from arbitrary directions and, hence, their influence is bias-free in rich scattering environments. However, even for multipath propagation with a single dominant reflection, for example the ground in [51], the influence of the multipath propagation can be decreased by increasing the aperture size. In addition, PDOA receivers are easy to implement using small narrow band antennas that provide high directional coverage with almost constant radiation patterns.…”

“…Hence, similar to TOA systems, where the correlation decreases for high distance differences, in PDOA systems, the correlation decreases for high angle differences. Thus, in the far field domain, multipath propagation strongly distorts the localization, whenever the multipath wave impinges from an only slightly different direction than the LOS, such as reflections on the ground, as in [51]. Unlike TOA systems, the multipath can distort the position estimation toward all directions, depending on the direction of the impinging wave, as in [7].…”

“…On the one hand, this implies that impinging waves from all directions distort the localization process. On the other hand, the influence of strong reflectors, as the ground in [51], is reduced. Further, the influence varies for small beacon movements, which can be well utilized in recursive filters.…”