In harsh environments where geographic positioning fails, communication between wireless nodes can be used to improve the accuracy of location information.

This paper presents Soli, a new, robust, high-resolution, low-power, miniature gesture sensing technology for human-computer interaction based on millimeter-wave radar. We describe a new approach to developing a radar-based sensor optimized for human-computer interaction, building the sensor architecture from the ground up with the inclusion of radar design principles, high temporal resolution gesture tracking, a hardware abstraction layer (HAL), a solidstate radar chip and system architecture, interaction models and gesture vocabularies, and gesture recognition. We demonstrate that Soli can be used for robust gesture recognition and can track gestures with sub-millimeter accuracy, running at over 10,000 frames per second on embedded hardware.

Location awareness is a key enabling feature and fundamental challenge in present and future wireless networks. Most existing localization methods rely on existing infrastructure and thus lack the flexibility and robustness necessary for large ad hoc networks. In this paper, we build upon SPAWN (sum-product algorithm over a wireless network), which determines node locations through iterative message passing, but does so at a high computational cost. We compare different message representations for SPAWN in terms of performance and complexity and investigate several types of cooperation based on censoring. Our results, based on experimental data with ultra-wideband (UWB) nodes, indicate that parametric message representation combined with simple censoring can give excellent performance at relatively low complexity.

Localization systems based on ultrawide bandwidth (UWB) technology have been recently considered for indoor environments, due to the property of UWB signals to resolve multipath and penetrate obstacles. However, line-of-sight (LoS) blockage and excess propagation delay affect ranging measurements thus drastically reducing the localization accuracy. In this paper, we first characterize and derive models for the range estimation error and the excess delay based on measured data from real ranging devices. These models are used in various multilateration algorithms to determine the position of the target. Using measurements in a real indoor scenario, we investigate how the localization accuracy is affected by the number of beacons and by the availability of priori information about the environment and network geometry. We also examine the case where multiple targets cooperate by measuring ranges not only from the beacons but also from each other. An iterative multilateration algorithm that incorporates information gathered through cooperation is then proposed with the purpose of improving the localization accuracy. Using numerical results, we demonstrate the impact of cooperation on the localization accuracy.

Figure 1. We explore interactive possibilities enabled by Google's project Soli (A), a solid-state short-range radar, capturing energy reflected of hands and other objects (B). The signal is unique in that it resolves motion in the millimeter range but does not directly capture shape (C). We propose a novel gesture recognition algorithm specifically designed to recognize subtle, low-effort gestures based on the Soli signal.