UWB sequential Monte Carlo positioning using virtual anchors

Abstract

We present a novel UWB indoor localization concept that performs the position estimation with a set of virtual anchor nodes, generated from a single physical anchor and floor plan information. Using range estimates to the virtual anchors, we perform multilateration to estimate the position of an agent. Previous work has shown the general applicability of this concept. In this contribution, we use a moving agent to exploit the correlation in successive positions using state-space concepts. A motion model for the agent and the measurement likelihood function allow for the use of the powerful framework of Bayesian state estimation. With this concept, we can propagate prior information on the agent position from one time step to the next. The statistical model for the ranging to the virtual anchors accounts for several imperfections, which lead to multimodal and heavy-tailed measurement distributions. We show how modified versions of the Kalman filter as well as a particle filter can account for these imperfections and yield accurate and robust position estimates. In a typical indoor pedestrian motion scenario, we can achieve an accuracy of about 45 cm for 90% of the estimates.

Ultra-wideband Radio Fuze Time Domain Doppler Effect Research

“…In the radio fuze system, The transmitter and receiver are located in the same projectile,the receiver signal is the target reflection signal,the duration time of signal is : The Doppler effect of UWB signal is the change of pulse width and pulse repetition cycle.,the pulse width of the echo signal and the repetition cycle is [6][7][8]:…”

Cooperative multipath-aided indoor localization

“…With the use of floor plan information, these reflections could be interpreted as originating from so called virtual anchors, which can be used for localization [4]. Utilizing these virtual anchors, localization was demonstrated with a single anchor [1]. Still, the performance was rather poor because, with the use of virtual anchors, ambiguities are added to the system so that at times no unique solution can be found.…”

“…In this paper, we reconsider the single anchor problem for indoor multipath localization from [1] and rewrite it as a Bayesian inference problem. This formulation allows us to include information from neighbors, naturally leading to a cooperative algorithm, by means of belief propagation.…”

“…The problem is represented with a factor graph, and belief propagation (BP) is used to extract the positions and their accuracy. The proposed cooperative algorithm leads to a significant improvement of the positioning accuracy compared to the non-cooperative method from [1]. …”

“…By reducing the number of anchors per room, overall costs are reduced. In [1], an algorithm that can estimate the position of a user using only one anchor and a priori floor plan information. However, performance for static localization was poor due to the presence of ambiguities.…”