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For a microwave locating system virtual synchronisation of low-cost free-running transmitter clocks is proposed. The synchronisation strategy is based on modelling the transmitter clocks. A stable stochastic model was designed by analysing the correlation function of the observables in the locating system i.e. time of arrival and carrier phase. Exploiting this model in a Kalman filter, a reference receiver estimates the current clock states and distributes them periodically to the mobile receivers enabling them to predict the transmitter clock deviations between updates. The impact of this synchronization scheme on the position accuracy using both simulated and real data is analysed

In this paper we study the problem of estimating the unknown delay(s) in a system where we receive a linear combination of several delayed copies of a known transmitted waveform. This problem arises in many applications such as timing-based localization or wireless synchronization. Since accurate delay estimation requires wideband signals, traditional systems need high-speed AD converters which poses a significant burden on the hardware implementation. Compressive sensing (CS) based system architectures that take measurements at rates significantly below the Nyquist rate and yet achieve accurate delay estimation have been proposed with the goal to alleviate the hardware complexity. In this paper, we particularly discuss the design of the measurement kernels based on a frequency-domain representation and show numerically that an optimized choice can outperform randomly chosen functionals in terms of the delay estimation accuracy

Multipath propagation is still one of the major problems in local positioning systems today. Especially in indoor environments, the received signals are disturbed by blockages and reflections. This can lead to a large bias in the user s time-of-arrival (TOA) value. Thus multipath is the most dominant error source for positioning. In order to improve the positioning performance in multipath environments, recent multipath mitigation algorithms based upon the concept of sequential Bayesian estimation are used. The presented approach tries to overcome the multipath problem by estimating the channel dynamics, using unscented Kalman filters (UKF). Simulations on artificial and measured channels from indoor as well as outdoor environments show the profit of the proposed estimator model. Furthermore, the quality of channel estimation applying the UKF and the channel sounding capabilities of the estimator are shown

The determination of orientation within time of arrival radio localisation systems is a widely discussed matter within the scientific world. In most cases, this goal is reached by using additional navigation sensors. Some other techniques exist, which are utilising only carrier phase measurements. For that purpose, the antenna configuration has to be chosen adequately. In the case considered here, a freely rotating and moving transmitter is equipped with a linearly polarised antenna. Two opposed circularly polarised antennas are used at the receiver. A general mathematical model for this basic measurement system is presented here. The electromagnetic field theory is deployed for the complete transmission chain up to the point where carrier phase measurements are obtained. As input to the model serve position and orientation of each of the transmitter and receiver antennas. The theory is verified by measurements with a rotating transmitter. The comparison of measured and calculated carrier phase values delivers a good match of theory and praxis