The increasing diffusion of mobile and portable devices provided with wireless connectivity makes the problem of distance measurement based on radio-frequency technologies increasingly important for the development of next-generation nomadic applications. In this paper, the performance limitations of two classic wireless ranging techniques based on received signal strength (RSS) and two-way time-of-flight (ToF) measurements, respectively, are analyzed and compared in detail. On the basis of this study, a data fusion algorithm is proposed to combine both techniques in order to improve ranging accuracy. The algorithm has been implemented and tested on the field using a dedicated embedded prototype made with commercial off-the-shelf components. Several experimental results prove that the combination of both techniques can significantly reduce measurement uncertainty.
The results obtained with the developed prototype are not accurate enough for fine-grained position tracking in Ambient Assisted Living applications. However, the platform can be successfully used for reliable indoor zoning, e.g., for omni directional and adjustable hazard proximity detection. Most importantly, the proposed solution is absolutely general, and it is quite simple and light from the computational point of view. Accuracy could be further improved by using a more isotropic antenna and by integrating the To F measurement technique at the lowest possible level on the same radio chip used for communication. Usually, this feature is not available in typical low-cost short-range wireless modules, e.g., for wireless sensor networks. Thus, the results of this research suggest that combining RSS with To F measurements could be a viable solution for chip manufacturers interested in adding ranging capabilities to their radio modules. A Data Fusion Technique for Wireless Ranging Performance Improvement
The fact, achieving both Omni directionality and accuracy in the short range is notoriously quite Hard, and consequently, it is still a hot research topic worldwide. Several approaches relying on different sensing technologies have been proposed for indoor positioning and ranging, e.g., based on laser rangefinders ultrasound devices infrared sensors inertial platforms and video cameras or combinations thereof. Camera-based solutions are very effective in terms of accuracy, even in the presence of partial occlusions. However, they are not always usable because of privacy issues and because they suffer from scalability problems. To overcome the directional constraint of such systems, pan tilt and unidirectional cameras have been also proposed. Their main drawback is the high computational burden when multiple targets have to be recognized and tracked. In addition, they could exploit the same wireless modules used for communication, and they are particularly suitable for wearable applications. In wireless RF ranging techniques, the distance between two objects is indirectly measured from some distance related parameters of the RF signals. The two most common approaches are based on received signal strength (RSS) and message time-of-flight (ToF) measurements. The RSS-based methods rely on the relationship between the measured received signal power and the transmitter–receiver distance. If the transmitted power and the signal propagation model are known, the distance from the transmitter can be estimated by reversing the equation of the model. Usually, the RSS can be easily measured without additional circuitry, because most of the integrated wireless chips are natively equipped with an RSS indicator. RSS-based ranging has been widely analyzed in recent years, both theoretically and experimentally.
The calibration procedure, about 5000 RSS and RTT values were collected by the MTS from the FA. The path loss coefficient can be estimated through linear regression, after applying the base-10 logarithm function to both terms. From this procedure, it follows that with negligible uncertainty. In the standard uncertainty and the RMSE patterns associated respectively, are plotted as a function of the real distance. The solid lines result from a Type-A uncertainty evaluation at different known distances from the FA, after removing the static position-dependent offsets. The dotted lines refer to the theoretical worst-case standard uncertainty respectively. Clearly, the theoretical and experimental uncertainty patterns are in good agreement. In particular, the uncertainty associated with the RSS data tends to grow with distance, whereas the uncertainty related to estimates is approximately constant, as expected. The dashed represent the experimental RMSE patterns including the effect of both random fluctuations and position-dependent offsets.