stochastic radio interferometric positioning in the 2.4 ghz range
This paper presents a novel Radio Interferometric Positioning System (RIPS), which we call Stochastic RIPS (SRIPS). Although RIPS provides centimeter accuracy, it is still not widely adopted due to (1) the limited set of suitable radio platforms and (2) the relatively long measurement and calibration times. SRIPS overcomes these practical limitations by (1) omitting the calibration phase of the existing RIPS and by (2) applying a novel positioning algorithm. SRIPS exploits the phenomenon of the small but stable difference between two transmitted frequencies that often exists when two radios are tuned to the same frequency. We obtain an experimental measure for this stability. This approach enables the implementation of RIPS on commonly available radio platforms, such as the CC2430, because fine-tuning in small steps relative to the beat frequencies for calibration is not required. In addition, we show that SRIPS calculates the position that provides the best fit to the set of measurements, given the underlying statistical and propagation models. Therefore, SRIPS converges more accurately to the true locations in a variety of situations of practical interest. Experiments in a 20x20 m 2 set-up verify this and show that our SRIPS CC2430 implementation reduces the number of required measurements by a factor of three, and it reduces the measurement time to less than 0.1 seconds, while providing accuracy similar to that of the existing RIPS implementation on the CC1000 platform, which requires seconds.
wireless sensor networks tracking localization radio interferometric localization rss-based localization