Accurate and low-cost sensor localization is a critical requirement for the deployment of wireless sensor networks in a wide variety of applications. In cooperative localization, sensors work together in a peer-to-peer manner to make measurements and then forms a map of the network. Various application requirements influence the design of sensor localization systems. In this article, the authors describe the measurement-based statistical models useful to describe time-of-arrival (TOA), angle-of-arrival (AOA), and received-signal-strength (RSS) measurements in wireless sensor networks. Wideband and ultra-wideband (UWB) measurements, and RF and acoustic media are also discussed. Using the models, the authors have shown the calculation of a Cramer-Rao bound (CRB) on the location estimation precision possible for a given set of measurements. The article briefly surveys a large and growing body of sensor localization algorithms. This article is intended to emphasize the basic statistical signal processing background necessary to understand the state-of-the-art and to make progress in the new and largely open areas of sensor network localization research.

Locating the nodes: cooperative localization in wireless sensor networks

Self-configuration in wireless sensor networks is a general class of estimation problems that we study via the Cramer-Rao bound (CRB). Specifically, we consider sensor location estimation when sensors measure received signal strength (RSS) or time-of-arrival (TOA) between themselves and neighboring sensors. A small fraction of sensors in the network have a known location, whereas the remaining locations must be estimated. We derive CRBs and maximum-likelihood estimators (MLEs) under Gaussian and log-normal models for the TOA and RSS measurements, respectively. An extensive TOA and RSS measurement campaign in an indoor office area illustrates MLE performance. Finally, relative location estimation algorithms are implemented in a wireless sensor network testbed and deployed in indoor and outdoor environments. The measurements and testbed experiments demonstrate 1-m RMS location errors using TOA, and 1- to 2-m RMS location errors using RSS.

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Relative location estimation in wireless sensor networks

http://www.eecs.northwestern.edu/~peters/references/LocalizationTechniquesMaoAustralia.pdf

Wireless sensor network localization techniques

For wireless sensor networks, received signal strength (RSS) and proximity (also known as connectivity) measurements have been proposed as simple and inexpensive means to estimate range between devices and sensor location. While RSS measurements are recognized to suffer from errors due to the random nature of the fading channel, proximity measurements, ie., knowing only whether or not two devices are in communication range, are often discussed without considering that they are affected by the same fading channel. Proximity measurements are actually just a binary quantization of RSS measurements. We use the Cramer-Rao bound (CRB) to compare the minimal attainable variances of unbiased sensor location estimators for the cases of RSS and proximity measurements. For completeness, we also present the CRB for sensor localization with systems using K-level quantized RSS (QRSS) measurements, of which proximity measurements are the special case: K=2. Examples are presented for the case of one unknown-location sensor, and for the case of a 5 by 5 grid of sensors. These examples show that lower bounds for standard deviation in proximity-based systems are, as a rule of thumb, about 50% higher than the bounds for RSS-based systems. Furthermore, results are presented which show how many bits of quantization are necessary for a QRSS-based system to nearly achieve the bounds of an unquantized RSS system.

/pdf/using-proximity-and-quantized-rss-for-sensor-localization-in-2ftls4m8cg.pdf

Using proximity and quantized RSS for sensor localization in wireless networks

A position location communication system (10) determines the position of a master radio (12) using a round-trip messaging scheme in which the time of arrive (TOA) of ranging messages is accurately determined to yield the range estimates required to calculate the position of the master radio via trilateration. The master radio transmits outbound ranging messages to plural reference radios (14, 16, 18, 20) which respond by transmitting reply ranging messages. Upon reception of the reply ranging message, the master radio determines the range to the reference radio from the signal propagation time calculated by subtracting the far-end turn around time from the round-trip elapsed time. Any combination of fixed or mobile radios of known positions can be used as the reference radios for another mobile radio in the system, thereby providing adaptability under varying transmission conditions. The individual radios do not need to be synchronized to a common time reference, thereby eliminating the need for highly accurate system clocks.

Method and apparatus for determining the position of a mobile communication device using low accuracy clocks

Our position-location technique provides location information within milliseconds and is integrated in a handheld direct-sequence spread-spectrum (DSSS) communications system. We use a standard DSSS waveform, with a state of-the-art chipping rate to provide a position location capability to an accuracy of less than 1 m in a severe multipath environment. We use a two-way time-of-arrival (TOA) measurement technique with 1-ppm clocks that eliminates the need to synchronize master and reference radio clocks. Several techniques improve TOA and, therefore, range accuracy. A loop back calibrates internal system delay. Frequency diversity orthogonalizes multipath with respect to direct path, and leading edge curve fitting of the direct path reduces the effect of multipath. Applications include location of urban war fighters, firefighters, police, and medical personnel and resources.

T. Dempsey

Papers

Mobile ranging using low-accuracy clocks