COCKTAIL: An RF-Based Hybrid Approach for Indoor Localization

Abstract

Traditional RF-based indoor positioning approaches use only Radio Signal Strength Indicator (RSSI)to locate the target object. But RSSI suffers significantly from the multi-path phenomenon and other environmental factors. Hence, the localization accuracy drops dramatically in a large tracking field. To solve this problem, this paper introduces one more resource, the dynamic of RSSI, which is the variance of signal strength caused by the target object and is more robust to environment changes. By combining these two resources, we are able to greatly improve the accuracy and scalability of current RF-based approaches. We call such hybrid approach COCKTAIL. It employs both the technologies of active RFID and Wireless Sensor Networks (WSNs). Sensors use the dynamic of RSSI to figure out a cluster of reference tags as candidates. The final target location is estimated by using the RSSI relationships between the target tag and candidate reference tags. Experiments show that COCKTAIL can reach a remarkable high degree of localization accuracy to 0.45m, which outperforms significantly to most of the pure RFbased localization approaches.

Estimation of a nonvisible field-of-view mobile target incorporating optical and acoustic sensors

“…In one arrangement, RF receivers form a wireless sensor network (WSN), and numerical techniques are used to localize an NFOV target by processing information from the received signals, such as signal intensity (Bertinato et al 2008;Dai et al 2012;Ni et al 2004;Zhang et al 2010;Liu et al 2007;Gezici 2008;Guvenc and Chong 2009). For example, Wang et al (2012) developed and applied a Bayesian filter using the chirp-spread-spectrum ranging.…”

Link Sensing-Adaptive Passive Object Localization in Wireless Sensor Networks

“…Compared with the work in [15,16], the proposed LSA-POL method covers a larger monitoring area and is more suitable for practical applications. In the process of localization, LSA-POL requires a smaller amount of data and a lower demand of bandwidth for wireless transmission than the approach in [5] and achieves a higher level of localization accuracy than Zhang's work [14,16] under the same simulation setup.…”

“…Instead, they use the RSSI signal for intrusion detection, but not for object localization [13]. In the signal dynamics model proposed by Zhang et al [14][15][16], the reference nodes are deployed as a grid array with an interval of 1 or 2 meters. When the target is present in one grid, it would affect several links between the reference nodes near the grid, and the degree of signal variations on different links is used to estimate the position of the object.…”

A real-time device-free localization system using correlated RSS measurements

“…Zhang et al [10][11][12][13] presented a dynamic model to describe the relation between the RSS variance and the target location, then utilized geometric method and the dynamic cluster-based probabilistic cover algorithm to solve the DFL problem. They also proposed a real-time DFL system [14].…”

“…Moussa and Youssef [8,9] modeled the DFL as a machine learning problem and adopted fingerprint-matching method to solve the problem. Zhang et al [10][11][12][13][14] proposed a signal dynamic model and used the geometric method and probabilistic http://jwcn.eurasipjournals.com/content/2013/1/186 cover algorithm based on dynamic clustering to localize targets. All these works require sufficient RSS measurements of wireless links, which are inefficient, have high-resource consumption, and sometimes are not even possible.…”