Integrating active and passive received signal strength-based localization

Active received signal strength (RSS)-based localization systems estimate the location of a device-equipped target by using the RSS measurements between the target's device and a set of known-location nodes. To improve the accuracy of such systems, additional information from other devices such as inertial sensors or antenna arrays have been used at the cost of increased power consumption and complexity. Recently, RSS-based device-free localization (DFL) systems have been developed that can estimate a human target's location using only the shadowing caused by the target on the radio links within the network, and without requiring the target to be equipped with a radio device. In this paper, we integrate the active and passive RSS-based localization approaches to estimate the location of a single human target using a maximum likelihood estimation framework. Through an outdoor experiment, we show that the integrated method results in increased localization accuracy as compared to using either active or passive RSS-based localization methods alone and without requiring additional sensors.