Bayesian Cooperative Localization Using Received Signal Strength With Unknown Path Loss Exponent: Message Passing Approaches

Emitter localization using received-strength-signal data

This paper considers a scenario in which signals from an emitter at an unknown location are received at a number of different collinear locations. The receiver can determine the received signal strength, but no other parameters of the signal. Postulating a log-normal transmission model with a constant but unknown path loss exponent and, also, an unknown transmit power and known noise variance, ...

Learning Indoor Localization using Radio Received Signal Strength

Accurate and simple source localization using differential received signal strength

Locating an unknown-position source using received signal strength (RSS) measurements in an accurate and low-complexity manner is addressed in this paper. Given that the source transmit power is unknown, we employ the differential RSS information to devise two computationally attractive localization methods based on the weighted least squares (WLS) approach. The main ingredients in the first al...

RSS-based Localization in Wireless Sensor Networks with Unknown Transmit Power and Path Loss Exponent using SDP Relaxation

This paper addresses the problem of locating multiple target nodes from noisy received signal strength (RSS) measurements in wireless sensor networks (WSNs), for the case where the transmit power and the path loss exponent (PLE) are simultaneously unknown. According to the conventional path loss model, the maximum likelihood (ML) estimator is derived by solving the nonlinear and nonconvex least...

Message Passing Approaches to Compressive Inference under Structured Signal Priors

Across numerous disciplines, the ability to generate high-dimensional datasets is driving an enormous demand for increasingly efficient ways of both capturing and processing this data. A promising recent trend for addressing these needs has developed from the recognition that, despite living in high-dimensional ambient spaces, many datasets have vastly smaller intrinsic dimensionality. When cap...