User data selection method using received signal strength indicator for semi-supervised learning in fingerprint localization

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, ...

Maritime Management Using Received Signal Strength Indicator

For location finding through Wireless Sensor Networks (WSNs), the RSSI is regularly assumed to have a linear signal strength by the whole of the logarithm of the communication distance. However, this is not true in certain cases because there are uncertainties in RSSI readings to obstacles, receiver interferences etc. In this complimentary, we specifically ask for the hand of a new RSSI-based c...

Learning Indoor Localization using Radio Received Signal Strength

Data Selection for Semi-Supervised Learning

The real challenge in pattern recognition task and machine learning process is to train a discriminator using labeled data and use it to distinguish between future data as accurate as possible. However, most of the problems in the real world have numerous data, which labeling them is a cumbersome or even an impossible matter. Semi-supervised learning is one approach to overcome these types of p...

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...