Global Optimization for Multiple Transmitter Localization

Abstract

We propose a global optimization approach to locating multiple transmitters within a geographic area. A set of sensor nodes are assumed to be present in the region and to measure total power received at their respective locations. These measurements are communicated to a processing node, which uses particle swarm optimization to find the transmitter locations that minimize the difference between the true received power and the estimated power based on the chosen propagation model. Clustering is used to generate initial estimates of the transmitter locations, thereby increasing the likelihood that the particle-based optimizer reaches the global minimum. Simulation results show that global optimization is an effective method for multiple transmitter localization and that generating "smart" initial conditions via clustering can yield an average performance improvement of over 25% compared to random initial conditions.

Measurement clustering criteria for localization of multiple transmitters

“…Localization involving multiple primary transmitters is studied in [11], [12], where it is assumed that the number of transmitters and the transmit power are known a priori. In [11], the particle swarm optimization technique is used, whereas in [12] the problem is solved by applying the expectationmaximization algorithm.…”

“…In [11], the particle swarm optimization technique is used, whereas in [12] the problem is solved by applying the expectationmaximization algorithm. An experimental study, employing a triangulation-based heuristic approach for multiple transmitter localization using synchronized sensing is presented in [13].…”

“…Therefore, to minimize the effect of cochannel interference, all the measurements should be clustered appropriately, where each measurement cluster represents the subset of measurements to be used in the localization of a particular transmitter. The the k-means clustering algorithm discussed in [11], [12] requires that the total number of cochannel transmitters in the network, say M , be known a priori. This clustering technique, which is based on a distance metric, uses only the position information of the secondary nodes, but the measured SS information is not taken into account.…”

Distributed Spatio-Temporal Spectrum Sensing: An Experimental Study

“…Hence, device localization and neighbor discovery techniques become essential in identifying white spaces in the spectrum. There has been recent interest in devising strategies for localizing the primary uses in a given area at a particular frequency [3], [4]. In [5], the authors propose and compare various neighbor discovery schemes for dynamic spectrum access in adhoc networks.…”

Opportunistic spectrum sharing with multiple cochannel primary transmitters

“…Localization using signal strength (SS) measurements of a primary transmitter with unknown transmit power based on a constrained least squares approach is considered in [14]. Localization involving multiple primary transmitters is studied in [15], [16], where it is assumed that the number of transmitters and their transmit powers are known a priori. An experimental study, employing a triangulation-based heuristic approach for multiple transmitter localization using synchronized sensing is presented in [17].…”