  Signals of opportunity (SOPs) are a promising technique that can be used for
relative positioning in areas where global navigation satellite system (GNSS)
information is unreliable or unavailable. This technique processes features of
the various signals transmitted over a broad wireless spectrum to enable a
receiver to position itself in space. This work examines the frequency
selection problem in order to achieve fast and accurate positioning using only
the received signal strength (RSS) of the surrounding signals. Starting with a
prior belief, the problem of searching for a frequency band that best matches a
predicted location trajectory is investigated. To maximize the accuracy of the
position estimate, a ranking-and-selection problem is mathematically
formulated. A knowledge-gradient (KG) algorithm from optimal learning theory is
proposed that uses correlations in the Bayesian prior beliefs of the frequency
band values to dramatically reduce the algorithm's processing time. The
technique is experimentally tested for a practical scenario of an unmanned
aerial vehicle (UAV) moving around a GPS-denied environment, with obtained
results demonstrating its validity and practical applicability.
