TY  - JOUR
T1  - Radio-Frequency Map Optimization for Indoor Positioning and Tracking
TT  - Radio-Frequency Map Optimization for Indoor Positioning and Tracking
AU  - Daniş, F. Serhan
PY  - 2025
DA  - September
Y2  - 2025
DO  - 10.35377/saucis...1644762
JF  - Sakarya University Journal of Computer and Information Sciences
JO  - SAUCIS
PB  - Sakarya University
WT  - DergiPark
SN  - 2636-8129
SP  - 410
EP  - 421
VL  - 8
IS  - 3
LA  - en
AB  - We introduce a parameter optimization strategy to enhance the accuracy of an indoor positioning system. The indoor positioning system of interest is composed of subsequent stages of processing of reference reduced signal strength indicator (RSSI) streams, Gaussian processes-based estimation of probabilistic radio-frequency maps, and an adaptive particle filter that is used to infer the trajectory of the tracked object. Each stage has its own model parameters, which can be evaluated by the accuracy of the final trajectory estimations given their ground-truth counterparts. We make use of an open dataset that includes RSSI data on reference points, RSSI data related to trajectories and their corresponding ground-truth positions. By being able to evaluate the estimations, we develop a Monte Carlo particle swarm optimization strategy to search for the best parameter configuration that minimizes the trajectory error. The time performance of the optimization strategy is also improved by artificially discretizing the parameters space, so that the stages can use the previously processed streams or radio maps. We show that the strategy can both improve accuracy and decrease the search time with respect to a grid-based search strategy.
KW  - Fingerprinting
KW  - Indoor Positioning
KW  - Monte Carlo Swarm Optimization
KW  - Radio Frequency Map Estimation
KW  - Reduced Signal Strength Indicator
N2  - We introduce a parameter optimization strategy to enhance the accuracy of an indoor positioning system. The indoor positioning system of interest is composed of subsequent stages of processing of reference reduced signal strength indicator (RSSI) streams, Gaussian processes-based estimation of probabilistic radio-frequency maps, and an adaptive particle filter that is used to infer the trajectory of the tracked object. Each stage has its own model parameters, which can be evaluated by the accuracy of the final trajectory estimations given their ground-truth counterparts. We make use of an open dataset that includes RSSI data on reference points, RSSI data related to trajectories and their corresponding ground-truth positions. By being able to evaluate the estimations, we develop a Monte Carlo particle swarm optimization strategy to search for the best parameter configuration that minimizes the trajectory error. The time performance of the optimization strategy is also improved by artificially discretizing the parameters space, so that the stages can use the previously processed streams or radio maps. We show that the strategy can both improve accuracy and decrease the search time with respect to a grid-based search strategy.
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UR  - https://doi.org/10.35377/saucis...1644762
L1  - https://dergipark.org.tr/en/download/article-file/4631536
ER  -