A novel ultrawide band locationing system using swarm enabled learning approaches

In this paper, an adaptive learning algorithm is developed using particle swarm to identify and mitigate the non-line of sight (NLOS) signals in ranging measurements. Training data is generated using the IEEE 802.15.4a UWB channel model for different conditions. Multiple metrics derived from this data are fused to identify the NLOS signals. Specifically, kurtosis, mean excess delay and root mean square delay are used as metrics for fusion. The fusion strategy is derived using PSO, considering the correlation between multiple classifiers. We compare the fusion methodology achieved by PSO for the correlated data set to the likelihood ratio based fusion methodology assuming independence. Further, the paper presents an NLOS mitigation approach derived using PSO. A scalar called ldquoerror mitigation ratio (EMR)rdquo is defined. The EMR transform a NLOS measurement into an equivalent LOS measurement. The PSO identifies the EMR using the training data. Application of PSO generated EMR enhances the positioning accuracy and is demonstrated in this paper for indoor wireless channel. This mitigation approach enables us to arrive at a position for the unknown node even when one of the measurements is identified as NLOS. Finally, PSO is used for multilateration to combine measurements from three nodes. Comparisons are done with the linearized least square method.