The Membership Degree Min-Max Localisation Algorithm

TL;DR

The paper presents the MD-Min-Max algorithm, an improved indoor localisation method that enhances the Min-Max approach using a membership function, achieving higher accuracy with low computational cost in WSNs.

Contribution

It introduces the MD-Min-Max algorithm, combining Min-Max localisation with a membership function for improved accuracy in indoor environments.

Findings

MD-Min-Max outperforms other algorithms in accuracy.

The method maintains low computational complexity.

Effective in real-world wireless sensor network deployments.

Abstract

We introduce the Membership Degree Min-Max (MD-Min-Max) localisation algorithm as a precise and simple lateration algorithm for indoor localisation. MD-Min-Max is based on the well-known Min-Max algorithm that computes a bounding box to estimate the position. MD-Min-Max uses a Membership Function (MF) based on an estimated error distribution of the distance measurements to improve the precision of Min-Max. The algorithm has similar complexity to Min-Max and can be used for indoor localisation even on small devices, e.g., in Wireless Sensor Networks (WSNs). To evaluate the performance of the algorithm, we compare it with other improvements of the Min-Max algorithm and maximum likelihood estimators, both in simulations and in a large real-world deployment of a WSN. Results show that MD-Min-Max achieves the best performance in terms of average positioning accuracy while keeping computational cost low compared to the other algorithms.