An Indoor Positioning Technique Based on a Feed-Forward Artificial Neural Network Using Levenberg-Marquardt Learning Method

Abstract

ABSTRACT:This paper presents an indoor positioning technique based on a multi-layer feed-forward (MLFF) artificial neural networks (ANN). Most of the indoor received signal strength (RSS)-based WLAN positioning systems use the fingerprinting technique that can be divided into two phases: the offline (calibration) phase and the online (estimation) phase. In this paper, RSSs were collected for all references points in four directions and two periods of time (Morning and Evening). Hence, RSS readings were sampled at a regular time interval and specific orientation at each reference point. The proposed ANN based model used Levenberg-Marquardt algorithm for learning and fitting the network to the training data. This RSS readings in all references points and the known position of these references points was prepared for training phase of the proposed MLFF neural network. Eventually, the average positioning error for this network using 30% check and validation data was computed approximately 2.20 meter.

Improved 3-D Indoor Positioning Based on Particle Swarm Optimization and the Chan Method

“…As we know, most WLAN positioning uses a fingerprint [30] matching method, but it is highly environment dependent, while the collection of a fingerprint library is difficult. Other feed-forward neural network (FNN) positioning algorithms [31,32] need to train the network parameters in advance, which take a great deal of time and a large amount of computation. Different from the methods in [18,[26][27][28][29]32], in the non-ideal environment of measurement error of TOA without a Gaussian distribution, the performance of the Chan positioning algorithm is limited due to the large initial solution error.…”

Improved Indoor Positioning Technique Based on a Geographic Weighted Regression

“…In the online phase of location fingerprinting, the location of user was estimated. The average positioning error for the proposed model containing 30% check and validation data was computed approximately 2.20 m (Pahlavani et al, 2017). Zou et al (2017) proposed a WiFi-based non-intrusive indoor positioning system for automatic online radio map construction and adaptation for calibration-free indoor localization.…”