Use of RF-based fingerprinting for indoor positioning by mobile technology platforms

A method for determining the position of a mobile technology platform within a structure, wherein the mobile technology platform is equipped with a gyroscope, a magnetometer and at least one accelerometer, the method comprising:

• detecting, at a position within the structure, the RF signature created by the RF signals received from a plurality of RF beacons disposed within the structure;
• and
• in conjunction with readings from the gyroscope, magnetometer and at least one accelerometer, comparing the detected RF signature to an RF fingerprint to determine the most likely location of the mobile technology platform within the structure;
• wherein the RF fingerprint is created by sampling the RF signatures at a plurality of locations within the structure, wherein the mobile technology platform uses at least one Bayes filter to calculate the probabilities of multiple beliefs to allow the mobile technology platform to infer its position and orientation at a site, wherein the at least one Bayes filter utilizes a true state, and wherein the true state is the actual location of the mobile technology platform and is assumed to be an unobserved Markov process in calculating the probabilities of multiple beliefs.

A method for determining the position of a mobile technology platform within a structure, wherein the mobile technology platform is equipped with a gyroscope, a magnetometer and at least one accelerometer, the method comprising:

• deploying a set of RF (radio frequency) beacons within the structure, wherein each RF beacon emits an RF signal;
• recording, at each of a set of sampling locations within the structure, the RF signature created by the RF signals received at the location;
• forming an RF fingerprint of the structure from the recorded RF signatures;
• and
• using the RF fingerprint, in conjunction with readings from the gyroscope, magnetometer and at least one accelerometer to determine the location of the mobile technology platform within the structure;
• wherein the mobile technology platform uses at least one Bayes filter to calculate the probabilities of multiple beliefs to allow the mobile technology platform to infer its position and orientation at a site, wherein the mobile technology platform uses sequential Monte Carlo (SMC) methods to calculate the probabilities of multiple beliefs to allow the mobile technology platform to infer its position and orientation at a site, wherein the SMC methods implement Bayesian recursion equations by using an ensemble based approach in which samples from a distribution are represented by a set of particles, with each particle having a weight assigned to it that represents the probability of that particle being sampled from a probability density function based on the calculated probabilities, wherein a particle filter is used to estimate the posterior density of the state variables, given the observation variables, and wherein the particle filter is designed for a hidden Markov Model in which a system on which the model is based consists of hidden and observable variables.