Combining a Modified Particle Filter Method and Indoor Magnetic Fingerprint Map to Assist Pedestrian Dead Reckoning for Indoor Positioning and Navigation

Abstract

Although advancement has been observed in global navigation satellite systems and these systems are widely used, they cannot provide effective navigation and positioning services in covered areas and areas that lack strong signals, such as indoor environments. Therefore, in recent years, indoor positioning technology has become the focus of research and development. The magnetic field of the Earth is quite stable in an open environment. Due to differences in building and internal structures, this type of three-dimensional vector magnetic field is widely available indoors for indoor positioning. A smartphone magnetometer was used in this study to collect magnetic field data for constructing indoor magnetic field maps. Moreover, an acceleration sensor and a gyroscope were used to identify the position of a mobile phone and detect the number of steps travelled by users with the phone. This study designed a procedure for measuring the step length of users. All obtained information was input into a pedestrian dead reckoning (PDR) algorithm for calculating the position of the device. The indoor positioning accuracy of the PDR algorithm was optimised using magnetic gradients of magnetic field maps with a modified particle filter algorithm. Experimental results reveal that the indoor positioning accuracy was between 0.6 and 0.8 m for a testing area that was 85 m long and 33 m wide. This study effectively improved the indoor positioning accuracy and efficiency by using the particle filter method in combination with the PDR algorithm with the magnetic fingerprint map.Sensors 2020, 20, 185 2 of 15 that provides all necessary factors. We should identify user requirements and combine different technologies to address those requirements with the lowest cost, highest range, and highest accuracy possible. Table 1 presents the current indoor positioning technologies [1], and Table 2 compares the more frequent use of wireless indoor position technologies [3].

Human Motion Likelihood Representation Map-Aided PDR Particle Filter

“…This is understandable to leverage the discrete prediction of the PDR associated with the discrete multihypothesis capabilities that the PF performs. Previously cited papers do this to achieve better localization [27][28][29] or to use of a kind of map [12,21].…”

A multisensor fusion algorithm of indoor localization using derivative Euclidean distance and the weighted extended Kalman filter

“…The smartPDR algorithm is limited to the environment where the magnetic field is not disturbed. Ning and Chen (2020) integrated indoor magnetic fingerprint and pedestrian tracking estimation using the improved particle filter algorithm. Sun et al (2020) used the genetic particle filter to integrate PDR and geomagnetism for localization.…”

Adaptive Inertial Sensor-Based Step Length Estimation Model

“…Various machine learning techniques and statistical estimation methods are often used to achieve higher accuracy, yet they depend upon comprehensive training. For instance, artificial neural networks [ 37 ] and Bayesian filters, such as particle filters [ 38 , 39 , 40 ], grid-based approaches [ 41 ], and Kalman filters [ 42 , 43 ], have already been employed. Integrating acceleration in the walking direction with respect to time [ 11 , 44 , 45 ] presents another means of estimating step length.…”