The Performance Analysis of the Map-Aided Fuzzy Decision Tree Based on the Pedestrian Dead Reckoning Algorithm in an Indoor Environment

In pedestrian inertial navigation, multi-sensor fusion is often used to obtain accurate heading estimates. As a widely distributed signal source, the geomagnetic field is convenient to provide sufficiently accurate heading angles. Unfortunately, there is a broad presence of artificial magnetic perturbations in indoor environments, leading to difficulties in geomagnetic correction. In this paper, by analyzing the spatial distribution model of the magnetic interference field on the geomagnetic field, two quantitative features have been found to be crucial in distinguishing normal magnetic data from anomalies. By leveraging these two features and the classification and regression tree (CART) algorithm, we trained a decision tree that is capable of extracting magnetic data from distorted measurements. Furthermore, this well-trained decision tree can be used as a reject gate in a Kalman filter. By combining the decision tree and Kalman filter, a high-precision indoor pedestrian navigation system based on a magnetically assisted inertial system is proposed. This system is then validated in a real indoor environment, and the results show that our system delivers state-of-the-art positioning performance. Compared to other baseline algorithms, an improvement of over 70% in the positioning accuracy is achieved.

Section: Geomagnetic Information Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving the Heading Accuracy in Indoor Pedestrian Navigation Based on a Decision Tree and Kalman Filter

Zhang²,

Wan

et al. 2020

“…Using these methods, the device must always point to the direction of pedestrian movement [18,19,20,21], but this limitation cannot be feasible all the times. Indoor map information and landmarks have been utilized to constrain the pedestrian’s trajectory and improve the localization performance in [30,31,32,33], however, when the trajectory covers more than the coverage of map information and landmarks, the localization accuracy will decrease significantly. Shen et al [34] proposed an indoor location method, which enhanced the heading estimation of PDR with the received signal strength indicator (RSSI) from the Wi-Fi access points (APs).…”

Section: Related Workmentioning

confidence: 99%

Pedestrian Dead Reckoning Based on Motion Mode Recognition Using a Smartphone

Wang

Liu

et al. 2018

This paper presents a pedestrian dead reckoning (PDR) approach based on motion mode recognition using a smartphone. The motion mode consists of pedestrian movement state and phone pose. With the support vector machine (SVM) and the decision tree (DT), the arbitrary combinations of movement state and phone pose can be recognized successfully. In the traditional principal component analysis based (PCA-based) method, the obtained horizontal accelerations in one stride time interval cannot be guaranteed to be horizontal and the pedestrian’s direction vector will be influenced. To solve this problem, we propose a PCA-based method with global accelerations (PCA-GA) to infer pedestrian’s headings. Besides, based on the further analysis of phone poses, an ambiguity elimination method is also developed to calibrate the obtained headings. The results indicate that the recognition accuracy of the combinations of movement states and phone poses can be 92.4%. The 50% and 75% absolute estimation errors of pedestrian’s headings are 5.6° and 9.2°, respectively. This novel PCA-GA based method can achieve higher accuracy than traditional PCA-based method and heading offset method. The localization error can reduce to around 3.5 m in a trajectory of 164 m for different movement states and phone poses.

“…K.W. Chiang et al put the focus on a fuzzy decision tree aided by map information to improve the accuracy and stability of PDR [18]. I. Miller et al proposed an indoor positioning scheme by fusing floor map and smartphone sensor data without additional infrastructure [19].…”

Section: Related Workmentioning

confidence: 99%

An Indoor Positioning Method for Smartphones Using Landmarks and PDR

Wang

Jiang

Guo

et al. 2016

Recently location based services (LBS) have become increasingly popular in indoor environments. Among these indoor positioning techniques providing LBS, a fusion approach combining WiFi-based and pedestrian dead reckoning (PDR) techniques is drawing more and more attention of researchers. Although this fusion method performs well in some cases, it still has some limitations, such as heavy computation and inconvenience for real-time use. In this work, we study map information of a given indoor environment, analyze variations of WiFi received signal strength (RSS), define several kinds of indoor landmarks, and then utilize these landmarks to correct accumulated errors derived from PDR. This fusion scheme, called Landmark-aided PDR (LaP), is proved to be light-weight and suitable for real-time implementation by running an Android application designed for the experiment. We compared LaP with other PDR-based fusion approaches. Experimental results show that the proposed scheme can achieve a significant improvement with an average accuracy of 2.17 m.