**1. Introduction**

With the progress of urbanization, the indoor environment has become an important place for human production and life. Indoor pedestrian navigation technology has been widely considered and studied by scholars in disaster relief and rescue, medical search and rescue, public security, anti-terrorism and other fields. Providing accurate navigation and positioning capabilities for pedestrians in indoor working environments is the basis for achieving indoor rescue work. In indoor working environments, the signals of global positioning system (GPS) [1], Beidou and other global navigation satellite systems (GNSS) [2] are seriously blocked, which makes it difficult to play the role of normal navigation and positioning and the incapacity to provide accurate navigation and positioning function for pedestrians. Therefore, it is necessary to carry out research on the pedestrian navigation method in the indoor satellite failure environment.

The current indoor pedestrian navigation technology mainly includes active navigation and passive navigation. Active navigation means that navigation and positioning must be carried out with the help of sensors other than itself, including ultra wide band

**Citation:** Wang, Z.; Xing, L.; Xiong, Z.; Ding, Y.; Sun, Y.; Shi, C. An Improved Pedestrian Navigation Method Based on The Combination of Indoor Map Assistance and Adaptive Particle Filter. *Remote Sens.* **2022**, *14*, 6282. https://doi.org/ 10.3390/rs14246282

Academic Editors: Yuwei Chen, Changhui Jiang, Qian Meng, Bing Xu, Wang Gao, Panlong Wu, Lianwu Guan and Zeyu Li

Received: 26 September 2022 Accepted: 8 December 2022 Published: 11 December 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

(UWB) [3], wireless fidelity (Wi-Fi) [4], bluetooth (BT) [5], ZigBee [6], radio frequency identification (RFID) [7], near field communication (NFC) [8] and other methods. While the positioning error of the active navigation algorithm does not accumulate over time, it is greatly affected by the indoor environment, obstacles, multipath propagation [9,10] and other environmental factors. It is necessary to arrange the source base station in advance and build a fingerprint database. The cost of construction and maintenance is high. In addition, the indoor rescue site is often accompanied by problems such as the unavailability of beacons caused by power system interruption. The relevant navigation and positioning technology is difficult to meet the availability requirements of indoor rescue positioning. Passive navigation refers to a navigation and positioning method that only relies on its own sensors, without relying on external sensor information sources. It mainly includes the methods of simultaneous localization and mapping (SLAM) based on the laser radar sensor [11] and visual sensor [12] (monocular camera, binocular camera, depth camera), and the methods based on inertial measurement unit (IMU). For the indoor rescue navigation and positioning system with high real-time requirements, there are shortcomings, such as the laser radar remaining unchanged, and the visual sensor may be affected by the indoor environment. As it is inconvenient for pedestrians to carry lidars, and visual sensors may be affected by indoor environment, these sensors cannot meet the needs of pedestrian navigation and positioning system for indoor rescue in actual use. With the development of technology, the micro electro–mechanical system (MEMS) [13] has been continuously improved and developed. As a kind of autonomous navigation and positioning equipment, a wearable inertial sensor based on MEMS technology has been widely studied in indoor pedestrian navigation. It only needs to fix the IMU on the human body and calculate pedestrian navigation parameters by collecting IMU data to realize autonomous navigation and positioning.

The pedestrian navigation algorithms based on IMU are mainly divided into two categories: The PDR algorithm and ZUPT algorithm. The pedestrian navigation method based on PDR estimates the step length, step number, heading and other parameters of the pedestrian in the walking process by collecting the acceleration, angular velocity and other data of the pedestrian, and calculates the pedestrian motion trace. In 2017, Dina [14] proposed a method to estimate the step length through the information of leg and foot inertial sensors of two navigation systems. In 2018, Xu [15] studied the PDR navigation algorithm based on handheld mobile phones. In order to improve the step length estimation accuracy of the algorithm for different users, she proposed a step length detection method based on state transition and a step length estimation method based on neural networks. In 2020, Ding [16] proposed a PDR navigation algorithm based on the relationship between waist inertial data and step length. Based on the ZUPT algorithm, the inertial sensor is installed on the foot. According to the algorithm, the velocity of the foot is zero in theory during the period of time when the foot contacts the ground during periodic movement, and the velocity during this period is used as the observation quantity to periodically correct the position and velocity of the human. In 2016, Ruppelt [17] proposed a navigation and positioning technology about ZUPT detection based on the finite state machine. It was used to analyze the gait cycle of human foot mounted IMU, which could detect the zero velocity interval more accurately. In 2017, Hsu [18] proposed a sensor fusion technology based on a two-stage quaternion extended Kalman filter for the inertial sensor cumulative error, and the error between the starting point and the end point was 2.01%. In 2018, Suresh [19] proposed the method of combining the ZUPT and the high pass filter. He applied the high pass filter to the complementary filtering, reducing the error drift of the angular velocity. In 2021, Abdallah [20] proposed a foot-mounted and synthetic aperture indoor navigation method based on inertial/ZUPT/depth neural network, which reduced the accumulated error of inertial navigation system through the integrated navigation algorithm based on ZUPT. From the working principle, the pedestrian navigation system (PNS), based only on inertial sensors, will diverge after a long time of operation.

The error of PNS relying only on inertial sensors will diverge over time. To solve this problem, scholars have studied a variety of methods to correct the navigation error. In 2016, Ilyas [21] studied the indoor geomagnetic assisted pedestrian navigation. A large amount of information interferes with the magnetometer, resulting in magnetic information distortion. In this regard, a magnetic anomaly detection method is proposed to compensate the abnormal data. In 2018, Song [22] proposed a two-stage Kalman filter, in which a magnetic sensor is installed at the waist and an inertial sensor is installed at the foot. Compared to the traditional pedestrian navigation method based on ZUPT, the error is reduced by 30%. In 2016, Diez [23] proposed an improved heuristic drift elimination algorithm (iHDE) to install the inertial sensor on the wrist. Compared to the algorithm, iHDE reduces the error by 95%. In 2018, Muhammad [24] used the indoor corridor for heading correction and proposed an HDE algorithm based on waist heading. The author divided the 360-degree heading into 16 equal sectors. When pedestrians moved along the orthogonal corridor direction or the main heading, the algorithm corrected the heading. If the motion trace is a curve or not moving along the main heading, no course correction will be made. In 2021, Kim [25] proposed a topological map construction method based on tge architectural plan and sensors to solve the problem that it took a lot of time to create indoor maps in real time. This method can provide a safe path, and the indoor plan can be updated more easily in the future, even if the internal structure of the building changes. Since 2009, the German Aerospace Center has studied the navigation method based on Foot SLAM [26,27], which is only based on inertial sensors and can maintain the navigation accuracy for a long time. References [28,29] combined indoor map and PF to modify the pedestrian navigation results obtained by the inertial sensor solution. This method greatly improves the navigation accuracy, but the computational efficiency is low. As the indoor geomagnetic interference is large, and the auxiliary navigation effect is not good, the HDE method needs to obtain indoor environmental constraint features in advance. The Foot SLAM method needs to form a closed loop of motion trajectory, which has great limitations in practical applications. For the MA method, the indoor architectural plan is relatively easy to obtain, and navigation and positioning are realized by combining the indoor map with PF.

For the PNS relying only on inertial sensors, in order to effectively solve the needs of pedestrian autonomous navigation under special tasks such as indoor rescue, this paper proposes an improved pedestrian navigation positioning method based on the combination of indoor MA and adaptive PF (IMAPF). In order to solve the problem of high precision localization when pedestrians enter an unfamiliar environment with unknown initial position and heading, a global search method based on MA is proposed. Aiming at the problem that a large number of particle operations are required under the unknown initial position and heading, which leads to low computational efficiency, an adaptive particle number calculation method is proposed. It solves the problem of high-precision navigation and positioning of indoor pedestrians for a long time and the positioning problem under the unknown initial position and heading, and improves the computational efficiency, accuracy and reliability of the indoor pedestrian navigation system.

The structure of this paper is as follows: Section 2 describes the algorithm in detail. In Section 3, the proposed algorithm is verified by the simulation and experiment. Section 4 discusses the results of the experimental activities proposed. Finally, Section 5 concludes the paper.
