**4. Discussion**

The purpose of this paper is to study an improved pedestrian navigation and location method based on the combination of indoor map assistance and adaptive particle filter. For the multi constraint integrated navigation method that only relies on the wearable inertial sensor node network for indoor pedestrians, there is an unavoidable problem of the accumulation and divergence of navigation errors. It is urgent to further improve the accuracy of the navigation system based on the available indoor auxiliary information. Considering that the indoor architectural plan is the most basic and accessible information source in the indoor rescue process, a navigation and positioning method based on the combination of indoor map assistance and particle filter is proposed. This method makes full use of the existing indoor map constraint information to assist in improving the performance of the pedestrian navigation and positioning system based on inertial sensor network. In this paper, the algorithm framework of indoor pedestrian navigation based on map assistance is designed. Combined with the characteristics that pedestrians cannot actually go through the walls and other obstacles when moving in buildings. By establishing the filtering algorithm under the property of particle "not going through the wall", the effective constraints on navigation error are realized; In view of the pedestrian's initial entry into an unfamiliar indoor environment, a map aided localization algorithm based on global search is proposed under the condition of unknown initial position and heading; In order to solve the problem that a large number of particles are required to complete the global search, which leads to low computational efficiency, a particle resampling method based

on adaptive particle number is proposed. While maintaining the accuracy of navigation and positioning, it also improves the computing efficiency and achieves accurate indoor positioning in unfamiliar environments. On this basis, based on indoor map constraints, the problem of inertial accumulation error divergence is well suppressed, which provides a strong support for pedestrian indoor navigation and positioning with high precision and reliability.

Through the verification and analysis of simulation data and measured data, the pedestrian navigation and positioning method based on the combination of improved indoor map assistance and adaptive particle filter proposed in this paper is suitable for the conditions of known and unknown initial position and heading. In the indoor environment of about 2600 m2, when the total distance exceeds 415.44 m, the mean error and the maximum error of the position relative to the reference point are both less than 2 m. It effectively suppresses the pedestrian navigation error based on inertial devices, and greatly improves the calculation efficiency, which can meet the needs of indoor pedestrians for a long time.

In fact, in the process of motion, both lateral and longitudinal errors are derived from step length error and heading errors. In a one-step correction process, if it is calculated that the coordinates of a particle in the lateral or longitudinal direction are in the inaccessible area, the particle is in the inaccessible area and needs to be resampled and given new navigation parameters. The pedestrian position is then calculated by the weighted average method.

The method proposed in this paper also has some limitations. It is based on the indoor building plan, and combines the characteristics of particles "not going through the wall" to constrain and modify the pedestrian trace. When the indoor structure is simple and the environment is open, then the distance between the walls on both sides of the walkway is very far, it is difficult to correct the pedestrian trace through this method.
