For existing indoor localization algorithm has low accuracy, high cost in deployment and maintenance, lack of robustness, and low sensor utilization, this paper proposes a particle filter algorithm based on multi-sensor fusion. The pedestrian’s localization in indoor environment is described as dynamic system state estimation problem. The algorithm combines the smart mobile terminal with indoor localization, and filters the result of localization with the particle filter. In this paper, a dynamic interval particle filter algorithm based on pedestrian dead reckoning (PDR) information and RSSI localization information have been used to improve the filtering precision and the stability. Moreover, the localization results will be uploaded to the server in time, and the location fingerprint database will be built incrementally, which can adapt the dynamic changes of the indoor environment. Experimental results show that the algorithm based on multi-sensor improves the localization accuracy and robustness compared with the location algorithm based on Wi-Fi.
The indoor positioning navigation system can provide navigation service for users in public places such as large complex buildings, and has wide application prospect [
In recent decades, with the rapid development of integrated circuits, smart phones had made great progress in data storage and data processing, and embedded many micro-sensors, such as the accelerometer, the gyroscopes, and the magnetometers and so on [
This paper mainly studies a particle filter algorithm based on multi-sensor fusion indoor pedestrian localization, and combines the smartphone with the traditional positioning technology. The first step, the sensors built-in smartphone can predict the user’s movement and observation status, as Bayesian estimates of the movement model and observation model, and establish the fingerprint database of indoor environment. The second step, the particle filter algorithm can filter and fusion the movement model and the observation model. The last step, the localization results will be uploaded to the server in time, and the location fingerprint database will be built incrementally, which can adapt the dynamic transform of the indoor environment.
The sensors built-in smartphone, such as acceleration sensors, gyroscopes, gravitational acceleration and magnetometers, can track the location of the pedestrian in the indoor environment.
The wireless module and other sensors built-in the smartphone can predict the pedestrian’s environment state and the pedestrian’s movement state. The proposed algorithm can fuse information of the pedestrian’s movement and observation, and upload the pedestrian’s location information to the application layer.
This algorithm is usually divided into two stages: the offline training phase and online positioning phase. In the first stage, we should set many reference points
in this stage and collect the reference data from Wi-Fi access point (AP), such as signal strength, arrival angle, and frequency and so on. Next, we store the reference data with the location information into the database as a set of fingerprint data. In the second stage, we use the smartphone to detect the signal data received at the location to be determined, and then compare the signal data with the database through the corresponding algorithm. Next, we get the user’s actual location information.
The authors in [
This paper improves the positioning precision of PDR system by fusion pedestrian gait information, indoor environment information and RSSI, because of PDR system cannot get absolute position information. We assume the pedestrian’s initial position, and get the relative position information from PDR system. We improve the positioning precision of the proposed algorithm by combine the indoor environment to filter the positioning results. But the above problem is a typical nonlinear problem, this means that the conventional linear fusion algorithm cannot get outstanding results. To solve those problems, we select the particle filter algorithm to fuse the multi-sensor data, which not only has
Database | AP1 | AP2 | APn | Class | |
---|---|---|---|---|---|
better flexibility, extensive practicality, but also can improve the positioning precision.
The pedestrian’s localization is described as dynamic system state estimation problem [
Motion equation:
Observation equation:
where
In this paper, the particle filter can achieve the integration of location information to avoid the integral operation in Bayesian estimation, and provide location data for indoor location-based services.
Due to the complexity of the pedestrian walk and the low cost of the positioning system in the indoor environment, we use the triangulation of step and azimuth to obtain the relative position information of pedestrians in this paper.
where
Which mainly need to solve three problems: step numbers, step length and motion detection.
The pedestrian’s movement behavior includes the movement direction and the movement distance. The data of the acceleration and the angular velocity will transform when person in the process of walking [
In this paper, we use the acceleration sensors, gyroscopes, gravitational acceleration and magnetometers to perceive the user’s motion behavior. The acceleration sensors are able to determine the changes in pedestrian’s acceleration, it is represented by the acceleration component of the three-dimensional direction. The human body step model is established by data which is collected by acceleration sensors filtering and feature extraction. The direction sensor can capture changes in the direction of motion of the pedestrian. This paper uses the empirical formula to calculate the step size, and correct the user’s step size dynamically [
The KNN localization algorithm calculates the cosine similarity of the RSSI vector measured at the anchor point and the RSSI vector measured at each RP point. The cosine similarity of the two vectors is shown below:
And then through the query fingerprint library to find the most similar
In this paper, particle filter [
It is assumed that each particle has the following state information:
The user’s position of the map is
Assume that the user’s initial position is
It is the user’s location with the update process over time. In the process of pedestrian movement, introducing the weight of particle as the smooth factor of PDR system. As we know, different particle weights will produce different paths.
By observing the environment in which each particle spreads, to verify that the propagation of the particles is reasonable, and observe the degree of similarity between the possible position of pedestrians represented by each particle and the actual location of pedestrians. The particles closer to the true position of the pedestrian will be given a larger weight, and vice versa. The particle weight is calculated as follows:
Normalized weight calculation:
Pedestrian location determination can be based on two criteria: Maximum posterior probability and Weighted Criteria. This paper uses the second method to calculate the position.
With the increase of filtering time, the degradation of particles will occur, the importance of weight may be concentrated to a small number of particles, the need for its resampling, increase the weight of the larger number of particles.
Based on the above, an indoor localization algorithm based on multi-sensor fusion is proposed, as shown in
In order to test the positioning performance of the proposed algorithm in this paper, a lot of experiments are carried out. Here are the specific experimental contents of testing the positioning performance based on the solution to realize pedestrian indoor positioning, and analyze the experimental results.
This paper chooses the eighth teaching building of Guilin University of Electronic Science and Technology as an experimental site. The experimental site is 21 meters long and 18 meters wide, and four wireless routers were installed in this experimental site. Experimental site structure shown in
In this experiment, the experimental site will be divided into 50 * 50 small squares according to the laying of floor tiles, 150 small squares in the horizontal direction and 201 in the vertical direction. The size of each small lattice is 0.5 m * 0.5 m.
Data acquisition of acceleration, direction, and Wi-Fi signal strength is achieved by programming based on Android 4.5. The experimenter who hand-
held Huawei mobile phone is walking at the normal pace in the experimental site according to the expected trajectory, and this phone has built-in Acceleration sensors, gyroscopes, gravitational acceleration and magnetometers.
In order to verify the effectiveness of the pedometer, the experimenter performed 50 times experiments on 100 step numbers. The pedometer results is shown in
In the positioning algorithm validation process, the result of location and Wireless signal strength will be uploaded to the database in time, and the location fingerprint database will be built incrementally. The fingerprint data table of location fingerprint database is shown in
In this paper, the result of location will be counted by the localization algorithm based on multi-sensor information fusion, and the result of location will be updated the original information in the database, and add the time stamp.
In this experiment, 1000 sets of fingerprint data were collected at each observation point, and the collected data is processed by Gaussian filtering in order to improve the accuracy of fingerprint data.
The experimenter is walking in the experimental site with expected trajectory, and collecting the information include acceleration, direction and Wi-Fi signal strength.
Position | Time | AP1 | AP2 | AP3 | AP4 |
---|---|---|---|---|---|
(1.2, 1.1) | 2016-11-12 10:32:13 | −35.6667 | −70.1618 | −71.4524 | −67.5 |
(2.4, 7.8) | 2016-11-12 10:32:24 | −38.258 | −68.7749 | −72.1597 | −65.9253 |
(4.15, 5.25) | 2016-11-12 10:32:38 | −47.4828 | −68.1205 | −75.1807 | −64.9457 |
(5.01, 6.88) | 2016-11-12 10:33:05 | −46.8 | −61.3243 | −73 | −68.3333 |
(11.21, 3.09) | 2016-11-12 10:33:17 | −59.0192 | −68.7097 | −74.3265 | −75.1613 |
Algorithm | Average error |
---|---|
Multi-senor fusion (N = 100) | 0.35 (93.9%) |
Multi-senor fusion (N = 1000) | 0.27 (95.2%) |
Wi-Fi fingerprint location | 1.65 (52.3%) |
Compared with
In Section 4.3, the experimenter walked at normal speed, and took the smart phone in his hand. In the next experiment, the experimenter will walk slowly, run or put the smart phone in pocket. Under the above three experimental conditions, the average errors are shown in
Due to the existing indoor positioning technology which has many deficiencies, such as high cost, low positioning accuracy and low sensor utilization, an indoor pedestrian localization algorithm based on multi-sensor information fusion is proposed in this paper. The sensor built-in smartphone can obtain pedestrian’s movement information, such as acceleration, step size, direction and so on. The proposed algorithm fused PDR and RSSI can improve the accuracy and the stability. From the above experimental results, the average error of the proposed method is above 0.35 meters in the range of
Normal | Walk slowly | Running | the smart phone in pocket | |
---|---|---|---|---|
Multi-senor fusion | 0.35 | 0.39 | 0.41 | 0.54 |
Wi-Fi | 1.65 | 1.79 | 2.15 | 2.35 |
This work is supported by the National Natural Science Foundation of China (No.61371107), the Guangxi Experiment Center of Information Science (No.LD16061X), the Guangxi Natural Science Foundation (No.2016GXNSFBA38014), and the China Postdoctoral Science Foundation (No.2016M602921XB) and the Opening Project of Guangxi Key Laboratory of UAV Remote Sensing (No. WRJ2016KF01).
Xu, X.Y., Wang, M., Luo, L.Y., Meng, Z.B. and Wang, E.L. (2017) An Indoor Pedestrian Localization Algorithm Based on Multi-Sensor Information Fusion. Journal of Computer and Communications, 5, 102-115. https://doi.org/10.4236/jcc.2017.53012