## 4 Experimental Results  
To verify the proposed approach, a field test was carried out on Level 2 of the Bolz Hall, Civil & Environmental Engineering building, Ohio State University, United States. The geometry of the building consists of labs, offices and classrooms, as shown in Figure 2. We divided the floor plan into 20 cells on topology. Typically, there is one cell per room. We also segmented the two long hallways into cells, which are Cells 1-4 and Cells 5-7 denoted in Figure 2. The first hallway connects the entrance of the building to the test area and the second corridor connects the right hand side eight administration offices (Cells 13-20) to the main hallway. All training data collection took place during five days covering different times of the day. During the collection, students and staffs walked around normally as usual.  
Figure 5: HMM.  
We analysed the correlation between the RSS measurements in the training data for each cell, and presented the efficiency of the proposed localisation system for both stop and go movement and dynamic walking data. The minimum training size and the affect of different \(K\) mixture component were investigated to attain certain room level localisation accuracy. A comparison was carried out between the mean RSS and the EM imputation method in terms of replacing the missing values in the training data.  
In the filed test, nine android devices were used for the crowdsourcing data collection (see Table 1). For Wi-Fi data collection, the CPS App developed by Mr. [PERSON] was used [62]; each Wi-Fi scan records the timestamp, location ID, MAC address, network name and RSS values for all visible APs. The devices collected signals from the university public base stations about which we had no prior information. The data collection consisted of three stages: calibration, static training and real kinematic walking data collection.  
Calibration data collection was conducted to get the coefficients for each device with respect to the reference device (see Section 3.2.1). The nine devices were put on a trolley and one user pushed the trolley around the test area and stopped at random in various cells. Each device collected 200 scans at each location. In this paper, we use Device 7 as the reference device, and the calibration coefficients for the other seven devices are displayed in Table 2. The calibrated RSS measurements for each device is calculated as:  
\[\widehat{RSS}_{d}=M_{d}\times RSS_{d}+C_{d} \tag{21}\]  
where \(RSS_{d}\) is the raw RSS measurements taken by device \(d\), \(M_{d}\), \(C_{d}\) are the calibration coefficients and \(\widehat{RSS}_{d}\) is the calibrated RSS measurements for device \(d\).  
After segmentation, each device was randomly assigned multiple RPs (normally 5-10) within each cell to collect training data in static mode. Note that the locations of the RPs were physically different against each device. Then, the training data for each cell is obtained by fusing the data collected at all the RPs from every available device. The locations of the RPs need not to be known, and every device generally chose different times to enter into the cell to assure that the training samples are covering the whole space and time variant features. At each RP, each device was designed to collect 200-400 scans in static mode; each scan records the timestamps, point ID, MAC address, network name and RSS values for all visible APs in the environment.