## 2 Background and Related Works  
[PERSON] et al. in [2] and [PERSON] et al. in [11] proposed the CS-based method and the G-SSL method respectively, to reduce the workload of the radio map construction in the offline phase. Both methods, aim to reduce the number of reference points (RP) and RSS measurements. Also, [14; 15; 16] explore crowdsourcing-based methods to reduce the deployment workload by engaging the users to participate in radio map construction.  
In [21], an RSS pre-processing method called the \"sliding correlation time window filter\" (SCTW) is used to reduce the noise in the measured RSS values. Similarly, in this paper, a sliding time window is used to average the RSS values collected in every RP to improve the accuracy of RSS measurements. However, this filter only uses a small number of the RSS values in the radio map and most of the information in the radio map is abandoned.  
[PERSON] et al. [22] used a path-loss model to improve the reliability of the measured RSS values. In the offline phase of their method, a set of channel parameters are estimated for each access point. In the online phase, the user's location is found based on the calculated RSS values using the stored channel parameters. Their method results in a reliable localization thanks to the stability of the estimated channel parameters. In [23], [PERSON] et al. proposed a D-model to estimate the radio signal strength in indoor areas. The experiments in their paper proved that the proposed D-model is capable of estimating the RSS values with a high accuracy. Also their method models the wall attenuation more accurately compared to the method of [22]. Although the simulation result showed that the proposed method is fit for RFID positioning system, when this method is used in WiFi positioning system, the result is not satisfactory.  
The signal propagation method gives us some inspiration, we proposed signal propagation-based outlier reduction technique (SPORT) to smooth the RSS collections in both the offline phase and the online phase and improve the localization accuracy [24]. In this method, we investigate the relationship of RSS values between adjacent locations using a signal propagation model and show that the outliers can be corrected using a signal propagation model. Experimental results show that SPORT greatly smoothens the radio map and improves the location accuracy.  
In order to minimize the fluctuation of RSS values, [PERSON] et al. [25] developed a method using a Particle Filter (PF). Particle filters are used to perform non-linear and non-Gaussian estimations. However, in the online phase, a large number of particles have to be used in order to obtain a high positioning accuracy. Consequently, the computational cost is high which may be unacceptable for some indoor positioning applications.  
[PERSON] et al. [26] proposed a method based on the singular value thresholding (SVT) to recover the missing RSS values both in offline and online phases. In that paper, the authors argued that the positioning performance degrades significantly when some of the APs are occasionally turned off such as in a green WLAN system. Therefore, they proposed an SVT-based method to estimate the missing RSS values both in the radio map and the online RSS readings. They showed that their SVT-based method could achieve an acceptable positioning performance.