## 1 Introduction  
Localization is known to be useful in various domains, such as medical care, police, firefighting, industries, museums, airports, and shopping malls. Specifically, the positions of defined objects in the mentioned domains are sought for using localization systems. A few examples of such objects include pedestrians in a building or airport, auto-motives on the road, foreign objects in the human body, mobile animals in a game park, and merchandise on a shelf. A key example is the precise positioning of mobile objects is one key challenge in navigation studies. Positioning systems are classified as either indoor or outdoor depending on their development purposes and areas of application. Outdoor positioning systems have had a huge number of breakthroughs in terms of the accuracy and precision of the tools in the market today. Such outdoor positioning tools include the global positioning system (GPS), the BeiDou, and the Quasi-Zenith Satellite System (QZSS). The same outdoor tools are not accurate for indoor environments given the high levels of signal interference, absorption, multipath propagation, and fading effects in indoor scenarios. Therefore, indoor positioning solutions are currently a major work on progress in the navigation research community. Various technologies, including infrared, Bluetooth, ultra-Wide band (UWB), wireless fidelity (Wi-Fi), RFID, cameras, sound, and magnetic systems, are basically used as work-benches for localization data generation. Different indoor positioning solutions based on these technologies [1; 2; 3; 4; 5; 6; 7; 8; 9; 10] have been studied. Positioning approaches proposed in [1; 2; 3; 4] are based on image data collected from indoor environments, while those in [5; 6; 7; 8] were based on received signal strength indicator (RSSI) data. Conventional image-based positioning approaches [1; 2; 3; 4] use images of various kinds generated from the target indoor environment. Examples of such images include photographic images [11], radar signal graphical data [12], and RSSI CWT graphs [13]. Such images are used in the prediction or estimation of indoor positions and hence are used as fingerprints. This is what is referred to as combining image-based positioning with finger-print based positioning. So far, very limited studies [13] show that fingerprint data has been applied in deep learning systems for localization purposes. Notably, the most prominent fingerprint data transformed to feature sets are the RSSI. In order to build deep learning-based localization systems, indoor feature representation is very critical. It is said features which compose the fingerprints used as indoor datasets. Popular feature representation approaches include the use of the time domain, frequency domain, and joint time-frequency domain. Examples of time domain features include mean, variance, standard deviation, root mean square, mean crossing rate, and many more. Examples of frequency domain features include spectral energy commonly referred to as PSD and the discrete fast Fourier transform coefficient. An example of a joint time-frequency feature is the wavelet coefficient. The CWT, therefore, falls under the time-frequency domain. Studies such as [PERSON] et al. [13] have used CWT-based images to build a feature set from RSSI data usable for indoor localization using CNN and K-nearest neighbors (k-NN). Notably, [PERSON] et al. [13] was the first to use CWT-based images to solve indoor localization problems using deep learning. In said study, the Morlet mother wavelet was used to build CWT graphical images which were the basis of the indoor fingerprint regarded as the dataset. This paper proposes a CWT-based approach for formulating the indoor fingerprint using RSSI data but introduces data augmentation in order to improve on the quality of the realized CWT-based dataset for purposes of improving the localization accuracy. Data augmentation is a technique which is used specifically to increase the size of a dataset due to data scarcity [14].