## 2 Related Work  
The existing literature focuses on efficient indoor positioning techniques that are low cost and are accurate in diverse environments. An ideal IPS would work in numerous indoor scenarios. To analyze the best indoor localization framework, a brief comparative investigation of the literature is presented. Existing research shows that machine learning models benefit IPS frameworks both in terms of cost and precision. Extensive research on handcrafted and deep learning models has assisted in achieving noise robustness in indoor positioning systems [22]. With the development of advanced computational devices, the employment of deep neural models and advanced machine learning frameworks has become possible [23; 24]. Deep neural networks such as AlexNet, DagNet, GoogleNet, ResNet, InceptionV3, VGG-16, MobileNet, and ZFNet require 2D input data, which is not available in the case of fingerprint base localization [25]. Indoor positioning systems developed for tracking objects broadly fall in two main classes: wireless signal and vision frameworks. Vision-based frameworks employ computer vision algorithms on the images captured by mounted cameras serving in indoor environments. Vision-based methods detect the desired object in non-overlapping camera networks of buildings by classifying the robust visual features extracted from the region of interest [26]. Vision-based methodologies are highly precise but computationally expensive. The high frame-rate and extensive resolution of images requires expensive processors and graphics processing unit for the recurrent neural networks to recognize and annotate the desired object in the input camera feeds. Moreover, vision-based frameworks also suffer from geometric and photometric variations, including occlusions and illumination and viewpoint variations. Compared to vision-based methods, wireless signal-based methods are cost-effective and require fewer computational resources [27]. Wireless signal-based indoor positioning frameworks rely both on geometric and fingerprinting methods. The method developed in [28; 29] considers indoor localization as a Gaussian and KNN regression problem. Regression-based models have less complexity, but they focus only on classification of pre-existing fingerprint data and ignore robust feature extraction. The work in [30] considers the importance of features to improve the precision of indoor localization by including continuous wavelet transforms (CWT) on raw RSS fingerprint data. CWT converts the 1D vector RSS data into 2D image data, with which pre-existing deep neural network models are easily compatible. CWT transformation is an additional stage before feature extraction that improve precision at the cost of the computational complexity of the model. Other works can be found in the literature using principal component analysis for feature extraction [31; 32]. Deep neural network (DNN) frameworks are much more sensitive to the format of input data, whereas RSS fingerprint data is vectorized data with a limited set of values for each geographical coordinate. Therefore, most IPSs incorporate handcrafted statistical models in their machine learning frameworks for indoor localization. Handcrafted statistical models are less complex and embrace the problem's specific modifications. The authors in [33] integrated a statistical hypothesis test on asymptotic relative efficiency (ARE) to optimize signal distribution at the site coverage area. Another work [34] introduces multi-output least square support vector machine (M-LS-SVM) regression to improve classification of RSS fingerprint data. Localization in [35] is achieved by fusion of grid-independent and grid-dependent least-square classifications. The authors of [36] used a neural network-based algorithm to correct the camera tilt angle, and they used nueral networks to establish a relationship between LED images and distance. However, the noise generated by reflection, which affects IPS performance, has not been considered.