You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Indoor Fingerprint Positioning Based on Wi-Fi: An Overview 
Chapter Title:  Application of Channel Propagation Model: 
1. Abstract of Cited Paper (BIB001):  In this paper, a new indoor positioning system based on incident angle measurement of infrared lights has been suggested. Though there have been various researches on indoor positioning methods using vision sensor or ultrasonic sensor, they have not only advantages, but also disadvantages. To minimize the disadvantages they have, this new method using incident angle of infrared light has been invented. In a new positioning system, there are three infrared emitters on fixed known positions. An incident angle sensor measures the angle differences between each two emitters. Measured angle differences determine a position. This method is available only inside the triangle which is composed of three emitters. Mathematical problems to determine the position with angle differences and position information of emitters has been solved. To solve the non-linear equations without prior position information, iterative linearization process has been used. Simulations and experiments have been implemented to show the performance of this new positioning system. The results of simulation were good. Since there existed problems of noise and signal conditioning, the experimented has been implemented in limited area. But the results were acceptable. This new positioning method can be applied to any indoor systems that need absolute position informations. 
2. Abstract of Cited Paper (BIB002):  A Bayesian method for dynamical off-line estimation of the position and path loss model parameters of a WLAN access point is presented. Two versions of three different on-line positioning methods are tested using real data. The tests show that the methods that use the estimated path loss parameter distributions with finite precisions outperform the methods that only use point estimates for the path loss parameters. They also outperform the coverage area based positioning method and are comparable in accuracy with the fingerprinting method. Taking the uncertainties into account is computationally demanding, but the Gauss-Newton optimization method is shown to provide a good approximation with computational load that is reasonable for many real-time solutions. 
3. Abstract of Cited Paper (BIB003):  Aiming to solve one of the serious problems of radio-signal strength (RSS) indoor positioning (namely, fluctuation of RSS values due to reflective waves), a novel indoor-positioning method based on RSS distribution modeling (called “RSS distribution modeling using the mirror-image method,” RDMMI), was developed and evaluated. With RDMMI, a model of RSS distribution is created by using measured RSS and considering reflective waves. RDMMI achieves an average positioning error of 3.1 m, which exceeds that of conventional nearest-neighbor (NN) method by 0.6 m. It also accomplishes average positioning error of 3.7 m with a small set of randomly chosen RSS measurements. Furthermore, even in the case that radio source positions are unknown, RDMMI exceeds NN method. 
4. Abstract of Cited Paper (BIB004):  The positioning based on Wireless Local Area Networks (WLAN) is one of the most promising technologies for indoor location-based services, generally using the information carried by Received Signal Strengths (RSS). One challenge, however, is the huge amount of data in the radiomap database due to the enormous number of hearable Access Points (AP) that could make the positioning system very complex. This paper concentrates on WLAN-based indoor location by comparing fingerprinting, path loss and weighted centroid based positioning approaches in terms of complexity and performance and studying the effects of grid size and AP reduction with several choices for appropriate selection criterion. All results are based on real field measurements in three multi-floor buildings. We validate our earlier findings concerning several different AP selection criteria and conclude that the best results are obtained with a maximum RSS-based criterion, which also proved to be the most consistent among the different investigated approaches. We show that the weighted centroid based low-complexity method is very sensitive to AP reduction, while the path loss-based method is also very robust to high percentage removals. Indeed, for fingerprinting, 50% of the APs can be removed safely with a properly chosen removal criterion without increasing the positioning error much.

Literature Review:
The radar BIB001 system analyzed the ground attenuation factor channel model (FAF) BIB003 , and found that when this model was associated with a large-scale path loss model, it was flexible enough to be used in different indoor space structures. Based on the FAF, researchers built a model called the wall attenuation factor channel model (WAF) used in radar, as shown in Equation (5). In Equation (6), n represents the distance path attenuation rate, where P(d 0 ) represents the signal strength value under the condition that the distance between the sender and receiver equals reference distance d 0 ; and d represents the distance between the sender and receiver. C indicates the maximum number of walls that the factor works; nW represents the number of walls between the sender and receiver; and WAF represents the wall attenuation factor. Using the WAF model, the median of accuracy of positioning can reach 4.3 m. Nurminen et al. BIB005 used the classical logarithmic model (power-law model) as the indoor channel propagation model. The classical logarithmic mode combines LOS (Line of Sight) and NLOS (Not Line of Sight) propagation by using numbers of correction factors to offset the uncertainty caused by these two propagations. Equation (7) shows the logarithmic model Nurminen BIB005 used. P rx (d) is shown as a logarithm, where d 0 represents the reference distance. n represents the path fading index and W-N(0, σ 2 ) represents a random variable as normal distribution, which indicates the slow fading (shadow fading) of indoor signal propagation. Nurminen BIB005 utilized this model based on a large number of fingerprint data measured to optimize his research results, which had some effects. Laitinen et al. BIB004 improved the model proposed by Nurminen BIB005 and built the single slope model, shown in Equation BIB002 . In Equation (8), n ap represents the effect of path loss on the ap-th AP where d i,ap represents the distance between the ap-th AP and the i-thRP, which is calculated as Euclidean distance. P i,ap represents the signal strength measured at the i-thRP sent from the ap-th AP, where P Tap represents the signal propagation strength of the ap-th AP, and η i,ap represents a noise factor as normal distribution. Unlike Nurminen BIB005 , Laitinen BIB004 obtained only a few pieces of fingerprint data at several key RPs and then obtained the entire radio map through the model. Laitinen BIB004 optimized the fingerprint database in this way, which improved positioning efficiency. Alternatively, there are other new ways to achieve positioning using accurate modeling in combination with other methods like those discussed in reference  .