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: A survey of hybridisation methods of GNSS and wireless LAN based positioning system 
Chapter Title:  Lower level feature fusion 
1. Abstract of Cited Paper (BIB001):  Indoor positioning systems that make use of received signal strength based location fingerprints and existing wireless local area network infrastructure have recently been the focus for supporting location-based services in indoor and campus areas. A knowledge and understanding of the properties of the location fingerprint can assist in improving design of algorithms and deployment of position location systems. However, most existing research work ignores the radio signal properties. This paper investigates the properties of the received signal strength reported by IEEE 802.11b wireless network interface cards. Analyses of the data are performed to understand the underlying features of location fingerprints. The performance of an indoor positioning system in terms of its precision is compared using measured data and a Gaussian model to see how closely a Gaussian model may fit the measured data. 
2. Abstract of Cited Paper (BIB002):  Indoor positioning systems are platforms required to provide location information for indoor location-aware computing. This study focuses on a type of indoor positioning systems called location fingerprinting technique that utilizes received signal strength indication (RSSI) of wireless local area network (WLAN) interfaces. Thus far, many characteristics of RSSI have not been thoroughly investigated. Many researchers have relied on the RSSI as sensor information to determine objects location while ignoring the characteristic of RSSI. Therefore, this paper looks into underlying mechanism of RSSI from the indoor positioning systems perspective. In particular, the distributions of RSSI from five different IEEE 802.11b/g WLAN interfaces are investigated using extensive measurement experiments. Desired properties of WLAN interfaces for location fingerprinting are also identified based on the measurement results. The ultimate goal is to use additional knowledge gained here to improve positioning performance and to model location fingerprinting based indoor positioning systems. 
3. Abstract of Cited Paper (BIB003):  For a growing number of aviation applications of the Global Positioning System (GPS) and of other Global Navigation Satellite Systems (GNSS), it is essential to establish a rigorous bound on measurement error. Most existing bounding methods rely on representing the actual measurement error distribution with a conservative, continuous model (e.g., a Gaussian "overbound"). We propose a conservative, discrete model as a practical alternative. A key limitation of continuous error models is validation, particularly in the distribution tails where comparatively little statistical data is available. With a discrete model, it is easy (1) to define a minimally conservative core region, where data are plentiful, and (2) to introduce a highly conservative tail region, where data are sparse. The trade-off is increased computational complexity, as no closed-form expression exists for convolution of non-Gaussian error distributions. We propose a particular form of a discrete error distribution, which we call the NavDEN model. Through application to a heavy-tail GPS data set, we demonstrate that the NavDEN model compares favorably to Gaussian models, both in providing more margin for tail uncertainty and, at the same time, in providing generally tighter protection levels (PLs) when multiple distributions are convolved.

Literature Review:
The potential performance of data fusion schemes based upon pseudo ranges and WPS features (Section 3.4) is still higher. But so are the complexity and the costs. When fusing information on data level, a less processed level, more meaningful information can be retrieved from the signals. That assists evaluating the quality of data. Accessing the pseudo ranges allows integrity monitoring (e.g. RAIM) or a multipath mitigating strategy. When using a Kalman filter for the PVT computation, the innovation of the filter may provide some useful information with respect to errors and outliers. Having access to the fingerprint data base of a WPS, knowledge about the actual number of APs, etc., used for the position estimation, may help to get a useful QoI. Information about the number of recently appeared and unknown APs and the number of disappeared APs may also help to evaluate the reliability of the position estimate. Integrating this kind of information into the data fusion of GNSS and WPS, faulty measurements can be excluded from the position estimation. Thus, the accuracy can be significantly increased. Where on the other hand, the position fusion systems risk greater errors caused by false detection. The uncertainty of measurements can be modelled more appropriate, may be according to certain environments, frequent scenarios or different motions. The probabilistic models for pseudo ranges usually follow a Gaussian distribution BIB001 , which is justified with overbounding BIB004 . RSS measurements are often modelled as Gaussian distributed too, but this rarely fits well the measurements BIB003 BIB002 . An access to the al- gorithms allows to refine the noise models, which can improve the precision of position estimates. By feeding features of GNSS and WPS into a common position estimation process, less signals from one of the subsystems are required to compute a position. This makes the overall system much more robust and increases its coverage. Otherwise only one positioning subsystem or none at all would be usable. Indeed, those approaches, like particle filtering, are much more complex and costly. Both subsystems are supposed to operate in parallel. Power consumption is larger than that of all former approaches and the up to day mobile devices may reach their limits, when contemplating processing power. Table 1 summarises the statements of this section, comparing hybrid positioning approaches based on the four data fusion levels with GNSS and WPS standalone positioning systems.