## 2 Navigation System for Visually Impaired Pedestrians
### Related Works on 3 DMA GNSS  
One popular approach to improve GNSS positioning in urban canyons is using a 3D building model to identify and even correct the NLOS reception error. Different research has proposed to simulate the propagation path and model the error for code [18] and carrier phase [19] measurements. Research also incorporates the Fresnel zone analysis to predict GNSS multipath, NLOS, and diffraction effects in urban areas [20]. 3D building models demonstrate a massive improvement in positioning, namely 3D mapping-aided (3 DMA) GNSS [21]. 3 DMA GNSS has greatly impacted urban positioning in recent years, especially since huge improvements in smartphone positioning have been demonstrated [22]. An existing study uses the 3D building model to exclude the NLOS satellites; the weighted least squares (WLS) solution can therefore be enhanced [23]. However, we have to know the exact position to give a correct prediction and correction. As a result, the solution is usually determined as a particle-based approach. Position hypothesis candidates are distributed and measurements are modelled as the prediction at each candidate. The candidate with the highest similarity between modelled and actual received measurements is assumed to be the receiver location. Two basic categories of 3 DMA GNSS algorithms are shadow matching and ranging-based 3 DMA GNSS. Shadow matching [24; 25] matches the satellite visibility at different locations. The received satellites are assumed to be the LOS, while the non-received ones are assumed to be NLOS. The ephemeris provides the satellites' position and matches the visibility at each candidate to find the highest visibility similarity. There is also research to further improve the shadow matching by particle filter [26]. Another 3 DMA GNSS is the ranging-based method. The receiver location is determined by comparing the modelled and received pseudorange. Pseudorange measurements are modelled at each candidate. For the NLOS-predicted satellite, the NLOS reflection delay is also modelled based on a geometrical or statistical approach. The absolute position of the reflecting point has to be determined for the geometrical approach. A popular approach is ray-tracing [27; 28]. It tests and validates the reflection path over each potential reflector, creating a high computational load. Therefore, research on using GPU to accelerate the computing process is relevant [29]. Moreover, an effective computational version called skymask 3 DMA GNSS [30] was introduced. It determines the reflecting point over an enhanced skymask. Besides reflection delay, ray-tracing simulation can also calculate the GNSS signal strength based on the multipath propagation model [31; 32]. A research study was conducted on the use of the ray-tracing technique to identify propagation obstructions and quantity propagation errors [33]. The study proposed measuring the position integrity as a set-based approach to bound the remaining systematic uncertainty. The statistical approach, also known as likelihood-based ranging [34], uses a skew-normal distribution to model the NLOS delay measurements and then remap the errors to the LOS with the normal distribution. Performance of shadow matching and ranging-based 3 DMA GNSS are different due to the healthy satellite and building geometry distribution. Shadow matching usually outperforms in the across-street direction, while ranging-based 3 DMA GNSS obtains higher accuracy in the along-street direction. The complementary nature of the two approaches inspired researchers to integrate them. The latest study shows that an integrated solution of 3 DMA GNSS can provide positioning accuracy of around 10 m or less in urban canyons with both single-frequency [34] and multi-frequency [35] measurements. However, most of these approaches only concern positioning in a single epoch. Hence the performance is not robust for continuous positioning. As a result, a temporal connection is required to improve the reliability of urban GNSS positioning.