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Among the hidden map4/11/2023 ![]() ![]() Map matching technique was proposed to assist navigation systems in global navigation satellite system (GNSS)-denied environments, based on the integration of BSSD–INS and road network models applying hidden Marcov model and Viterbi algorithm. A Kalman filter algorithm was used to integrate BSSD and inertial navigation system (INS)-based smartphone MEMS. In this research, the performance of the Xiaomi MI 8 smartphone’s single-frequency precise point positioning was tested in kinematic mode using the between-satellite single-difference (BSSD) technique. Smartphone microelectromechanical systems (MEMS) are attractive due to their small size and low cost however, they suffer from long-term drift, which highlights the need for additional aiding solutions using road network that can perform efficiently for longer periods. High-end inertial sensors are not preferred due to their high cost. The integration between navigation systems is necessary to maintain a reliable solution. ![]() The demand for smartphone positioning has grown rapidly due to increased positioning accuracy applications, such as land vehicle navigation systems used for vehicle tracking, emergency assistance, and intelligent transportation systems. This can be accomplished by (1) developing an undifferenced and between-satellite single-difference (BSSD) PPP technique using single-frequency GNSS 140 observations (2) evaluating the model results in kinematic mode using a Xiaomi MI 8 smartphone and a high-end geodetic receiver (3) providing an integrated positioning model based on combining the BSSD PPP results with the smartphone inertial sensors output (4) developing a rectified road network mapping for the area of interest through a new map matching model based on hidden Marcov model (HMM) and Viterbi algorithms (Luo et al., 2017) (5) combining the positioning of the integrated system with a map matching model to obtain a restricted land vehicle navigation solution and (6) assessment of the restricted integrated system in comparison with the reference RTK relative positioning solution. The main purpose of this research is to improve the smartphone's positioning accuracy for land vehicle navigation applications in environments where GPS accuracy has either deteriorated or its signal has been blocked. We have evaluated our method on a public dataset and observed an average accuracy of 91% in automatically identifying map-matching errors, thus helping analysts to significantly reduce manual effort for map-matching quality assurance. In this work, we propose the first method to automatically identify map-matching errors in the absence of ground truth, i.e., only using the recorded trajectory points and the map-matched route. Although research has focused on improving map-matching algorithms, to our knowledge no attempts have been made to automatically classify and identify the residual map-matching errors. Thus, the identification of map-matching errors without ground truth is a time-consuming and mundane task. Identification of these map-matching errors in the absence of ground truth can only be achieved by visual inspection and reasoning. However, due to the unavoidable errors in the recorded trajectory points and the incomplete map data, map-matching algorithms may match points to incorrect segments, leading to map-matching errors. Map-matching algorithms snap a set of trajectory points observed by a satellite navigation system to the most likely route segments of a map. Map-matching of trajectory data has widespread applications in vehicle tracking, traffic flow analysis, route planning, and intelligent transportation systems. The results show a significant improvement in mobile phone positioning and high and low sampling of GPS data. The HMM-based map-matching algorithm is validated on a vehicle trajectory using GPS and mobile phone data. The sequence consists of hidden states in the HMM model. HMM-based map-matching exploits the Viterbi algorithm to find the optimized road link sequence. In this work, the hidden Markov chain model was built to establish a map-matching process, using the geometric data, the topologies matrix of road links in road network and refined quad-tree data structure. The HMM is a statistical model well known for providing solutions to temporal recognition applications such as text and speech recognition. This paper introduces a novel map-matching algorithm based on a hidden Markov model (HMM) for GPS positioning and mobile phone positioning with a low sampling rate. However, most existing map-matching algorithms process GPS data with high sampling rates, to achieve a higher correct rate and strong universality. ![]() ![]() Numerous map-matching techniques have been developed to improve positioning, using Global Positioning System (GPS) data and other sensors. ![]()
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