A Hidden Markov Model-Based Map-Matching Algorithm for Wheelchair Navigation

2009 ◽  
Vol 62 (3) ◽  
pp. 383-395 ◽  
Author(s):  
Ming Ren ◽  
Hassan A. Karimi
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Viterbi Algorithm ◽  
Hidden Markov ◽  
Markov Chain Model ◽  
Computational Time ◽  
Chain Model ◽  
Map Matching ◽  
Matching Algorithm ◽  
Matching Techniques

Application of map-matching techniques to GPS positions can provide accurate vehicle location information in challenging situations. The Hidden Markov Model (HMM) is a statistical model that is 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 HMM for GPS-based wheelchair navigation. Given GPS positions, a hidden Markov chain model is established by using both geometric data and the topology of sidewalk segments. The map-matching algorithm employs the Viterbi algorithm to estimate correct sidewalk segments as hidden states in a HMM in order to match GPS trajectory on the corresponding segment sequence. The HMM-based map-matching algorithm was validated on a campus sidewalk network for wheelchair navigation. The results show an improvement in tracking a wheelchair in dense urban conditions both in accuracy and in computational time.

scholarly journals An Online Map Matching Algorithm Based on Second-Order Hidden Markov Model

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiao Fu ◽  
Jiaxu Zhang ◽  
Yue Zhang
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Second Order ◽  
Map Matching ◽  
Trajectory Data ◽  
Matching Problem ◽  
Matching Algorithm ◽  
Study Results

Map matching is a key preprocess of trajectory data which recently have become a major data source for various transport applications and location-based services. In this paper, an online map matching algorithm based on the second-order hidden Markov model (HMM) is proposed for processing trajectory data in complex urban road networks such as parallel road segments and various road intersections. Several factors such as driver’s travel preference, network topology, road level, and vehicle heading are well considered. An extended Viterbi algorithm and a self-adaptive sliding window mechanism are adopted to solve the map matching problem efficiently. To demonstrate the effectiveness of the proposed algorithm, a case study is carried out using a massive taxi trajectory dataset in Nanjing, China. Case study results show that the accuracy of the proposed algorithm outperforms the baseline algorithm built on the first-order HMM in various testing experiments.

scholarly journals The Application of Baum-Welch Algorithm in Multistep Attack

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Yanxue Zhang ◽  
Dongmei Zhao ◽  
Jinxing Liu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov Models ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Simulation Experiments ◽  
Forward Algorithm ◽  
Better Than

The biggest difficulty of hidden Markov model applied to multistep attack is the determination of observations. Now the research of the determination of observations is still lacking, and it shows a certain degree of subjectivity. In this regard, we integrate the attack intentions and hidden Markov model (HMM) and support a method to forecasting multistep attack based on hidden Markov model. Firstly, we train the existing hidden Markov model(s) by the Baum-Welch algorithm of HMM. Then we recognize the alert belonging to attack scenarios with the Forward algorithm of HMM. Finally, we forecast the next possible attack sequence with the Viterbi algorithm of HMM. The results of simulation experiments show that the hidden Markov models which have been trained are better than the untrained in recognition and prediction.

scholarly journals Log-Viterbi algorithm applied on second-order hidden Markov model for human activity recognition

2018 ◽  
Vol 14 (4) ◽  
pp. 155014771877254 ◽  
Author(s):  
Yang Sung-Hyun ◽  
Keshav Thapa ◽  
M Humayun Kabir ◽  
Lee Hee-Chan
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Activity Recognition ◽  
Human Activities ◽  
Time Complexity ◽  
Viterbi Algorithm ◽  
Hidden Markov ◽  
Elderly Care ◽  
Second Order ◽  
Assistive Living

Recognition of human activities is getting into the limelight among researchers in the field of pervasive computing, ambient intelligence, robotic, and monitoring such as assistive living, elderly care, and health care. Many platforms, models, and algorithms have been developed and implemented to recognize the human activities. However, existing approaches suffer from low-activity accuracy and high time complexity. Therefore, we proposed probabilistic log-Viterbi algorithm on second-order hidden Markov model that facilitates our algorithm by reducing the time complexity with increased accuracy. Second-order hidden Markov model is efficient relevance between previous two activities, current activity, and current observation that incorporate more information into recognition procedure. The log-Viterbi algorithm converts the products of a large number of probabilities into additions and finds the most likely activity from observation sequence under given model. Therefore, this approach maximizes the probability of activity recognition with improved accuracy and reduced time complexity. We compared our proposed algorithm among other famous probabilistic models such as Naïve Bayes, condition random field, hidden Markov model, and hidden semi-Markov model using three datasets in the smart home environment. The recognition possibility of our proposed method is significantly better in accuracy and time complexity than early proposed method. Moreover, this improved algorithm for activity recognition is much effective for almost all the dynamic environments such as assistive living, elderly care, healthcare applications, and home automation.

Sign in / Sign up

Export Citation Format

Share Document