A prediction method of indoor moving trajectory data based on hmm model
A prediction method and model technology, which is applied to the prediction of indoor mobile trajectory data and the field of indoor trajectory prediction under big data, which can solve problems such as increased calculation amount, discontinuous state transition probability, and state stay
Active Publication Date: 2020-10-23
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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The indoor trajectory position prediction method based on the HMM model is to divide the space into disjoint regions and use the region to represent the trajectory points in order to simplify the trajectory data and improve the speed of prediction calculations; however, the classical HMM model has hidden state discontinuity, In addition, there are floor attributes in the indoor space, so that there are the same position coordinates between different floors, resulting in prediction failure
At present, the method to solve the transition probability of the discontinuous hidden state is 0 is to use the historical data multiple times in gradients during the establishment of the HMM, extract the trajectory points at periodic intervals, and calculate the state transition matrix together with the continuous trajectory points to obtain the discontinuous The two state transition probabilities; however, this algorithm requires multiple visits to historical data. When the amount of historical data storage is accumulated, the amount of calculation becomes larger, and the model overfitting occurs
The state stay problem refers to the situation that among the first N trajectory points, there are multiple consecutive points belonging to the same hidden state, and the rotation probability of the state transition probability matrix is 0, which leads to the failure of prediction; Obtain the experience value so that the probability of model rotation is not 0; since this method requires artificial settings, the prediction progress on different trajectory data is quite different
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[0058] The present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments.
[0059] This embodiment provides a method for predicting the position of a spatial movement trajectory based on an HMM model, the process of which is as follows figure 1 shown; the specific steps are as follows:
[0060] Step 1: Based on the historical trajectory data, calculate the side length of the grid unit in the indoor space, and grid the indoor space;
[0061] Step 1.1: The method of calculating the side length of the grid unit is: take the minimum distance between consecutive and non-repeating points of the historical trajectory sequence as the side length of the grid unit, and grid the indoor space model; in this way, grid projection is performed on the trajectory sequence It can improve the integrity of the trajectory sequence and avoid the improper selection of the side length of the indoor space grid unit, resulting in the loss of ...
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The invention belongs to the field of indoor movement trajectory management and prediction, and specifically provides a method for predicting indoor movement trajectory data based on an HMM model, so as to realize prediction based on HMM trajectory data in an indoor space. Firstly, based on the historical trajectory sequence, the minimum Euclidean distance between the continuous and non-repeating points of the historical trajectory sequence is taken as the side length of the grid unit, and the indoor space model is layered and gridded to generate a grid space; based on the grid space, Project the historical trajectory data, generate grid sequences, preprocess the grid sequences, and generate historical grid trajectory databases; then, perform clustering on the basis of the DBSCAN algorithm to generate a clustering information base, and based on the clustering information The table constructs the HMM model; finally, based on the trained HMM model, the Viterbi algorithm is used for prediction. The invention provides a method for predicting the location of indoor trajectory data, which can effectively improve the accuracy of predicted trajectory data, and further optimize the performance of the indoor trajectory data management system.
Description
technical field [0001] The invention belongs to the field of indoor trajectory management and prediction, and in particular relates to indoor trajectory prediction under big data, specifically a method for predicting indoor trajectory data based on an HMM model. Background technique [0002] With the rapid development of wireless interconnection technology, location-based services have been widely used; location-based services are divided into outdoor location services and indoor location services, and positioning technology is one of the core technologies of location services. In large indoor environments, such as shopping malls, hospitals, airports, etc., users have an increasing demand for location-based services. With the increasingly sophisticated indoor positioning technology of Bluetooth and WIFI, and the popularization of smart terminals, a large amount of trajectory data of moving objects has been collected; how to reasonably analyze, efficiently store, and accurate...
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IPC IPC(8): H04W4/029H04W4/33
CPCH04W4/029H04W4/33
Inventor李波张睿霖刘民岷
OwnerUNIV OF ELECTRONICS SCI & TECH OF CHINA