CN103079168A - Distributed motion node positioning method based on hidden Markov model - Google Patents

Abstract

The invention discloses a distributed motion node positioning method based on a hidden Markov model. The distributed motion node positioning method comprises the following steps of: setting a certain time interval, counting location information, generating probability matrixes of location transfer respectively for different users, counting encounter information, and generating the probability of encountering other user nodes at each location; then, dividing a plurality of subsegments according to the current existing locations, wherein each time interval of each subsegment has the encounter information only without known location, and the locations of the head and tail of each subsegment are known; and then, determining the maximal probability estimation on internal unknown paths at fixed head and tail locations by using the known locations of the fixed head and tail of each subsegment, the hidden Markov chain model, and a Viterbi forward and backward algorithm in combination with a dynamic programming algorithm. According to the invention, higher positioning precision is achieved, and the distributed motion node positioning method is suitable for a large-area mobile network with very strong user movability, particularly for network scenes with sparse distribution in daily life.

Description

Localization method based on the distributed movement node of hidden Ma Shi model

Technical field

The present invention relates to the localization method of user node in the mobile communications network, particularly a kind of localization method of the distributed movement node based on hidden Ma Shi model belongs to network communications technology field.

Background technology

The localization method of user node is a technology that has been widely studied in the mobile communications network, and it has widely application scenarios, is very important application technology.Existing location technology can be divided into two large classifications substantially: based on the mode of finding range with based on the mode of connectedness.

Localization method based on range finding need to be measured each internodal relative distance, and higher to the required precision of measuring, main metering system is based on RSS (signal strength signal intensity that namely is subject to), the angle of arriving signal, difference of time of arrival (toa) etc.; On the contrary, based on the localization method utilization of connectedness be connectedness between the node, thereby avoided the high accurancy and precision of high cost to measure requirement, so such localization method can adapt to various scenes.

Instantly employed localization method based on connectedness needs the node environment of strong density, yet nowadays a lot of mobile network environments is because the network area is excessive, the user node mobility is excessively strong, and network topology is very easy to change, and therefore all only has relatively low connectedness in reality scene.This positions for node in the mobile network and has brought very large difficulty, localization method among the existing mobile network the most constructive meaning be MCL (being the Monte Carlo localization algorithm), but MCL depends on highdensity stationary nodes very much, therefore is difficult to adapt to the practical application in mobile network's scene.

By the analysis to history data set, we have found such phenomenon, and the mobility of user node has shown the regularity of very strong temporal and spatial correlations, the more important thing is, between the mobility of user node and the person of meeting of user node very strong correlation is arranged.Based on this rule, the present invention proposes a kind of localization method that is adapted to sparse two mobility network.

Summary of the invention

The objective of the invention is to overcome the deficiency that prior art too relies on highdensity stationary nodes environment, a kind of localization method of the distributed movement node based on hidden Ma Shi model is provided, comparatively sparse for user node density, and with only be in the communication radius with interior other user nodes could communications scene, use the hidden Markov chain model and realize location to this user node.

The present invention adopts following technical proposals to achieve the above object:

A kind of localization method of the distributed movement node based on hidden Ma Shi model is used for the mobile network to the location of user node, it is characterized in that, may further comprise the steps:

(1) system initialization is set communication radius and the time interval, and the statistics positional information also generates minute other position transfer probability matrix for the different user node, meet information and be created on each position collision probability with other user nodes of statistics;

(2) described user node obtains the information that itself and other user node meets by WiFi access AP, and described user node and other user nodes meet and refer to: this user node and other user nodes are accessed this AP or are in the interior AP of this AP communication radius in the interval at one time;

(3) described user node obtains itself present position by accessing fixing AP or meeting with fixing other user nodes, and described fixing AP and other fixing user nodes have known location;

(4) mark off some subsegments according to current existing known location, within each time interval of this subsegment, only meet information and without known position, the beginning of each subsegment and end have fixing known location;

(5) with the beginning of each subsegment of step (4) gained and the known location at end, use the hidden Markov chain model, utilize the Viterbi forward backward algorithm to determine beginning and end position are fixed and the maximum probability estimation in inner unknown path in conjunction with dynamic programming algorithm.

Described beginning and end position are fixed and definite step of the maximum probability in inner unknown path estimation is as follows:

(1) by historical information statistics, initial set up position transition probability matrix A and with the collision probability B of other user nodes;

(2) from initial position g, be designated as e in the information of meeting of described user node of next time interval and other user nodes _t, and be δ at the probability that this moment may move to all i _i, utilize the position transfer matrix A and knownly meet information calculations out ${\mathrm{\δ}}_i=A_{\mathrm{gi}}*B_{{\mathrm{ie}}_t};$

(3) make t=t+1, the information of meeting of this user node and other user nodes is designated as e within next time interval _t, and the probability that moves to all j within next time interval is δ _j, utilize ${\mathrm{\δ}}_j=\underset{i}{\max }{\mathrm{\δ}}_i*A_{\mathrm{ij}}*B_{{\mathrm{ie}}_t},$ And record $\underset{i}{\arg \max }{\mathrm{\δ}}_i*A_{\mathrm{ij}}*B_{{\mathrm{ie}}_t};$

(4) repeating step (3) is until t surpasses the afterbody time;

(5) establishing h is the known present position of afterbody, by the maximum probability δ that is in the unknown path of position j before the known arrival afterbody of front 4 steps _j, establish from position j, utilize ${\mathrm{\δ}}_h=\underset{j}{\max }{\mathrm{\δ}}_j*A_{\mathrm{jh}}*B_{{\mathrm{he}}_t},$ And record ${\mathrm{\δ}}_h=\underset{j}{\max }{\mathrm{\δ}}_j*A_{\mathrm{jh}}*B_{{\mathrm{je}}_t};$

(6) optimal path of searching for and recording according to per step, backstepping goes out to have the path T of maximum probability.

The present invention utilize the information of meeting that can directly observe, calculate most possible path position with the hidden Markov chain model, thereby realize the location to user node.

The invention has the advantages that:

(1) localization method of the present invention can adapt to the mobile network in large zone, be applicable to very strong user node mobility, even network topology changes, position error of the present invention can great changes have taken place, simultaneously the present invention is applicable to the more sparse network scenarios of real-life distribution, and the strong assumption of network environment being made with respect to the MCL method has stronger adaptive capacity.

(2) the present invention is distributed localization method, rather than the method for routing of being controlled by control centre, user node only needs within the communication radius of oneself, communicating by letter that other nodes that statistics is met with oneself are a small amount of is just passable, and do not need to carry out between every pair of user node communication, reduced the spending of energy consumption and communication.

(3) the present invention utilize the information of meeting that can directly observe, calculate most possible path position with hidden Ma Shi model, can reach higher positioning accuracy.

Embodiment

The localization method of the distributed movement node based on hidden Ma Shi model of the present invention is based on connective mode, take full advantage of mobility and the correlation between the user node of meeting, obtain that user node records with communication context in the history of other user nodes meet on the basis of information, set up hidden Ma Shi model, calculate the purpose that most possible path position reaches the user node location.Described method is specially adapted to the sparse mobile network's scene of user node.

The below is described in further detail each step of the present invention:

The localization method of described distributed movement node based on hidden Ma Shi model is used for the mobile network to the location of user node, and it may further comprise the steps:

(1) system initialization, set communication radius and the time interval, the statistics positional information, generation is for minute other position transfer probability matrix of different user node, be that the residing position of user node previous moment is at the state-transition matrix of next moment present position, the statistics information of meeting is created on each locational and collision probabilitys other user nodes, namely this time be engraved on this position simultaneously the probability that meets with other user nodes set.

(2) described user node is accessed AP by WiFi, and in the interval other user nodes of accessing among this AP is arranged at one time, is this user node and other user nodes to meet; If other user nodes of access and the AP of this AP in the communication distance scope are arranged, also meet for these other user nodes and this user node.In other words, user node obtains the information that itself and other user node meets by WiFi access AP, and described user node and other user nodes meet and refer to: this user node and other user nodes are accessed this AP or are in the interior AP of this AP communication radius in the interval at one time.

(3) position of some AP, therefore described user node is when accessing this AP by WiFi, just can obtain current location, perhaps certain user's node be fixed and its present position known, then described user node is accessed this fixing AP or is met with fixing user node and namely knows this moment present position.In other words, described user node obtains itself present position by accessing fixing AP or meeting with fixing other user nodes, and described fixing AP and other fixing user nodes have known location.

(4) mark off some subsegments according to current existing known location, the information of within each time interval of this subsegment, only meeting, there is not known position, namely do not meet with any fixed node in this subsegment, the beginning of each subsegment and end all have fixing known location, and namely the beginning and end in each subsegment all meets with known stationary nodes.

(5) with the fixedly beginning of each subsegment of step (4) gained and the known location at end, use the hidden Markov chain model, utilize the Viterbi forward backward algorithm to determine beginning and end position are fixed and the maximum probability estimation in inner unknown path in conjunction with the relevant step of dynamic programming algorithm.

Described beginning and end position are fixed and definite step of the maximum probability in inner unknown path estimation is as follows:

(1) initial set up position transition probability matrix A, this state-transition matrix comes out by historical information, be arranged on this position and gather the probability matrix B that meets with other user nodes simultaneously, this matrix also comes out by historical information;

(2) from initial position g, the information of meeting at described user node of next time interval and other user nodes that is come out by historical information is designated as e _t, and be δ at the probability that this moment may move to all i _i, utilize the position transfer matrix A and knownly meet information calculations out

(3) make t=t+1, this user node and other user node information of meeting are designated as e within next time interval _t, and the probability that moves to all j within next time interval is δ _j, utilize

Move to the probability of j when wherein i represents previous moment at position i and in next time interval, obtaining the δ of maximum probability _jIn time, recorded $\underset{i}{\arg \max }{\mathrm{\δ}}_i*A_{\mathrm{ij}}*B_{{\mathrm{ie}}_t};$

(4) repeating step (3) namely calculates the end that next time interval is exactly this subsegment until t surpasses the afterbody time;

(5) establishing h is the known present position of afterbody, by the maximum probability δ that is in the unknown path of position j before the known arrival afterbody of front 4 steps _j, establish from position j, utilize ${\mathrm{\δ}}_h=\underset{j}{\max }{\mathrm{\δ}}_j*A_{\mathrm{jh}}*B_{{\mathrm{he}}_t},$ And record ${\mathrm{\δ}}_h=\underset{j}{\max }{\mathrm{\δ}}_j*A_{\mathrm{jh}}*B_{{\mathrm{je}}_t};$

(6) according to the search of per step and the optimal path recorded, namely so that the previous position of maximum probability, along the parameter of every back maximum probability, backstepping goes out to have the path T of maximum probability.

Below be Implementation of pseudocode of the present invention, i.e. source program:

The present invention is by the analysis to history data set, strong correlation between other user nodes of using the mobility of described user node and meeting and the regularity of temporal and spatial correlations, utilize that described user node records with communication context in the information of meeting of other user nodes, calculate most possible path position with the hidden Markov chain model, realize the location to user node.The present invention has reached higher positioning accuracy, is applicable to the mobile network in the very strong large zone of user mobility.

Claims (2)

1. the localization method based on the distributed movement node of hidden Ma Shi model is used for the mobile network to the location of user node, it is characterized in that, may further comprise the steps:

(1) system initialization is set communication radius and the time interval, and the statistics positional information also generates minute other position transfer probability matrix for the different user node, meet information and be created on each position collision probability with other user nodes of statistics;

(2) described user node obtains the information that itself and other user node meets by WiFi access AP, and described user node and other user nodes meet and refer to: this user node and other user nodes are accessed this AP or are in the interior AP of this AP communication radius in the interval at one time;

(3) described user node obtains itself present position by accessing fixing AP or meeting with fixing other user nodes, and described fixing AP and other fixing user nodes have known location;

(4) mark off some subsegments according to current existing known location, within each time interval of this subsegment, only meet information and without known position, the beginning of each subsegment and end have fixing known location;

(5) with the beginning of each subsegment of step (4) gained and the known location at end, use the hidden Markov chain model, utilize the Viterbi forward backward algorithm to determine beginning and end position are fixed and the maximum probability estimation in inner unknown path in conjunction with dynamic programming algorithm.

2. the localization method of the distributed movement node based on hidden Ma Shi model according to claim 1 is characterized in that, described beginning and end position is fixed and definite step of the maximum probability estimation in inner unknown path is as follows:

(1) by historical information statistics, initial set up position transition probability matrix A and with the collision probability B of other user nodes;

(2) from initial position g, be designated as e in the information of meeting of described user node of next time interval and other user nodes _t, and be δ at the probability that this moment may move to all i _i, utilize the position transfer matrix A and knownly meet information calculations out ${\mathrm{\δ}}_i=A_{\mathrm{gi}}*B_{{\mathrm{ie}}_t};$

(3) make t=t+1, the information of meeting of this user node and other user nodes is designated as e within next time interval _t, and the probability that moves to all j within next time interval is δ _j, utilize ${\mathrm{\δ}}_j=\underset{i}{\max }{\mathrm{\δ}}_i*A_{\mathrm{ij}}*B_{{\mathrm{ie}}_t},$ And record $\underset{i}{\arg \max }{\mathrm{\δ}}_i*A_{\mathrm{ij}}*B_{{\mathrm{ie}}_t};$

(4) repeating step (3) is until t surpasses the afterbody time;

(5) establishing h is the known present position of afterbody, by the maximum probability δ that is in the unknown path of position j before the known arrival afterbody of front 4 steps _j, establish from position j, utilize ${\mathrm{\δ}}_h=\underset{j}{\max }{\mathrm{\δ}}_j*A_{\mathrm{jh}}*B_{{\mathrm{he}}_t},$ And record $\underset{j}{\arg \max }{\mathrm{\δ}}_j*A_{\mathrm{jh}}*B_{{\mathrm{je}}_t};$

(6) optimal path of searching for and recording according to per step, backstepping goes out to have the path T of maximum probability.