CN101754247B - Mine positioning system based on multiple linear regression and positioning method thereof - Google Patents

Abstract

The invention discloses a mine positioning system based on multiple linear regression and a positioning method thereof. The positioning system comprises an anchor node module, a mobile node module, a positioning formula parameter calculation module, an RSSI value filtering module and an RSSI value positioning module, and a mobile node collects the RSSI value of the broadcast information of an anchor node, carries out regression analysis on data and obtains a positioning formula and parameters; when the system normally works, the mobile node captures the RSSI values of the broadcast information of a plurality of anchor nodes in certain cycle time, the data with relatively low reliability are filtered out by substituting all RSSI values in the filtering module when the cycle time is up, and finally own position is calculated by using a regression equation and parameters and the position is sent to an aboveground monitoring center through the forwarding-direct transfer of the anchor node. The invention has simple calculation process and high positioning precision, can provide real-time and precise well personnel information for a safety monitoring system, and can provide accurate target place for search and rescue work after an accident and provide reliable guarantee for rescuing personnel life in time.

Description

A kind of mine navigation system and localization method thereof based on multiple linear regression

Technical field

The present invention relates to a kind of mine navigation system and localization method thereof; Be specifically related to a kind of receiving signal strength values (RSSI; Received Signal Strength Indicator) combines with the multiple linear regression scheme, have the mine navigation system and the localization method thereof based on multiple linear regression of advantages such as positioning accuracy height, reliable operation, amount of calculation be little.

Background technology

At present, all kinds of mine disasters happen occasionally, and not only cause huge property loss, and also direct threats miner's life security.Existing monitoring mining system is based on wireline equipment mostly on the market, in case blast and cave in, most of circuit can be damaged, thus monitoring system the back takes place in accident just can't operate as normal.In recent years; Wireless sensor network based on Zigbee (IEEE802.15.4) technology has obtained using widely; Characteristics such as its self-organizing, survivability just can remedy the shortcoming of existing monitoring mining system; Even after various mine disasters take place, the information of down-hole still can be through wireless network transmissions to Surveillance center, for carrying out of rescue operation provides Useful Information.

Publication number is that the patent of invention of CN 101369944A discloses a kind of mine safety wireless location system and distribution method thereof based on ZigBee and RFID; This system uses the ZigBee node to carry out the data route; But use the RFID technology to gather people's position; This method can only judge whether certain workman has got into the scope that certain RFID label is radiated, and can't in whole tunnel, accurately locate.Because the position error of this localization method is very big, so it can only be used for workman's attendance checking system, and can't be applied to the rescue after the mine disaster.

The mine detection system requires to obtain exactly, in real time personnel in the pit's positional information.Some navigation system proposes to use the scheme of GPS location, but the tunnel of down-hole generally is positioned at below the 500-1000 rice of the face of land the very difficult arrival of gps signal.Wireless sensor network also has some conventional positioning methods, for example: DV-Hop, APIT, barycenter methods etc. mostly are based on the topological structure of node random distribution, are not suitable for this long and narrow area of mine laneway.And the error of these localization methods is rescued after the locating information under this error also can't be used for mine disaster mostly at about 20% of wireless range.In some methods based on measurement (Range-Based); They derive mostly theoretically; And suppose that internodal distance learns; Simulate the result of localization method then according to these distance values, but in the applied environment of reality, two internodal distances often are difficult to accurate Calculation and come out.

Summary of the invention

Goal of the invention: in order to overcome the deficiency that exists in the prior art; It is a kind of receiving signal strength values (RSSI that the present invention provides; Received Signal Strength Indicator) combines with the multiple linear regression scheme, have the mine navigation system and the localization method thereof based on multiple linear regression of advantages such as positioning accuracy height, reliable operation, amount of calculation be little.

Technical scheme: for realizing above-mentioned purpose; A kind of mine navigation system of the present invention based on multiple linear regression; Comprise anchor node module, mobile node module, ranging formula parameter calculating module, RSSI value filtering module and RSSI value locating module; Said anchor node module comprises one group of anchor node, and anchor node is equally spaced to be arranged in the mine laneway; Said mobile node module comprises the mobile node that is worn on miner or the vehicle, and mobile node is monitored the broadcast message of anchor node, therefrom extracts the coordinate that receives signal strength values RSSI and anchor node, and is saved in the internal memory linked list data structure; Position coordinates, the RSSI value that on this position coordinates receive and this RSSI corresponding anchor node coordinate of said ranging formula parameter calculating module statistics mobile node in the tunnel; And substitution data analysis tool; The method of use multiple linear regression calculates the relation formula of mobile node physical location and anchor node broadcast message, i.e. each parameter value in regression equation and the equation; Said RSSI value filtering module is regularly carried out Filtering Processing to the value in the linked list data structure; Said RSSI value locating module is the position of after Filtering Processing is accomplished, obtaining mobile node according to ranging formula and parameter value.

Be somebody's turn to do localization method, may further comprise the steps based on the mine navigation system of multiple linear regression:

1. be arranged among the mine laneway ID number and coordinate information of anchor node broadcasting oneself with anchor node is equally spaced; Mobile node is worn on miner and the vehicle, and all the sensors node organizes themselves into wireless sensor network after arranging completion and opening power;

2. mobile node some places collection position coordinate, the RSSI value that on this position coordinates, receives and corresponding anchor node coordinate of this RSSI in the tunnel; The ranging formula parameter calculating module is updated to the data that collect in the data analysis tool; Calculate the relation formula of anchor node RSSI value and mobile node physical location, i.e. each parameter value in regression equation (7) and the formula through the method for multiple linear regression:

$Y=\underset{i=1}{\overset{3}{\mathrm{\Σ}}}{b}_i{x}_i+b_0---\left(7\right)$

Wherein, Y is the coordinate of mobile node, b _iBe regression coefficient, b ₀It is a constant coefficient;

Said regression equation (7) is to derive according to formula (4), formula (5) and formula (6):

$R_i=\frac{{\mathrm{RSSI}}_i}{\underset{j=1}{\overset{3}{\mathrm{\Σ}}}{\mathrm{RSSI}}_j}---\left(4\right)$

x _i＝R _iX _i (5)

Wherein, R _iBe scale factor, RSSI _iThe signal strength values that receives from i anchor node for mobile node,

For mobile node receives 3 summations than big RSSI values in the anchor node data, X _iCoordinate for the corresponding anchor node of RSSI value;

$Y=\underset{i=1}{\overset{3}{\mathrm{\Σ}}}{b}_i{R}_i{X}_i+b_0---\left(6\right)$

Wherein, Y is the coordinate of mobile node, X _iBe the coordinate of anchor node, b _iBe regression coefficient, b ₀Be a constant coefficient, R _iBe scale factor;

3. in system's operate as normal, ID number and coordinate information of anchor node fixed time broadcast oneself, mobile node is monitored anchor node information at some cycles in the time, from information, extract the coordinate of RSSI value and this anchor node, and is saved in the internal memory linked list data structure;

4. when arrive cycle time and need the location; RSSI value filtering module is carried out Filtering Processing to the value in the linked list data structure; Promptly calculate the mean value and the standard deviation of all RSSI values; Through the bigger data of gaussian filtering formula filtering error, obtain the mean value of remaining data again, at last with this mean value as the RSSI value of mobile node to this anchor node;

5. RSSI value locating module parameter that filtered RSSI value substitution ranging formula (6) and substitution regression analysis are drawn is calculated the position coordinates of mobile node, and is sent to aboveground PC to the forwarding of position coordinates through anchor node always.

Has only a life cycle of jumping with coordinate information ID number of the broadcasting of said anchor node.

Said step 4. in, timing is 5 seconds, regularly not then mobile node was not monitored the anchor node broadcast message all the time and was saved in this locality to RSSI value and anchor node coordinate in 5 seconds, 5 seconds timing arrives are carried out data processing and location Calculation again.

All anchor node anchor1 that the listHead that said internal memory linked list data structure comprises the chained list that each mobile node keeps and this mobile node can listen to are to anchorN, and N represents the number of anchor node; Each anchor node comprises five attributes, promptly after the ID anchorNum of this anchor node, anchor node coordinate anchorPos, the pointer NextAnchor that points to next anchor node, RSSI value NextValue that mobile node receives this anchor node, the Filtering Processing mobile node to the RSSI mean value AverageRSSI of this anchor node; The RSSI value NextValue that said mobile node receives this anchor node comprises two attributes; Promptly receive the pointer NextValue of the next RSSI value that RSSI value value, this anchor node of this anchor node receive; If there is not next value, the value of this pointer NextValue is NULL.

Beneficial effect: a kind of mine navigation system and method thereof based on multiple linear regression of the present invention is applied to wireless sensor network technology in the location of mine laneway; When mine disaster and all kinds of accident took place, system still can be through wireless mode transmission data; The present invention is the large-scale distributed calculating of simple, the easy realization of computational process not only; And positioning accuracy is high; Can provide in real time for safety monitoring system, accurate personnel in the pit's information; Also the accurate target place can be provided for the search-and-rescue work after the accident, for timely rescue personnel life provides reliable guarantee.

Description of drawings

Fig. 1 arranges and the transfer of data sketch map for anchor node;

Fig. 2 is the anchor node workflow diagram;

Fig. 3 is the mobile node workflow diagram;

Fig. 4 is for preserving the linked list data structure figure of RSSI value.

Embodiment

Below in conjunction with accompanying drawing and embodiment the present invention is done explanation further.

A kind of mine navigation system of the present invention based on multiple linear regression; Comprise anchor node module, mobile node module, ranging formula parameter calculating module, RSSI value filtering module and RSSI value locating module; The anchor node module comprises one group of anchor node, and anchor node is equally spaced to be arranged in the mine laneway; The mobile node module comprises the mobile node that is worn on miner or the vehicle, and mobile node is monitored the broadcast message of anchor node, therefrom extracts the coordinate that receives signal strength values RSSI and anchor node, and is saved in the internal memory linked list data structure; Position coordinates, the RSSI value that on this position coordinates receive and this RSSI corresponding anchor node coordinate of ranging formula parameter calculating module statistics mobile node in the tunnel; And substitution data analysis tool; The method of use multiple linear regression calculates the relation formula of mobile node physical location and anchor node broadcast message, i.e. each parameter value in regression equation and the equation; RSSI value filtering module is regularly carried out Filtering Processing to the value in the linked list data structure; RSSI value locating module is the position of after Filtering Processing is accomplished, obtaining mobile node according to ranging formula and parameter value.

The localization method of a kind of mine navigation system based on multiple linear regression of the present invention is such in reality:

As shown in Figure 1, be arranged among the mine laneway anchor node 1 is equally spaced, mobile node 2 is worn on miner and the vehicle, all the sensors node arrange accomplish and opening power after organize themselves into wireless sensor network.Mobile node 2 can be mobile with being intended in the tunnel, and route is in sink node 3 in the direction of arrows for downhole data, and sink node 3 compiles the back to data and sent in the aboveground PC 5 by EPA 4.

Mobile node diverse location in the tunnel listens to ID number of a plurality of anchor nodes, coordinate and to the RSSI value of this anchor node, and sends to these data in the aboveground PC.PC is noted the physical location of mobile node in the tunnel and anchor node RSSI value and the coordinate that on this position, receives, and in this way in the tunnel many places gather numerical value.From all anchor node RSSI that mobile node listens to, select 3 bigger values as the subsequent treatment object.In order to reflect mobile node and selected 3 anchor nodes degree of closeness spatially, introduced scale factor R _i,

$R_i=\frac{{\mathrm{RSSI}}_i}{\underset{j=1}{\overset{3}{\mathrm{\Σ}}}{\mathrm{RSSI}}_j}$ Formula (4)

Wherein, RSSI _iThe signal strength values that the expression mobile node receives from i anchor node,

The expression mobile node receives the summation of 3 big RSSI values in the anchor node data; Scale factor R _iBig more, explain that mobile node is spatially approaching more with i node.

Suppose to have in the mine laneway M fixed anchor nodes B _i(1≤i≤M), its coordinate is X _i(because mine laneway height and width value are all also very little, so the tunnel is considered to straight line here).Suppose a certain moment, the coordinate of mobile node is Y, uses the RSSI of these moment 3 bigger anchor nodes to be worth scale factor, multiply by the coordinate of this anchor node again, that is:

x _i=R _iX _iFormula (5)

When i=1, with maximum substitution in 3 RSSI values; When i=2, with second largest value substitution in 3 RSSI values; When i=3, with minimum value substitution in 3 RSSI values.The positioning equation model of then setting up is:

$Y=\underset{i=1}{\overset{3}{\mathrm{\Σ}}}{b}_i{R}_i{X}_i+b_0$ Formula (6)

The final regression equation that then obtains in conjunction with formula (5)

$Y=\underset{i=1}{\overset{3}{\mathrm{\Σ}}}{b}_i{x}_i+b_0$ Formula (7)

B wherein _iBe regression coefficient, b ₀It is a constant coefficient.

With mobile node at diverse location Y _iCalculate x in the coordinate substitution formula (5) of RSSI value that receives and anchor node _i, obtaining all training samples, partial data is as shown in table 1:

Table 1 regression equation training sample

X wherein _1iRepresent the result of the corresponding anchor node coordinate substitution formula (5) of maximum RSSI value, x _2iThe result of the anchor node coordinate substitution formula (5) of RSSI value correspondence in the middle of representing, x _3iRepresent the result of the corresponding anchor node coordinate substitution formula (5) of minimum RSSI value.

In the Matlab Software tool, the data that X in the training sample is listed as form the matrix X that a N capable 3 is listed as, and the data that Y is listed as form the matrix Y that a N capable 1 is listed as, and wherein N is the number of training sample.Following two orders of input in the order line window then:

X=[X, ones (91,1)]; Order (1)

[b, bint, r, rint, stats]=regress (Y, X); Order (2)

Thereby obtain following equation:

Y=2.8277x ₁+ 0.0531x ₂-0.079x ₃+ 4.6967 formula (8)

4 constant terms here can be based on the changes of tunnel environment and are changed, but can use same group of parameter under the same or analogous environment.

In system's operate as normal, as shown in Figure 2, behind the anchor node electric power starting, wait for and do two things: when needing the data of route, upstream nodes is transmitted data; When ID number of needs broadcasting oneself during with coordinate information, send a broadcast message towards periphery, broadcast message can only be transmitted the distance of a jumping; As shown in Figure 3; Be engraved in the broadcast message of anchor node around monitoring during mobile node; After receiving the anchor node broadcast message, therefrom extract the coordinate that receives signal strength values (RSSI) and this anchor node, the information corresponding each anchor node is saved in the linked list data structure.As shown in Figure 4, all anchor node anchor1 that the listHead that the internal memory linked list data structure comprises the chained list that each mobile node keeps and this mobile node can listen to are to anchorN, and N represents the number of anchor node; Each anchor node comprises five attributes; Promptly the ID anchorNum of this anchor node, anchor node coordinate anchorPos, point to the pointer NextAnchor of next anchor node; If there is not next value, the value of this pointer be after NULL, the mobile node RSSI value NextValue that receives this anchor node, the Filtering Processing mobile node to the RSSI value AverageRSSI of this anchor node; The RSSI value NextValue that said mobile node receives this anchor node comprises two attributes; Promptly receive the pointer NextValue of the next RSSI value that RSSI value value, this anchor node of this anchor node receive; If there is not next value, the value of this pointer NextValue is NULL.

When arrive cycle time and need the location; RSSI value filtering module is carried out Filtering Processing to the value in the linked list data structure; Promptly calculate the mean value and the standard deviation of all RSSI values; Through the lower data of gaussian filtering formula confidence values, obtain the mean value of remaining data again, concrete grammar is following: chained list can use formula List _IjJ RSSI value of the i anchor node that expression, its implication receive for this mobile node, n _iBe the total number of RSSI value that i anchor node listened in (was the cycle to carry out Filtering Processing and location Calculation with 5 seconds) at 5 seconds, i=1 ... N, n _i=1 ... 10,0≤j≤n _iAfter being timed in 5 seconds, calculate the average value mu of each chained list _iAnd standard deviation sigma _i, wherein

${\mathrm{\μ}}_i=\frac{1}{n_i}\underset{j=1}{\overset{n_i}{\mathrm{\Σ}}}{\mathrm{List}}_{\mathrm{Ij}}$ Formula (1)

${\mathrm{\σ}}_i=\sqrt{\frac{1}{n_i}\underset{j=1}{\overset{n_i}{\mathrm{\Σ}}}({\mathrm{\μ}}_i-{\mathrm{List}}_{\mathrm{Ij}})}$ Formula (2)

And then calculate in each chained list number range at interval (μ _i-σ _i, μ _i+ σ _i) in the average value mu of all values _i', following with formulae express:

${{\mathrm{\μ}}_i}^{\′}=\frac{1}{n_i^{\′}}\underset{j=1}{\overset{n_i^{\′}}{\mathrm{\Σ}}}\left\{{\mathrm{List}}_{\mathrm{Ij}}\right|{\mathrm{List}}_{\mathrm{Ij}}\∈({\mathrm{\μ}}_i-{\mathrm{\σ}}_i,{\mathrm{\μ}}_i+{\mathrm{\σ}}_i)\}$ Formula (3)

N wherein _i' be interval (μ _i-σ _i, μ _i+ σ _i) number of interior numerical value.After this step calculating, μ _i', i=1 ... The RSSI value of N the anchor node that N receives for this mobile node.

This mean value as the RSSI value of mobile node to this anchor node, is saved in this anchor node RSSI mean value AverageRSSI in the data structure.

At last; RSSI value locating module calculates the position coordinates of mobile node according to ranging formula (6); Method is following: in linked list data structure, only travel through the RSSI mean value of anchor node, and therefrom find out three maximums, so obtained the anchor node of 3 RSSI value maximums and their coordinate.These 3 RSSI values are calculated scale factor R according to the computational methods of formula (4) _i, again with scale factor and corresponding coordinate X _iIn the substitution formula (6), just can calculate the position coordinates Y of mobile node in conjunction with 4 parameters in the formula (8).At last the forwarding of position coordinates through anchor node is sent to aboveground PC always.

The above only is a preferred implementation of the present invention; Be noted that for those skilled in the art; Under the prerequisite that does not break away from the principle of the invention, can also make some improvement and retouching, these improvement and retouching also should be regarded as protection scope of the present invention.

Claims (6)

1. mine navigation system based on multiple linear regression; It is characterized in that: comprise anchor node module, mobile node module, ranging formula parameter calculating module, RSSI value filtering module and RSSI value locating module; Said anchor node module comprises one group of anchor node, and anchor node is equally spaced to be arranged in the mine laneway; Said mobile node module comprises the mobile node that is worn on miner or the vehicle, and mobile node is monitored the broadcast message of anchor node, therefrom extracts the coordinate that receives signal strength values RSSI and anchor node, and is saved in the internal memory linked list data structure; Position coordinates, the RSSI value that on this position coordinates receive and this RSSI corresponding anchor node coordinate of said ranging formula parameter calculating module statistics mobile node in the tunnel; And substitution data analysis tool; The method of use multiple linear regression calculates the relation formula of mobile node physical location and anchor node broadcast message, i.e. each parameter value in regression equation and the equation; Said RSSI value filtering module is regularly carried out Filtering Processing to the value in the linked list data structure; Said RSSI value locating module is the position of after Filtering Processing is accomplished, obtaining mobile node according to ranging formula and parameter value.

2. the localization method of the described mine navigation system based on multiple linear regression of a claim 1 is characterized in that may further comprise the steps:

1. be arranged among the mine laneway ID number and coordinate information of anchor node broadcasting oneself with anchor node is equally spaced; Mobile node is worn on miner and the vehicle, and all the sensors node organizes themselves into wireless sensor network after arranging completion and opening power;

2. mobile node some places collection position coordinate, the RSSI value that on this position coordinates, receives and corresponding anchor node coordinate of this RSSI in the tunnel; The ranging formula parameter calculating module is updated to the data that collect in the data analysis tool; Calculate the relation formula of anchor node RSSI value and mobile node physical location, i.e. each parameter value in regression equation (7) and the formula through the method for multiple linear regression:

$Y=\underset{i=1}{\overset{3}{\mathrm{\Σ}}}{b}_i{x}_i+b_0---\left(7\right)$

Wherein, Y is the coordinate of mobile node, b _iBe regression coefficient, b ₀It is a constant coefficient;

Said regression equation (7) is to derive according to formula (4), formula (5) and formula (6):

$R_i=\frac{{\mathrm{RSSI}}_i}{\underset{j=1}{\overset{3}{\mathrm{\Σ}}}{\mathrm{RSSI}}_j}---\left(4\right)$

x _i＝R _iX _i (5)

Wherein, R _iBe scale factor, RSSI _iThe signal strength values that receives from i anchor node for mobile node,

For mobile node receives 3 summations than big RSSI values in the anchor node data, X _iCoordinate for the corresponding anchor node of RSSI value;

$Y=\underset{i=1}{\overset{3}{\mathrm{\Σ}}}{b}_i{R}_i{X}_i+b_0---\left(6\right)$

Wherein, Y is the coordinate of mobile node, X _iBe the coordinate of anchor node, b _iBe regression coefficient, b ₀Be a constant coefficient, R _iBe scale factor;

3. in system's operate as normal, ID number and coordinate information of anchor node fixed time broadcast oneself, mobile node is monitored anchor node information at some cycles in the time, from information, extract the coordinate of RSSI value and this anchor node, and is saved in the internal memory linked list data structure;

4. when arrive cycle time and need the location; RSSI value filtering module is carried out Filtering Processing to the value in the linked list data structure; Promptly calculate the mean value and the standard deviation of all RSSI values; Through the bigger data of gaussian filtering formula filtering error, obtain the mean value of remaining data again, at last with this mean value as the RSSI value of mobile node to this anchor node;

5. RSSI value locating module parameter that filtered RSSI value substitution ranging formula (6) and substitution regression analysis are drawn is calculated the position coordinates of mobile node, and is sent to aboveground PC to the forwarding of position coordinates through anchor node always.

3. the localization method of the mine navigation system based on multiple linear regression according to claim 2 is characterized in that: have only a life cycle of jumping with coordinate information ID number of said anchor node broadcasting.

4. the localization method of the mine navigation system based on multiple linear regression according to claim 2; It is characterized in that: said step 4. in; Timing is 5 seconds, and regularly not then mobile node was not monitored the anchor node broadcast message all the time and was saved in RSSI value and anchor node coordinate in the mobile node internal memory in 5 seconds; 5 seconds timing arrives are carried out data processing and location Calculation again.

5. the localization method of the mine navigation system based on multiple linear regression according to claim 2; It is characterized in that: all anchor node anchor1 that the listHead that said internal memory linked list data structure comprises the chained list that each mobile node keeps and this mobile node can listen to are to anchorN, and N represents the number of anchor node; Each anchor node comprises five attributes, promptly after the ID anchorNum of this anchor node, anchor node coordinate anchorPos, the pointer NextAnchor that points to next anchor node, RSSI value NextValue that mobile node receives this anchor node, the Filtering Processing mobile node to the RSSI value AverageRSSI of this anchor node.

6. the localization method of the mine navigation system based on multiple linear regression according to claim 5; It is characterized in that: the RSSI value NextValue that said mobile node receives this anchor node comprises two attributes; Promptly receive the pointer NextValue of the next RSSI value that RSSI value value, this anchor node of this anchor node receive; If there is not next value, the value of this pointer NextValue is NULL.

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