CN102264097A - Method for positioning GSM (global system for mobile communication) mobile communication network terminal - Google Patents
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Abstract
The invention discloses a method for positioning a GSM (global system for mobile communication) mobile communication network terminal. The method comprises the following steps: a. dividing grids for a region covered by a mobile communication network according to the established sizes, and numbering each grid; b. establishing an initial radio broadcasting model of the grid according to a geographical feature and a radio broadcasting feature of each grid; c. generating feature data of the radio broadcasting model of each grip according to DT (drive test) data; d. acquiring a signaling data from the mobile communication network, screening out each conversation in the signaling data, extracting a test report in the conversation, and screening the test report; e. analyzing the matching degree of the screened test report and the feature data of each grip one by one, and calculating a probability value; and f. finding centers of the grids with the highest probabilities, namely the positions of the mobile communication network terminal of the conversation. The positioning method has the characteristics of fast positioning speed and easiness of implementation.
Description
Technical field
The present invention relates to the mobile communication technology field, specifically a kind of GSM mobile communications network method of locating terminal.
Background technology
Be accompanied by the continuous development of GSM mobile communication technology, communications industry service object has begun by the conversion of face to point.GSM mobile communications network terminal positioned in mobile communication service play an important role.As at network optimization work, its working surfaces is optimized by simple network operation performance index, refine to and grasps individual consumer's service quality.For example: operator wishes some users' professional use habit, network service quality are gone and the professional geography information correspondence that takes place; So that more careful understanding user's service scenario could go to promote the network competitiveness from most basic user's perception aspect like this; At the user, the simple traditional business that relies on realizes (as: voice call, note, multimedia message etc.) also can not satisfy the demand of user to new business, such as: emerging services such as customer location inquiry, user terminal location are familiar with by increasing user.As seen the location technology of mobile communications network terminal has risen to vital status for the development of mobile communication business.
The geographical location information of GSM mobile communications network terminal is the basis of realizing work such as the network optimization, service optimization, new business analysis.GSM mobile communications network method of locating terminal at present commonly used mainly contains: CGI (cell global identity)+TA (Timing Advance) alignment by union, utilize UL-TOA location, E-TOD location, A-GPS assist location.More than four kinds of localization methods be localization method at the GSM network, exist such as deciding deficiencies such as low precision, fringe cost height, realizability be limited.Specific as follows:
1, the shortcoming of CGI+TA alignment by union mainly is the non-constant of positioning accuracy, is approximately about 550 meters;
2, the shortcoming of UL-TOA location:
1) Chinese GSM network is not supported LCS business, realizability deficiency at present;
2) initial outlay height.Because LMU is general and BTS is total to the location, the ratio of BTS and LMU is 1: 1, and whole net need be set up a large amount of LMU, invests bigger.Existing GSM net will be realized also need transforming synchronously;
Network burden increased when 3) traffic carrying capacity was big.When location requirement increased, mobile phone needed the frequent switching command of executing, and the signaling transmission quantity of GSM net is increased;
4) positioning accuracy is poor, and positioning accuracy is 100 meters magnitudes;
5) locating speed is slow, generally need the measurement data of 3 BTS just can finish location work, so locating speed is slower, is unfavorable for a large number of users geographical information query fast.
3, the shortcoming of E-OTD location algorithm:
1) Chinese GSM network is not supported LCS business, realizability deficiency at present;
2) existing mobile phone can not be applicable to the E-OTD locate mode, needs update software or all replacements;
3) initial outlay height.Though the ratio of LMU and BTS is between 1: 3 to 1: 5, whole net is still needed to set up a large amount of LMU, invest bigger;
4) precision is low.Because be subjected to the influence of RTD and geometric distance parameter simultaneously, positioning accuracy is lower.In addition, multipath effect will influence positioning accuracy (especially urban area);
5) locating speed is slow, generally need the measurement data of a plurality of BTS just can finish location work, so locating speed is slower, is unfavorable for a large number of users geographical information query fast.
4, the shortcoming of A-GPS location
1) need more produce extra data traffic expense;
2) mobile phone cost, volume and power increase.Increase GP S positioning function in the mobile phone, then must increase relevant hardware, cause the mobile phone cost to increase.After increasing the function of GPS, will inevitably increase power consumption and volume.
3) the actual number of users of opening is few, and the user who opens the A-GPS function in the existing network is generally much smaller than start user's 0.1%, and in real network optimization and service optimization work, data volume is limited, and practical value is limited.
Drawback at above-mentioned GSM mobile communications network method of locating terminal existence, the inventor is actively studied and is innovated, so that a kind of brand-new GSM mobile communications network method of locating terminal to be provided, thereby solve the problem that existing GSM mobile communications network method of locating terminal exists.
Summary of the invention
In order to solve the above-mentioned problems in the prior art, the invention provides a kind of GSM mobile communications network method of locating terminal, it is fast that this localization method has locating speed, the characteristics that are easy to realize.
In order to solve the problems of the technologies described above, the present invention has adopted following technical scheme:
GSM mobile communications network method of locating terminal comprises the steps:
A. grid is divided according to the size of formulating in the zone that mobile communications network is covered, and gives each grid numbering;
B. the initial wireless propagation model of setting up this grid according to the geographical feature and the radio propagation characteristics of each grid;
C. generate the characteristic of the radio transmission model of each grid according to the DT data;
D. gather signaling data from mobile communication network, filter out the conversation each time in the signaling data, the measurement report (MR) in the conversation is extracted, and measurement report is screened;
E. the matching degree of the characteristic of the measurement report after the Analysis and Screening and each grid one by one calculates probable value;
F. find the center of the highest several grids of probable value, be the position of the mobile communications network terminal of this conversation.
Further, wherein among the step c generation of characteristic comprise the steps:
1), calculates the concrete grid that the DT data cover, and, be recorded in respectively in the two-dimensional array according to grid with rxlev value, rxqual value and the TA value of the main plot in the DT data and six adjacent areas according to the latitude and longitude information of DT data;
2), calculate of characteristic and weighting foundation and the reference of above each grid at each sub-district based on the data set that step 1) counted;
3) travel through the grid that all do not have characteristic;
4) calculate the quantity of the grid that characteristic is arranged around each grid that does not have characteristic;
5) from around have the maximum grid that does not have characteristic of quantity of the grid of characteristic to begin to handle;
6) from around have the grid of characteristic to extract characteristic, earlier go out a set of feature data according to mean value computation, travel through the sub-district in the characteristic of extracting again, find the grid at these places, sub-district successively, calculate the azimuthal relation of current grid and place, sub-district grid and sub-district, extrapolating current grid should increase still at the probability of occurrence of sub-district and reduce, and the amount that changes, thereby calculates the characteristic of current grid;
7) repeating step 5)-6), till all grids all have characteristic.
The generation of the characteristic further, step 2 wherein) is as follows:
I. calculate back-end crop average, back-end crop intermediate value, the extreme difference of this grid at the Rxlev of certain sub-district, wherein poor as the desired value basis of calculation with the back-end crop average, extreme difference and standard deviation are as the weighting foundation of this grid Rxlev, the back-end crop intermediate value is done the weighting reference, 2 times of standard deviations of back-end crop mean value are as the effective bound of this grid, and the TA value is as effectively reference;
Ii. calculate back-end crop average, back-end crop intermediate value, the extreme difference of this grid at the Rxqual of certain sub-district, wherein poor as the desired value basis of calculation with the back-end crop average, extreme difference and standard deviation are as the weighting foundation of this grid Rxqual, the back-end crop intermediate value is done the weighting reference, 2 times of standard deviations of back-end crop mean value are as the effective bound of this grid, and the TA value is as effectively reference;
Iii. with all data of calculating among top step I and the ii characteristic as this grid.
Further, randomly draw 90% DT data and be used to produce the grid characteristic, remaining 10% is used to calculate grid characteristic coefficient and checking.
Further, the step of reckoning grid characteristic coefficient and checking is as follows:
A) measurement report in the DT data of extraction remaining 10% filters out the measurement report that effective adjacent sub-district is less than 4, after the measurement report that filters abnormal data;
B) according to formula
Y=g (a, b, c)=and a+b* (1/|MRrxlev-SDrxlev|)+c* (1/|MRrxqual-SDrxqual|), in the formula
A, b, c are the characteristic coefficient of this grid, default a=b=c=1,
In the DT data of MRrxlev for residue 10%, the Rxlev value of current measurement report;
MRrxqual in like manner is in the DT data of residue 10%, the Rxqual value of current measurement report;
SDrxlev is this grid Rxlev standard deviation that data statistics goes out based on DT,
SDrxqual is this grid Rxqual standard deviation that data statistics goes out based on DT;
C) calculate the matching degree y of the identical grid in all main plots, select 5 the highest grids of result;
D) calculate 5 grid errors respectively, preserve the value of 5 error amounts and a, b, c;
E) finely tune the value of a, b, c at random;
F) repeating step c)-e) 5-10 time;
G) variation of statistical analysis a, b, c according to genetic algorithm, is found a, the b that helps to improve matching degree, the variation tendency of c for the influence of grid error;
H) value of correction a, b, c, repeating step c once more)-g), evolution number of times+1;
I) no longer include up to evolution and help matching degree and improve, perhaps the raising of matching degree is very little, and number of times 〉=50 of perhaps evolving stop above step, preserve and obtain a, the b of approximate optimal solution, the value of c, as the characteristic coefficient of this grid.
Compared with prior art, beneficial effect of the present invention is:
1, consumer positioning quantity is big.Because the scope of the network operation, maintenance work is whole network users, therefore require to carry out the whole network user in real time the positioning analysis of service quality; One of data source of the inventive method is the wireless measurement report (MR) of full mobile communications network, these data be under the dedicated mode all user MS to the measurement report of Serving cell, neighbor cell, therefore these data are used for our location algorithm, just can calculate the whole network user geographical location information relevant easily, the ground physics and chemistry O﹠M work of network level is achieved with service.
2, positioning accuracy height.The positioning accuracy of conventional, theoretical location algorithm is generally all more than 100 meters, have in addition at 550 meters; Such positioning accuracy is for the network optimization, the service optimization meaning and little of user class; The coverage of general base station, urban district is at 500-1000 rice, and user distribution and complexity thereof in its coverage, as indoor, outdoor, general user district, intensive building; Such precision is accurately to grasp the wireless service situation in concrete zone.
Method of the present invention is used the means of network grid division, grid propagation model revision, its positioning accuracy is about 40 meters after tested, accuracy rate is more than 98%, than algorithm before qualitative leap has been arranged with regard to positioning accuracy, such positioning accuracy has great practical significance to the network optimization, the service optimization work of user class undoubtedly.
3, speed is fast.Locate for the location amount of the whole network level extremely important fast; We generally need the measured value of 5 wireless measurements reports to be averaged for sole user's grid location Calculation (with reference to content of the present invention), be that the cycle that mobile phone terminal takies TCH (Traffic Channel) measurement report is 480ms, article 5, the time of measurement report, be approximately 480ms*5=2.4 and just can finish second; Increase positioning accuracy if desired, can be according to being averaged of many wireless measurements report, be its computing time: T=480ms*N (N: the quantity of measurement report); For the calculating of mass users, we have adopted four figures it is investigated to ask and the support of cloud computing, but satisfying magnanimity user's parallel geographical information calculations.
4, save cost.Method of the present invention need not the input of other network hardwares and terminal hardware; Its primary data source is directly gathered the measurement report of Abis interface, and it is convenient to gather, and data are reliably effective, do not influence the normal operation of existing network.
5, be convenient to the advantage that realizes.LCS function in the GSM standard is not used in China at present, if open, the realization of UL-TOA location and OTD location algorithm also needs a large amount of network rebuildings, and the difficulty of realization is bigger; And the realization of A-GPS depends on the renewal of user terminal and the change of user's use habit more; And our location algorithm is convenient to realize very much, and the correction of the propagation model factor relies on daily DT work both can finish; Directly gather the measurement report of Abis interface for primary data source, it is convenient to gather, and data are reliably effective.
Embodiment
Below in conjunction with specific embodiment the present invention is described in further detail, but not as a limitation of the invention.
Term implication related among the present invention is as follows:
The rxlev value: the average received level is to describe the statistical parameter of receiving signal strength signal intensity (level);
Rxqual value: signal receiving quality, the statistical parameter of description collection of letters wireless link signals quality.
The TA value: the last time that Timing Advance, serving BS are received in advance.
DT data: the measurement data of driving.
MR: measurement report.
GSM mobile communications network method of locating terminal comprises the steps:
1. the division of network grid
By to the physics and chemistry information processing of GSM network ground, GSM the whole network is carried out grid division, its grid size is 40 meters of 40 meters *.The setting of this grid size size had both guaranteed locating accuracy, had guaranteed that again locating accuracy is more than 98%.As dwindling lattice dimensions again, though precision increases, locating accuracy will reduce greatly.If enlarge lattice dimensions, little to the locating accuracy influence, but precision can reduce greatly.Existing other localization methods then can not reach precision of the present invention.
2. according to the grid geographic properties, as intensive building, locations such as residential area, road, suburb, on the basis of considering its nature of radio propagation (as diffraction, diffraction characteristic etc.), set up various wireless environments initial wireless propagate propagation model.Because in wireless network planning, the radio transmission loss is a very crucial parameter, and it is determining the correctness of program results.Because the wireless propagation environment in the practical application is very complicated, need summarize the relationship of parameters such as radio transmission loss and frequency, distance, antenna height, just radio transmission model by the theoretical research and the method for actual test.For example: the Okumura-Hata model is chosen in general 900Mhz urban district:
L
d＝69.55+26.16lgf-13.82lghb-a(h
m)+(44.9-6.55lgh
b)lgd
L wherein
dBe path loss, f is a carrier frequency, h
bBe the antenna for base station effective depth, a (h
m) be the portable antenna correction factor, d is that travelling carriage arrives base station distance.
3. data of gathering based on DT
Gather a large amount of DT data,,, be used to adjust the propagation model of grid so that generate and adjust the characteristic of network grid propagation model as the modeling basic data of entire city.For the grid of not testing,, obtain its characteristic by the method that evolution is calculated.The DT data are divided into two parts to be used, and randomly draws 90% and is used to produce the grid characteristic, and remaining 10% is used to calculate grid characteristic coefficient and checking.
4. the generation of grid characteristic
A) at first, with the geographic area that system will cover, the size according to formulating is divided into grid, gives each grid numbering, conveniently to set up Query Database, improves computational efficiency.
B) randomly draw out 90% in the DT data, latitude and longitude information according to the DT data that extract, calculate the grid that the DT data cover, and, be recorded in respectively in the two-dimensional array according to grid with rxlev value, rxqual value and the TA value of the main plot in the DT data and six adjacent areas;
C) based on the two-dimensional array that step b) counted, calculate of characteristic and weighting foundation and the reference of above each grid at each sub-district, computational methods are as follows;
I. calculate back-end crop average, back-end crop intermediate value, the extreme difference of this grid at the Rxlev of certain sub-district, poor with the back-end crop average as the desired value basis of calculation, extreme difference and standard deviation are as the weighting foundation of this grid Rxlev, the back-end crop intermediate value is done the weighting reference, 2 times of standard deviations of back-end crop mean value are as the effective bound of this grid, and the TA value is as effectively reference;
Ii. calculate back-end crop average, back-end crop intermediate value, the extreme difference of this grid at the Rxqual of certain sub-district, poor with the back-end crop average as the desired value basis of calculation, extreme difference and standard deviation are as the weighting foundation of this grid Rxqual, the back-end crop intermediate value is done the weighting reference, 2 times of standard deviations of back-end crop mean value are as the effective bound of this grid, and the TA value is as effectively reference;
Iii. with all data that calculate among top step I and the ii: the back-end crop average that the Rxlev of current grid and Rxqual relate to, back-end crop intermediate value, extreme difference, standard deviation, TA value, as the characteristic of this grid, preservation is got up;
D) next handle the grid that is not covered again by the DT data;
E) travel through the grid that all do not have characteristic;
F) calculate the quantity of the grid that characteristic is arranged around each grid that does not have characteristic;
G) from around have the maximum grid that does not have characteristic of quantity of the grid of characteristic to begin to handle;
H) from around have the grid of characteristic to extract characteristic, go out a set of feature data (all data that calculate among i above being about in the step c) and the ii are all got average respectively) according to mean value computation earlier, travel through the sub-district in the characteristic of extracting again, find the grid at these places, sub-district successively, calculate the azimuthal relation of current grid and place, sub-district grid and sub-district, extrapolating current grid should increase still at the probability of occurrence of sub-district and reduce, and the amount that changes, thereby obtain the Rxlev of current grid and the back-end crop average that Rxqual relates to, the back-end crop intermediate value, extreme difference, standard deviation, the TA value;
I) Rxlev of the current grid that step h is calculated and back-end crop average, back-end crop intermediate value, extreme difference, standard deviation, TA value that Rxqual relates to preserve as the characteristic of current grid;
J) repeating step e)-i), till all grids all have characteristic.
5. computation grid characteristic coefficient and checking
A) extraction step 3b) MR (measurement report) in remaining 10% the DT data filters out the MR that effective adjacent sub-district is less than 4, after the MR that filters abnormal data, such as the main plot not in orientation range, adjacent sub-district hypertelorism, TA checking computations are not inconsistent or the like;
B) hit rate of the following formula computation grid of foundation:
Y=g (a, b, c)=and a+b* (1/|MRrxlev-SDrxlev|)+c* (1/|MRrxqual-SDrxqual|), in the formula
A, b, c are the characteristic coefficient of this grid, default a=b=c=1;
In the DT data of MRrxlev for residue 10%, the Rxlev value of current MR;
MRrxqual in like manner is in the DT data of residue 10%, the Rxqual value of current MR;
SDrxlev is this grid Rxlev standard deviation that data statistics goes out based on DT;
SDrxqual is this grid Rxqual standard deviation that data statistics goes out based on DT;
C) calculate the hit rate y of the identical grid in all main plots, select 5 the highest grids of matching degree; Matching degree refers to (1/|MRrxlev-SDrxlev|) and (1/|MRrxqual-SDrxqual|) in the formula.Certainly the value number can also can be less than 5 more than 5, but precision and amount of calculation can reach a balance point when choosing 5, has lacked to make precise decreasing, during more than 5, though precision is increased, the amount of calculation that increases will increase greatly, lower efficiency;
D) calculate the error of 5 grids respectively, the value of preserving 5 error amounts and a, b, c;
E) finely tune the value of a, b, c at random;
F) repeating step b)-e) 5-10 time, according to concrete city size, number of repetition is lower than 5 meetings declines to a great extent precision, be higher than 10 times and then precision no longer included too much influence, but computational efficiency can reduce greatly.So along with the scale difference of using, considering under all multifactor prerequisites such as hardware environment, customer demand, corresponding time, can in this scope of 5-10, decide in its sole discretion and pursue precision or computational efficiency;
G) variation of statistical analysis a, b, c according to genetic algorithm, is found a, the b that helps to improve hit rate, the variation tendency of c for the influence of grid error;
H) revise the value of a, b, c, once more repeating step b)-g), evolution number of times (step b)-i) number of repetition)+1;
I) no longer include up to evolution and help hit rate and improve, perhaps the raising of hit rate is very little, and number of times 〉=50 of perhaps evolving stop above step, preserve and obtain a, the b of approximate optimal solution, the value of c, as the final characteristic coefficient of this grid.Adopt remaining 10% DT data to adjust the accuracy rate that characteristic coefficient can improve the location.And adopt remaining 10% DT data to verify locating accuracy, can between precision and accuracy rate, find best balance.
6. gather the signaling in the order of A+Abis message, carry out signalling analysis:
A) go here and there out complete each time conversation;
B) MR in will conversing extracts, and filters out the MR that effective adjacent sub-district is less than 4, after the MR that filters abnormal data, such as the main plot not in orientation range, adjacent sub-district hypertelorism, TA checking computations are not inconsistent or the like.Because when adjacent sub-district is less than 4, the computing meeting that participates in the step d) probable value is less than 5 times, can be less than 25 thereby the back participates in the point of k-means clustering algorithm, and precision can decline to a great extent;
C) all grid characteristics of relating to of the traversal of the MR after Analysis and Screening main plot one by one, calculate the involved sub-district of itself and current talking and the matching degree of level range respectively, according to matching degree, calculate probable value, the matching degree here refers to be exactly below (1/|MRrxlev-SDrxlev|) and (1/|MRrxqual-SDrxqual|) in the formula of step d);
D) computing formula of probable value:
Y=g (a, b, c)=and a+b* (1/|MRrxlev-SDrxlev|)+c* (1/|MRrxqual-SDrxqual|), in the formula
MRrxlev is the Rxlev value of current MR;
MRrxqual in like manner is the Rxqual value of current MR;
SDrxlev is this grid Rxlev standard deviation that data statistics goes out based on DT;
SDrxqual is this grid Rxqual standard deviation that data statistics goes out based on DT;
Characteristic coefficient a, b, c get the optimal coefficient that draws in the step 5;
E) find out 5-7 the highest grid of probable value y, data such as its plane coordinates, rxlev, rxqual, probability weight are preserved as the base data of next step analysis;
F) repeating step c) and d), up to handling all MR;
G) gather all and preserve the base data of getting off, obtain one group of discrete dot pattern two-dimensional space distributed data set.
7. to the discrete set carrying out of dot pattern two-dimensional space distributed data further data analysis and derivation:
A) in most cases, above discrete point can distribute by the cluster shape, therefore at first all discrete points is carried out the analysis of k-means clustering algorithm.Concrete steps are as follows:
I. select k object as initial cluster center arbitrarily from n data object;
Ii. circulation is (iii) to (iv) till each cluster no longer changes;
Iii. according to the average (center object) of each cluster object, calculate the distance of each object and these center object; And again corresponding object is divided according to minimum range;
Iv. recomputate the average (center object) of each (changing) cluster;
B) if cluster has only one group, jump to step d);
C) in the set of many group clusters, find maximum one group of set element;
D) calculate the geometric center of all elements on two dimensional surface in this cluster, this center is positioning result.
8. finish the location.
Above embodiment is an exemplary embodiment of the present invention only, is not used in restriction the present invention, and protection scope of the present invention is defined by the claims.Those skilled in the art can make various modifications or be equal to replacement the present invention in essence of the present invention and protection range, this modification or be equal to replacement and also should be considered as dropping in protection scope of the present invention.
Claims (7)
1.GSM the mobile communications network method of locating terminal is characterized in that, comprises the steps:
A. grid is divided according to the size of formulating in the zone that mobile communications network is covered, and gives each grid numbering;
B. the initial wireless propagation model of setting up this grid according to the geographical feature and the radio propagation characteristics of each grid;
C. generate the characteristic of the radio transmission model of each grid according to the DT data;
D. gather signaling data from mobile communication network, filter out the conversation each time in the signaling data, the measurement report in the conversation is extracted, and measurement report is screened;
E. the matching degree of the characteristic of the measurement report after the Analysis and Screening and each grid one by one calculates probable value;
F. find the center of the highest several grids of probable value, be the position of the mobile communications network terminal of this conversation.
2. GSM mobile communications network method of locating terminal according to claim 1 is characterized in that wherein the generation of characteristic comprises the steps: among the step c
1), calculates the concrete grid that the DT data cover, and, be recorded in respectively in the two-dimensional array according to grid with rxlev value, rxqual value and the TA value of the main plot in the DT data and six adjacent areas according to the latitude and longitude information of DT data;
2), calculate of characteristic and weighting foundation and the reference of above each grid at each sub-district based on the data set that step 1) counted;
3) travel through the grid that all do not have characteristic;
4) calculate the quantity of the grid that characteristic is arranged around each grid that does not have characteristic;
5) from around have the maximum grid that does not have characteristic of quantity of the grid of characteristic to begin to handle;
6) from around have the grid of characteristic to extract characteristic, earlier go out a set of feature data according to mean value computation, travel through the sub-district in the characteristic of extracting again, find the grid at these places, sub-district successively, calculate the azimuthal relation of current grid and place, sub-district grid and sub-district, extrapolating current grid should increase still at the probability of occurrence of sub-district and reduce, and the amount that changes, thereby calculates the characteristic of current grid;
7) repeating step 5)-6), till all grids all have characteristic.
3. GSM mobile communications network method of locating terminal according to claim 2 is characterized in that, wherein step 2) in the generation of characteristic as follows:
I. calculate back-end crop average, back-end crop intermediate value, the extreme difference of this grid at the Rxlev of certain sub-district, wherein poor as the desired value basis of calculation with the back-end crop average, extreme difference and standard deviation are as the weighting foundation of this grid Rxlev, the back-end crop intermediate value is done the weighting reference, 2 times of standard deviations of back-end crop mean value are as the effective bound of this grid, and the TA value is as effectively reference;
Ii. calculate back-end crop average, back-end crop intermediate value, the extreme difference of this grid at the Rxqual of certain sub-district, wherein poor as the desired value basis of calculation with the back-end crop average, extreme difference and standard deviation are as the weighting foundation of this grid Rxqual, the back-end crop intermediate value is done the weighting reference, 2 times of standard deviations of back-end crop mean value are as the effective bound of this grid, and the TA value is as effectively reference;
Iii. with all data of calculating among top step I and the ii characteristic as this grid.
4. GSM mobile communications network method of locating terminal according to claim 2 is characterized in that, randomly draws 90% DT data and is used to produce the grid characteristic, and remaining 10% is used to calculate grid characteristic coefficient and checking.
5. GSM mobile communications network method of locating terminal according to claim 4 is characterized in that, calculates that the step of grid characteristic coefficient and checking is as follows:
A) measurement report in the DT data of extraction remaining 10% filters out the measurement report that effective adjacent sub-district is less than 4, after the measurement report that filters abnormal data;
B) according to formula
Y=g (a, b, c)=and a+b* (1/|MRrxlev-SDrxlev|)+c* (1/|MRrxqual-SDrxqual|), in the formula
A, b, c are the characteristic coefficient of this grid, default a=b=c=1;
In the DT data of MRrxlev for residue 10%, the Rxlev value of current measurement report;
MRrxqual in like manner is in the DT data of residue 10%, the Rxqual value of current measurement report;
SDrxlev is this grid Rxlev standard deviation that data statistics goes out based on DT,
SDrxqual is this grid Rxqual standard deviation that data statistics goes out based on DT;
C) calculate the matching degree of the identical grid in all main plots, select 5 the highest grids of result;
D) calculate 5 grid errors respectively, preserve the value of 5 error amounts and a, b, c;
E) finely tune the value of a, b, c at random;
F) repeating step c)-e) 5-10 time;
G) variation of statistical analysis a, b, c according to genetic algorithm, is found a, the b that helps to improve matching degree, the variation tendency of c for the influence of grid error;
H) value of correction a, b, c, repeating step c once more)-g), evolution number of times+1;
I) no longer include up to evolution and help matching degree and improve, perhaps the raising of matching degree is very little, and number of times 〉=50 of perhaps evolving stop above step, preserve and obtain a, the b of approximate optimal solution, the value of c, as the characteristic coefficient of this grid.
6. GSM mobile communications network method of locating terminal according to claim 1 is characterized in that wherein the computing formula of step e probable value is as follows:
Y=g (a, b, c)=and a+b* (1/|MRrxlev-SDrxlev|)+c* (1/|MRrxqual-SDrxqual|), in the formula
MRrxlev is the Rxlev value of current measurement report;
MRrxqual is the Rxqual value of current measurement report;
SDrxlev is this grid Rxlev standard deviation that data statistics goes out based on DT;
SDrxqual is this grid Rxqual standard deviation that data statistics goes out based on DT;
A, b, c are characteristic coefficient.
7. GSM mobile communications network method of locating terminal according to claim 1 is characterized in that wherein the size of grid is 40m * 40m among the step a.
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