CN102721945B - Time difference of arrival filtering method, device and feature data storage method - Google Patents
Time difference of arrival filtering method, device and feature data storage method Download PDFInfo
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Abstract
The invention discloses a TDOA (Time Difference of Arrival) filtering method, belonging to the location technology field. The method comprises the following steps of: sorting TDOA data acquired by a calibration point in an ascending order; orderly executing subtraction on every two of TDOA data, dividing the TDOA data into multiple groups according to absolute values of obtained differences; and further dividing the TDOA data in each group into multiple small groups, calculating the standard deviation of the TDOA data in each small group, taking the TDOA data in the small group of which the standard deviation is less than 0.5 as TDOA data corresponding to one path; calculating the standard deviation of the TDOA data corresponding to each path, calculating the mean value of the TDOA data contained in the small group of which the standard deviation is less than 0.5, and taking the mean value as the value of the TDOA of the path. The invention further discloses a feature data storage method. The methods provided by the invention can greatly improve the location precision of the calibration point, thereby laying a high-precision data basis for location work of the calibration point.
Description
Technical field
The present invention relates to field of locating technology, particularly differ from filtering method, device and characteristic storage means a kind of time of arrival.
Background technology
In current indoor positioning navigation field, the location technology based on WLAN, WSN and RFID is main trend.But because most indoor positioning technology is based on received signal strength (RSSI, Received Signal Strength Indicator), affected greatly by the factors such as indoor environment variation, multipath, signal dropout, interference, cause position stability lower.Meanwhile, the prior art pair magnanimity information utilization factor relevant to position lower (less than 5%), lacks real-time intelligent processing and the service system of navigating with positional information.
Along with the development of location technology, location-based application is more and more, becomes an important directions of current techniques and application development.For indoor positioning technology such as indoor orientation method UWB, RFID, Zigbee, WiFi, be mostly to adopt the mesh fitting technology of disposing node, thereby system need to gather a large amount of characteristics, building database, workload is large, promotes comparatively difficulty; Satellite-based location, if GPS location is to be mainly used in outdoor positioning.
For indoor positioning technology such as WLAN, WSN, be subject to environmental interference and be unfavorable for to problems such as universe popularizations, cellular base station (TOA time of arrival based on Mobile High-Speed Data Transport Network has been proposed in prior art, Time OfArrival) and time of arrival poor (TDOA, Time Difference ofArrival) location technology.These technology all need to measure in advance a large amount of calibration points, also just need to store the calibration point of TDOA/TOA, thereby just must have the Database Systems of storage calibration point.
In realizing process of the present invention, inventor finds, existing various localization method has their limitation separately, the node that some needs are disposed is too much, and cost is higher, need to gather a large amount of characteristics, building database, workload is large, promotes comparatively difficulty, and some hybrid locating method positioning precisioies are lower again.For example, in TDOA/TOA characteristic information gatherer process, because the TDOA feature of positioning signal there will be wave band, beforehand research found that, in fixed point collection apparatus, because signal generation diffraction, scattering, reflection etc. cause signal to produce multipath, change, eigenwert fluctuates near may concentrating on different path values.Now, need process measuring eigenwert, therefore, the quality that how to improve TDOA becomes a key breakthrough points, and it is extremely important that TDOA filtering method also just becomes.Need badly and want a kind of effective TDOA to gather filtering method, to solve the low problem of positioning precision existing in prior art.
Summary of the invention
In order to solve the problem of prior art, the embodiment of the present invention provides a kind of time of arrival poor filtering method, device and characteristic storage means.Described technical scheme is as follows:
Differ from a filtering method time of arrival, and described method comprises:
Difference data time of arrival that calibration point is collected carries out ascending order arrangement;
By described time of arrival difference data according to order, subtract each other between two, according to the absolute value of the difference obtaining by described time of arrival difference data be divided into many groups;
Difference data time of arrival in every group is further divided into some groups, calculates the standard deviation of difference data time of arrival in each group, using standard deviation be less than 0.5 group included time of arrival difference data as difference data time of arrival that footpath is corresponding;
Calculate every footpath corresponding time of arrival difference data standard deviation, standard deviation is less than to difference data computation of mean values time of arrival comprising in 0.5 described group, difference time of arrival using described average as described footpath.
Described by described time of arrival difference data according to order, subtract each other between two, according to the absolute value of the difference obtaining by described time of arrival difference data be divided into many groups, comprising:
Described time of arrival of difference data after sequence is subtracted each other between two according to order, when TDOA (i+1)-TDOA (i) > 1, TDOA (i) all time of arrival of difference data is before divided into one group; Wherein, described i is the numbering of difference data time of arrival, i=1,2,3 ... N, described N is difference data sum time of arrival;
By remaining difference data repetition time of arrival said process, until all difference datas time of arrival all complete grouping.
After described time of arrival, difference data divided into groups, also comprise:
Add up the quantity of difference data time of arrival in each grouping, by time of arrival, the quantity of difference data is less than difference data sum 10% grouping time of arrival and gives up.
Described difference data time of arrival in every group is further divided into some groups, comprises:
Difference data time of arrival in every group is carried out to ascending order arrangement, and according to difference data arrangement time of arrival number sequence, average difference data time of arrival by every group is divided into 15～30Ge group.
In each group of described calculating time of arrival difference data standard deviation, comprising:
Calculate the average of difference data time of arrival in each group
wherein, a
ifor each of the difference data of every a small group time of arrival, n be in every a small group time of arrival difference data number;
Calculate again the standard deviation of difference data time of arrival in each group
Described method also comprises:
According to footpath every described corresponding time of arrival difference data quantity, determine the significance level in every footpath; The maximum footpath of difference data time of arrival is maximum footpath.
Differ from a filter time of arrival, and described device comprises sequencing unit, grouped element, minute unit, footpath and glide filter unit, wherein,
Described sequencing unit, carries out ascending order arrangement for difference data time of arrival that calibration point is collected;
Described grouped element, for by described time of arrival difference data according to order, subtract each other between two, according to the absolute value of the difference obtaining by described time of arrival difference data be divided into many groups;
Unit, described minute footpath, for difference data time of arrival of every group is further divided into some groups, calculate the standard deviation of difference data time of arrival in each group, using standard deviation be less than 0.5 group included time of arrival difference data as difference data time of arrival that footpath is corresponding;
Described glide filter unit, for calculate every footpath corresponding time of arrival difference data standard deviation, standard deviation is less than to difference data computation of mean values time of arrival comprising in 0.5 described group, difference time of arrival using described average as described footpath.
Described device further comprises that effective grade gets unit, for according to footpath every described corresponding time of arrival difference data quantity, determine the significance level in every footpath; Choose footpath that significance level is high as effective diameter.
A characteristic storage means, is applied to differ from the storage that filtering method obtains data time of arrival as above, and described method comprises:
The scope of wish location is divided into a plurality of regions, the base station IDs that receives signal base station in the IDYu region, region in each region is stored;
Difference data time of arrival that each calibration point in each region is collected is stored, and the form of storage includes but not limited to: coordinate information and the elevation information of difference data time of arrival corresponding to region ID, base station IDs, every footpath, received signal strength data, calibration point.
Described method also comprises:
The base station number of required storage is set according to the requirement of actual location precision, is minimumly not less than 3, is the highlyest not more than 12, and preferred value is 6.
Difference data time of arrival that described every footpath is corresponding, comprising:
Only store maximum diameter and time difference data time of arrival corresponding to large footpath.
The beneficial effect that the technical scheme that the embodiment of the present invention provides is brought is:
By a plurality of TDOA data that each calibration point is collected sort, after calculated difference, the TDOA data of each calibration point are divided into groups, again every group of TDOA data are segmented, obtain the TDOA data combination behind minute footpath, also be about to a plurality of TDOA data and be divided into respectively different footpaths, then calculate the value of the TDOA data in every footpath.A large amount of TDOA data of calibration point collection can be divided into data corresponding to different footpaths thus, by the analysis and calculation to every footpath, can improve greatly the positioning precision of calibration point, thereby provide for utilizing calibration point to position the data basis that precision is very high.Meanwhile, the embodiment of the present invention also provides the storage means of TDOA characteristic, in order to the TDOA data behind storage minute footpath, has realized the storage of the characteristic calibration point in wide scope and is unlikely to data volume and to localization process, brings huge burden too greatly.
Accompanying drawing explanation
In order to be illustrated more clearly in the technical scheme in the embodiment of the present invention, below the accompanying drawing of required use during embodiment is described is briefly described, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skills, do not paying under the prerequisite of creative work, can also obtain according to these accompanying drawings other accompanying drawing.
Fig. 1 is the TDOA filtering method principle flow chart that the embodiment of the present invention one provides;
Fig. 2 is the TDOA filter structural representation that the embodiment of the present invention two provides;
Fig. 3 is the characteristic storage means principle flow chart that the embodiment of the present invention three provides.
Embodiment
For making the object, technical solutions and advantages of the present invention clearer, below in conjunction with accompanying drawing, embodiment of the present invention is described further in detail.
In actual location feature data acquisition, the TDOA feature of positioning signal there will be wave band, and beforehand research found that, fixes a point in collection apparatus, and due to multipath reason of changes, eigenwert fluctuates near may concentrating on different path values.What the eigenwert here referred to is exactly the TDOA data that gather, the number of signals receiving from base station by antenna, due to signal in communication process due to reflection or the reason such as diffraction, the TDOA value (propagation time difference) that just there will be so the some buildingss of signal process that send from base station to reflex to acquisition terminal position when signal receives may (be established this value for a at a numerical value, claim that a is article one footpath) fluctuation up and down, and receiver receives that then the signal that sends base station blocks by diffraction through buildings that different (establishing this value is b to the TDOA value of acquisition terminal position and by the TDOA value reflecting back again simultaneously, be called second footpath), what the data that gather in terminal may this time obtain is the value through reflecting back, what obtain is the value of coming through diffraction next time, so just occurred that the data that collect are at a, near the namely fluctuation in two footpaths of these two values of b., now, need process to measuring eigenwert separating multiple diameter information from a large amount of raw data.And be unlikely to data volume in order to realize the storage of the characteristic calibration point in wide scope, to localization process, bring huge burden too greatly, need the database format that will set up in suitable wide scope badly.The embodiment of the present invention is divided footpath by the TDOA data to calibration point collection, in order to improve positioning precision.
Embodiment mono-
As shown in Figure 1, be the TDOA filtering method principle flow chart that the embodiment of the present invention one provides, specific as follows:
Step 10, the TDOA data that calibration point is collected are carried out ascending order arrangement.
Here, in order to locate needs, in fact each calibration point will gather hundreds of TDOA data, and these data bulks are huge and mixed and disorderly, first need these TDOA data to sort.Conventionally according to the value of TDOA data, carry out ascending order arrangement, the size of the data based all values of TDOA that also soon each calibration point will collect, from little toward sorting greatly.
Step 20, subtracts each other the data based order of TDOA between two, according to the absolute value of the difference obtaining, TDOA data is divided into many groups.
Here, the data based order of TDOA after sequence need to be subtracted each other between two, when TDOA (i+1)-TDOA (i) >1, TDOA (i) all TDOA data are before divided into one group.I is the numbering of difference data time of arrival, i=1,2,3 ... N, N is TDOA data sums.By remaining TDOA Data duplication said process, until all TDOA data all complete grouping.
Specifically, the value of the TDOA data that calibration point collects all approaches, in TDOA data array after ascending order is arranged, from first data, consequent TDOA data deduct preceding paragraph TDOA data, if the difference obtaining is less than 1, these two TDOA data should be divided at same group.So analogize, until consequent, subtract difference that preceding paragraph obtains and be greater than 1, illustrate that two TDOA data differences of consequent and preceding paragraph are at this moment larger, should not reallocate at same group.At this moment, all before TDOA data are all distributed in to a group, the more consequent TDOA data of take are now as initial, continue to carry out the consequent calculating that subtracts preceding paragraph, same, difference is less than 1 TDOA data and divides at same group backward, until difference is greater than at 1 o'clock, second group of TDOA data is also assigned.So analogize, until all TDOA data have all completed grouping.
The theoretical foundation of doing is like this TDOA data that calibration point collects, if a paths is propagated and to be come, the value difference of its TDOA data can be not large, like this, the TDOA data that difference is larger may not be just that propagate in same footpath, thereby need to be assigned to different groups, here path corresponding to different groups, only here, be rough minute footpath, its result is very inaccurate.
Further, add up the quantity of TDOA data in each grouping, the grouping that the quantity of TDOA data is less than to TDOA data sum 10% is given up.Namely after all TDOA data have all been divided into groups, the quantity of TDOA data in lower each grouping of statistics, if the quantity of TDOA data seldom in some groupings, be less than 10% of all TDOA data of this calibration point sum, illustrate that the TDOA data in this grouping may be by a not too important propagated, thereby, can give up this grouping, namely give up this not too important path.
Step 30, is further divided into some groups by the TDOA data in every group, calculates the standard deviation of TDOA data in each group, and standard deviation is less than to the included TDOA data of 0.5 group as the TDOA data that footpath is corresponding.
Here, the TDOA data in every group need to be continued to be divided into several groups, the method for grouping has two kinds, and a kind of is first all TDOA data to be carried out to ascending order arrangement, then according to order by all TDOA data average be divided into several groups.For example, if there are 100 TDOA data, be divided into the words of 10Ge group, need, according to order, front 10 TDOA data are divided into first group, 10 TDOA data that continue are divided into the second group, by that analogy, average is divided into 10Ge group by 100 TDOA data, 10 TDOA data of each group.
In another method, the random exactly TDOA data by every group are average is divided into several groups, and the TDOA data bulk in each group is identical, but the TDOA data in each group are Random assignments.
Each concrete group need to be divided into how many groups, need to set according to actual needs, generally need to be assigned as 15Dao30Ge group, optimum value Wei20Ge group.
After being divided into group, calculate respectively the standard deviation of each group.The computation process of standard deviation is: first obtain the average of every a small group TDOA data, mean value computation formula is:
a wherein
ieach TDOA data for every a small group in the middle of group; N is the TDOA data amount check of every a small group.Calculate standard deviation, the computing formula of standard deviation is again:
tDOA data corresponding to those standard deviations that the poor value of finding out again group's Plays is less than 0.5, and these data are deposited again, as the TDOA data that footpath is corresponding.
This step is the further step in minute footpath, by this step, has in fact completed the work in minute footpath.Every TDOA data corresponding to footpath are determined.
In this time, can carry out the differentiation of main footpath and time critical path.Store in footpath after all refinements are divided, obtains every footpath TDOA data amount check, and what TDOA data amount check was maximum is main footpath, and taking second place is two footpaths, by that analogy, can obtain all footpaths and corresponding significance level.Conventionally the footpath obtaining here can be not a lot, and generally also with regard to 2 or 3, the data in more footpath, due to not too important, have been given up in step 20.
Step 40, calculates the standard deviation of TDOA data corresponding to every footpath, standard deviation is less than to the TDOA data computation of mean values comprising in 0.5 group, the TDOA value using average as described footpath.
Following step is exactly to ask the process of the TDOA data in maximum diameter and time large footpath, adopts the method for average here, utilizes glide filter, and coefficient can be set as required, is 0.65 for suitable herein.
If TDOA's adds up to count, the sum of Wsize=σ * count(TDOA data in footpath), from i=1, obtain TDOA (i)---the standard deviation of TDOA (i+Wsize), until i=count-Wsize.The value of these standard deviations relatively, the average of the TDOA of standard deviation minimum (i)---TDOA (i+Wsize) i.e. the TDOA value in footpath for this reason.The standard deviation is here minimum, has in fact comprised that standard deviation is less than all TDOA data in 0.5 group.
Certainly, such method can calculate the value of the TDOA in all footpaths, but in fact only need to calculate the value of the TDOA in topmost two footpaths.Because in the process of actual location, more footpath can not be improved the precision of location significantly.
Embodiment bis-
The embodiment of the present invention provides a kind of TDOA filter, and referring to Fig. 2, this device comprises sequencing unit 100, grouped element 200, minute unit, footpath 300 and glide filter unit 400, specific as follows:
Sequencing unit 100, carries out ascending order arrangement for the TDOA data that calibration point is collected.
200 grouped elements, for the data based order of TDOA is subtracted each other between two, are divided into many groups according to the absolute value of the difference obtaining by TDOA data.
Minute unit, footpath 300, for the TDOA data of every group are further divided into some groups, calculates the standard deviation of TDOA data in each group, and standard deviation is less than to the included TDOA data of 0.5 group as the TDOA data that footpath is corresponding.
Glide filter unit 400, for calculating the standard deviation of TDOA data corresponding to every footpath, is less than standard deviation the TDOA data computation of mean values comprising in 0.5 group, the TDOA value using average as described footpath.
Preferably, this device also comprises that effective grade gets unit 500, for according to the quantity of TDOA data corresponding to every footpath, determines the significance level in every footpath; Choose footpath that significance level is high as effective diameter.
It should be noted that: the TDOA filter that above-described embodiment provides is when filtering operation, only the division with above-mentioned each functional module is illustrated, in practical application, can above-mentioned functions be distributed and by different functional modules, completed as required, the inner structure that is about to device is divided into different functional modules, to complete all or part of function described above.In addition, the TDOA filter that above-described embodiment provides and the embodiment of the method for filtering belong to same design, and its specific implementation process refers to embodiment of the method, repeats no more here.
Embodiment tri-
Referring to Fig. 3, the embodiment of the present invention provides a kind of characteristic storage means, is applied in the storing process of the data that TDOA filtering method that above-mentioned two embodiment provide and filter obtain, wherein:
Step 1000, is divided into a plurality of regions by the scope of wish location, and the base station IDs that receives signal base station in the IDYu region, region in each region is stored.
In storage data, first need base station information corresponding to area information and region that form stores is all, namely region ID and a base station IDs.This form is exactly whole region form.The division in region can determine by coordinate on map, and its table format is as follows:
| Region ID | Base station IDs 1 | Base station IDs 2 | Base station IDs 3 | Base station IDs 4 | Base station IDs 5 | Base station IDs 6 |
Here, need the quantity of the base station of storage to set according to the requirement of actual location precision, be minimumly not less than 3, be the highlyest not more than 12, preferred value is 6.
Step 2000, the TDOA data that each calibration point in each region is collected are stored, and the form of storage includes but not limited to: coordinate information and the elevation information of TDOA data corresponding to region ID, base station IDs, every footpath, received signal strength data, calibration point.
After having stored area information and base station information, follow-uply need to store the data that in each concrete region, calibration point collects.Can be divided into different storage lists stores.The name of each storage list can be determined with region ID.Mainly be divided into the following information: area coordinate ID, TDOA1
_ 1, TDOA2
_ 1... TDOAN
_ 1, this represents the numerical value of maximum diameter of the TDOA on each road, then TDOA1
_ 2, TDOA2
_ 2... TDOAN
_ 2, being that the numerical value in time large footpath of each road TDOA, N are determined by the number of combinations of number of base stations, 6 base stations are
road TDOA value; RSSI
_ 1, RSSI
_ 2, RSSI
_ Nfor field intensity corresponding to each base station, the X of calibration point, Y coordinate, add the elevation information Height_1 of calibration point, Height_2, Height_6, this is the value directly reading from receiver, depositing in database is in order to utilize elevation information to realize floor switching and indoor and outdoor switching in position fixing process.Here introduce the elevation information of calibration point, object is to choose calibration point when doing architecture, for example, the calibration point of 15th floor, Yi Dong building Li and 5th floors is different, we found before this calibration point ID and then had a look the elevation information whether elevation information of this calibration point obtain with our locating terminal the same in calibration point data storages, with this, determined the accuracy of calibration point.
Its form is as follows:
| Data tabular name | Attribute |
| Region ID | Regional number (reading from map) |
| Base station IDs 1 | Base station number 1 |
| …… | …… |
| Base station IDs 6 | Base station number 6 |
| TDOA1 _1 | The value of TDOA1 maximum diameter |
| …… | …… |
| TDOA15 _1 | The value of TDOA15 maximum diameter |
| TDOA1 _2 | The value in TDOA1 large footpath |
| …… | …… |
| TDOA15 _2 | The value in TDOA15 large footpath |
| RSSI _1 | The field intensity value of base station 1 |
| …… | …… |
| RSSI _6 | The field intensity value of base station 6 |
| CoordinateX | The horizontal ordinate of calibration point |
| CoordinateY | The ordinate of calibration point |
| Height_Pt | The elevation information of calibration point |
Here to store the data instance in two footpaths of a calibration point, in fact can store the TDOA data in all footpaths.Certainly, in actual applications, the TDOA data in two maximum footpaths of storage are enough.
Especially, storage is TDOA data here, in fact can also, by the storage of TOA data, calculate TDOA data in calling data again.
In sum, the embodiment of the present invention by a plurality of TDOA data that each calibration point is collected sort, after calculated difference, the TDOA data of each calibration point are divided into groups, again every group of TDOA data are segmented, obtain the TDOA data combination behind minute footpath, also be about to a plurality of TDOA data and be divided into respectively different footpaths, then calculate the value of the TDOA data in every footpath.A large amount of TDOA data of calibration point collection can be divided into data corresponding to different footpaths thus, by the analysis and calculation to every footpath, can improve greatly the positioning precision of calibration point, thereby provide for utilizing calibration point to position the data basis that precision is very high.Meanwhile, the embodiment of the present invention also provides the storage means of TDOA characteristic, in order to the TDOA data behind storage minute footpath, has realized the storage of the characteristic calibration point in wide scope and is unlikely to data volume and to localization process, brings huge burden too greatly.
The invention described above embodiment sequence number, just to describing, does not represent the quality of embodiment.
One of ordinary skill in the art will appreciate that all or part of step that realizes above-described embodiment can complete by hardware, also can come the hardware that instruction is relevant to complete by program, described program can be stored in a kind of computer-readable recording medium, the above-mentioned storage medium of mentioning can be ROM (read-only memory), disk or CD etc.
The foregoing is only preferred embodiment of the present invention, in order to limit the present invention, within the spirit and principles in the present invention not all, any modification of doing, be equal to replacement, improvement etc., within all should being included in protection scope of the present invention.
Claims (5)
1. differ from a filtering method time of arrival, it is characterized in that, described method comprises:
Difference data time of arrival that calibration point is collected carries out ascending order arrangement;
By described time of arrival difference data according to order, subtract each other between two, according to the absolute value of the difference obtaining by described time of arrival difference data be divided into many groups;
Difference data time of arrival in every group is further divided into some groups, calculates the standard deviation of difference data time of arrival in each group, using standard deviation be less than 0.5 group included time of arrival difference data as difference data time of arrival that footpath is corresponding;
Calculate every footpath corresponding time of arrival difference data standard deviation, standard deviation is less than to difference data computation of mean values time of arrival comprising in 0.5 described group, difference time of arrival using described average as described footpath;
Described by described time of arrival difference data according to order, subtract each other between two, according to the absolute value of the difference obtaining by described time of arrival difference data be divided into many groups, comprising:
Described time of arrival of difference data after sequence is subtracted each other between two according to order, when TDOA (i+1)-TDOA (i) >1, TDOA (i) all time of arrival of difference data is before divided into one group; Wherein, described i is the numbering of difference data time of arrival, i=1,2,3 ... N, described N is difference data sum time of arrival;
By remaining difference data repetition time of arrival said process, until all difference datas time of arrival all complete grouping.
2. the method for claim 1, is characterized in that, after described time of arrival, difference data divided into groups, also comprises:
Add up the quantity of difference data time of arrival in each grouping, by time of arrival, the quantity of difference data is less than difference data sum 10% grouping time of arrival and gives up.
3. the method for claim 1, is characterized in that, described difference data time of arrival in every group is further divided into some groups, comprising:
Difference data time of arrival in every group is carried out to ascending order arrangement, and according to difference data arrangement time of arrival number sequence, average difference data time of arrival by every group is divided into 15～30Ge group.
4. the method for claim 1, is characterized in that, in each group of described calculating time of arrival difference data standard deviation, comprising:
Calculate the average of difference data time of arrival in each group
wherein, a
ifor each of the difference data of every a small group time of arrival, n be in every a small group time of arrival difference data number;
Calculate again the standard deviation of difference data time of arrival in each group
5. the method for claim 1, is characterized in that, described method also comprises:
According to footpath every described corresponding time of arrival difference data quantity, determine the significance level in every footpath; The maximum footpath of difference data time of arrival is maximum footpath.
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