CN104053129A - Wireless sensor network indoor positioning method and device based on sparse RF fingerprint interpolations - Google Patents
Wireless sensor network indoor positioning method and device based on sparse RF fingerprint interpolations Download PDFInfo
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Abstract
The invention discloses a wireless sensor network indoor positioning method and device based on sparse RF fingerprint interpolations. The device comprises a fingerprint data measuring and collecting module, a fingerprint interpolation module and a fingerprint matching module. According to the fingerprint data measuring and collecting module, for a preset wireless sensor network, the strength of signals which are sent by main nodes and received by the positions of all reference nodes is measured, the repeatedly-measured average values of the strength of the signals sent from all main nodes are used as scene fingerprint data, and the scene fingerprint data of all the reference nodes and corresponding position coordinates are stored into an RF fingerprint database. By means of the fingerprint interpolation module, an ultimate RF fingerprint database is obtained through data interpolations according to the scene fingerprint data of the measured nodes, which are collected by the fingerprint data measuring and collecting module. The fingerprint matching module is used for conducting fingerprint matching on the scene fingerprint data of the measured nodes to achieve positioning according to the ultimate RF fingerprint database. The positioning device and method are good in robustness, high in adaptability, high in fault tolerance, low in cost and power consumption, and easy to implement.
Description
Technical field
The present invention relates to wireless communication technology field, be specifically related to the continuous location technology of dynamic node in the application of indoor wireless sensing network.
Background technology
The development comparative maturity of the outdoor positioning system based on GPS and also obtained more widely application.But due to indoor, there is no a gps signal, GPS indoor positioning is also inapplicable.In recent years, a lot of indoor positioning technology have been worked out both at home and abroad, for example: ultrasonic wave location, infrared ray location, WLAN (wireless local area network) location etc.These localization methods on the whole, the poor or power consumption of precision greatly with or poor practicability.And at security protection, assets and personnel positioning, Internet of Things Based Intelligent Control and management domain, accurate indoor positioning specification requirement is strong; Study a kind of low-power consumption, low cost, new indoor navigation system that positioning precision is higher is very necessary.
In recent years, mobile communication technology and wireless sensor network (Wireless Sensor Network, WSN) had obtained development fast, and the research of wireless indoor location becomes a popular research field.Wireless location refers to by the cooperation of wireless terminal and wireless network, determines the position of certain user (or node) in network with specific location algorithm.The adaptable field of wireless location is very extensive, comprises military, commercialization and civil area, as the emergency relief in automobile navigation, fire or earthquake, personnel at risk's (or old man and children) tracking, location-based information issue etc.
The technology such as wireless sensor network is integrated transducer, embedded, network and radio communication, distributed information processing, by dispose a large amount of, cheap sensor node in guarded region, with communication, form the network system of a self-organizing, come in perception, acquisition and processing network's coverage area can perceptive object information, and send to user terminal.Node can dynamically add network, or existing node in alternative networks, makes network have very strong robustness for individual node invalid.Due to the low cost of WSN, be convenient to large-scale application, make to take Position Research that WSN is carrier to be given widely and pay close attention to.
Wireless location technology refers to that the characteristic parameter of the wireless signal to receiving analyzes, and then according to specific algorithm, calculates the position at testee place.Conventional location technology comprises: measure signal intensity (RSSI), sense (arrive angle, be called for short AOA), signal transmission time [(be called for short TOA the time of advent), (time of advent is poor, is called for short TDOA)] etc.Algorithms of different, the precision of location is also different from loss.Because indoor positioning scope is generally relatively little, and now indoor positioning is generally that what to utilize is that radiofrequency signal requires very high (ns order of magnitude precision) for TDOA method evaluated error, as the error location error of the actual 0.1us of having has 30m, common distance-finding method error based on the radiofrequency signal time difference is large, and high-precision TDOA method cost is high.
Distance-finding method based on RSSI does not have this restriction, its signal propagation model spaciousness among a small circle in (10～100m) approach theoretical value, and the location technology based on RSSI is without being used extras, and indoor positioning technology is all generally the methods that adopt based on RSSI.But large based on RSSI distance model measurement and positioning error, therefore study a kind of novel localization method based on RSSI very necessary.
Summary of the invention
The application has proposed a kind of wireless sensor network indoor orientation method based on sparse radio-frequency fingerprint interpolation.The method need not be set up signal propagation accurate model and realize location.The method is under the fixed situation of network topology, to measure the unique RSSI vector value in certain position, and the RSSI vector is here that the data that gathered by a plurality of reference nodes (reference node is generally fixing beaconing nodes) combine.Because the RSSI vector value of this position comprises the impact that the environment such as construction wall are propagated radiofrequency signal, as reflection, diffraction, decline, shade and multipath effect etc., during test, the radio-frequency fingerprint of certain position is conventionally combined and is represented with the coordinate of RSSI Vector Groups and this position.
According to an aspect of the present invention, a kind of wireless sensor network indoor positioning device based on sparse radio-frequency fingerprint has been proposed, this device comprises: finger print data is measured and acquisition module, to the wireless sensor network setting in advance, measure the signal strength signal intensity of the host node transmitting that the signal of each reference node receives in exemplary position, the mean value of the signal strength signal intensity that each host node transmitting repeatedly recording in this position is come, as scene finger print data, deposits described scene finger print data and the relevant position coordinate of all reference nodes in fingerprint database; Fingerprint interpolating module, measures and the scene finger print data of the tested node that acquisition module collects according to finger print data, by data multidimensional interpolation, obtains grade radio-frequency fingerprint database eventually; Fingerprint matching module, according to the described radio-frequency fingerprint of level eventually database, carries out fingerprint matching to realize location to the scene finger print data of tested node.
According to a further aspect in the invention, a kind of wireless sensor network indoor orientation method based on sparse radio-frequency fingerprint interpolation is provided, comprise: within the scope of wireless sensor network, arrange host node and reference node, the coordinate of the RSSI Vector Groups measuring and each reference node position is gathered as RSSI scene finger print data, generate elementary radio-frequency fingerprint database; Utilize finger print data in described elementary radio-frequency fingerprint database, by data interpolating, obtain level radio-frequency fingerprint database eventually; According to the described radio-frequency fingerprint of level eventually database, the RSSI scene finger print data of tested node is carried out to fingerprint matching, to carry out the location of unknown node in reference node position range.
This localization method has merged signal strength signal intensity telemetry (RSSI) and sparse radio-frequency fingerprint method this two kinds of methods, i.e. information using RSSI vector value as sparse radio-frequency fingerprint.The method has overcome that the former precision is low, the latter sets up the large shortcoming of scene fingerprint database workload, realizes higher positioning accuracy, low cost and low-power consumption.
Accompanying drawing explanation
Fig. 1 is a kind of indoor positioning plant system drawing based on sparse radio-frequency fingerprint interpolation according to the embodiment of the present invention;
Fig. 2 is according to the flow chart of a kind of indoor orientation method based on sparse radio-frequency fingerprint interpolation of the embodiment of the present invention;
Fig. 3 is a kind of room layout schematic diagram in the embodiment of the present invention;
Fig. 4 is same room location node position view in the embodiment of the present invention;
Fig. 5 is the RSSI Changing Pattern curved surface schematic diagram that in the embodiment of the present invention, host node is propagated at same room;
Fig. 6 is the position view of chummery location node not in the embodiment of the present invention;
Fig. 7 is the RSSI Changing Pattern curved surface schematic diagram that in the embodiment of the present invention, host node is propagated at chummery not;
Fig. 8 is the tested node R SSI measured value of same room and predicted value relative error record sheet in one embodiment of the invention;
Fig. 9 is the tested node coordinate measured value of same room and predicted value absolute error record sheet in one embodiment of the invention;
Figure 10 is the not tested node R SSI measured value of chummery and predicted value and error log table in one embodiment of the invention;
Figure 11 is the not tested node coordinate measured value of chummery and predicted value absolute error record sheet in one embodiment of the invention.
Embodiment
Below in conjunction with accompanying drawing, the embodiment of the present invention is described.
According to one embodiment of present invention, provide the positioner of the wireless sensor network based on sparse radio-frequency fingerprint interpolation, in the wireless sensor network that this device can be used for setting in advance.In this network packet, contain a plurality of reference nodes and a plurality of host node setting in advance.As shown in Figure 1, this device comprises: finger print data measurement module 1011, to the wireless sensor network setting in advance, measure the signal strength signal intensity of the host node transmitting receiving on the position of each reference node; Finger print data acquisition module 1012, the mean value of the signal strength signal intensity that each host node transmitting repeatedly recording a specific reference node position gathers as scene finger print data, deposits described scene finger print data and the relevant position coordinate of all reference nodes in radio-frequency fingerprint database; Fingerprint interpolating module 1013, according to the scene finger print data of the tested node collecting, obtains level radio-frequency fingerprint database eventually by data interpolating; And fingerprint matching module 102, according to whole level radio-frequency fingerprint database, the scene finger print data of tested node is carried out to fingerprint matching to realize location.
According to another embodiment of the present invention, provide the wireless sensor network locating method based on sparse radio-frequency fingerprint interpolation.As shown in Figure 2, the method comprises the following steps:
1. fingerprint database generates
First at the indoor host node (step S001) that places.The host node position of laying is generally at indoor edge vertices place, and in the process of test, host node position remains unchanged.Then in host node signal spread scope, arrange reference node (step S001).In this example, be in every 10m*10m grid, to arrange 1～2 (if positioning accuracy request is high, the several reference nodes of many layouts that can be suitable).Successively travelling carriage (instrument of witness mark node and the RSSI of nodes of locations place value) is put on the position of reference node, measures the signal strength signal intensity RSSI (step S002) receiving from each host node.By repeatedly measuring, show that the multi-C vector of mean value (mean value of other host node signal strength signal intensities is also like this) composition of the signal strength signal intensity that host node transmitting of receiving on this position comes, as scene finger print data, deposits radio-frequency fingerprint database (step S003) in.The rest may be inferred, obtains the radio-frequency fingerprint data of all reference nodes, deposits radio-frequency fingerprint database (step S004) in.
2. the concrete expression mode of fingerprint base
Because the radiofrequency signal at certain ad-hoc location can influence each other, tend to cause radiofrequency signal unstable.During networking in test between node, should eliminate such interference (for example, while making each node transmit interval certain hour, thereby avoid clashing).Based on this hypothesis, each position in building has unique radiofrequency signal characteristic vector value.Conventionally unique radiofrequency signal characteristic vector value of He Gai position, a position is made to the as a whole scene fingerprint of describing this position in building.In the present embodiment, adopt the radio-frequency fingerprint information in database that is combined as of RSSI signal strength signal intensity and its position data.Therefore, can use compound orientational vector (K, L) to represent the radio-frequency fingerprint data in database.Wherein, K represents the position data of coordinate of this position and so on, and L represents the RSSI scene finger print data of this position, the multi-C vector of the RSSI mean value sending over for each host node receiving on this position.
Its expression formula can be expressed as follows:
(K,L)＝{(x,y),[RSSI
1，RSSI
2,......RSSI
n]} (1)
The method of its collection is: the signal sending out with certain frequency collection host node on this position, (general sampling rate is 5Hz to form a collection of signal intensity samples data, 10 samples are used in each base station), then calculate the mean value of these signal intensity samples data, a part of scene finger print data using the mean value finally obtaining as this position, gathers and comes from other host nodes at the scene finger print data of this position similarly.The position coordinates here can be mapped to three-dimensional.
3. bilinear interpolation value-based algorithm
Finger print data interpolation carries out interpolation to RSSI value in (x, y) plane, and the method that can adopt comprises the most contiguous interpolate value, bilinear interpolation value, cubic convolution interpolation, Lagrange's interpolation.Wherein the most contiguous interpolate value is simple and directly perceived, but precision is not high; Lagrange's interpolation calculates simple, if but increase a node, basic function will recalculate, and has greatly increased amount of calculation; Cubic convolution method interpolate value computational accuracy is high, but amount of calculation is too large.According to the inventor's development test, find to adopt bilinear interpolation value method, can realize amount of calculation is 1/4 of cubic convolution method
n(n refers to the quantity that needs interpolation point) and precision are higher.Therefore, overall balance precision and amount of calculation, utilize different interpolation methods to carry out mapping analysis, the data error that bilinear interpolation draws is less, and amount of calculation is low), in the present embodiment, indoor positioning finger print data interpolation method adopts bilinear interpolation value method (step S005).
Bilinear interpolation value-based algorithm principle:
It is (i+u that the floating-point coordinate that interpolation point coordinate obtains by reciprocal transformation is set, j+v), wherein i, j are nonnegative integer, and u, v are [0,1) interval floating number, the value f (i+u, j+v) of this interpolation point can coordinate be (i, j), (i+1 in original image, j), (i, j+1), (i+1, j+1) corresponding four seat target values around determine
f(i+u，j+v)＝(1-u)(1-v)f(i，j)+(1-u)vf(i，j+1)
A (2)
+u(1-v)f(i+1，j)+uvf(i+1，j+1)
The value that wherein f (i, j) representative function (i, j) is located, by that analogy.Can obtain level fingerprint database (step S006) eventually from elementary radio-frequency fingerprint database thus.
4. unknown node is located
Should in reference node position range, carry out the location of unknown node, the maximum of reference node coordinate and minimum value are the borders of orientation range.In to unknown node position fixing process, gather each host node at the RSSI at this unknown node place multi-C vector (step S008), the eventually level scene fingerprint database that completes interpolation of take is foundation, use proximity matching algorithm to carry out fingerprint matching (step S007), finally determine the position (step S009) of unknown node.
5. fingerprint matching algorithm
Unknown node positioning precision is played to key effect, and except setting up the problem of fingerprint database, another one problem is exactly how by certain effective algorithm, to obtain the position of unknown node.K nearest neighbor algorithm is mainly used in the field of Data Mining Classification, pattern recognition.And the present invention carries out fingerprint matching by employing k nearest neighbor matching algorithm, calculate unknown node position.This algorithm is organized the corresponding reference point of adjacent signal intensity level by choosing K, then chooses an optimum position as the estimated position of mobile node.
If f
ijthat i reference point of off-line phase (obtaining the stage of scene finger print information) receives the signal strength signal intensity mean value from j host node, f
jthat on-line stage (being actual measurement positioning stage) records the signal strength values from j host node at mobile node, i=1,2 ..., m, j=1,2 ..., n, wherein m is reference point number, n is host node number.F
jwith data f
ijbetween distance can be expressed as:
In the result obtaining by above formula, choose from small to large K d
ibe worth corresponding reference point, with following formula, calculate the average of their position coordinateses and as a result of export:
(x wherein
i, y
i) be i the corresponding coordinate of reference point being selected in fingerprint database.
If have N value and this K d in K value
ithe mean value difference of value is all>=1, this N value should be given up, concrete operations are as follows:
In upper table, be around two tuples and the d of 10 nodes of unknown node
ivalue, gets K=4 in this example, minimum 4 d
ibe respectively d
2, d
4, d
5and d
10, the coordinate that calculates nodes of locations is (11.1,6.3).
Mean value
for:
As from the foregoing, with reference to node 4 and 5, give up, calculate unknown node position for (11.8,5.8);
The position of measuring unknown node through test is (12,5), therefore gives up the reference node that indivedual deviation averages are larger, and positioning precision is higher.
Embodiment:
As shown in Figure 3, at 4 host nodes of house interior administration of 20m*20m, with #, represent, the reference point shown in Fig. 4 represents with *, and tested node represents with o.By bilinear interpolation, show that each host node is in the net surfaces trrellis diagram of indoor RSSI, as shown in Figure 5, in figure 001,002,003,004 is respectively #1, #2, #3, the indoor RSSI three dimensional network of #4 trrellis diagram, can obtain real indoor RSSI three-dimensional grid distribution map by each figure stack.In Fig. 3 20m*30m not chummery interior nodes #2 and #3 be fixed to another room, #1 and #4 invariant position, in Fig. 6, reference point represents with *, tested node represents with o.Fig. 7 is RSSI net surfaces trrellis diagram after interpolation, and in figure, 001,002,003,004 is respectively #1, #2, and #3, the indoor RSSI three dimensional network of #4 trrellis diagram, can obtain real indoor RSSI three-dimensional grid distribution map by each figure stack.Listed in Fig. 8 is same coordinate 4 dimension RSSI predicted value and measured values in same room, and the average relative error value of location is that in 6.49%, Fig. 9, actual coordinate is 1.525 with prediction coordinate absolute error mean value; Listed in Figure 10 is same coordinate 4 dimension RSSI predicted value and measured values in chummery not, and the average relative error value of location is that in 7.42%, Figure 11, actual coordinate is 1.78 with prediction coordinate absolute error mean value.
It will be appreciated by those skilled in the art that accompanying drawing is the schematic diagram of a preferred embodiment, the module in accompanying drawing or flow process can be accepted or rejected according to implementing concrete condition of the present invention.
It will be appreciated by those skilled in the art that the module in the device in embodiment can be distributed in the device of embodiment according to embodiment description, also can carry out respective change and be arranged in the one or more devices that are different from the present embodiment.The module of above-described embodiment can be merged into a module, also can further split into a plurality of submodules.
The invention described above embodiment sequence number, just to describing, does not represent the quality of embodiment.
Disclosed is above only several specific embodiment of the present invention, and still, the present invention is not limited thereto, and the changes that any person skilled in the art can think of all should fall into protection scope of the present invention.
Claims (10)
1. the wireless sensor network indoor positioning device based on sparse radio-frequency fingerprint interpolation, comprising:
Finger print data is measured and acquisition module, to the wireless sensor network setting in advance, measure the signal strength signal intensity of the host node transmitting receiving on the position of each reference node, the mean value of the signal strength signal intensity that each host node transmitting repeatedly recording in this position is come, as scene finger print data, deposits described scene finger print data and the relevant position coordinate of all reference nodes in radio-frequency fingerprint database;
Fingerprint interpolating module, measures and the scene finger print data of the tested node that acquisition module collects according to finger print data, by data interpolating, obtains grade radio-frequency fingerprint database eventually;
Fingerprint matching module, according to the described radio-frequency fingerprint of level eventually database, carries out fingerprint matching to realize location to the scene finger print data of tested node.
2. device as claimed in claim 1, is characterized in that: described fingerprint interpolating module is measured finger print data and the scene finger print data of the tested node that acquisition module collects carries out bilinear interpolation to obtain level radio-frequency fingerprint database eventually.
3. device as claimed in claim 1 or 2, is characterized in that: described fingerprint matching module is carried out fingerprint matching to realize location according to K nearest neighbor algorithm to the scene finger print data of tested node.
4. the wireless sensor network indoor orientation method based on sparse radio-frequency fingerprint, comprising:
Within the scope of wireless sensor network, arrange host node and reference node, the coordinate of the RSSI vector value measuring and each reference node position is gathered as RSSI scene finger print data, generate elementary radio-frequency fingerprint database;
Utilize finger print data in described elementary radio-frequency fingerprint database, by data interpolating, obtain level radio-frequency fingerprint database eventually;
According to the described radio-frequency fingerprint of level eventually database, the RSSI scene finger print data of tested node is carried out to fingerprint matching, to carry out the location of unknown node in reference node position range.
5. method according to claim 4, wherein, utilize finger print data in elementary radio-frequency fingerprint database to obtain level radio-frequency fingerprint database eventually by bilinear interpolation value method, described bilinear interpolation value method is included in (x, y) in plane, RSSI value is carried out to interpolation, comprising:
For object pixel, it is (i+u, j+v) that the floating-point coordinate that coordinate obtains by reciprocal transformation is set, wherein i, j are nonnegative integer, u, v be [0,1) interval floating number, the value f (i+u of this pixel, j+v) in original image, coordinate is (i, j), (i+1, j), (i, j+1), (i+1, j+1) the corresponding value of four pixels around determines as follows
f(i+u,j+v)＝(1-u)(1-v)f(i,j)+(1-u)vf(i,j+1)+u(1-v)f(i+1,j)+uvf(i+1,j+1)
Wherein, the pixel value that f (i, j) presentation video (i, j) is located.
6. method according to claim 4, is characterized in that, described fingerprint matching adopts K positioned adjacent algorithm, comprising:
By choosing K, organize the corresponding reference point of adjacent signal intensity level, then choose an optimum position as the estimated position of mobile node;
If f
ijthat i reference point of off-line phase receives the signal strength signal intensity mean value from j host node, f
jthat on-line stage mobile node records the signal strength values from j host node, i=1,2 ..., m, j=1,2 ..., n, wherein m is reference point number, n is host node number, f
jwith data f
ijbetween distance table be shown:
In above-mentioned definite distance results, choose from small to large K d
ibe worth corresponding reference point, with following formula, calculate the average output of the position coordinates of described reference point
(x wherein
i, y
i) be i the corresponding coordinate of reference point being selected in described fingerprint database;
Selected K d
imiddle than other d
ithe one or more d that is worth little preset range
ibe worth, by weights method, determine the position of transfer point.
7. method according to claim 4, is characterized in that, the step that described elementary radio-frequency fingerprint database generates comprises:
Arrange host node and select the position of reference node;
Measure successively the signal strength signal intensity of being launched by each host node on the position of each reference node, the multi-C vector that the mean value of the repeatedly measured value that each host node recording on this position is transmitted forms is as scene finger print data;
The rest may be inferred, obtains the scene finger print data of all reference nodes, deposits radio-frequency fingerprint database in.
8. method according to claim 4, it is characterized in that, in described database, finger print data is used compound orientational vector (K, L) represent wherein, K represents the information of described reference point locations, L represents the RSSI scene finger print data of this position, the multi-C vector of the RSSI mean value sending over for host node on this position
Its expression formula is:
(K,L)＝{(x，y)，[RSSI
1,RSSI
2，......RSSI
n]}
The method of described collection comprises: the signal sending out with the frequency collection host node of being scheduled in described reference point locations, form a collection of signal intensity samples data, then calculate the mean value of these signal intensity samples data, a part of scene finger print data using the mean value finally obtaining as this position, and
Collection comes from other host nodes at the scene finger print data of this position.
9. method according to claim 4, the location of wherein carrying out unknown node in reference node position range comprises:
Using the maximum of reference node coordinate and the minimum value border as orientation range;
Collect this each host node of unknown node place RSSI multi-C vector herein;
According to the described scene of level eventually fingerprint database, use K proximity matching algorithm, determine the position of described unknown node.
10. according to the method described in claim 4-9 any one, it is characterized in that, described layout node is in the scope of certain area, to dispose a plurality of host nodes, participation in the election examination point, tested node, and described method also comprises:
By bilinear interpolation, obtain each host node in the net surfaces trrellis diagram of indoor RSSI.
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