CN110082715A - The weighted mass center localization method of environment self-adaption - Google Patents
The weighted mass center localization method of environment self-adaption Download PDFInfo
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Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/0278—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves involving statistical or probabilistic considerations
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/06—Position of source determined by co-ordinating a plurality of position lines defined by path-difference measurements
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
- H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
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Abstract
The invention discloses a kind of weighted mass center localization methods of environment self-adaption, comprising: signal propagation distance d is calculated according to the relational model of RSSI and signal propagation distance d in the signal strength RSSI for acquiring beaconing nodes；Different beaconing nodes are distributed with different modified weight coefficients using the characteristic that k-nearest neighbor arest neighbors is classified, corrects weight；The coordinate of calculate node.On the basis of being based on weighted mass center location algorithm, by the weighted factor in k-nearest neighbor dynamic corrections weighted mass center method, the irrationality of weight selection is avoided, to realize that environment self-adaption positions.
Description
Technical field
The present invention relates to sensor network technology fields, more particularly to a kind of weighted mass center positioning side of environment self-adaption
Method can be used for indoor positioning navigation.
Background technique
In recent years, with mobile communication, the rise and development of radio network technique, the neck such as agricultural, industry, business, military affairs
The researcher in domain increasingly pays close attention to wireless sensor network (Wireless Sensing Networks, WSN) and wireless local area
Net (Wireless Local Area Networks, WLAN) etc. is towards indoor location technology.In buildings such as large supermarkets
Interior arrangement wireless sensing net node carries out division region to different classes of commodity, and arranges one for the shelf of every class commodity
Wireless sensor network node, the portable mobile terminal of customer are equivalent to a mobile node, and such customer is in shopping
The article bought needed for oneself can be quickly searched according to their own needs, and supermarket can push some preferential letters to customer
Breath, can also save a large amount of shopping guide's information overhead, and shopping entire in this way is more intelligent.And WSN node hardware is low in cost,
So can arrange on a large scale.Indoor positioning has very wide commercial application prospect in smart home, currently, exclusively for intelligence
The wireless sensor technology that energy household provides proposes wireless family automatic network (Wireless Home Automation
Networks, WHANs).Currently, Zigbee, Z-Wave, INSTEON based on IEEE 802.15.4 related protocol standard,
Wavenis and IP-based technology all provide theory and technology for WHANs related hardware standard and agreement and support.Currently in this way
Application demand it is more and more wider, but due to being limited by the indoor environment of positioning accuracy, location efficiency and complexity, indoor positioning
Technology development is seriously hindered.
In open outdoor environment, global position system GPS provides point-device location information, however due to room
The particularity of interior environment, it is currently some to be difficult to be transplanted in the application of indoor positioning for outdoor positioning algorithm and system.It is indoor
It is the most close occasion of people's production and living relationship, in the indoor environments such as large stadium, market, airport, realizes and be accurately positioned
As the emerging and hot fields studied at present.If child walks to lose during market shopping, how to allow parent's use most short
Time-tracking is to the position of child；During underground parking, driver how to be allowed to be known that when no arrival parking lot
Parking position situation, these problems require indoor positioning technologies to solve.Accurate indoor positioning information, can be to available sky
Between and inventory's substance realize efficiently management；Can navigate police, fire fighter, soldier, the specific room of health care worker completion
Interior task；Intelligent space, general fit calculation etc. all be unable to do without location-based service, therefore indoor positioning has broad application prospects, and is
The hot spot of current research.
Indoor positioning mostly uses weighted mass center algorithm to be positioned, and traditional location technology excessively relies on dynamic environment and leads
Cause positioning accuracy lower, the present invention is therefore.
Summary of the invention
In order to solve above-mentioned technical problem, the purpose of the present invention is to propose to a kind of weighted mass centers of environment self-adaption
Localization method passes through the weighting in k-nearest neighbor dynamic corrections weighted mass center method on the basis of being based on weighted mass center location algorithm
The factor, avoids the irrationality of weight selection, to realize that environment self-adaption positions.
The technical scheme is that
A kind of weighted mass center localization method of environment self-adaption, comprising the following steps:
S01: acquiring the signal strength RSSI of beaconing nodes, is calculated according to the relational model of RSSI and signal propagation distance d
Obtain signal propagation distance d；
S02: distributing different beaconing nodes different modified weight coefficients using the characteristic that k-nearest neighbor arest neighbors is classified,
Correct weight；
S03: the coordinate of calculate node.
In preferred technical solution, the beaconing nodes in the step S01 periodically send self information to surrounding；It is right
The same beaconing nodes repeatedly record the information that the beaconing nodes are broadcasted, and to the beaconing nodes signal strength received
RSSI carries out ascending order and finds out its average value, and identical value is grouped to and is calculated the number m of every group of identical valuei。
In preferred technical solution, the amendment weight in the step S02, comprising:
Arest neighbors value is chosen using k-nearest neighbor, by maenvalueIt is compared with the every group of sample to be tested classified
And RSSI similarity distance is calculated, RSSI value corresponding to k the smallest RSSI similarity distances, selects probability of occurrence most before selecting
High RSSI value obtains N group arest neighbors value by k-nearest neighbor；Calculate revised weight wij,Wherein, SijBeaconing nodes B is received for unknown nodei,BjSignal strength ratio, Sij
=Ri/Rj, i=1 ..., N, j=1 ..., N, i ≠ j,For correction factor, N is the number of beaconing nodes.
In preferred technical solution, the calculate node coordinate
Wherein, xBj、yBjFor beaconing nodes BjCoordinate.
The invention also discloses a kind of weighted mass center positioning systems of environment self-adaption, comprising:
Data reception processing unit acquires the signal strength RSSI of beaconing nodes, according to RSSI's and signal propagation distance d
Signal propagation distance d is calculated in relational model；
Modified weight unit distributes different beaconing nodes different weights using the characteristic that k-nearest neighbor arest neighbors is classified
Correction factor corrects weight；
Coordinate calculating unit, the coordinate of calculate node.
In preferred technical solution, the data reception processing unit includes RSSI data processing unit, beaconing nodes week
Phase property sends self information to surrounding；The information that the beaconing nodes are broadcasted repeatedly is recorded to the same beaconing nodes, and right
The beaconing nodes signal strength RSSI received carries out ascending order and finds out its average value, and identical value is grouped and is calculated
The number m of every group of identical valuei。
In preferred technical solution, the amendment weight of the modified weight unit, comprising:
Arest neighbors value is chosen using k-nearest neighbor, by maenvalueIt is compared with the every group of sample to be tested classified
And RSSI similarity distance is calculated, RSSI value corresponding to k the smallest RSSI similarity distances, selects probability of occurrence most before selecting
High RSSI value obtains N group arest neighbors value by k-nearest neighbor；Calculate revised weight wij,
Wherein, SijBeaconing nodes B is received for unknown nodei,BjSignal strength ratio, Sij=Ri/Rj, i=1 ..., N, j=
1 ..., N, i ≠ j,For correction factor, N is the number of beaconing nodes.
In preferred technical solution, calculate node coordinate in the coordinate calculating unit
Wherein, xBj、yBjFor beaconing nodes BjCoordinate.
Compared with prior art, the invention has the advantages that
1, the present invention passes through k-nearest neighbor dynamic corrections weighted mass center method on the basis of being based on weighted mass center location algorithm
In weighted factor, avoid weight selection irrationality, thus realize environment self-adaption position.Universality is strong, robustness
Height, positioning accuracy are improved significantly.
2, positioning device of the present invention is simple, without excessive hardware node, saves and positions cost, low power mode of operation,
Area coverage is wide.Simultaneously in complicated indoor environment, it is capable of providing accurate reliable location information.
Detailed description of the invention
The invention will be further described with reference to the accompanying drawings and embodiments:
Fig. 1 is RSSI information collection flow chart of the present invention；
Fig. 2 is that serial data of the present invention reads flow chart；
Fig. 3 is RSSI flow chart of data processing figure of the present invention；
Fig. 4 is environment self-adaption algorithm implementation flow chart of the present invention；
Fig. 5 is the weighted mass center positioning flow figure of environment self-adaption of the present invention；
Fig. 6 is modified weight flow chart of the present invention.
Specific embodiment
In order to make the objectives, technical solutions and advantages of the present invention clearer, With reference to embodiment and join
According to attached drawing, the present invention is described in more detail.It should be understood that these descriptions are merely illustrative, and it is not intended to limit this hair
Bright range.In addition, in the following description, descriptions of well-known structures and technologies are omitted, to avoid this is unnecessarily obscured
The concept of invention.
Embodiment:
Positioning software part first is mainly made of slave computer and host computer, and slave computer is mainly realized and adopted to nodal information
The coordinate being positioned to finally is shown on interface by collection and upload function, host computer mainly to the realization of innovatory algorithm.
Location hardware is realized on the CC2640 node module of TI, and the exploitation of slave computer is mainly in BLE protocol stack
It completes, BLE protocol stack is operated in TI-RTOS system, and TI-RTOS kernel is the customization version of SYS/BIOS, as a reality
When, seize, the operating system of multithreading is in combination with synchronous and scheduling tool (XDC Tools).SYS/BIOS inner core managing four
The execution thread of a level: hardware interrupts, software interrupt, task and hiding idle function.System electrification is first from main ()
Function brings into operation, and is mainly made of hardware initialization, creation task and enabled interruption, starting BIOS.Host scanner program thing
Part is created in main task, has detected whether that GAP_DEVICE_INFO_EVENT event is touched by continuous poll in main task
Hair passes through serial ports function if any the information collection for then entering execution beaconing nodes inside the event, and by the information finally acquired
SerialPrintValue () output, Fig. 1 is the work flow diagram of data information acquisition.
In the finder that the present invention designs, host computer exchanges data with CC2640 host node by serial ports.Beacon section
Point constantly sends position and the id information of itself, and unknown node constantly acquires the information of the beaconing nodes of surrounding, then unknown section
For point by being communicated with host node, the packet information received is passed to upper computer end by serial ports by host node.It is upper
The data received are carried out data processing by generator terminal, weight correction factor eventually by k-nearest neighbor dynamic acquisition.It is broadly divided into serial ports
Data receipt unit, RSSI data processing unit, k-nearest neighbor realize unit, specific as follows:
(1) serial data receiving unit
It is emphasis how the data host computer that the transmission of the next generator terminal comes up receives processing, realizes that serial ports reads journey by C#
The corresponding parameter of setting, the general serial ports parameter being arranged with slave computer as, herein selection may be selected in host computer interface for sequence
Parameter is as follows: port can choose COM1-COM10, Configuration of baud rate 115200, stop position 1, data bit 8, no odd even
Check bit.DataReceived event, trigger event after serial ports receives data are defined, then the data received are placed on reception
In byte arrays, complete this group of data to be received such as 100ms that be delayed before receiving this group of data receive next group again, and flow chart is such as
Shown in Fig. 2.
(2) RSSI data processing unit
Since the RSSI value received from serial ports is of different sizes, so to carry out data processing classification to RSSI value.In C#
It is middle to carry out ascending sort with Array.Sort (rssiArr) function, average value is then obtained by rssi=Sum/Count, is led to
One group can be placed on for every group of identical value and find out the number m of every group of identical value by crossing LINQ in .NET and operating to data seti, and
Establish each beaconing nodes information aggregate, by GetDistByRssi () function can calculate RSSI average value it is corresponding away from
From value, RSSI mean value respective distances set, detailed process is as shown in Figure 3.
(3) k-nearest neighbor realizes unit
The distance value d gone out in (2) by RSSI mean value computationiWeight as i-th of node.To improve weighted mass center
The performance of location algorithm is that different beaconing nodes distribute different correction factors by k-nearest neighbor.The innovatory algorithm of this paper was realized
Journey are as follows: dll file generated by C# Calling MATLAB first passes to the numerical value array for sequencing sequence in C# and having screened
MATLAB function interface calls directly k nearest neighbor classifier functions in modified hydrothermal process programThe highest value Predict_rssii of probability of occurrence is sought, by Predict_rssii generation
Enter correction factors formula (2) and (3) can get the correction factor of different beaconing nodes, to realize the amendment to weight.Then
The revised weight of each beaconing nodes was placed in W [], unknown node can be found out according to improved weighted mass center algorithm routine
Coordinate Est_Target.x, Est_Target.y, detailed process is as shown in Figure 4.
Wireless signal can be obtained by radio signal propagation principle in a kind of weighted mass center localization method of environment self-adaption
The relational model of received signal strength RSSI and signal propagation distance d when being propagated in non-free space.This method is using most
Small square law is fitted the relational model, by repeatedly measuring RSSI and d in actual environment, seeks current environment signal biography
Broadcast the A and n in model.Then according to distance dependent between the value and reference mode and unknown node of weighting correction factor n, i.e., not
Know that node is closer with beaconing nodes, weighting correction factor distribution is higher；Unknown node is remoter with beaconing nodes, weights correction factor
Distribute lower conclusion.Reasonable modified weight coefficient finally is distributed to different beaconing nodes using k-nearest neighbor sort feature, is made
The positioning accuracy of weighted mass center algorithm further increases.
Parameter A relevant to actual environment and path loss coefficient n are fitted by least square method, can generally be passed through
It measures the received signal strength RSSI at range transmission node 1m and obtains A value, it will be according to varying environment for path loss constant n
Take different values.The multiple RSSI of interval measurement at a certain distance of the straight line far from transmitting node are usually taken, minimum two is then passed through
Multiplication fitting, seeks the parameter A and n in signal model.
As shown in Figure 5,6, the main of reasonable weighting correction factor is distributed to different beaconing nodes using k nearest neighbor classification
Steps are as follows:
(1) beaconing nodes periodically send the information in relation to itself to surrounding, and information is mainly node ID, itself position
The information such as set；
(2) unknown node receives the information of beaconing nodes, and it is wide repeatedly to record beaconing nodes institute to the same beaconing nodes
The information broadcast, and ascending order is carried out to the beaconing nodes signal strength RSSI received and finds out its average value, by identical value
The number for being grouped and calculating every group of identical value is mi；
(3) chooses arest neighbors value using k-nearest neighbor, and k value is 3.By maenvalueIt is to be measured with classified every group
Sample compares and calculates RSSI similarity distance, RSSI value corresponding to k the smallest RSSI similarity distances, choosing before selecting
The highest RSSI value of probability of occurrence is selected, N group arest neighbors value P can be obtained by above-mentioned k-nearest neighbornr={ (ID1,R1),(ID2,
R2),...,(IDi,Ri)}；IDiFor the ID of i-th of node, RiFor the RSSI value of i-th of node.
(4) distance of obtained unknown node to each reference mode is d by, and substitutes into environment self-adaption weighted mass center
Algorithmic formula (1), (2), (3), can obtain unknown node coordinate are as follows:
Sij=Ri/Rj, i=1 ..., N, j=1 ..., N, i ≠ j (3)
Wherein, xBj、yBjFor beaconing nodes BjCoordinate, wijFor weighted value, SijBeaconing nodes are received for unknown node
Bi,BjSignal strength ratio, N is the number of beaconing nodes,For correction factor.
It should be understood that above-mentioned specific embodiment of the invention is used only for exemplary illustration or explains of the invention
Principle, but not to limit the present invention.Therefore, that is done without departing from the spirit and scope of the present invention is any
Modification, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.In addition, appended claims purport of the present invention
Covering the whole variations fallen into attached claim scope and boundary or this range and the equivalent form on boundary and is repairing
Change example.
Claims (8)
1. a kind of weighted mass center localization method of environment self-adaption, which comprises the following steps:
S01: acquiring the signal strength RSSI of beaconing nodes, is calculated according to the relational model of RSSI and signal propagation distance d
Signal propagation distance d；
S02: different beaconing nodes are distributed with different modified weight coefficients using the characteristic that k-nearest neighbor arest neighbors is classified, is corrected
Weight；
S03: the coordinate of calculate node.
2. the weighted mass center localization method of environment self-adaption according to claim 1, which is characterized in that the step S01
In beaconing nodes periodically to surrounding send self information；It is wide that beaconing nodes institute is repeatedly recorded to the same beaconing nodes
The information broadcast, and ascending order is carried out to the beaconing nodes signal strength RSSI received and finds out its average value, by identical value
It is grouped and calculates the number m of every group of identical valuei。
3. the weighted mass center localization method of environment self-adaption according to claim 2, which is characterized in that the step S02
In amendment weight, comprising:
Arest neighbors value is chosen using k-nearest neighbor, by maenvalueIt compares and counts with the every group of sample to be tested classified
RSSI similarity distance is calculated, RSSI value corresponding to k the smallest RSSI similarity distances, selects probability of occurrence highest before selecting
RSSI value obtains N group arest neighbors value by k-nearest neighbor；Calculate revised weight wij,Its
In, SijBeaconing nodes B is received for unknown nodei,BjSignal strength ratio, Sij=Ri/Rj, i=1 ..., N, j=
1 ..., N, i ≠ j,For correction factor, N is the number of beaconing nodes.
4. the weighted mass center localization method of environment self-adaption according to claim 3, which is characterized in that the calculate node
Coordinate
Wherein, xBj、yBjFor beaconing nodes BjCoordinate.
5. a kind of weighted mass center positioning system of environment self-adaption characterized by comprising
Data reception processing unit acquires the signal strength RSSI of beaconing nodes, according to the relationship of RSSI and signal propagation distance d
Signal propagation distance d is calculated in model；
Modified weight unit distributes different beaconing nodes different modified weights using the characteristic that k-nearest neighbor arest neighbors is classified
Coefficient corrects weight；
Coordinate calculating unit, the coordinate of calculate node.
6. the weighted mass center positioning system of environment self-adaption according to claim 5, which is characterized in that the data receiver
Processing unit includes RSSI data processing unit, and beaconing nodes periodically send self information to surrounding；To the same beacon
Node repeatedly records the information that the beaconing nodes are broadcasted, and carries out ascending order to the beaconing nodes signal strength RSSI received
And its average value is found out, identical value is grouped to and is calculated the number m of every group of identical valuei。
7. the weighted mass center positioning system of environment self-adaption according to claim 6, which is characterized in that the modified weight
The amendment weight of unit, comprising:
Arest neighbors value is chosen using k-nearest neighbor, by maenvalueIt compares and counts with the every group of sample to be tested classified
RSSI similarity distance is calculated, RSSI value corresponding to k the smallest RSSI similarity distances, selects probability of occurrence highest before selecting
RSSI value obtains N group arest neighbors value by k-nearest neighbor；Calculate revised weight wij,Wherein,
SijBeaconing nodes B is received for unknown nodei,BjSignal strength ratio, Sij=Ri/Rj, i=1 ..., N, j=1 ...,
N, i ≠ j,For correction factor, N is the number of beaconing nodes.
8. the weighted mass center positioning system of environment self-adaption according to claim 7, which is characterized in that the coordinate calculates
Calculate node coordinate in unit
Wherein, xBj、yBjFor beaconing nodes BjCoordinate.
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