CN108111966A - A kind of indoor orientation method based on compressed sensing and neighbour's difference comparsion - Google Patents
A kind of indoor orientation method based on compressed sensing and neighbour's difference comparsion Download PDFInfo
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- CN108111966A CN108111966A CN201611044836.7A CN201611044836A CN108111966A CN 108111966 A CN108111966 A CN 108111966A CN 201611044836 A CN201611044836 A CN 201611044836A CN 108111966 A CN108111966 A CN 108111966A
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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
- H04W4/02—Services making use of location information
- H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
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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
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- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
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- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
Abstract
The invention discloses a kind of indoor orientation methods based on compressed sensing and neighbour's difference comparsion, this method builds indoor grid map first, unknown node signal strength is gathered by anchor node, the signal strength values collected are formed into compression sampling vector, restructing algorithm is tracked by base again and accurately recovers original signal, the sequence number for recovering signal greatest member is the grid sequence number residing for unknown node, and unknown node coarse positioning is grid element center coordinate；Then it is compared using the RSSI differences of the adjacent anchor node of unknown node, obtains being accurately positioned for unknown node.The present invention program is combined by compressed sensing with neighbour's difference comparsion, unknown node position is accurately positioned by known anchor node, reduce compressed sensing quantification gradation, reduce computation complexity, the accuracy of positioning is improved simultaneously, in addition the program avoids the increase of cost without additionally being aided in using other measuring apparatus or instrument.
Description
Technical field
The present invention relates to indoor orientation methods, and in particular to determines a kind of interior based on compressed sensing and neighbour's difference comparsion
Position method.
Background technology
Surely belong to global positioning system using most convenient alignment system at present, not only positioning accuracy is high, round-the-clock, but also
Strong interference immunity, therefore it is a kind of feasible solution to carry out node location estimation using it.But from volume, cost and consumption
Energy etc. is many-sided to be considered, seems inapplicable in the wireless sensor network of large scale deployment.Also, in wireless sensor network
Indoor application in, due to the masking of building, cause indoor GPS signal weaker and can not realize and be accurately positioned.In recent years, with
Integrated mill to the transformation of production of intelligent, it is necessary to the foundation of location information service and various large stadiums, to indoor position
It puts that the demand of information service is also growing, so the research of indoor positioning technologies causes the extensive concern of people, becomes related
One research hotspot of technical field.Sensor node is due to its customizable, small size and the characteristic for being easy to networking so that base
There is unique advantage in the indoor positioning technologies of wireless sensor network.
Being currently based on the location technology of wireless sensor network, there is antijamming capability, real-time and energy consumptions etc.
Deficiency.Because during locating information acquisition, there are the acquisition and processing of mass data, in addition, sensor, which exists, calculates energy
The weakness of power, storage and limited battery capacity.A kind of novel signal acquisition for being referred to as compressed sensing, processing method, can be upper
It states problem and solution is provided.It is openness or being capable of sparse table on a certain transform domain that its principle is that original signal needs to have
Show, measurement condensation matrix can recover original signal under the conditions of constraint isometry is met with maximum probability.It breaches traditional
The limitation of nyquist sampling theorem employs intelligence sample and is sampled instead of classical signal, therefore can be much smaller than Nai Kuisi
In the case of special sampling rate, obtain sampled signal and pass through restructing algorithm and signal is recovered with maximum probability.Utilize compressed sensing
During theory acquisition signal, intelligence sample and compression can be completed simultaneously, reduces the acquisition of hash, is then dropped in position fixing process
Low data volume and memory space, while reduce sensor energy consumption.In addition, the main calculation amount of compressed sensing concentrates on signal
Reconstruction stage, information gathering stage calculation amount is smaller, meets the demand of wireless sensor network positioning just.However compression sense
Know algorithm complexity, when building extensive area network, system real time will be subject to serious challenge.
Paper " research and realization of the RSS indoor locating systems based on compressed sensing, 2011, doctor's thesis, Feng
Orientation problem, is described as the reconstruction of sparse signal by occasion ", thus alignment system according only to terminal device to coming from a small amount of nothing
The measurement of line access point RSS, applied compression perception principle solve to realize location estimation by a L1 norm minimum.It examines
Consider the time-varying characteristics of RSS, algorithm uses different coarse positioning mechanisms and different AP selection mechanisms, to improve the stabilization of system
Property and stability.
Paper " the radio frequency indoor positioning algorithm research based on LANDMARC and compressed sensing, 2015, master's thesis,
Ma Jun " proposes a kind of two section type location algorithm being combined based on LANDMARC area lockings and the locking of compressed sensing position.It is first
First, zone location, quick lock in target region scope are realized using LANDMARC location algorithms；It is re-introduced into compressed sensing reason
By, in the region of locking, establish virtual reference label, determine calculation matrix scale using regularization orthogonal matching pursuit compress
Sensing reconstructing algorithm obtains the location information of target.
The current above method all goes then to reuse compression into the preliminary judgement of row position in advance using additional means
Cognitive method is accurately positioned, however the above method is required for additional equipment (handheld terminal, RFID label tag etc.) progress auxiliary
Primary Location is helped, is unfavorable for large-scale promotion and application.
The content of the invention
Present invention aims to overcome that the deficiencies in the prior art, especially solve the existing indoor positioning based on compressed sensing
Method computation complexity is high, can not realize large-scale precision indoor orientation problem.
In order to solve the above technical problems, the present invention adopts the following technical scheme that：
A kind of indoor orientation method based on compressed sensing and neighbour's difference comparsion, this method comprise the following steps, wherein：
S1：Create indoor grid map.According to indoor environment and the position of anchor node, according to specified step-length by indoor ring
Border is divided into grid map.
S2：Dynamic calculation matrix is built.Using the anchor node around unknown node, dynamic is obtained according to signal propagation principle
Calculation matrix.
S3：Compressed sensing refactoring localization.Unknown node signal strength, the signal strength that will be collected are gathered by anchor node
Value forms compression sampling vector, then tracks restructing algorithm by base and accurately recover original signal, recovers the sequence of signal greatest member
Number it is the grid sequence number residing for unknown node, unknown node coarse positioning is grid element center coordinate.
S4：Neighbour's difference comparsion.For the unknown node of coarse positioning, grid element center is obtained to the RSSI of adjacent anchor node
Value, corresponding with compression sampling vector RSSI value form squared difference and, the corresponding mesh coordinate of gained minimum value is not
Know being accurately positioned for node.
The advantageous effect of the scheme of the invention is, is combined by compressed sensing with neighbour's difference comparsion, is felt by compressing
Know and coarse positioning first is carried out to unknown node position, provide the approximate region of unknown node, then pass through optimal anchor node and unknown section
Point neighbour grid carries out difference comparsion, is accurately positioned, and reduces the complexity that compressed sensing accurately calculates, and it is real to improve system
Shi Xing avoids compressed sensing and calculates this complicated defect, while the program is without additionally using other measuring apparatus or instrument
It is aided in, avoids the increase of cost.
Description of the drawings
Fig. 1 is a kind of indoor orientation method flow chart based on compressed sensing and neighbour's difference comparsion of the present invention.
Fig. 2 is unknown node in the specific embodiment of the present invention with there is the anchor node schematic diagram of correspondence.
Fig. 3 is unknown node borderline region schematic diagram in the specific embodiment of the present invention.
Fig. 4 is neighbour's difference comparsion schematic diagram in the specific embodiment of the present invention.
Specific embodiment
Below in conjunction with the accompanying drawings and specific embodiment the present invention is carried out in further detail with complete explanation.It is appreciated that
It is that specific embodiment described herein is only used for explaining the present invention rather than limitation of the invention.
As shown in Figure 1, a kind of indoor orientation method based on compressed sensing and neighbour's difference comparsion, this method includes following
Step：
S1：Create indoor grid map.According to indoor environment and the position of anchor node, according to specified step-length by indoor ring
Border is divided into grid map.
As shown in Fig. 2, in localization region, M sensor node is evenly arranged as anchor node, the space bit of anchor node
Put it is known that then using anchor node set China and foreign countries border maximum be anchor node as boundary point, construct the grid of 2 dimensional region, grid
Step-length is b, shares N number of small grid.
S2：Dynamic calculation matrix is built.Using the anchor node around unknown node, dynamic is obtained according to signal propagation principle
Calculation matrix.
There are multiple anchor nodes for having correspondence therewith near unknown node, can be obtained according to signal propagation principle
Calculation matrix Φ=Ms × N is represented as follows：
Wherein Pi,jI-th of anchor node is measured to the signal strength of j-th of grid element center for signal propagation model, i ∈ [1,
Ms], MsTo there is the anchor node total number of correspondence with unknown node, j ∈ [1, N], N are the possibility corresponding to unknown node
Area grid divides total number.D is anchor node to the Euclidean distance of unknown node, can be represented with formula 2, wherein anchor node E, not
The coordinate for knowing node F points is respectively (xe,ye)、(xf,yf)；d0For reference distance, 1m is set to；P0For unknown node under reference distance
Transmitting signal strength, be set to -35dB；η and δ is path-loss factor, and with the difference of environment, value is different.It thus can be with
Obtaining each and unknown node has the anchor node of correspondence to the calculation matrix Φ of N number of grid element center.
S3：Compressed sensing refactoring localization.
As shown in figure 3, meshes number N is generally far larger than 1, so thus constructing the s=[s that degree of rarefication is 11,s2,...,
sN] input signal of the vector as compressed sensing, s in formulan=0 or 1 (n=1,2 ..., N), when there is unknown section in n-th of grid
During point, sn=1, otherwise sn=0.There to be the M of correspondence with unknown nodesA anchor node is sensed as the measurement of calculation matrix
Device measures each measurement sensor to the signal strength values (RSSI) of grid element center using signal propagation model, thus obtains Ms
A 1 × N-dimensional vector, can be by this MsA vector is configured to MsThe calculation matrix of × N, by MsA sensor measures the letter of unknown node
Number intensity forms MsThe compression sampling vector y=(y of × 1 dimension1,y2,...,yMs), wherein yiRepresent the i-th (i ∈ [1, Ms]) a anchor
Node measures the signal strength values of unknown node, works as Ms＞ MmaxWhen, M before being chosen from compression sampling vector ymaxA measured value structure
The compression sampling vector y of Cheng Xinnew, according to equation 3 below to input signalRecovered：
Wherein ε represents the energy of noise., the sequence number for recovering signal greatest member is the grid sequence residing for unknown node
Number, unknown node coarse positioning is grid element center coordinate.
S4：Neighbour's difference comparsion.As shown in figure 4, for unknown node coarse positioning in grid w0Afterwards, select compression sampling to
Measure the corresponding 4 anchor node M of 4 values maximum in y1,M2,M3,M4As optimal anchor node, net is obtained by calculation matrix Φ
Lattice w0And 8 grid element center { w of surrounding1,w2,...w8To 4 optimal anchor node M1,M2,M3,M4RSSI vector, 9 obtained
A 4 × 1 dimension RSSI vectors yij=[yM1,j,yM2,j,yM3,j,yM4,j] (j=0,1,2 ..., 8), respectively with compression sampling vector
The new vector y that 4 maximum RSSI are formed in yfIt makes the difference, acquires squared difference and be described as follows with formula 4：
Take DjMinimum value corresponding to grid element center coordinate j as positioning result.Assuming that target institute can be accurately positioned
In grid, then the worst average localization error of system is b/2, and b is mesh spacing.
It is above-mentioned for the preferable embodiment of the present invention, but embodiments of the present invention and from the limitation of the above,
His any Spirit Essence without departing from the present invention with made under principle change, modification, replacement, combine, simplification, should be
The substitute mode of effect, is included within protection scope of the present invention.
Claims (1)
1. a kind of indoor orientation method based on compressed sensing and neighbour's difference comparsion, which is characterized in that comprise the following steps：
S1：Indoor grid map is created, according to indoor environment and the position of anchor node, is drawn indoor environment according to specified step-length
It is divided into grid map；
S2：Dynamic calculation matrix is built, and using the anchor node around unknown node, is obtained dynamic according to signal propagation principle and is measured
Matrix；
S3：Compressed sensing refactoring localization gathers unknown node signal strength, the signal strength values structure that will be collected by anchor node
Into compression sampling vector, then restructing algorithm is tracked by base and accurately recovers original signal, the sequence number for recovering signal greatest member is
It is the grid sequence number residing for unknown node, unknown node coarse positioning is grid element center coordinate；
S4：Neighbour's difference comparsion for the unknown node of coarse positioning, obtains grid element center to the RSSI value of adjacent anchor node,
Corresponding with compression sampling vector RSSI value form squared difference and, the corresponding mesh coordinate of gained minimum value is unknown section
Point is accurately positioned.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109661030A (en) * | 2018-12-07 | 2019-04-19 | 南京工业大学 | Unknown object location algorithm in wireless sensor network based on dynamic grid |
| CN109862510A (en) * | 2019-03-28 | 2019-06-07 | 合肥工业大学 | A kind of compressed sensing based convex domain localization method |
| US10761200B1 (en) | 2019-02-27 | 2020-09-01 | Osram Gmbh | Method for evaluating positioning parameters and system |
-
2016
- 2016-11-24 CN CN201611044836.7A patent/CN108111966A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109661030A (en) * | 2018-12-07 | 2019-04-19 | 南京工业大学 | Unknown object location algorithm in wireless sensor network based on dynamic grid |
| CN109661030B (en) * | 2018-12-07 | 2020-11-13 | 南京工业大学 | Unknown target positioning algorithm based on dynamic grid in wireless sensor network |
| US10761200B1 (en) | 2019-02-27 | 2020-09-01 | Osram Gmbh | Method for evaluating positioning parameters and system |
| CN109862510A (en) * | 2019-03-28 | 2019-06-07 | 合肥工业大学 | A kind of compressed sensing based convex domain localization method |
| CN109862510B (en) * | 2019-03-28 | 2020-11-24 | 合肥工业大学 | Convex region positioning method based on compressed sensing |
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