CN104080169B - A kind of underwater wireless sensor network dynamic self-adapting localization method - Google Patents
A kind of underwater wireless sensor network dynamic self-adapting localization method Download PDFInfo
- Publication number
- CN104080169B CN104080169B CN201410328637.3A CN201410328637A CN104080169B CN 104080169 B CN104080169 B CN 104080169B CN 201410328637 A CN201410328637 A CN 201410328637A CN 104080169 B CN104080169 B CN 104080169B
- Authority
- CN
- China
- Prior art keywords
- mrow
- node
- msub
- coordinate
- estimation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Landscapes
- Position Fixing By Use Of Radio Waves (AREA)
Abstract
The invention discloses a kind of underwater wireless sensor network dynamic self-adapting localization method, belong to underwater wireless sensor network field of locating technology, comprise the following steps：Step 1: the degree of accuracy of anchor node online evaluation itself statement position coordinates；Step 2: node structure dynamic updatable positioning group to be positioned；Step 3: positioning group carries out the multi-hop distance estimations of deviation adaptively correcting；Step 4: solve node location holding space to be positioned；Step 5: node coordinate estimation to be positioned and accuracy evaluation.The present invention passes through the degree of accuracy online evaluation to anchor node statement position coordinates and demarcation, reduce adverse effect of the anchor node site error to positioning performance, pass through the multi-hop distance estimations of deviation adaptively correcting, improve the accuracy of multi-hop distance estimations, online cognition and dynamic self-adapting ability of the sensing net node to complicated localizing environment are enhanced, improves the positioning performance of underwater wireless sensor network.
Description
Technical field
The invention belongs to underwater wireless sensor network field of locating technology, particularly towards complicated ocean environmental applications
Underwater wireless sensor network node is self-positioning, specially a kind of underwater wireless sensor network dynamic self-adapting localization method.
Background technology
Underwater wireless sensor network (Underwater Wireless Sensor Networks) is that marine information perceives
And a revolution of application field, extensive random placement execution information in complicated marine environment are perceived, gather, handle and passed
The task such as defeated is that it is typically applied, such as naval battle field Situation Awareness, marine environmental monitoring, submarine target locating and tracking etc..Section
It is the critical support technology of underwater sensing net that point is self-positioning, and the premise of such application, because perceived information generally needs
The positional information for having node accompanies.During self-positioning, node to be positioned needs to rely on corresponding reference information, such as joins
Examine distance of the coordinate of node, node to be positioned to reference mode etc..Ideally, anchor node position and node are often assumed that
Between measurement distance etc. reference information it is accurate.But in actual applications, the reference information needed for position fixing process can be inevitably
Influenceed by complicated marine environment, multi-source does not know that the appearance of noise will seriously reduce the performance of alignment system, specific next
Say：By the anchor node position deviation that self performance is limited and environmental disturbances occur, limited by node range capability and multipath is imitated
Range error caused by should waiting, the multi-hop accumulated error for being influenceed to occur by the network condition of reference link etc..
Existing location algorithm assumes that anchor node site error obeys normal state with range error according to central-limit theorem mostly
Distribution, or directly assume to obey preferable zero-mean gaussian distribution, and then Error processing is carried out using least square method thought,
To reduce the influence to positioning precision.But the actual effect of this kind of method is often not very managed in complicated ocean environmental positioning
Think.Reason has three：Firstth, for the deployed environment of underwater sensing net, we are difficult to obtain all noises in advance to be accurately distributed
Rule and characteristic parameter, error Normal Distribution even standardized normal distribution is directly assumed in the case of prior information deficiency
It is irrational；Secondth, some scholars consider the problem of prior information deficiency, and noise characteristic ginseng is carried out using Monte Carlo method
Several statistical inferences, but this kind of location algorithm based on traditional statistical method On-line testing noise characteristic needs to rely on mass efficient
Measurement sample, and for the underwater sensing net node of resource critical constraints, great amount of samples is obtained by duplicate measurements
Mode its cost be unaffordable；3rd, above two method is all based on more source noise Gaussian distributeds such one
It is individual it is assumed that but actually not obeying Gauss point strictly with a variety of noises of complex environment position fixing process
Therefore, it is necessary to for prior information is insufficient, multi-source noise profile rule is not known present in position fixing process,
The problems such as effective ranging sample number deficiency, exploring one kind can overcome caused by reference information inaccuracy not under condition of small sample
Profit influences and has the underwater wireless sensor network section of relatively strong online cognition and dynamic self-adapting ability to complicated localizing environment
The self-positioning new method of point.
The content of the invention
It is an object of the invention to provide a kind of underwater wireless sensor network dynamic self-adapting localization method, solves multi-source not
Know under influence of noise while anchor node site error be present and the underwater wireless sensor network section of multi-hop distance estimations deviation
The self-positioning problem of point；Under condition of small sample, by stating that anchor node the degree of accuracy of position coordinates carries out online evaluation and mark
It is fixed, influence of the anchor node site error to positioning performance is reduced, by the multi-hop distance estimations of deviation adaptively correcting, reduces section
Influence of the point multi-hop distance estimations deviation to positioning performance, strengthens online cognition under node complexity localizing environment and dynamic is adaptive
Should be able to power, improve the positioning performance of underwater wireless sensor network.
The present invention proposes a kind of underwater wireless sensor network dynamic self-adapting localization method, specifically includes following steps：
Step 1: the degree of accuracy of anchor node online evaluation itself statement position coordinates；
(1) all anchor nodes with the statement coordinate (or proven initial coordinate during node deployment) of initial time for base
Standard, the Euclidean distance using between the n-th renewal coordinate in the online evaluation time and reference coordinate as position bias sample point,
Gather n times (normal conditions n≤5), structure anchor node position bias sample collection；
(2) Bootstrap double samplings are carried out to position bias sample collection by the method for nonparametric sampling with replacement；
(3) the constant Bootstrap samples of successive independent B capacity of extraction；To meet the sampling of Bootstrap methods
It is required that simultaneously control node amount of calculation, normal conditions B take 200；
(4) Bootstrap sample averages are calculated, ask for standard deviation
(5) degree of accuracy η of anchor node statement position coordinates is calculatedi：Wherein R is the communication radius of node；
(6) the reference rank of anchor node is demarcated：The degree of accuracy is higher than the anchor node of default precision threshold, is demarcated as one-level reference
Node；Less than default precision threshold, it is demarcated as common anchor node；Default precision threshold normal conditions are taken in [0.95,1] section
Real number；
Step 2: node structure dynamic updatable positioning group to be positioned；
(1) the one-level reference node points that node statistics to be positioned itself one are skipped in multi-hop threshold range, reach coordinate and estimate
During the minimum quantity of calculation, all one-level reference modes and the common intermediate node for participating in information forwarding are included into positioning group, and turn
Step 3；Less than coordinate estimation minimum quantity, it is demarcated as being unsatisfactory for location condition node；Multi-hop threshold value normal conditions take less
In 5 natural number；The minimum quantity of coordinate estimation is 4 under three-dimensional deployment scenario, is 3 under two-dimentional deployment scenario；
(2) when the position coordinates degree of accuracy of one-level reference mode is decreased below default precision threshold, it is marked again
It is set to common anchor node, and eliminates positioning group；
(3) ordinary node obtains position coordinates and precision is higher than default precision threshold, is demarcated as two level reference mode；
In new locating periodically, it is unsatisfactory for location condition node and scope of statistics is expanded to two level reference mode, when I and II reference node
When points sum reaches the minimum quantity of coordinate estimation, structure positioning group, and go to step three；Otherwise, it is next fixed to continue waiting for
Bit period；
Step 3: positioning group carries out the multi-hop distance estimations of deviation adaptively correcting；
(1) the original ranging sample set between node and reference mode to be positioned is built, and calculates the equal of original ranging sample
Value；The original ranging sample number of normal conditions is not more than 5；
(2) the secondary Bootstrap double samplings of B ' are independently carried out in succession to original ranging sample set, and calculates its average；Weight
Frequency in sampling B ' normal conditions take 200；
(3) estimate deviation is examined WhereinFor j-th of Bootstrap sample
Average,For the average of original ranging sample；IfShow Estimation of Mean unbiased；IfShow
Value estimation is higher；IfThen show that Estimation of Mean is relatively low；
(4) estimator of offset correction is calculated WillBig row is arrived from childhood
Sequence, obtain
(5) the distance estimations bound after adaptively correcting is calculated, the multi-hop that confidence level is 1- α is asked for and estimates apart from section
Meter：Take Q1ForInteger part, i.e.,Take Q2ForExtractWithAs lower bound of the multi-hop apart from interval estimation and the upper bound；
Step 4: the node location holding space to be positioned of solution interval；
(1) one kind being made up of the distance between node to be positioned and neighboring reference node estimation interval bound is extracted
Constraint space；
(2) two classes that extraction is made up of the multi-hop distance estimations between node to be positioned and multi-hop reference mode constrain empty
Between；The upper bound of two class constraint spaces is the upper bound in multi-hop distance estimations section, and the lower bound of constraint space is communication radius and multi-hop
Less one between the lower bound of distance estimations section, extraction formula is：
Wherein, Sja(x) it is node N to be positionedaWith multi-hop reference mode NjThe two class constraint spaces formed, R is node
Communication radius,For the lower bound in multi-hop distance estimations section, | | Xj-Xa||2For NaWith NjBetween Euclidean distance,
For the upper bound in multi-hop distance estimations section；
(3) one kind all in group will be positioned, two class constraint spaces are replaced with interval number；Ask for the friendship of all interval numbers
Collection, and as the position holding space of node interval to be positioned；
Step 5: node coordinate estimation to be positioned and accuracy evaluation；
(1) the position holding space after node interval to be positioned is scanned, asks for each interval number subset in space
Center, and as the sample set of node coordinate to be positioned；The optimal point estimation of node coordinate to be positioned is asked for, is calculated
Formula is：
Wherein,For node N to be positionedaThe optimal point estimation of coordinate,For one in node coordinate sample set to be positioned
Individual sample, XiTo position group's internal reference node NiCoordinate, d 'aiFor the distance estimations intermediate value after adaptively correcting ΩaIt is the position holding space behind section；
(2) accuracy evaluation is carried out to the estimated coordinates of node；Positioning precision is higher than default precision threshold, is demarcated as two level
Reference mode, and go to step two；Otherwise, it is demarcated as having completed positioning node, this estimated coordinates is final coordinate；Precision is commented
The formula estimated is：
Wherein, ηaFor positioning node NaThe Evaluation accuracy of estimated coordinates, ηkTo participate in k reference node of positioning in positioning group
The positioning precision of point, Xa=[xa,ya,za]TFor node N to be positionedaEstimated coordinates, Xi=[xi,yi,zi]TSat for reference mode
Mark,
The advantage of the invention is that：
(1) present invention proposes a kind of underwater wireless sensor network dynamic self-adapting localization method, by anchor node sound
The online evaluation of the bright coordinate degree of accuracy, reduce the adverse effect that anchor node site error treats the estimation of positioning node coordinate；Institute
The Bootstrap small sample methods of estimation of use require no knowledge about the distribution of sample space, it is only necessary at one compared with small sample number
Relevant parameter can be estimated on the basis of amount, the energy expenditure of sample collection has not only been reduced but also has improved the actual effect of online evaluation.
(2) present invention proposes a kind of underwater wireless sensor network dynamic self-adapting localization method, by more hop distances
Estimation carries out deviation adaptively correcting, reduces adverse effect of the multi-hop distance estimations error to positioning performance, improves coordinate
The precision of estimation, enhance dynamic self-adapting ability of the node under complicated localizing environment.
(3) present invention proposes a kind of underwater wireless sensor network dynamic self-adapting localization method, by section to be positioned
The position holding space of point carries out section processing, reduces computation complexity, enhances the practicality of system.
Brief description of the drawings
Fig. 1 is a kind of underwater wireless sensor network dynamic self-adapting localization method implementation steps flow proposed by the present invention
Figure；
Fig. 2 is that the schematic diagram that group carries out single-hop and multi-hop distance estimations is positioned in the present invention；
Fig. 3 is node N to be positioned in the present inventionaPosition holding space schematic diagram；
Fig. 4 is to node N in the present inventionaPosition holding space carry out the schematic diagram that sectionization is handled；
Fig. 5 is the three-dimensional deployment schematic diagram of underwater wireless sensor network in the present invention；
Fig. 6 is that average localization error of the new method proposed by the present invention with conventional method under heterogeneous networks degree of communication contrasts
Figure.
Embodiment
The present invention is described in further detail below in conjunction with the accompanying drawings.The present invention is a kind of underwater wireless sensor network
Dynamic self-adapting localization method, implementation steps especially by following steps as shown in figure 1, realize：
Step 1: the degree of accuracy of anchor node online evaluation itself statement position coordinates；
(1) anchor node NiIt is (or proven initial during node deployment with the statement coordinate of each assessment cycle initial time
Coordinate) Xi (0)=[xi (0),yi (0),zi (0) ,]TOn the basis of, with n-th renewal coordinate X in online evaluation time Δ ti (n)With it is first
Beginning coordinate Xi (0)Between Euclidean distance | | Xi (n)-Xi (0)||2As a position bias sample pointCollection n times, establishes anchor
Node location bias sample collectionTo improve the ageing of assessment and controlling communication to consume, update times n
It should lack as far as possible, normal conditions n≤5；The assessment time Δ t should be controlled within a shorter period, generally correspond to 3
~5 positioning times it is cumulative；Thus the sample set V obtainediFor small sample set；
(2) to small sample set ViCarry out Bootstrap double samplings：By the method for nonparametric sampling with replacement from original sampleThe Bootstrap samples that capacity is n are repeatably extracted at random
(3) the Bootstrap samples that successive independent B capacity of extraction is n, are obtainedb
=1,2 ..., B；To meet the sampling requirement of Bootstrap methods and control node amount of calculation, the Bootstrap sample numbers B leads to
Reason condition takes 200；
(4) to b-th of Bootstrap sample, its average is calculated：B=1,2 ..., B；
(5) standard deviation deviateed anchor node position is estimated
(6) anchor node position coordinates degree of accuracy η is calculatedi：
(7) the reference rank of anchor node is demarcated：If anchor node NiηiMore than default precision threshold λ, then it is demarcated as one-level
Reference mode；Otherwise, it is demarcated as common anchor node；The default precision threshold λ, normal conditions take the reality in [0.95,1] section
Number；
Step 2: node structure dynamic updatable positioning group to be positioned；
(1) all anchor nodes broadcast one group " positioning bag ", include anchor node self ID, coordinate, the position coordinates degree of accuracy, ginseng
Examine for the levels the information such as other indications；All one group of node broadcasts " ranging bag ", by information exchange, obtain institute in itself communication context
There are the information such as ID, the neighbor distance of neighbor node；
(2) node N to be positionedaCount the one-level reference node points m in itself neighbor nodea：Such as maReach coordinate estimation
Minimum quantity, i.e. ma>=4, then node to be positioned by all neighbours' one-level reference modes include positioning group；Such as the m in the range of a jumpa
＜ 4, then the 2 one-level reference modes skipped in the range of multi-hop threshold value ζ are searched successively, until maWhen >=4, by all one-level reference nodes
Point and the common intermediate node of participation information forwarding include positioning group；Otherwise, the node is demarcated as being unsatisfactory for location condition section
Point；The multi-hop threshold value ζ, normal conditions take the natural number no more than 5；
(3) for being unsatisfactory for location condition node, with the progress of position fixing process, if in the range of its multi-hop threshold value ζ
Ordinary node obtains position coordinates and precision is higher than default precision threshold λ, then by this, positioning node has been demarcated as two level reference
Node, and count two level reference node points ni；Such as ni+mi>=4, then by all one-level reference modes in the range of ζ hop counts, two level
Reference mode and the common intermediate node of participation information forwarding include positioning group；
(4) with the progress of position fixing process, if the position coordinates of the firsts and seconds reference mode in positioning group is accurate
Degree is decreased below default precision threshold λ, then re-scales the reference mode for common anchor node or ordinary node, and reject
Go out to position group；
Step 3: positioning group carries out the multi-hop distance estimations of deviation adaptively correcting；
Node N to be positionedaWith reference mode NiThe schematic diagram of progress single-hop and multi-hop distance estimations is as shown in Fig. 2, in figure
Closed square is node to be positioned, and black circle is reference mode；
(1) node N to be positioned is builtaWith reference mode NiBetween original ranging sample set
And calculate the average of original ranging sampleThe sample number n takes the natural number no more than 5；
(2) Bootstrap double samplings are carried out to original ranging sample set：From original sample
By the method for sampling with replacement, the Bootstrap samples that capacity is n are extracted
(3) the Bootstrap samples that successive independent M capacity of extraction is n,
Wherein j=1,2 ..., M；For j-th of Bootstrap sample, its average is calculatedJ=1,2 ..., M；For
Meet the sampling requirement of Bootstrap methods and control node amount of calculation, Bootstrap sample number M normal conditions take 200；
(4) estimate deviation is examined If
Show Estimation of Mean unbiased；IfShow that Estimation of Mean is higher；IfThen show that Estimation of Mean is relatively low；
The deviation of multi-hop distance estimationsNormal conditions are more than 0；
(5) estimator of offset correction is calculated WillBig row is arrived from childhood
Sequence, obtain
(6) the distance estimations bound after adaptively correcting：Take Q1ForInteger part, i.e.,
TakeExtractionAsEstimation；
(7) the node N to be positioned that confidence level is 1- α in group is positionedaWith reference mode NiBetween multi-hop distance estimationsIt is ultimately determined to
Step 4: the node location holding space to be positioned of solution interval；
(1) extraction is by node N to be positionedaWith neighboring reference node NiThe distance between estimation interval bound formed
A kind of constraint space：
(2) extraction is by node N to be positionedaWith multi-hop reference mode NjBetween two classes that are formed of multi-hop distance estimations about
Beam space；The upper bound of two class constraint spaces is the upper bound in multi-hop distance estimations section, the lower bound of constraint space for communication radius and
Less one between the lower bound of multi-hop distance estimations section, extraction formula is：
Wherein R0For the communication radius of node；
(3) node N to be positioned is asked foraAll one kind, the common factor S of two class constraint spacesa(x), i.e. NaPosition can hold
Space：
Wherein, k is with node N to be positionedaFor the number of positioning group's internal reference node of core；
As shown in figure 3, node N to be positionedaWith reference mode NiFor neighbor node, the annulus formed for it is a kind of about
Beam space, NaWith multi-hop reference mode NjThe annulus of composition is two class constraint spaces, and dash area is two constraint spaces
Common factor part, i.e. NaPosition holding space；
(4) section NaPosition holding space：By node N to be positionedaA certain constraint space Sap(x) interval number is usedTo replace, whereinFor interval number θap ILower bound,For area
Between number θap IThe upper bound；Two interval number θap IAnd θaq ICommon factor be：
NaPosition holding space behind section is：
Node N to be positionedaThe top view of position holding space behind section is as shown in Figure 4；
Step 5: node coordinate estimation to be positioned and accuracy evaluation；
(1) node coordinate estimation to be positioned；
The position holding space Ω in the sectionaIt is all k θ in positioning groupai ICommon factor, be geometrically one group
Cubical set, is designated as Ωa={ Θa1,Θa2,…,Θan}；By each cube ΘanCenterAs section to be positioned
Point NaOne sample of coordinate, asks for centerFormula be：
Node N to be positionedaCoordinate estimationAsked for by following formula：
Wherein, XiTo position group's internal reference node NiCoordinate, d 'aiFor the distance estimations intermediate value after adaptively correcting The node N to be positioned asked foraThe optimal point estimation of coordinateFor：
(2) to node NaEstimated coordinates carry out accuracy evaluation：Node N to be positionedaEstimated coordinates be Xa=[xa,ya,
za]T, the coordinate for participating in k one-level reference mode of its position fixing process is Xi=[xi,yi,zi]T, i=1,2 ..., k；NaDetermine
Position precision ηaAsked for by following formula：
Wherein：
If there is two level reference mode to participate in positioning, positioning precision η in positioning groupaAsked for by following formula：
Wherein ηkTo participate in the positioning precision of k two level reference mode of positioning in positioning group；
If positioning precision ηaMore than default precision threshold λ, then by this positioning node NaIt is demarcated as two level reference mode；It is no
Then, this node is demarcated as having completed positioning node, node NaFinal estimated coordinates be
Embodiment
As shown in figure 5,200 sensor nodes of random placement in 300m × 300m × 100m three-dimensional spatial area,
Wherein anchor node ratio is 10%, is represented with five-pointed star, its ID is respectively 1-20；Node number to be positioned is 180, and use is solid
Round dot represents that its ID is respectively 21-200.All nodes all possess distance measurement function, and the dotted line between node represents two nodes can be with
Direct Communication is carried out, the length of dotted line represents the Euclidean distance between two nodes.Maximum measure distance frequency n between node is 5, anchor section
Point position bias sample ViBootstrap double sampling numbers B be 200, original ranging sample D between nodeiaDouble sampling number
M is 200.Default node location Evaluation accuracy threshold value λ is 0.97, and the confidence level of multi-hop distance estimations is 0.95.Position group
The multi-hop threshold value ζ of interior progress ranging and information forwarding is 4.Anchor node site error is obedience Rayleigh with multi-hop distance estimations error
Distribution, the non-Gaussian noise that standard deviation is 0.02 times of actual range.By adjusting the communication radius of node, by network-in-dialing degree from
4 is incremented by successively to 13.
It is adaptive using a kind of underwater wireless sensor network dynamic proposed by the invention respectively under above-mentioned network environment
Answer localization method and traditional DV-distance algorithms to carry out node self-localization, obtain Sensor Network under heterogeneous networks degree of communication
Average localization error situation of change is as shown in Figure 6.The solid line for being marked with hollow square is dynamic self-adapting localization method of the present invention
Average localization error, be marked with empty circles dotted line be conventional mapping methods average localization error.With network-in-dialing
The increase of degree, average localization error first become big and then diminished rapidly, and the variation tendency of two kinds of algorithms is essentially identical, but DV-
The position error of distance algorithms is higher than localization method proposed by the present invention.The situation for occurring diminishing after first becoming big be because
Only have a small number of nodes to position when network-in-dialing degree is 4, as degree of communication increases, the more hop nodes in part participate in fixed
Position, the nodes that can complete positioning increase, but because multi-hop evaluated error now is larger, cause average localization error
Have and raise up by a small margin.As network-in-dialing degree increases, multi-hop distance estimations error diminishes, and average localization error can be gradually reduced.
The average localization error lower than DV-distance algorithm about more than 20% of the present invention, this explanation to anchor node site error and
The rejection ability of multi-hop distance estimations error is better than conventional method effect.
It should be noted that this embodiment is merely to illustrate technical scheme and unrestricted, although with reference to preferable
The present invention is described in detail embodiment, it will be understood by those within the art that, can be to the technology of the present invention
Scheme is modified or equivalent substitution, without departing from the spirit and scope of technical solution of the present invention.
Claims (6)
1. a kind of underwater wireless sensor network dynamic self-adapting localization method, is realized by following steps：
Step 1: the degree of accuracy of anchor node online evaluation itself statement position coordinates；
(1) all anchor nodes are on the basis of proven initial coordinate when the statement coordinate or node deployment of initial time, with
The Euclidean distance between n-th renewal coordinate and reference coordinate in the line assessment time gathers n as position bias sample point
It is secondary, structure anchor node position bias sample collection；
(2) Bootstrap double samplings are carried out to anchor node position bias sample collection by the method for nonparametric sampling with replacement；
(3) the constant Bootstrap samples of successive independent B capacity of extraction；
(4) Bootstrap sample averages are calculated, ask for standard deviation
(5) degree of accuracy η of anchor node statement position coordinates is calculatedi：R is the communication radius of node；
(6) the reference rank of anchor node is demarcated：The degree of accuracy is higher than the anchor node of default precision threshold, is demarcated as one-level reference node
Point；The degree of accuracy is less than default precision threshold, is demarcated as common anchor node；
Step 2: node structure dynamic updatable positioning group to be positioned；
(1) the one-level reference node points that node statistics to be positioned itself one are skipped in multi-hop threshold range, reach coordinate estimation
During minimum quantity, all one-level reference modes and the common intermediate node for participating in information forwarding are included into positioning group, and go to step
Three；Less than coordinate estimation minimum quantity, it is demarcated as being unsatisfactory for location condition node；
(2) when the position coordinates degree of accuracy of one-level reference mode is decreased below default precision threshold, re-scaled for
Common anchor node, and eliminate positioning group；
(3) position coordinates will be obtained and precision is demarcated as two level reference mode higher than the ordinary node of default precision threshold；
New locating periodically, it is unsatisfactory for location condition node and scope of statistics is expanded to two level reference mode, when I and II reference mode
When number sum reaches the minimum quantity of coordinate estimation, structure positioning group, and go to step three；
Step 3: positioning group carries out the multi-hop distance estimations of deviation adaptively correcting；
(1) the original ranging sample set between node and reference mode to be positioned is built, calculates the average of original ranging sample；
(2) the secondary Bootstrap double samplings of B ' are independently carried out in succession to original ranging sample set, and calculates its average；
(3) estimate deviation is examined WhereinFor the average of j-th of Bootstrap sample,For the average of original ranging sample；
(4) estimator of offset correction is calculated WillBig sequence is arrived from childhood, is obtained
Arrive
(5) calculate adaptively correcting after distance estimations bound, ask for confidence level be 1- α multi-hop apart from interval estimation：
Take Q1ForInteger part, i.e.,TakeExtractWithAs
Lower bound and the upper bound of the multi-hop apart from interval estimation；
Step 4: the node location holding space to be positioned of solution interval；
(1) a kind of constraint that extraction is made up of the distance between node to be positioned and neighboring reference node estimation interval bound
Space；
(2) two class constraint spaces being made up of the multi-hop distance estimations between node to be positioned and multi-hop reference mode are extracted；
(3) all a kind of, two class constraint spaces in positioning group are replaced with interval number；The common factor of all interval numbers is asked for, by it
Position holding space as node interval to be positioned；
Step 5: node coordinate estimation to be positioned and accuracy evaluation；
(1) the position holding space after sweep interval, the center of each interval number subset in space is asked for, is made
For the sample set of node coordinate to be positioned；Ask for the optimal point estimation of node coordinate to be positioned；
(2) accuracy evaluation is carried out to the estimated coordinates of node；Positioning precision is higher than default precision threshold, is demarcated as two level reference
Node, and go to step two；Otherwise, it is demarcated as having completed positioning node, this estimated coordinates is final coordinate；
It is characterized in that：Original ranging sample number in described step three (1) is not more than 5；(2) the double sampling number B ' in takes
200；(3) if inJudge Estimation of Mean unbiased, ifJudge that Estimation of Mean is higher, if
Judge that Estimation of Mean is relatively low.
A kind of 2. underwater wireless sensor network dynamic self-adapting localization method according to claim 1, it is characterised in that：
The anchor node position bias sample number n of (1) is not more than 5 in the step 1；(3) the double sampling number B in takes 200 times；
(6) the default precision threshold in takes the real number in [0.95,1] section.
A kind of 3. underwater wireless sensor network dynamic self-adapting localization method according to claim 1, it is characterised in that：
Multi-hop threshold value in described step two (1) takes the natural number no more than 5, and the minimum quantity of coordinate estimation is disposed in three-dimensional
In the case of be 4, under two-dimentional deployment scenario be 3.
A kind of 4. underwater wireless sensor network dynamic self-adapting localization method according to claim 1, it is characterised in that：
Two its upper bound of class constraint space in described step four (2) are the upper bound in multi-hop distance estimations section, and its lower bound is logical
Less one is interrogated between radius and multi-hop distance estimations section lower bound, and extraction formula is：
<mrow>
<msub>
<mi>S</mi>
<mrow>
<mi>j</mi>
<mi>a</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mo>{</mo>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
<mo>{</mo>
<mi>R</mi>
<mo>,</mo>
<msub>
<mover>
<mi>D</mi>
<mo>^</mo>
</mover>
<mrow>
<msub>
<mi>ja</mi>
<mrow>
<mi>l</mi>
<mi>o</mi>
<mi>w</mi>
<mi>e</mi>
<mi>r</mi>
</mrow>
</msub>
</mrow>
</msub>
<mo>}</mo>
<mo>&le;</mo>
<mo>|</mo>
<mo>|</mo>
<msub>
<mi>X</mi>
<mi>j</mi>
</msub>
<mo>-</mo>
<msub>
<mi>X</mi>
<mi>a</mi>
</msub>
<mo>|</mo>
<msub>
<mo>|</mo>
<mn>2</mn>
</msub>
<mo>&le;</mo>
<msub>
<mover>
<mi>D</mi>
<mo>^</mo>
</mover>
<mrow>
<msub>
<mi>ja</mi>
<mrow>
<mi>u</mi>
<mi>p</mi>
<mi>p</mi>
<mi>e</mi>
<mi>r</mi>
</mrow>
</msub>
</mrow>
</msub>
<mo>}</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, Sja(x) it is node N to be positionedaWith reference mode NjThe two class constraint spaces formed, R are the communication half of node
Footpath,For the lower bound in multi-hop distance estimations section, | | Xj-Xa||2For NaWith NjBetween Euclidean distance,For more hop distances
The upper bound of estimation interval.
A kind of 5. underwater wireless sensor network dynamic self-adapting localization method according to claim 1, it is characterised in that：
The formula for asking for the optimal point estimation of node coordinate to be positioned in described step five (1) is：
<mrow>
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mover>
<mi>X</mi>
<mo>^</mo>
</mover>
<mi>a</mi>
</msub>
<mo>=</mo>
<munder>
<mrow>
<mi>arg</mi>
<mi> </mi>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
</mrow>
<msub>
<mi>X</mi>
<mi>a</mi>
</msub>
</munder>
<mfrac>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>k</mi>
</munderover>
<msqrt>
<mrow>
<msup>
<mrow>
<mo>(</mo>
<mo>|</mo>
<mo>|</mo>
<msubsup>
<mi>&theta;</mi>
<mrow>
<mi>a</mi>
<mi>n</mi>
</mrow>
<mo>*</mo>
</msubsup>
<mo>-</mo>
<msub>
<mi>X</mi>
<mi>i</mi>
</msub>
<mo>|</mo>
<msub>
<mo>|</mo>
<mn>2</mn>
</msub>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
<mo>-</mo>
<msup>
<msubsup>
<mi>d</mi>
<mrow>
<mi>a</mi>
<mi>i</mi>
</mrow>
<mo>&prime;</mo>
</msubsup>
<mn>2</mn>
</msup>
</mrow>
</msqrt>
</mrow>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>k</mi>
</munderover>
<mo>|</mo>
<mo>|</mo>
<msubsup>
<mi>&theta;</mi>
<mrow>
<mi>a</mi>
<mi>n</mi>
</mrow>
<mo>*</mo>
</msubsup>
<mo>-</mo>
<msub>
<mi>X</mi>
<mi>i</mi>
</msub>
<mo>|</mo>
<msub>
<mo>|</mo>
<mn>2</mn>
</msub>
</mrow>
</mfrac>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mtable>
<mtr>
<mtd>
<mrow>
<mi>s</mi>
<mi>u</mi>
<mi>b</mi>
<mi>j</mi>
<mi>e</mi>
<mi>c</mi>
<mi>t</mi>
<mi> </mi>
<mi>t</mi>
<mi>o</mi>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mi>X</mi>
<mi>a</mi>
</msub>
<mo>&Element;</mo>
<msub>
<mi>&Omega;</mi>
<mi>a</mi>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mtd>
</mtr>
</mtable>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein,For node N to be positionedaThe optimal point estimation of coordinate,For a sample in node coordinate sample set to be positioned
This, XiTo position group's internal reference node NiCoordinate, d 'aiFor the distance estimations intermediate value after adaptively correcting ΩaIt is the position holding space behind section.
A kind of 6. underwater wireless sensor network dynamic self-adapting localization method according to claim 1, it is characterised in that：
It is to the formula of node estimated coordinates progress accuracy evaluation in described step five (2)：
<mrow>
<msub>
<mi>&eta;</mi>
<mi>a</mi>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>k</mi>
</munderover>
<msub>
<mi>&eta;</mi>
<mi>k</mi>
</msub>
</mrow>
<mi>k</mi>
</mfrac>
<mo>&times;</mo>
<mo>(</mo>
<mrow>
<mn>1</mn>
<mo>-</mo>
<mfrac>
<mi>&delta;</mi>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>k</mi>
</munderover>
<msqrt>
<mrow>
<mo>&lsqb;</mo>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>x</mi>
<mi>a</mi>
</msub>
<mo>-</mo>
<msub>
<mi>x</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
<mo>+</mo>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>y</mi>
<mi>a</mi>
</msub>
<mo>-</mo>
<msub>
<mi>y</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
<mo>+</mo>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>z</mi>
<mi>a</mi>
</msub>
<mo>-</mo>
<msub>
<mi>z</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
<mo>&rsqb;</mo>
</mrow>
</msqrt>
</mrow>
</mfrac>
</mrow>
<mo>)</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>3</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, ηaFor positioning node NaThe Evaluation accuracy of estimated coordinates, ηkTo participate in k reference mode of positioning in positioning group
Positioning precision, Xa=[xa,ya,za]TFor node N to be positionedaEstimated coordinates, Xi=[xi,yi,zi]TFor the coordinate of reference mode,da′iFor the distance estimations intermediate value after adaptively correcting
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410328637.3A CN104080169B (en) | 2014-07-10 | 2014-07-10 | A kind of underwater wireless sensor network dynamic self-adapting localization method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410328637.3A CN104080169B (en) | 2014-07-10 | 2014-07-10 | A kind of underwater wireless sensor network dynamic self-adapting localization method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104080169A CN104080169A (en) | 2014-10-01 |
| CN104080169B true CN104080169B (en) | 2017-11-14 |
Family
ID=51601164
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410328637.3A Active CN104080169B (en) | 2014-07-10 | 2014-07-10 | A kind of underwater wireless sensor network dynamic self-adapting localization method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104080169B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10379239B2 (en) * | 2014-11-25 | 2019-08-13 | Fairfield Industries Incorporated | Method and computer system for determining seismic node position |
| CN105137393B (en) * | 2015-07-31 | 2017-08-01 | 石川 | A kind of space multisensor method for rapidly positioning for network |
| CN111194081B (en) * | 2018-11-14 | 2020-12-29 | 中国科学院声学研究所 | Node positioning method combined with underwater acoustic network protocol |
| CN110167124B (en) * | 2019-05-21 | 2020-07-07 | 浙江大学 | Target tracking method of underwater wireless sensor network with self-adaptive transmission power |
| CN110166934B (en) * | 2019-05-21 | 2020-08-18 | 长安大学 | Mobile underwater acoustic network self-positioning method based on dynamic reference node selection |
-
2014
- 2014-07-10 CN CN201410328637.3A patent/CN104080169B/en active Active
Non-Patent Citations (4)
| Title |
|---|
| Cube-scan-based three dimensional localization for large-scale Underwater wireless Sensor Networks;任永吉，于宁等;《IEEE international systems conference》;20120426;全文 * |
| Multi-Hop Localization Algorithm Based on Grid-Scanning for Wireless Sensor Networks;万文江，郭晓雷，于宁等;《Sensors》;20110331;第11卷(第4期);全文 * |
| Nonparametric Bootstrap-Based Multihop Localization Algorithm for Lare-Scale Wireless Sensor Networks in Complex Environments;任永吉，于宁等;《International Journal of Distributed Sensor Networks》;20130430;第2013卷(第7期);全文 * |
| Set-Membership Adaptive Localization Algorithm with Time-Varting Error Bounds for Underwater Wireless Sensor Networks;任永吉，于宁等;《International Journal of Distributed Senor Networks》;20130531;第2013卷(第3期);第3页左栏第3段至第6页左栏第4段 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104080169A (en) | 2014-10-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104080169B (en) | A kind of underwater wireless sensor network dynamic self-adapting localization method | |
| Rabbat et al. | Decentralized source localization and tracking [wireless sensor networks] | |
| CN103401922B (en) | Distributed localization apparatus and method based on game method in wireless sensor network | |
| CN107113764B (en) | Method and device for improving positioning performance of artificial neural network | |
| CN102883428B (en) | Based on the node positioning method of ZigBee wireless sensor network | |
| CN104125538B (en) | The secondary localization method and device of RSSI signal intensities based on WIFI network | |
| CN104902562B (en) | A kind of indoor orientation method based on multilayer fingerprint matching | |
| CN106412828A (en) | Approximate point-in-triangulation test (APIT)-based wireless sensor network node positioning method | |
| CN106646356A (en) | Nonlinear system state estimation method based on Kalman filtering positioning | |
| CN104684081B (en) | The Localization Algorithm for Wireless Sensor Networks of anchor node is selected based on distance cluster | |
| CN102905365B (en) | Network node positioning method of wireless sensor | |
| CN103747419B (en) | A kind of indoor orientation method based on signal strength difference and dynamic linear interpolation | |
| CN101873691A (en) | Method for positioning wireless sensor network node without ranging based on connectedness | |
| CN105704652A (en) | Method for building and optimizing fingerprint database in WLAN/Bluetooth positioning processes | |
| CN102621522B (en) | Method for positioning underwater wireless sensor network | |
| CN104363653B (en) | A kind of passive type localization method for eliminating ambient noise | |
| CN105635964A (en) | Wireless sensor network node localization method based on K-medoids clustering | |
| CN104185272A (en) | WSN location method based on WSDV-Hop (Weighted and Selected DV-Hop) | |
| CN105682026A (en) | Improved DV-Hop localization method based on hop count threshold optimal average hop distance | |
| CN103415072B (en) | Based on the localization method estimating distance in a kind of radio sensing network | |
| CN107708202A (en) | A kind of wireless sensor network node locating method based on DV Hop | |
| CN106199500A (en) | Fingerprint characteristic localization method and device | |
| CN103249144A (en) | C-type-based wireless sensor network node location method | |
| CN103369670A (en) | Improved DV-hop (distance vector-hop) location method based on hop count optimization | |
| CN106125037A (en) | Indoor wireless focus based on WiFi signal intensity and Micro Model backtracking localization method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB03 | Change of inventor or designer information | ||
| CB03 | Change of inventor or designer information |
Inventor after: Ren Yongji Inventor after: Wang Guangyuan Inventor after: Zhong Jianlin Inventor after: Huang Juan Inventor after: Song Yanbo Inventor after: Liu Tao Inventor after: Wang Weiya Inventor after: Guo Haiyan Inventor before: Ren Yongji Inventor before: Zhong Jianlin Inventor before: Huang Juan Inventor before: Song Yanbo Inventor before: Liu Tao Inventor before: Wang Weiya Inventor before: Guo Haiyan |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |