CN104486833B - The indoor wireless tomography Enhancement Method of interfering link is deleted based on motion prediction - Google Patents
The indoor wireless tomography Enhancement Method of interfering link is deleted based on motion prediction Download PDFInfo
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Abstract
The invention discloses a kind of indoor wireless tomography Enhancement Methods for deleting interfering link based on motion prediction, belong to target detection and tracking technical field in wireless network.The intersection point set being made of shadow fading link is found using cluster, estimation of the center of set as initial time target location, the estimation of current target position is obtained to delete the interfering link of distance objective position farther out using the positioning result of target movement model and last moment by Kalman filter, RTI imagings are carried out finally by shadow fading link, update using Kalman to obtain target current time position on the basis of RTI.Non-shadow is excluded based on motion prediction to decline the influence of link, effectively eliminates false target, and attenuation effect caused by more acurrate protrusion exists due to target, and target positioning is more accurate to making under multi-path environment, improves image quality, realizes more excellent dynamic tracking.
Description
Technical field
The present invention relates to a kind of wireless chromatographic imaging system localization methods, especially a kind of to delete interference based on motion prediction
The more accurate measurement and positioning model of wireless chromatographic imaging system of link, the target detection and tracking technology in wireless network that belongs to are led
Domain.
Background technology
Wireless tomography (RTI) be it is a kind of positioning the technology with tracking target using radio node, it is according to monitoring
Link attenuation caused by target occlusion, to monitoring regional imaging, realizes the locating and tracking to target in region.Wireless chromatography at
Any device is carried as technology does not need target, and it only needs received signal strength (RSS) value of radio node, it is now big
Most wireless devices can provide.Therefore the technology can very easily extend in existing network till now without adding
Add any hardware.The technology practical application area is very extensive, such as：Indoor Video, fire rescue etc..
The indoor application of wireless chromatography imaging technique is not also very ripe at present, and error is larger, improves nothing under indoor environment
Some technologies of line tomography performance all do not consider the problem of link interference, are not all chains indoors under environment
Road decaying is all to be blocked to cause by the path sighting distance (LOS), that is to say, that not all decaying link is all shadow fading
Link.In fact, the larger multipath of power is blocked or rapid fading can cause link attenuation as caused by multipath, if
It is directly imaged using these links, it will image quality and positioning accuracy is made to generate very big error.
When target monitor moved in region when, for the link between two radio nodes of Mr. Yu, target is to the chain
The influence on road can be divided into two kinds of situations：LOS path is blocked the case where not being blocked with LOS path, as depicted in figs. 1 and 2.
In Fig. 1 and Fig. 2, there is 4 transmission paths between transmitting node and receiving node, wherein one is LOS path, in addition 3 are
Reflection path.Here only to facilitate analysis, the actually number of indoor multipath are much larger than 4.It is understood that 3 in this example
The propagation path in path is changeless, and the signal in these paths is possible to meeting by target occlusion, still at different times
The propagation path of these signals is constant, i.e., whether each path is distinguished as in the unique of different moments by target occlusion.Such as
In Fig. 1, the LOS path i.e. direct path (LOS path) that is blocked is blocked.In Fig. 2, LOS path is not blocked, the 3rd article
Path is blocked, and the 2nd paths are just the same in two kinds of situation, that is, Fig. 1 and Fig. 2.4th paths are due to target
Reflection and scattering generate, and propagation path is related with the position of target, therefore the paths are time-varying.
Assuming that the signal of LOS path is2nd, 3 reflection path is not generated by target, it is assumed that this two roads
The signal of diameter isγpIndicate whether pth paths are blocked, if γp=1 indicates pth paths
Not by target occlusion, γ on the contraryp=0 indicates pth paths by target occlusion.4th paths signal by target reflection
Or scattering generates, because the paths are time-varying, it is assumed that this kind of path signal isHere ApAnd θp(p=1,
2,3 ...) indicate that pth paths receive the amplitude and phase of signal, then link l is in discrete instants t=1,2 ... when
Received signal strength RSS values are:
In the case that aimless, the 1st, 2,3 propagation path is only existed, time-varying is not contained in signal then receiving
, the RSS values of link l are：
When there is no other disturbances when the influence of noise for not considering circuit, and in environment, expressed RSS values r in (2) formulal
It is constant.
When target occurs in monitoring region, the RSS values of link l may change, then RSSs of the link l in t moment
The variable quantity of value, that is, link l is in the receiving signal attenuation amount of t moment：
Δrl,t=rl,t-rl, l=1,2 ..., L. (3)
Wherein L is the link sum monitored in region, is determined by radio node quantity.The power attenuation for receiving signal may
There are following three kinds of situations to generate：1. direct path is by target occlusion, the 2. stronger reflection paths of certain power are by target occlusion, 3.
The RSS rapid fadings caused by target reflects or scatters the time-varying multipath generated.As shown in formula 1, received signal strength is every
Path signal phasor superposition as a result, the variation of certain paths signal may make RSS occur great variety.
In wireless tomography mainly using be that the decaying of LOS path signal carries out the imaging of target location, but
It is that we are not aware that the decaying of which link is caused by being blocked due to LOS path in advance.If we are simply considered that public affairs
The link l that formula 3 provides is the decaying of LOS path signal in the receiving signal attenuation of t moment and is applied to wireless chromatography
In imaging, then the target location etc. that will imaging results be generated with prodigious interference, generate false target or mistake, this is showed
As being known as the multi-path jamming in wireless tomography.
Multi-path jamming influence most significant on wireless tomography is presented as that the presence of multi-path jamming may make wireless level
Image checking is to multiple false targets.In wireless tomography, target is presented as the bright spot in image.Multi-path jamming makes
In image other than the bright spot of target, also many false bright spots, to the number of erroneous estimation target.In addition, empty
Decoy can take away the part energy of target so that major heading brightness dies down, it could even be possible to less than some false target
Brightness causes the target location estimation of mistake.
In general, the third above-mentioned situation is so that receiving signal is in because target reflects or scatters the time-varying multipath generated
The characteristic for revealing RSS rapid fadings either imaging noise, such as can pass through low pass filtered by the method for Time Domain Processing by signal
Wave device is eliminated.But to the stronger reflection path of Mr. Yu power by target occlusion the case where because such case is in time domain
Feature of feature when being blocked with LOS path in time domain it is identical, therefore the treating method of time domain is stronger to certain power anti-
The rays diameter such case that is blocked is helpless.
So far there are no people propose the similar stronger reflection path of certain power be blocked such case the problem of with
And the solution of relevant issues, and similar research was done in other field also nobody.
Invention content
For the wireless prodigious problem of chromatography imaging technique positioning effects of multipath link pair, the present invention proposes one kind and is based on
Motion prediction deletes the indoor wireless tomography Enhancement Method of interfering link, before RTI imagings, by into line link intersection point
Spatial characteristic detects and deletes interfering link.Normally due to multi-path jamming and the intersection point distance objective of existing interfering link is true
Position is farther out.Since we do not know target actual position, can by Kalman filter using target movement model and
The positioning result at last moment obtains the estimation of current target position to delete the interference chain of distance objective position farther out
Road.Since this method requires initial time target location it is known that the present invention is found using cluster is made of shadow fading link
Intersection point set, the center of set can be as the estimation of initial time target location, the target location obtained based on Kalman
Estimation, and interfering link is deleted to obtain shadow fading link set, finally by shadow fading according to the location estimation
Link carries out RTI imagings, updates using Kalman to obtain target current time position on the basis of RTI.Experiments have shown that interference chain
Either image quality or target tracking accuracy is all significantly improved after road is deleted.
The indoor wireless tomography Enhancement Method of the present invention that interfering link is deleted based on motion prediction, including as follows
Step：
Step 1：When target is located at monitoring region, each link received signals intensity i.e. variation of RSS values is measured：
Step 1.1：Configuration node
Monitoring region is located at xoy coordinate planes, and o is coordinate origin；Equidistant be deployed in of η radio node is monitored into region
Around, the height that all nodes are placed at i.e. all nodes placements on the same xoy coordinate planes is identical, and each node
A unique ID number is assigned as mark, the coordinate of each node is it is known that be (αq,βq), q=1,2 ..., η, wherein q are
Node serial number；These radio nodes constitute L=η (η -1)/2 wireless links；
Each node sends signal according to preset agreement and sequential, and receives and measure what other nodes were sent out
The RSS values of wireless signal, i.e.,：In t moment, the node transmission data that number is q, other nodes receive data and measure reception letter
Number intensity；At the next moment, the node transmission data that number is q+1, other nodes receive data and measure received signal strength；
Step 1.2：Measure the l articles link t moment RSS variation deltas rl,t：
When measuring monitoring region first does not have target, the RSS values of each of the links are rl, wherein l is the number of the link；So
Measuring target afterwards, each of the links are in discrete instants t=1 when monitoring in region, and 2 ... the RSS values r ofl,t, to obtain the l articles
Link is in the variable quantity of the RSS values of t moment：
Δrl,t=rl,t-rl, l=1,2 ..., L. (4)
Δrl,tDecay for negative value expression；
Step 2：To step 1 obtain each of the links t moment RSS values variation delta rl,tIt is handled, is obtained
The decline link set l at current timet；
Step 2.1：In current time t, RSS variation deltas r that each of the links receivel,tFirst pass around sliding average filter
For wave device to eliminate the noise become soon, 2 ω+1 are that sliding filter window is long；
Δrl,iFor link l the RSS values at t- ω≤i≤t+ ω moment variable quantity, then filtering after link l in t
The RSS variable quantities at quarter are：
Step 2.2：A threshold value is set, the unconspicuous link of decaying is removed；That is, if discrete instants t=1,2 ...
When link l meet following formula moment link l just claimed to be decaying link：
WhereinIt is drop threshold, then t moment decline link set is：
Step 3：Obtain t moment decline link set ltIn it is all decaying links intersection point (uk,vk) structure in monitoring region
At set, i.e. intersection point set ρt；
Preferably, the method for obtaining the intersecting point coordinate of arbitrary both links is as follows：
In t moment, it is assumed that belong to set ltA link two node coordinates be respectively (αi,βi) and (αj,βj), belong to
In ltAnother link two node coordinates be respectively (αm,βm) and (αn,βn), then the intersecting point coordinate of this both links
(uk,vk) meet：
The solution matrix form of this formula can be expressed as
Wherein []-1Inverse of a matrix matrix is sought in expression.
Step 4：The initial position of target is obtained when carving t=1 at the beginning
Step 4.1：With clustering algorithm by the t moment obtained in step 3 decaying link intersection point set ρtAccording to cluster
Characteristic is grouped, to find the class for having and significantly building up；
Assuming that K is the number of t moment intersection point set cluster, Φj,tIt is the set of intersection point in t moment cluster j, | Φj,t| table
Show the number at the midpoints cluster j；Find the center (C each clustered under t momentx,j,Cy,j) keep following object function minimum
Change：
Here (Cx,j,Cy,j) it is cluster j i.e. intersection point set Φ under t momentj,tCenter position coordinates；
Step 4.2：T moment initializes K=1 and assumes that it includes the most cluster of intersection point at the moment to cluster j to be, detects such
Whether interior intersection point meetsWherein (uk,vk)∈Φj,t, and R is preset apart from threshold
Value；If meet if iteration ends, otherwise K values add 1, return to step 4.1, which is found, makes the minimization of object function shown in formula (11)
(Cx,j,Cy,j)；It is final to obtain the number K clustered under t moment；
Step 4.3：Select set of the cluster as shadow fading link intersection point；General shadow fading link intersection point collection
The number of intersection point is maximum in conjunction, therefore the set of the intersection point of link that is blocked of these LOS paths and initial position can be under
Formula provides：
WhereinIt indicates in t moment using the most cluster J of intersection point number in K cluster as with bright
The class of aobvious aggregation properties,It is the coordinate estimation of target current time position；It is initial time as t=1, can obtains
Initial time target location is
Step 5：Position based on Kalman filter prediction target in discrete instants t
When t >=2, the current time i.e. position of t moment target is estimated according to the target location at previous moment, that is, t-1 moment
It sets, is initial time as t=1, the target location of initial time is what step 4.3 obtainedAssuming that target is monitoring
It is uniform motion in region, then the equation of motion of target is
According to the uniform motion model of target, it is known that：
XtFor the state variable of 4 dimensions, including coordinates of targets and speed；T is the sampling time interval to target state,WithIt is target respectively in t moment in the directions the x speed and the directions y speed of monitoring region xoy planes, (xt,yt) it is that target exists
Position coordinates of the t moment in monitoring region xoy planes；Assuming that the noise ε on the directions x and the directions y of xoy planest=[εx,t,
εy,t]TIt is Gaussian Profile, the covariance matrix of noise isIts value is determined according to target state；
Followed by Kalman filter obtain target t moment position coordinates estimated value：Assuming that the mesh that t moment obtains
The two-dimensional coordinate of cursor position observed quantity, that is, target is Yt, the wherein moment target locations t=1 are from target initial position estimation
What step 4.3 obtainedThe moment target location observed quantities of t >=2 YtIt is obtained from the imaging results of wireless tomography, Yt
And XtRelationship be：
Yt=HXt+wt (15)
Wherein assume wtBe mean value be zero, covariance matrix isGaussian distributed observation error,It is
The variance of measurement error；Observing matrix H is：
So Kalman filter theory obtains target and is in the location estimation of t moment according to the following formula
Wherein Pt|t-1It is prediction of the t-1 moment least mean-square error matrixes to t moment least mean-square error matrix, is referred to as
Minimum prediction Square Error matrix, Pt-1|t-1It is t-1 moment least mean-square error matrixes；KtIt is t moment Kalman gains；
It is prediction of the t-1 moment to t moment dbjective state variable；It is state variable of the target at the t-1 moment,For
State variableThe first two element；
Step 6：According to obtained target location estimationDelete decaying link setIn non-shadow decline
Link obtains shadow fading link subset ξt；Method is as follows：
If point (uk, vk）∈ltIt is the intersection point of shadow fading link, then the distance between target and the intersection point must expire
Foot：
WhereinIt is that t moment target location coordinate is obtained according to the target location at t-1 moment in previous step
Estimated value, RthFor distance threshold, value is more than the distance threshold R in step 4.2；If intersection point be unsatisfactory for formula (18) this
Condition, then it is the intersection point of shadow fading link to judge this intersection point not, to remove the friendship in the intersection point set of decaying link
For point to get to the intersection point set of formula (18) is met in decaying link, the decaying link where these intersection points constitutes t moment shade
The link set that declines ξt；
Step 7：Shadow fading link set ξ obtained by above-mentioned stepstObtain the position detection at target current time
Amount；Method is as follows：
The xoy planes in monitoring region are divided into grid, and Δ υ is the length of side of grid, NrAnd NcIt is every row and each column packet respectively
The meshes number contained, the weight matrix of wireless tomographyWherein d=1,2 ..., Nr×NcTable
Show grid number, | ξt| indicate t moment shadow fading link set ξtThe number of middle shadow fading link；
By formulaThe imaging array of t moment target is found out, whereinμ
It is Tikhonov regularization parameters, Δ xd,tIndicate d-th of element in t moment, 1≤d≤NrNc, from formula (5) as a result, I is unit matrix；Due to cannot be guaranteed that middle all elements are
Positive value, need to be by negative pressure be assigned to zero and to wherein element simultaneously divided by middle maximum value be normalized
Wherein indicated the RSS attenuations of the grid d in discrete instants t.Element by is by row
Stack arrangement is at Nr×NcTwo-dimensional matrix can be imaged；Most bright spot is considered as the positional value of target in image, then by following formula
Imaging results are provided to which the target location obtained is：
YtIt is the position detection amount i.e. two-dimensional coordinate of target of the target described in step 5, for updating Kalman filter
The target location observed quantity of device；SymbolIt indicating to the downward rounding of data, D indicates to decay the serial number of maximum grid, 1≤D≤
NrNc；
Step 8：Obtain target current time location updating：
According to following Kalman filter theory obtain target t moment location updating：
Wherein Pt|tFor t moment least mean-square error matrix,It is the state variable of t moment；The first two elementThe as updated value of t moment target location.
The prior art is compared, advantageous effect of the present invention is, proposed by the present invention to delete interfering link based on motion prediction
RTI Enhancement Methods, the relationship of comprehensive descision each shadow fading link intersection point and target kinetic characteristic, based on motion prediction exclude
False target is effectively eliminated in the influence of some non-shadow decline links, and more acurrate protrusion is caused by target exists
Attenuation effect, to keep target positioning under multi-path environment more accurate, the more excellent dynamic tracking of realization.
Description of the drawings
Fig. 1：The explanation that target influences communication environments：LOS path is blocked；
Fig. 2：The explanation that target influences communication environments：LOS path is not blocked；
Fig. 3：The flow chart of indoor wireless tomography Enhancement Method is deleted based on motion prediction interfering link；
Fig. 4：Influence of the interfering link to RTI：Monitor the link decayed in region；
Fig. 5：Utilize the cluster result for the intersection point that K-means algorithms obtain；
Fig. 6：The weight model of RTI；
Fig. 7：The position of node and the explanation of target trajectory.
Specific implementation mode
The present invention is described in detail below in conjunction with drawings and examples, while also describing technical solution of the present invention
The technical issues of solution and advantageous effect, it should be pointed out that described embodiment is intended merely to facilitate the understanding of the present invention,
And any restriction effect is not played to it.
Flow chart of the present invention is shown in attached drawing 3, described to delete the enhancing of interfering link indoor wireless tomography based on motion prediction
Method specifically comprises the following steps：
Step 1：The variation of all link received signals intensity (RSS) values is obtained when target is located at monitoring region；
Step 1.1：Configuration node
Monitoring region is located at xoy coordinate planes, and o is coordinate origin.By the wireless section of η support IEEE802.15.4 agreement
Point is equidistant to be deployed in around monitoring region, and all nodes are placed at i.e. all node quilts on the same xoy coordinate planes
The height of placement is identical, and each node is assigned a unique ID number as mark, and the coordinate of each node is it is known that be
(αq,βq), q=1,2 ..., η, wherein q are node serial number.Each node sequentially sends and receives successively in the way of token ring
Signal, sometime, sequence node number is the node transmission data of q, other nodes receive data and measure that receive signal strong
Degree.At the next moment, sequence node number be q+1 node transmission data, other nodes receive data and measure receive signal it is strong
Degree.These radio nodes may be constructed the wireless links of L=η (η -1)/2, and each node is according to preset agreement and sequential
The RSS values for the wireless signal that other nodes are sent out can be measured.
Step 1.2：Measure the l articles link t moment RSS variation deltas rl,t：
When measuring monitoring region first does not have target, the RSS values of each of the links are rl：
Wherein l is the number of the link, and P is the number of the reflection path when monitoring region and not having target；ALOSAnd θLOSTable
Show the direct path i.e. amplitude and phase of LOS path reception signal when monitoring region and not having target, BpAnd θp(p=1,2,
3 ... P) indicate that pth reflection path receives the amplitude and phase of signal.
Then measuring target, each of the links are in discrete instants t=1 when monitoring in region, and 2 ... the RSS values r ofl,t, adopt
It sample interval should be sufficiently small to keep up with the speed of target movement：
Wherein γLOSIndicate whether direct path is blocked：If γLOS=1 indicates that direct path is not hidden by target
Gear, otherwise γLOS=0 indicates direct path by target occlusion；
γpIndicate whether pth reflection path is blocked：If γp=1 indicates pth reflection path not by target
It blocks, on the contrary γp=0 indicates pth reflection path by target occlusion.
Cz(t) and θz(t) (z=1,2,3.....Z) indicates that the z articles generates multipath signal by the reflection of target or scattering
Amplitude and phase, Z for due to target reflection or the number of multipath signal that generates of scattering.
It is in the variable quantity of the RSS values of t moment to obtain the l articles link：
Δrl,t=rl,t-rl, l=1,2 ..., L.
Step 2：To step 1 obtain each of the links t moment RSS values variation delta rl,tIt is handled, is obtained
The decline link set l at current timet；
Step 2.1：In current time t, the RSS variation deltas r that receivesl,tFirst have to by moving average filter with
The noise become soon is eliminated, 2 ω+1 are that sliding filter window is long, and the selection of the window length should be appropriate, and usual window length preferably takes
Value ranging from 3~7.
Δrl,iFor link l the RSS values at i (t- ω≤i≤t+ ω) moment variable quantity, then filtering after link l exist
The RSS variable quantities of t moment are：
Step 2.2：In general if link is blocked, no matter LOS path or the stronger reflection path of energy are hidden
Gear, RSS values can all show stronger decaying.Therefore a threshold value can be set, removes the unconspicuous link of decaying, if from
Dissipate moment t=1,2 ... when link l meet following formula just claim moment link l be decaying link：
WhereinIt is drop threshold,Selection with give detection probability it is related, when detection probability is met the requirements
The preferred value range of drop threshold is generally -1dB~-3dB.Then t moment decline link set is：
As shown in figure 4, giving the set of all decaying links in detection zone.
Step 3：According to t moment decline link set ltObtain the intersection point of each link t moment in monitoring region in the set
Set ρt；
Preferably, the method for obtaining the intersecting point coordinate of arbitrary both links is as follows：
In t moment, it is assumed that belong to set ltA link two node coordinates be respectively (αi,βi) and (αj,βj), belong to
In ltAnother link two node coordinates be respectively (αm,βm) and (αn,βn), then the intersecting point coordinate of this both links
(uk,vk) meet：
The solution matrix form of this formula can be expressed as
Wherein []-1Inverse of a matrix matrix is sought in expression.T moment decline link set l is calculated accordinglytIn all decaying chains
The intersection point on road intersection point collection for constituting in monitoring region is：
Step 4：The initial position of target is obtained when carving t=1 at the beginning
Step 4.1：The t moment for using clustering algorithm (K-means clustering algorithms as preferred method) that will be obtained in step 3
Decaying link intersection point set ρtIt is grouped according to Clustering features to find the class for having and significantly building up.Assuming that K is t moment intersection point
Gather the number of cluster, Φj,tIt is the set of intersection point in t moment cluster j, | Φj,t| indicate the number at the midpoints cluster j.Using K-
Means methods find suitable cluster centre (C under t momentx,j,Cy,j) find suitable cluster and make following object function most
Smallization：
Here (Cx,j,Cy,j) it is cluster j i.e. intersection point set Φ under t momentj,tCenter, that is, find under t moment and each cluster
Center.
Step 4.2：Usually we, which do not know, be divided into these intersection points how many clusters, if these intersection points are all LOS
The link that path the is blocked i.e. intersection point of shadow fading link, then clusters number should be just 1.But if there is intersection point be not
The intersection point of shadow fading link should just increase the number of cluster.Therefore, t moment initializes K=1 and assumes that it is the moment to cluster j
The maximum cluster of element number, detects whether the intersection point in such meets
Wherein R is distance threshold, the generally radius of objective contour.The iteration ends if meeting, otherwise K=K+1, return to step
4.1 find the (C for making object function (11) minimizex,j,Cy,j), it is final to obtain the number K clustered under t moment.As shown in figure 5,
It is 3 to obtain final cluster number, and each cluster shows certain Clustering features.
Step 4.3：We need the set for selecting a cluster as shadow fading link intersection point after cluster is completed.
The number of intersection point is maximum in general shadow fading link intersection point set, because of the intersection point for the link that those non-LOS paths are blocked
Typically isolated, they will form a cluster with many intersection points and be difficult.Therefore these LOS paths are blocked
The set and initial position of the intersection point of link can be given by：
WhereinIt indicates in t moment using the maximum cluster J of intersection point number in K cluster as with bright
The class of aobvious aggregation,It is the coordinate estimation of target current time position.It is initial time as t=1, can obtains initial
Moment target location is
Step 5：Position based on Kalman filter prediction target in discrete instants t
Estimate the current time i.e. position of t moment target according to the target location at previous moment, that is, t-1 moment (t >=2),
It is initial time as t=1, the target location of initial time is what step 4.3 obtainedAssuming that target is in monitoring region
Interior is uniform motion, then the equation of motion of target is
According to the uniform motion model of target, it is known that：
XtFor the state variable of 4 dimensions, including coordinates of targets and speed.T is the sampling time interval to target state,WithIt is target respectively in t moment in the directions the x speed and the directions y speed of monitoring region xoy planes, (xt,yt) it is target
In position coordinates of the t moment in monitoring region xoy planes, target is obtained in the position of t moment followed by Kalman filter
Set coordinate.Assuming that the noise ε on the directions x and the directions y of xoy planest=[εx,t,εy,t]TIt is Gaussian Profile, the covariance of noise
Matrix isIts value is generally determined according to target state.
Followed by Kalman filter obtain target t moment position coordinates estimated value：Assuming that the mesh that t moment obtains
The two-dimensional coordinate of cursor position observed quantity, that is, target is Yt, the wherein moment target locations t=1 can be obtained from target initial position estimation
To i.e.T ＞ 1 moment target location observed quantities YtIt can be obtained from the imaging results of RTI.YtAnd XtRelationship be：
Yt=HXt+wt (15)
Wherein assume wtBe mean value be zero, covariance matrix isGaussian distributed observation error,It is
The variance of measurement error.Observing matrix H is：
The location estimation that so Kalman filter theory can obtain t moment according to the following formula is
Wherein Pt|t-1It is prediction of the t-1 moment least mean-square error matrixes to t moment least mean-square error matrix, is referred to as
Minimum prediction Square Error matrix, Pt-1|t-1It is t-1 moment least mean-square error matrixes；(subscript is t-1 | t-1 and t | the title of t
For least mean-square error matrix, subscript is t | t-1 is minimum prediction Square Error matrix).KtIt is t moment Kalman gains.
It is prediction of the t-1 moment to t moment state variable.It is state variable of the target at the t-1 moment,For shape
State variableThe first two element.
Step 6：According to obtained target location estimationDelete decaying link set ltIn non-shadow decline
Link obtains shadow fading link subset ξt；
For Mr. Yu link, only target be located at LOS path either it is closer apart from LOS path when, this chain
Lu Caineng observes that RSS decays.Therefore, if having been known for the Position Approximate of targetIt may determine that should
Whether the decaying of link causes since LOS path is blocked, to delete the interfering link that those non-LOS paths are blocked.
If intersection point (uk, vk)∈ltIt is the intersection point of shadow fading link, then the distance between target and the intersection point must satisfy：
Wherein it is that t moment target location coordinate is obtained according to the target location at t-1 moment in previous step
Estimated value, RthFor distance threshold, value is generally greater than the distance threshold R in step 4.2.If intersection point is unsatisfactory for formula (18)
This condition, then it is the intersection point of shadow fading link to judge this intersection point not, to remove in the intersection point set of decaying link
The intersection point, therefore new intersection point collection can be obtained, i.e., intersection point concentrates all intersection points all to meet formula (18), so as to obtain
T moment shadow fading link set
Step 7：Shadow fading link set ξ obtained by above-mentioned stepstObtain the position detection at target current time
Amount；
Monitoring region is divided into grid, and Δ υ is the length of side of grid, NrAnd NcIt is every row respectively and grid that each column includes
Number, the weight matrix that RTI is obtained first with oval weight model (are based on wirelessly penetrating referring to patent one kind
The safety monitoring system of frequency meshed network detection, 201120506303.2), wherein d=1,2 ..., Nr×NcIndicate grid number,
|ξt| indicate t moment t moment shadow fading link set ξtThe number of middle shadow fading link.RTI weight models are as shown in Figure 6.
By formulaThe imaging array of t moment target is found out, whereinμ is
Tikhonov regularization parameters, Δ xd,tIndicate d-th of element in t moment, 1≤d≤NrNc,
From formula (5) as a result, I is unit matrix；Due to cannot be guaranteed that middle all elements are positive value, negative that need to be by
Pressure be assigned to zero and to wherein element simultaneously divided by middle maximum value be normalized to obtain
Wherein indicate the RSS attenuations of the grid d in discrete instants t.Element by is by row stack arrangement at Nr×NcTwo
Dimension matrix can be imaged；Most bright spot is considered as the positional value (but target has volume occupy-place) of target in image, then by following public affairs
Formula provides RTI imaging results to which the target location obtained is：
YtIt is the position detection amount i.e. two-dimensional coordinate of target of the target described in step 5, for updating Kalman filter
The target location observed quantity of device；SymbolIt indicating to the downward rounding of data, D indicates to decay the serial number of maximum grid, 1≤D≤
NrNc；
Step 8：Obtain target current time location updating：
The location updating of t moment can be obtained according to following Kalman filter theory：
Wherein Pt|tFor t moment least mean-square error,It is the state variable of t moment.The first two elementThe as updated value of t moment target location.It elaborates to the present invention with reference to concrete signal example：
In this experiment, the node that we support IEEE802.15.4 agreements using 14.The working frequency of these nodes
For 2.4GHz.IEEE802.15.4 defines the channel from 11 to 26 in 2.4GHz frequency ranges, we use 11 in this laboratory
Channel.
These nodes measure RSS and transmit data on base-station node.We use a kind of similar with token ring logical
Letter agreement to obtain the RSS values of each of the links in real time.In t moment, sequence node number is the node transmission data of q, other sections
Point receives data and measures received signal strength.At the t+1 moment, sequence node number is the node transmission data of q+1, other nodes
It receives.As q=14, so that it may update once the RSS values of all links with measuring, q is assigned to 1 again at this time, is restarted
The new measurement of one wheel.In order to accelerate measuring speed, in the measurements, the information that each node is sent is the RSS with other nodes
Value.Base-station node only receives data and is transmitted to RSS values in local PC by serial ports.
In this experiment, node is placed in a common office environment.This environment is a typical room
Interior multi-path environment, the inside are dispersed with desk, chair, books, wall, the reflectors such as computer.14 nodes are arranged so that, wherein
10 nodes are placed on desk, and in addition 4 nodes are fixed on holder and are consistent with the height of node on desk.
The distribution of these nodes is as shown, the distance on desk between node is 1m, therefore 14 nodes can cover 5*4=20 squares
The monitoring region of rice.The arrangement of 14 nodes is as shown in Figure 7 in this experiment.
We measure in monitoring region the static RSS values of link when without target first, then measure target and exist and monitoring
The RSS values of link when in region.The target trajectory that we test in this experiment is rectangle, as shown in Figure 7.Local PC exists
After receiving signal, operation data processing routine, so that it may to obtain imaging and the positioning result of target.
By analyze comparative experiments simulation result can be seen that traditional RTI methods imaging results be disturbed influence it is very big,
Picture quality is poor.In RTI figures other than the bright spot of target, also a lot of other false bright spots, these bright spots are distributed in
In entire imaging region.Even at a time, the position that target should occur does not occur bright spot, and bright spot appears in it
His position.On the contrary, the imaging results that method proposed by the present invention obtains will be substantially better than traditional RTI methods, the bright spot of target and
Actual position is sufficiently close to, and other than target highlight, and almost without other main bright spots, image is cleaner.This
It is since method proposed by the present invention deletes interfering link so that image quality obtains very big enhancing.
It can be seen that many bright spots from the image that traditional RTI methods obtain so that the estimation of target number occurs wrong
Accidentally.Also include the target of falseness certainly to detect the number of target in figure.The image of generation first passes around binary conversion treatment,
Threshold value is chosen for 0.6 in this experiment, and gray value is assigned to 1 more than 0.6, and 0 is assigned to less than 0.6.Binaryzation can incite somebody to action
The several disjoint regions of image segmentation, the number for then counting disjoint range in bianry image can be obtained by the number of target
Mesh.From in experimental result it is concluded that only detecting a target at the time of 99% in proposition method of the present invention, only
It just will appear the situation of multiple targets at certain moment.And the probability of traditional technique in measuring to single target is 80%, Er Qie
Many moment can detect 3 even 4 targets.
It can be seen that the target trajectory that traditional RTI methods obtain is true with target in certain positions from target following track
Real movement locus falls far short, this is because interfering link so that serious offset has occurred in bright spot in the image generated so that mesh
There is very big error in target location estimation.However, for method proposed by the present invention, deleted due to the use of the method for motion prediction
In addition to those interfering links, the case where estimated location substantial deviation actual position, is substantially not present, therefore seems to make from figure
The track estimation obtained with motion prediction method is more smooth.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
It is familiar with the people of the technology within the technical scope disclosed by the invention, it will be appreciated that the transformation and replacement expected should all be covered at this
Within the scope of invention, therefore, the scope of protection of the invention shall be subject to the scope of protection specified in the patent claim.
Claims (9)
1. deleting the indoor wireless tomography Enhancement Method of interfering link based on motion prediction, which is characterized in that comprising as follows
Step：
Step 1：When target is located at monitoring region, the variation of each link received signals intensity (RSS values) is measured：
Step 1.1：Configuration node
Monitoring region is located at xoy coordinate planes, and o is coordinate origin；Equidistant be deployed in of η radio node is monitored into region week
It encloses, the height that all nodes are placed at i.e. all nodes placements on the same xoy coordinate planes is identical, and each node quilt
The unique ID number of distribution one is as mark, and the coordinate of each node is it is known that be (αq,βq), q=1,2 ..., η, wherein q are section
Point number；These radio nodes constitute L=η (η -1)/2 wireless links；
Each node sends signal according to preset agreement and sequential, and receive and measure other nodes sent out it is wireless
The RSS values of signal, method are as follows：In t moment, the node transmission data that number is q, other nodes receive data and measure reception
Signal strength；At the next moment, the node transmission data that number is q+1, other nodes receive data and measure receive signal it is strong
Degree；
Step 1.2：Measure the l articles link t moment RSS variation deltas rl,t：
When measuring monitoring region first does not have target, the RSS values of each of the links are rl, wherein l is the number of the link；Then it surveys
Measuring target, each of the links are in discrete instants t=1 when monitoring in region, and 2 ... the RSS values r ofl,t, to obtain the l articles link
It is in the variable quantity of the RSS values of t moment：
Δrl,t=rl,t-rl, l=1,2 ..., L. (4)
Δrl,tDecay for negative value expression；
Step 2：To step 1 obtain each of the links t moment RSS values variation delta rl,tIt is handled, is obtained current
The decline link set at moment
Step 2.1：In current time t, RSS variation deltas r that each of the links receivel,tFirst pass around moving average filter
To eliminate the noise become soon, 2 ω+1 are that sliding filter window is long；
Δrl,iFor link l the RSS values at t- ω≤i≤t+ ω moment variable quantity, then filtering after link l in t moment
RSS variable quantities are：
Step 2.2：A threshold value is set, the unconspicuous link of decaying is removed；Method is as follows, if discrete instants t=1,
2 ... when link l meet following formula just claim moment link l be decaying link：
WhereinIt is drop threshold, then t moment decline link set is：
Step 3：Obtain t moment decline link setIn it is all decaying links intersection point (uk,vk) constituted in monitoring region
Set, and it is referred to as intersection point set ρt；
Step 4：The initial position of target is obtained when carving t=1 at the beginning
Step 4.1：With clustering algorithm by the t moment obtained in step 3 decaying link intersection point set ρtAccording to Clustering features
Grouping, to find the class for having and significantly building up；
Assuming that K is the number of t moment intersection point set cluster, Φj,tIt is the set of intersection point in t moment cluster j, | Φj,t| indicate poly-
The number at the midpoints class j；Find the center (C each clustered under t momentx,j,Cy,j) make following the minimization of object function：
Here (Cx,j,Cy,j) it is intersection point set Φ under t momentj,tCenter position coordinates；
Step 4.2：T moment initializes K=1 and assumes that it includes the most cluster of intersection point at the moment to cluster j to be, is detected in such
Whether intersection point meetsWherein (uk,vk)∈Φj,t, and R is preset distance threshold；Such as
Fruit meets then iteration ends, and otherwise K values add 1, and return to step 4.1, which is found, makes the minimization of object function shown in formula (11)
(Cx,j,Cy,j)；It is final to obtain the number K clustered under t moment；
Step 4.3：Select set of the cluster as shadow fading link intersection point；In general shadow fading link intersection point set
The number of intersection point is maximum, thus the set of the intersection point of link that is blocked of these LOS paths and initial position can by following formula to
Go out：
WhereinIt indicates in t moment using the most cluster J of intersection point number in K cluster as with apparent poly-
Collect the class of characteristic,It is the coordinate estimation of target current time position；It is initial time as t=1, can obtains initial
Moment target location is
Step 5：Position based on Kalman filter prediction target in discrete instants t
When t >=2, the current time i.e. position of t moment target is estimated according to the target location at previous moment, that is, t-1 moment, works as t
It is initial time when=1, the target location of initial time is what step 4.3 obtainedAssuming that target is in monitoring region
For uniform motion, then the equation of motion of target is
According to the uniform motion model of target, it is known that：
XtFor the state variable of 4 dimensions, including coordinates of targets and speed；T is the sampling time interval to target state,WithIt is target respectively in t moment in the directions the x speed and the directions y speed of monitoring region xoy planes, (xt,yt) it is target in t
The position coordinates being engraved in monitoring region xoy planes；Assuming that the noise ε on the directions x and the directions y of xoy planest=[εx,t,
εy,t]TIt is Gaussian Profile, the covariance matrix of noise isIts value is determined according to target state；
Followed by Kalman filter obtain target t moment position coordinates estimated value：Assuming that the target position that t moment obtains
It is Y to set the observed quantity i.e. two-dimensional coordinate of targett, the wherein moment target locations t=1 obtain i.e. step from target initial position estimation
4.3 acquisitionsThe moment target location observed quantities of t >=2 YtIt is obtained from the imaging results of wireless tomography, YtAnd Xt
Relationship be：
Yt=HXt+wt (15)
Wherein assume wtBe mean value be zero, covariance matrix isGaussian distributed observation error,It is to measure
The variance of error；I is unit matrix；Observing matrix H is：
So Kalman filter theory obtains target and is in the location estimation of t moment according to the following formula
Wherein Pt|t-1It is prediction of the t-1 moment least mean-square error matrixes to t moment least mean-square error matrix, is referred to as minimum
Predict Square Error matrix, Pt-1|t-1It is t-1 moment least mean-square error matrixes；KtIt is t moment Kalman gains；It is t-1
Prediction of the moment to t moment dbjective state variable；It is state variable of the target at the t-1 moment,For state
VariableThe first two element；R is covariance matrix；
Step 6：According to obtained target location estimationDelete decaying link setIn non-shadow decline link,
Obtain shadow fading link subset ξt；Method is as follows：
If pointIt is the intersection point of shadow fading link, then the distance between target and the intersection point must satisfy：
WhereinIt is that the estimation of t moment target location coordinate is obtained according to the target location at t-1 moment in previous step
Value, RthFor distance threshold, value is more than the distance threshold R in step 4.2；If intersection point be unsatisfactory for formula (18) this
Part, then it is the intersection point of shadow fading link to judge this intersection point not, to remove the intersection point in the intersection point set of decaying link,
The intersection point set for meeting formula (18) in decaying link is obtained, the decaying link where these intersection points constitutes t moment shade and declines
Fall link set ξt；
Step 7：Shadow fading link set ξ obtained by above-mentioned stepstObtain the position detection amount at target current time；Side
Method is as follows：
The xoy planes in monitoring region are divided into grid, and Δ υ is the length of side of grid, NrAnd NcEvery row and each column include respectively
Meshes number, the weight matrix of wireless tomographyWherein d=1,2 ..., Nr×NcIndicate net
Lattice number, | ξt| indicate t moment shadow fading link set ξtThe number of middle shadow fading link；
By formulaThe imaging array of t moment target is found out, whereinμ
It is Tikhonov regularization parameters, Δ xd,tIndicate t momentIn d-th of element, 1≤d≤NrNc,From formula (5) as a result, I is unit matrix；Due to cannot be guaranteedMiddle all elements are
Positive value, need byIn negative pressure be assigned to zero and to wherein element simultaneously divided byMiddle maximum value, obtains normalized
Result afterwardsWhereinIndicate the RSS attenuations of the grid d in discrete instants t；It willIn element
By row stack arrangement at Nr×NcTwo-dimensional matrix can be imaged；Most bright spot is considered as the positional value of target in image, then by following
Formula provides imaging results to which the target location obtained is：
YtIt is the position detection amount i.e. two-dimensional coordinate of target of the target described in step 5, for updating Kalman filter
Target location observed quantity；SymbolIndicate that, to the downward rounding of data, D indicates the serial number of the maximum grid of decaying, 1≤D≤NrNc；
Step 8：Obtain target current time location updating：
According to following Kalman filter theory obtain target t moment location updating：
Wherein Pt|tFor t moment least mean-square error matrix,It is the state variable of t moment；The first two elementThe as updated value of t moment target location.
2. the indoor wireless tomography Enhancement Method of interfering link is deleted based on motion prediction according to claim 1,
It is characterized in that, the η radio node supports IEEE802.15.4 agreements.
3. the indoor wireless tomography Enhancement Method of interfering link is deleted based on motion prediction according to claim 1,
It is characterized in that, in step 1.1, each node sequentially sends and receives signal successively in the way of token ring.
4. the indoor wireless tomography Enhancement Method of interfering link is deleted based on motion prediction according to claim 1,
It is characterized in that, in step 2.1, the value range of sliding filter window length is 3~7.
5. the indoor wireless tomography Enhancement Method of interfering link is deleted based on motion prediction according to claim 1,
It is characterized in that, in step 2.2,Value range be -1dB~-3dB.
6. the indoor wireless tomography Enhancement Method of interfering link is deleted based on motion prediction according to claim 1,
It is characterized in that, in step 3, the method for obtaining the intersecting point coordinate of arbitrary both links is as follows：
In t moment, it is assumed that belong to setA link two node coordinates be respectively (αi,βi) and (αj,βj), belong to
Another link two node coordinates be respectively (αm,βm) and (αn,βn), then the intersecting point coordinate (u of this both linksk,
vk) meet：
The solution matrix form of this formula can be expressed as
Wherein []-1Inverse of a matrix matrix is sought in expression.
7. the indoor wireless tomography Enhancement Method of interfering link is deleted based on motion prediction according to claim 1,
It is characterized in that, in step 4.1, clustering algorithm uses K-means.
8. the indoor wireless tomography Enhancement Method of interfering link is deleted based on motion prediction according to claim 1,
It is characterized in that, in step 4.1, preset distance threshold R values are the radius of objective contour.
9. the indoor wireless tomography Enhancement Method of interfering link is deleted based on motion prediction according to claim 1,
It is characterized in that, the weight matrix of wireless tomography is obtained using oval weight model in step 7.
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| CN110703195A (en) * | 2019-09-08 | 2020-01-17 | 天津大学 | Indoor visible light passive positioning method based on spatial filter |
| CN112818514B (en) * | 2021-01-05 | 2023-02-07 | 河南工业大学 | Wireless tomography method and ellipse weight model combining horizontal distance attenuation |
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