CN109922432A - Pass through the object localization method of optimization fingerprint elements number under wireless communications environment - Google Patents
Pass through the object localization method of optimization fingerprint elements number under wireless communications environment Download PDFInfo
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Abstract
The invention discloses pass through the object localization method of optimization fingerprint elements number under a kind of wireless communications environment, based on short-distance wireless communication positioning system, using positioning accuracy and computational efficiency as target, using neural network as research tool, the accuracy and calculating cycle of combined training process feedback continue to optimize intrinsic dimensionality；In the training stage according to the historical track of positioning target, and finger print information composed by the collected characteristic information of institute is trained under these tracks, neural network is taken to carry out regression analysis, so that location information and characteristic information can be realized mapping, simultaneously according to the element number of the feature vector on the adjustment corresponding position of test process result, positioned to realize with most reasonable element number；Finally in test phase, by having completed the training pattern of indoor each position, specific location will be detected positioning target indoors.The present invention can choose the element number of optimal feature vector for current intelligence in position fixing process, to improve the efficiency of collection apparatus and provide more accurate location information.
Description
Technical field
The present invention relates to one kind to be based on deep learning visual angle, builds the indoor locating system based on short-distance wireless communication,
Belong to wireless communication field of locating technology.
Background technique
Fingerprint recognition is the important method for solving the problems, such as indoor positioning.So-called fingerprint (finger print information), just referring to will be traditional
Location algorithm in received signal strength (RSSI), arrival time (TOA), reaching time-difference (TDOA) etc. position target acquisition
The characteristic set that the characteristic information about each broadcast node arrived forms.So in the present invention, commonly using " fingerprint " to indicate
These characteristic informations from each anchor node are combined into a vector and calculated by collected feature.
Above-mentioned collected characteristic information is mainly directly used in calculating by traditional localization method, this is because flat in two dimension
In face, three anchor nodes in space can determine the position of positioning target by solving the secondary equation group of ternary, such as
Fruit then at least arranges four anchor nodes in view of the altitudes of positioning target in three-dimensional planar.But traditional positioning side
It is the one group of characteristic information put successively using current time from anchor that method calculates all every time, due to the multipath effect in space, is used
In the characteristic information of calculating in same position be all continually changing, so higher positioning accuracy can not be provided.
The feature vector that multiple groups feature forms then is used to indicate a certain specific location by the indoor orientation method based on fingerprint
Attribute, the relevance of finger print information and corresponding position is excavated by neural network (present invention be based on BP neural network), together
When the present invention also pass through neural network and go to learn optimal feature vector dimension.In an indoor locating system, target is positioned
The characteristic information put successively from each anchor can be constantly collected, and in meeting composition characteristic vector investment neural network, with fixed
The position of position target is trained as target.Since the element number of the fingerprint vector of selection is adjustable, if choosing
It takes the number of element lower, is then difficult to reflect location information, positioning accuracy is lower；, whereas if the corresponding finger in each position
Line length is too long, then the position number for participating in calculating in entire calculating process can tail off, and reduces so as to cause the efficiency of calculating.
So according to the training of neural network be system formulate a set of optimal intrinsic dimensionality can combine positioning accuracy and
Computational efficiency.
With the increase of indoor positioning business demand, using traditional localization method or only using only simple statistics side
Method is difficult to excavate the relevance of these characteristic informations and position, so using neural network using the theory of deep learning
Method is to solve data nonbalance in indoor positioning, overcomes the inexorable trend of various interference.
Summary of the invention
Goal of the invention: in order to overcome the deficiencies in the prior art, the present invention provides leads under a kind of wireless communications environment
The object localization method of optimization fingerprint elements number is crossed, this method theory neural network based is solved in short-distance wireless
To the optimization of the dimension of the finger print information of input neural network in communications localization system, realization combines fixed in position fixing process
The accuracy of position, and it is able to maintain higher computational efficiency.
Technical solution: to achieve the above object, the technical solution adopted by the present invention are as follows:
By the object localization method of optimization fingerprint elements number under a kind of wireless communications environment, with short-distance wireless communication
Based on positioning system, using positioning accuracy and computational efficiency as target, using neural network as research tool, combined training process is anti-
The accuracy and calculating cycle of feedback continue to optimize intrinsic dimensionality；Since the output valve of neural network is specific coordinate, so
What the positioning system was finally fed back is motion profile made of having multiple coordinate fittings.
In the present invention, the evaluation index of short-distance wireless positioning system is divided into positioning accuracy and computational efficiency.Positioning accuracy
It is that specific bit target finger print information collected passes through the error after calculating in neural network with physical location.Computational efficiency
Refer to the number in the location information of limited time range internal feedback, since the dimension of fingerprint will affect calculating often, institute
To choose the element number of suitable fingerprint vector when constructing neural network, simultaneously because the motility of object, is positioning
Number of coordinates in the process is no preset, so it is one that neural network cannot be simply considered that in building process
More classification problems, but one should be constructed and have the network structure for returning function.
In the training stage according to the historical track of positioning target, and the collected characteristic information institute, institute under these tracks
The finger print information of composition is trained, and is taken neural network to carry out regression analysis, is enabled location information and characteristic information real
It now maps, while according to the element number of the feature vector on the adjustment corresponding position of test process result, to realize with most
Reasonable element number is positioned；Finally in test phase, by having completed the training pattern of indoor each position, positioning
Specific location will be detected target indoors.
For neural network demand set forth above, since fingerprint is one-dimensional vector form, so using normal in the present invention
Basic framework of the BP neural network (BPNN, Back Propagation Neural Network) as neural network, makes
The recurrence output of neural network is realized as activation primitive with line rectification function (ReLU, Rectified Linear Unit).
BP neural network contains input layer, hidden layer, output layer three-decker.Input layer receives data, and output layer output data is preceding
One layer of neuron is connected to next layer of neuron, collects the information that upper one layer of neuron transmitting comes, will by ReLu activation primitive
Upper one layer of value passes to next layer, realizes nonlinear mapping.BP neural network has the machine of propagated forward and backpropagation
System, constantly optimizes network parameter, eventually learns to a regression model by fingerprint vector coordinates computed.
The present invention, using the regression model of neural network, realizes in short-distance wireless communication indoor positioning environment
The element number of suitable fingerprint vector is chosen under current intelligence to be positioned, as shown in Figure 5, comprising the following steps:
Step 1, it builds based on short-distance wireless communication indoor positioning environment, acquires fingerprint in the case where positioning target motion conditions
Information initializes the element number of fingerprint；
Step 2, finger print information is sliced according to different fingerprint elements numbers, and respective coordinates；
Step 3, step 1 and step 2 are repeated, acquires mass data under same fingerprint elements number；
Step 4, it is trained using BP neural network, using ReLU activation primitive, by training pattern, in test process
Record location precision and computational efficiency；
Step 5, increase the element number of fingerprint, repeat step 1-4, and step 4 is obtained into the knot of fingerprint elements not of the same race
Fruit is compared；It repeats step 5 more times, obtains the result under a variety of fingerprint elements numbers；
Step 6, optimal fingerprint elements number is found according to the result of step 5, obtains optimal training pattern；
Step 7, it according to the training pattern of step 6, is tested, completes building for positioning system.
It is preferred: to be recorded in T time and position target voltuntary movement in orientation range, while being recorded in T time every
The movement position at a moment obtains one group within the T moment from the finger print information A of all anchor nodesAlways, indicate are as follows:
AAlways={ a0,a1,a2,…aS}
Wherein, S is total characteristic information number；
By AAlwaysMultiple subsets are divided into according to receiving sequence, subset is expressed as Cm, wherein m ∈ { 1,2,3 ..., l }, each
The number of a is N, subset C in subsetm={ am-0,am-1,...,am-n-1, wherein the time interval of subset is τ, subset numberThis is because the element in subset has the characteristic information from different anchor nodes, it should be comprising working as in a subset
Characteristic information from all anchor nodes in preceding time interval τ；
After determining subset length N and characteristic information is combined into fingerprint, the input of BP neural network training process will
It can be determined, and subset CmCorresponding coordinateBy positioning target in CmMoving range in corresponding time interval τ
Central point is determined.
Preferred: BP neural network includes treatment process below in training process input and output:
Input:
1. the mark of the finger print information of each time interval τ and corresponding broadcast node, according to short-distance wireless communication system
The feature of system, by modifying the mark of anchor node to realize differentiation；
2., in the case where total amount is certain, each broadcast saves shared element number after given fingerprint vector element number
It should be identical；When the fingerprint characteristic dimension currently chosen is N, if there is M anchor node, then each anchor puts shared fingerprint successively
Element number should be
Output:
1. the coordinate of training process movement locus of object and corresponding finger print data on same time shaft, such as I
The fingerprint characteristic dimension currently chosen be N, then corresponding fingerprint can be expressed as A={ a0,a1,a2,…aN-1, wherein a
It is the combination of each feature, i.e. a=[RSSI, TDOA, TOA...], while we can be according to a0And aN-1The corresponding time
Axis finds corresponding coordinate position, and the coordinates of the fingerprint corresponding points can be identified by asking the midpoint of two o'clock, and (experiment shows to refer to
Line acquisition rate is very fast, and the error based on coordinate selected by the train interval received and actual coordinate can be ignored).
Preferred: the input vector of single training is Cm={ am-0,am-1,...,am-n-1, then corresponding output vector isAccording to the structure of BP neural network, ReLU function isWherein λ is disposed proximate to 0
Count or be directly disposed as 0；If Wij kFor the connection weight of -1 layer of j-th of neuron and kth layer of kth, bi kIt is i-th of kth layer
The biasing of neuron, then:
hi k=f (neti k)
And h is every layer of input element, the input of first layer is Cm, wherein neti kBe from the sum of upper one layer weight i.e.
Just be over the calculating process of forward-propagating above, needs to correct W by backpropagation in BP neural networkij kWith
bi k；Backpropagation is being executed it needs to be determined that loss functionIts
Middle β is weight coefficient, and 0 < β < 1, TcostIt is to calculate the time,Expression is instructed in certain iteration of neural network
Practice the output valve of process.
Objective function of the loss function as test process, i.e. test target simultaneously are as follows:
Finally according to the loss function of definition in the following manner to Wij kAnd bi kIt is updated:
Wherein, α is learning rate, and the correspondence N of loss function is recorded in the above process, by the result of test process come
Judge the optimal element number of fingerprint
Preferred: short-distance wireless communication positioning system includes anchor node, positioning node and top service device, according to short
The agreement of distance wireless communication, anchor node constantly send various characteristic informations to positioning node, and positioning node is by parsing this
A little information analyze the mark and characteristic information of anchor node；The positioning node anchor section that mode will receive by wireless communication
Point identification and corresponding characteristic information are transmitted to the server on upper layer；The information structuring that will will be collected into top service device
Optimal fingerprint elements number is found by the training process of neural network at finger print data, positioning accuracy is realized and calculates effect
The optimization of rate
The present invention compared with prior art, has the advantages that
The present invention is based on the visual angles of neural network theory to be fed back by being trained to the finger print information in positioning engineering
Positioning accuracy and computational efficiency under different fingerprint dimensions, so that it is determined that optimal fingerprint dimension under present systems.
Detailed description of the invention
Fig. 1 is the positioning system based on short-distance wireless communication.
Fig. 2 is fingerprint vector structure figure.
Fig. 3 is BP neural network basic block diagram.
Based on the neuron operational model of ReLU activation primitive when Fig. 4.
Fig. 5 is the indoor positioning optimization algorithm process under short distance radio communication system based on best fingerprint elements number.
Specific embodiment
In the following with reference to the drawings and specific embodiments, the present invention is furture elucidated, it should be understood that these examples are merely to illustrate this
It invents rather than limits the scope of the invention, after the present invention has been read, those skilled in the art are to of the invention various
The modification of equivalent form falls within the application range as defined in the appended claims.
By the object localization method of optimization fingerprint elements number under a kind of wireless communications environment, by arrange indoors with
Such as localizing environment of the short-range wireless communication apparatus such as bluetooth, RFID, wifi as anchor node, so that positioning target apperception week
The signal strength of mid-side node, reaching time-difference, the features such as arrival time, and using these features as the component of fingerprint, so
Afterwards by the training of neural network and test process enable positioning target it is adaptive position；It is needed in the training stage
According to the historical track of positioning target, and under these tracks, the collected characteristic information of institute is trained, and mainly takes mind
Regression analysis is carried out through network, so that location information and characteristic information can be realized mapping, while according to test process result
The element number of feature vector on quality adjustment corresponding position, is positioned to realize with most reasonable element number, simultaneous
Care for reliable positioning accuracy and higher operation efficiency；Finally in test phase, due to having completed indoor each position
Training pattern is built, and specific location will be detected positioning target indoors.The present invention can be directed in position fixing process
Current intelligence chooses the element number of optimal feature vector, to improve the efficiency of collection apparatus and provide more accurate
Location information.Positioning accuracy is specific bit target finger print information collected by after calculating in neural network and actual bit
The error set；Computational efficiency refers to the number in the location information of limited time range internal feedback；Finger print information refer to by about
Received signal strength, arrival time, reaching time-difference etc. position the collected characteristic information about each broadcast node of target
The characteristic set of composition.
The present invention is based on the visual angles of deep learning, study fingerprint under indoor positioning environment by building neural network model
The number of element.The present invention can improve the computational efficiency based on fingerprinting localization algorithm under the premise of guaranteeing positioning accuracy.
It is short-distance wireless communication indoor locating system structure chart as shown in Figure 1, it can be seen that whole system includes anchor section
The server on point (i.e. broadcast node), positioning node and upper layer.According to the agreement of short-distance wireless communication, anchor node can not
It is disconnected to send various characteristic informations to positioning node, positioning node by parsing these information, analyze the mark of anchor node with
And characteristic information.Positioning node can for example, by lora, the communications such as wifi the anchor node received is identified and
Corresponding characteristic information is transmitted to the server on upper layer.Algorithm proposed by the present invention will be completed in top service device, will be collected
The information structuring arrived finds optimal fingerprint elements number by the training process of neural network at finger print data, realizes positioning
The optimization of precision and computational efficiency.
It is one group of dactylotype with N number of element as shown in Figure 2, it can be seen that fingerprint is divided into M unit, this is
Because the information of M anchor node may be will receive simultaneously in time interval τ for positioning node.Due to these anchor sections
The information of point is sequentially random when being sent to server by positioning node, so needing in server to time interval τ
It is interior that the characteristic information from M anchor node is ranked up, form the form of Fig. 2.Illustratively restrictive condition is needed exist for, by
Be in the number of the signal strength of anchor node each in fingerprint vector it is identical, so always a if setting fingerprint elements
Number, then needing to guarantee that the signal strength information number of each anchor node in time interval τ will at least reach
By analyzing above, we set data acquisition total time as T, i.e., target is positioned in T time in orientation range
Voltuntary movement, while being recorded in the movement position at each moment in T time (this process can be by other localization methods, example
If camera positions, the modes such as binding sensor positioning obtain the position of actual motion, for the finger based on short-distance wireless communication
Line positioning provides object of reference).In this way, we will obtain one group of finger print information from all anchor nodes within the T moment.It can be with table
It is shown as:
AAlways={ a0,a1,a2,…aS}
Wherein S is to generally refer to line information number, since data can be in server repository, so coming from the present invention
The receiving time of the feature of anchor node and the position time of positioning target can correspond.According to described by Summary
The case where, it needs AAlwaysMultiple subsets are divided into according to receiving sequence, subset can be expressed as Cm, wherein m ∈ 1,2,3 ...,
L }, the number of a is N, subset C in each subsetm={ am-0,am-1,...,am-n-1, wherein the time interval of subset is τ, subset
NumberThis is because distance of each anchor node from positioning target is different, it will lead to anchor node and broadcasted in the unit time
The number that information is received is different, but needs to meet the signal strength for needing to meet each anchor node in time interval τ simultaneously
Number will at least reachAccording to constraint before, due to requiring the characteristic information number of each anchor node in N identical, so
The characteristic information that will lead to part is rejected.In conclusion when determining subset length N and completing collated in server
Journey, then the input of BP neural network training process will be determined, and subset CmCorresponding coordinateIt can pass through
Target is positioned in CmThe central point of moving range is determined in corresponding time interval τ.
It is the process that the structure of BP neural network and neuron pass through excitation function in the present invention as shown in Figure 3 and Figure 4.
BP neural network is widely used in practical applications using error inverse algorithm training feedforward neural network, BP mind
Classification or the regression forecasting of multiple target may be implemented through network.In the present invention mainly after the element number of given fingerprint
The recurrence that positioning coordinate is realized by BP neural network adjusts element number N by verifying regression model quality.
The input vector of single training is Cm={ am-0,am-1,...,am-n-1, then corresponding output vector isAccording to the structure of BP neural network, if ReLU function isWherein λ can be set to
0 is counted or is directly disposed as close to 0.If Wij kFor the connection weight of -1 layer of j-th of neuron and kth layer of kth, bi kFor kth layer
The biasing of i-th of neuron, then be easy to get:
hi k=f (neti k)
And h is every layer of input element, the input of first layer is exactly C in the present inventionm, wherein neti kIt is from upper one layer
The sum of weight is i.e.:
Just be over the calculating process of forward-propagating above, needs to correct W and b by backpropagation in BP neural network.
Backpropagation is being executed it needs to be determined that loss functionWherein β
It is weight coefficient, and 0 < β < 1, TcostIt is to calculate the time, mostlys come from service in entire position fixing process evaluation time of falling into a trap
Device needs arrange the finger print information received according to the mark of anchor node.The loss function can also be used as test simultaneously
The objective function of process, i.e. test target are as follows:
Finally W and b can be updated in the following manner according to the loss function of definition:
Wherein α is learning rate.According to the above process, the correspondence N of lower loss function will record in the present invention, due to initial
N will not take excessive, so while have lower computation rate, but positioning accuracy will not be too high, so such as Fig. 5 institute
N can be continuously increased in the present invention by showing, to avoid over-fitting, need to judge the member that fingerprint is optimal by the result of test process
Plain number.Fingerprint preparation process in test process needs consistent with the progress of corresponding training process, it should be noted that if
The positioning accuracy that the case where over-fitting so test process occurs in training pattern will not be too ideal, only chooses suitable fingerprint
Element number is just avoided that over-fitting.Indoors in orientation problem, due to the variation of environment, optimal N may be different, but
It is method provided by the present invention is general in the indoor positioning problem based on fingerprint.
The above is only a preferred embodiment of the present invention, it should be pointed out that: for the ordinary skill people of the art
For member, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also answered
It is considered as protection scope of the present invention.
Claims (7)
1. passing through the object localization method of optimization fingerprint elements number under a kind of wireless communications environment, it is characterised in that: with short distance
Based on wireless communication positioning system, using positioning accuracy and computational efficiency as target, using neural network as research tool, in conjunction with
The accuracy and calculating cycle of training process feedback continue to optimize intrinsic dimensionality；In the training stage according to the history of positioning target
Track, and finger print information composed by the collected characteristic information of institute is trained under these tracks, takes neural network
Regression analysis is carried out, so that location information and characteristic information can be realized mapping, while according to the adjustment pair of test process result
The element number of the feature vector on position is answered, is positioned to realize with most reasonable element number；Finally in test rank
Section, by having completed the training pattern of indoor each position, specific location will be detected positioning target indoors.
2. special according to claim 1 by the object localization method of optimization fingerprint elements number under wireless communications environment
Sign is: the following steps are included:
Step 1, it builds based on short-distance wireless communication indoor positioning environment, fingerprint letter is acquired in the case where positioning target motion conditions
Breath, initializes the element number of fingerprint；
Step 2, finger print information is sliced according to different fingerprint elements numbers, and respective coordinates；
Step 3, step 1 and step 2 are repeated, acquires mass data under same fingerprint elements number；
Step 4, it is trained using BP neural network, is recorded by training pattern in test process using ReLU activation primitive
Positioning accuracy and computational efficiency；
Step 5, increase the element number of fingerprint, repeat step 1-4, and by step 4 obtain the results of fingerprint elements not of the same race into
Row compares；It repeats step 5 more times, obtains the result under a variety of fingerprint elements numbers；
Step 6, optimal fingerprint elements number is found according to the result of step 5, obtains optimal training pattern；
Step 7, it according to the training pattern of step 6, is tested, completes building for positioning system.
3. special according to claim 2 by the object localization method of optimization fingerprint elements number under wireless communications environment
Sign is: being recorded in T time, positions target voltuntary movement in orientation range, while being recorded in each moment in T time
Movement position obtains one group within the T moment from the characteristic information A of all anchor nodesAlways, indicate are as follows:
AAlways={ a0,a1,a2,…aS}
Wherein, S is total characteristic information number；
By AAlwaysMultiple subsets are divided into according to receiving sequence, subset is expressed as Cm, wherein m ∈ { 1,2,3 ..., l }, each subset
The number of middle a is N, subset Cm={ am-0,am-1,...,am-n-1, wherein the time interval of subset is τ, subset number
This is because the element in subset has the characteristic information from different anchor nodes, it should include current time in a subset
Characteristic information from all anchor nodes in interval τ；
After determining subset length N and characteristic information is combined into fingerprint, the input of BP neural network training process will be by
It determines, and subset CmCorresponding coordinateBy positioning target in CmIn corresponding time interval τ in moving range
Heart point is determined.
4. special according to claim 3 by the object localization method of optimization fingerprint elements number under wireless communications environment
Sign is: BP neural network includes treatment process below in training process input and output:
Input:
1. the mark of the finger print information of each time interval τ and corresponding broadcast node, according to short distance radio communication system
Feature, by modifying the mark of anchor node to realize differentiation；
2., in the case where total amount is certain, each broadcast saves shared element number should after given fingerprint vector element number
It is identical；When the fingerprint characteristic dimension currently chosen is N, if there is M anchor node, then the element occupied that each anchor is put successively
Number should be
Output:
1. the coordinate of training process movement locus of object and corresponding finger print data on same time shaft, currently choose
Fingerprint characteristic dimension is N, and corresponding fingerprint representation is A={ a0,a1,a2,…aN-1, wherein a is the combination of each feature, i.e. a
=[RSSI, TDOA, TOA...], while according to a0And aN-1Corresponding time shaft find corresponding coordinate position, by asking two
The midpoint of point identifies the coordinates of the fingerprint corresponding points.
5. special according to claim 4 by the object localization method of optimization fingerprint elements number under wireless communications environment
Sign is: the input vector of single training is Cm={ am-0,am-1,...,am-n-1, then corresponding output vector is
According to the structure of BP neural network, ReLU function isWherein λ is disposed proximate to 0 number or straight
It connects and is set as 0；If Wij kFor the connection weight of -1 layer of j-th of neuron and kth layer of kth, bi kFor the inclined of i-th of neuron of kth layer
It sets, then:
hi k=f (neti k)
And h is every layer of input element, the input of first layer is Cm, wherein neti kBe from the sum of upper one layer weight i.e.
Just be over the calculating process of forward-propagating above, needs to correct W by backpropagation in BP neural networkij kAnd bi k；?
Backpropagation is executed it needs to be determined that loss functionWherein β is
Weight coefficient, and 0 < β < 1, TcostIt is to calculate the time,Indicate the training process in certain iteration of neural network
Output valve；
Objective function of the loss function as test process, i.e. test target simultaneously are as follows:
Finally according to the loss function of definition in the following manner to Wij kAnd bi kIt is updated:
Wherein, α is learning rate, and the correspondence N of loss function is recorded in the above process, is judged by the result of test process
The optimal element number of fingerprint.
6. special according to claim 5 by the object localization method of optimization fingerprint elements number under wireless communications environment
Sign is: short-distance wireless communication positioning system includes anchor node, positioning node and top service device, according to short-distance wireless
The agreement of communication, anchor node constantly send various characteristic informations to positioning node, and positioning node is divided by parsing these information
The mark and characteristic information of anchor node is precipitated；Positioning node by wireless communication mode by the anchor node received identify and
Corresponding characteristic information is transmitted to the server on upper layer；In top service device by by the information structuring being collected at fingerprint number
According to by the training process of neural network, finding optimal fingerprint elements number, realize the excellent of positioning accuracy and computational efficiency
Change.
7. special according to claim 6 by the object localization method of optimization fingerprint elements number under wireless communications environment
Sign is: the anchor node is the combination of one or more of bluetooth, RFID or wifi.
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| CN111405461A (en) * | 2020-03-16 | 2020-07-10 | 南京邮电大学 | Wireless indoor positioning method for optimizing equal-interval fingerprint sampling number |
| CN117930135A (en) * | 2024-03-21 | 2024-04-26 | 广东海洋大学 | TDOA moving target positioning method based on NRZND model |
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