CN107454570A - Maximum likelihood localization method with optimum choice is propagated based on minimal error - Google Patents
Maximum likelihood localization method with optimum choice is propagated based on minimal error Download PDFInfo
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- CN107454570A CN107454570A CN201710734544.4A CN201710734544A CN107454570A CN 107454570 A CN107454570 A CN 107454570A CN 201710734544 A CN201710734544 A CN 201710734544A CN 107454570 A CN107454570 A CN 107454570A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
Abstract
A kind of maximum likelihood localization method propagated based on minimal error with optimum choice, the maximum likelihood for being related to anchor node optimum choice improve localization method.The present invention is to effectively solve the problems, such as that communication distance evaluated error causes positioning precision relatively low.A kind of maximum likelihood localization method propagated based on minimal error with bubble sort method optimum choice of the present invention, unknown node is obtained to multiple sample values of distance estimations between each anchor node using the method for bilateral reciprocity distance estimations first, and statistical analysis, average statistical and the SS for obtaining each range estimation are poor；Then several distance estimations results of distance estimations average statistical and SS difference product value minimum are obtained using the method for bubble sort, and anchor node construction maximum likelihood positioning equation group corresponding to selecting, high-precision positioning result is finally obtained using criterion of least squares.
Description
Technical field
The present invention relates to high-precision distance estimations and wireless location technology.
Background technology
In actual wireless localizing environment, due to the influence of the undesirable elements such as noise, environment and measurement error, cause communication away from
There is larger error from estimation, cause maximum likelihood positioning precision relatively low.In view of the above-mentioned problems, the present invention is to anchor node redundancy
Under localizing environment, each anchor node is assessed to the product of communication distance estimation average statistical and SS difference between unknown node
Value, and using bubble sort method come distance value and anchor required during optimum choice maximum likelihood positioning equation set constructor
Node, the influence for reducing distance estimations error to positioning result is realized, so as to improve the purpose of maximum likelihood positioning precision.
The content of the invention
The invention aims to solve the problems, such as that communication distance evaluated error causes maximum likelihood positioning precision relatively low,
A kind of maximum likelihood localization method propagated based on minimal error with bubble sort method optimum choice is provided.
The maximum likelihood localization method of the present invention propagated based on minimal error with optimum choice, including following step
Suddenly：
Step 1: I+1 wireless sensor node of deployment, the anchor node and 1 unknown node of respectively I positioning, institute
Stating anchor node has nanoLOC rf receiver and transmitters；Wherein, I is the positive integer more than or equal to 5；
Step 2: each wireless sensor node is initialized, unknown node initially sets up wireless network, and waits
Other node applications add network；
Step 3: I anchor node scans the wireless network of unknown node foundation respectively, concurrent SCN Space Cable Network adds request data
Bag, application add the wireless network, if adding network success, perform step 4, otherwise, perform step 3；
Step 4: it is positive integer that initializing variable i, which is 1, i, and 1≤i≤I；
Step 5: unknown node sends Location Request packet by its rf receiver and transmitter to i-th of anchor node, the
After i anchor node receives Location Request packet, multiple Distance estimation is carried out using distance estimating algorithm and unknown node, obtained
The multiple measured value of distance between i-th of anchor node and unknown node, and carry out statistics calculating, using the average statistical of measured value as away from
From estimated result di_ u, the uncertainty d using the SS difference of measured value as distance estimations resulti_ σ, i=i+1；
Step 6: judging whether i value is more than I, if so, then performing step 7, step 5 is otherwise performed；
Step 7: according to the distance estimations result { d between unknown node and I anchor node1_u,d2_u,d3_ u ..., di_
U ..., dI_ u }, and uncertainty sequence { d corresponding to them1_σ,d2_σ,d3_ σ ..., di_ σ ..., dI_ σ } obtain error biography
Broadcast sequence Q={ d1_σ*d1_u,d2_σ*d2_u,d3_σ*d3_ u ..., di_σ*di_ u ..., dI_σ*dI_u}；
Step 8: error propagation sequence Q is ranked up, error propagation sequence Q '={ d ' after being sorted1_σ*d′1_
u,d′2_σ*d′2_u,d′3_σ*d′3_ u ..., d 'i_σ*d′i_ u ..., d 'I_σ*d′I_ u }, and take K wherein minimum value：
{d′1_σ*d′1_u,d′2_σ*d′2_u,d′3_σ*d′3_ u ..., d 'k_σ*d′k_ u ..., d 'K_σ*d′K_ u }, so that it is determined that K
Distance estimations result { d ' corresponding to value1_u,d′2_u,d′3_ u ..., d 'k_ u ..., d 'K_ u }, wherein K is positive integer, and 3<K<
I；
Step 9: the distance estimations result { d ' with reference to corresponding to K value1_u,d′2_u,d′3_ u ..., d 'k_ u ..., d 'K_
U }, and the coordinate information (x ' of K anchor node coordinate corresponding to K distance estimations result1, y '1), (x '2, y '2), (x '3, y
′3) ..., (x 'k, y 'k) ..., (x 'K, y 'K), according to criterion of least squares and maximum likelihood location algorithm, calculate unknown node
Coordinate.
Wherein, 5≤I≤12 in step 1.
Wherein, I values are 10 in step 1.
Wherein, K values are 8 in step 8.
Wherein, the number of multiple Distance estimation is 60~180 in step 5.
Wherein, the number of multiple Distance estimation is 150 in step 5.
Wherein, it is bubble sort to the mode that error propagation sequence Q is ranked up in the step 8.
Wherein, the distance estimating algorithm in the step 5 is SDS-TWR, RSSI, TOA, TDOA or AOA.
Wherein, the calculation formula of unknown node coordinate (x, y) is in step 9：
Wherein
The present invention has the following advantages that compared with background technology：
1st, can be in distance estimations average statistical and SS difference product using the method for bubble sort method optimum choice
The several of SS difference minimum are selected in the sequence of value, support is provided for the optimum choice of anchor node.
2nd, using based on minimal error propagation and bubble sort method optimum choice, distance estimations error is reduced to least square
The influence of positioning, realize high-precision least square positioning.
Brief description of the drawings
Fig. 1 is flow chart of the present invention.
Embodiment
Illustrate present embodiment with reference to Fig. 1, being propagated based on minimal error described in present embodiment and optimum choice are most
Maximum-likelihood localization method, comprises the following steps：
Step 1: I+1 wireless sensor node of deployment, the anchor node and 1 unknown node of respectively I positioning, it
All there is nanoLOC rf receiver and transmitters, and can be obtained using bilateral counterpart method measurement between any two node
Range estimation, wherein, I is the positive integer of user's setting, and 5≤I≤12, and I values are 10 in the present invention；
Step 2: each node is initialized in system, unknown node initially sets up wireless network, and waits other sections
Point application adds network；
Step 3: after I anchor node initializes successfully, nanoLOC rf receiver and transmitters scanning discovery is respectively adopted not
Know the wireless network that node is established, and network join request packet is sent by nanoLOC rf receiver and transmitters, application adds
Enter the wireless network, if adding network success, perform step 4, otherwise, perform step 3；
Step 4: it is positive integer that initializing variable i, which is 1, i, and 1≤i≤I；
Step 5: unknown node sends Location Request packet by its rf receiver and transmitter to i-th of anchor node, the
After i anchor node receives Location Request packet, using bilateral reciprocity distance-finding method, pass through 4J data between unknown node
Bag interaction, obtains the distance d between i-th of anchor node and unknown nodeiJ measured value：{di1,di2,di3,…,dij,…,
diJ, and statistics calculating is carried out, by the average statistical d of measured valuei_ u is used as distance diEstimated result, by the statistics of measured value
Standard deviation di_ σ is used as distance diThe uncertainty of estimated result, i=i+1, wherein j are positive integer, and 1≤j≤J, J are user
The positive integer of setting, and 60≤J≤180, in of the invention, J values are 150；
Step 6: judging whether i value is more than I, if so, then performing step 7, step 5 is otherwise performed；
Step 7: according to the distance estimations result { d between unknown node and I anchor node1_u,d2_u,d3_ u ..., di_
U ..., dI_ u }, and uncertainty sequence { d corresponding to them1_σ,d2_σ,d3_ σ ..., di_ σ ..., dI_ σ }, define error
Propagate sequence Q={ d1_σ*d1_u,d2_σ*d2_u,d3_σ*d3_ u ..., di_σ*di_ u ..., dI_σ*dI_u}；
Step 8: using caving area method, error propagation sequence Q '={ d ' after being sorted1_σ*d′1_u,d
′2_σ*d′2_u,d′3_σ*d′3_ u ..., d 'i_σ*d′i_ u ..., d 'I_σ*d′I_ u }, and take K wherein minimum value：{d′1_
σ*d′1_u,d′2_σ*d′2_u,d′3_σ*d′3_ u ..., d 'k_σ*d′k_ u ..., d 'K_σ*d′K_ u }, so that it is determined that it is corresponding away from
From estimated result { d '1_u,d′2_u,d′3_ u ..., d 'k_ u ..., d 'K_ u }, wherein K is the positive integer of user's setting, and 3<K<
I, K values are 8 in this patent；
Step 9: with reference to distance estimations result { d '1_u,d′2_u,d′3_ u ..., d 'k_ u ..., d 'K_ u }, and it is corresponding
Coordinate information (the x ' of K anchor node coordinate1, y '1), (x '2, y '2), (x '3, y '3) ..., (x 'k, y 'k) ..., (x 'K, y 'K),
And calculated according to criterion of least squares, the coordinate (x, y) of unknown node by formula (1)：
Wherein
Step 10: system judges whether maximum likelihood location Calculation task is completed, if it is, step 11 is performed, otherwise,
On next anchor point, step 4 is performed；
Step 11: terminate.
Present embodiment be to described in embodiment one it is a kind of based on minimal error propagate and optimum choice most
Maximum-likelihood localization method is described further, in present embodiment, using the method for bubble sort method optimum choice, can away from
It is anchor node from the several of the poor minimum of SS are selected in the sequence of estimation average statistical and the poor product value of SS
Optimum choice provides support.
In present embodiment, missed using being propagated based on minimal error with bubble sort method optimum choice, reduction distance estimations
Influence of the difference to least square positioning, realize high-precision least square positioning.
In present embodiment, the method for estimating distance of use can also be using other based on RSSI, TOA, TDOA and AOA etc.
Method for estimating distance.
In present embodiment, the sort method of use, other efficient sort methods can be also used.
In present embodiment, the localization method of use equally has for improving the maximum likelihood localization method under three-dimensional situation
Effect.
Claims (9)
1. the maximum likelihood localization method with optimum choice is propagated based on minimal error, it is characterised in that comprise the following steps：
Step 1: I+1 wireless sensor node of deployment, the anchor node and 1 unknown node of respectively I positioning, the anchor
Node has nanoLOC rf receiver and transmitters；Wherein, I is the positive integer more than or equal to 5；
Step 2: each wireless sensor node is initialized, unknown node initially sets up wireless network, and waits other
Node application adds network；
Step 3: I anchor node scans the wireless network of unknown node foundation respectively, concurrent SCN Space Cable Network adds request data package,
Application adds the wireless network, if adding network success, performs step 4, otherwise, performs step 3；
Step 4: it is positive integer that initializing variable i, which is 1, i, and 1≤i≤I；
Step 5: unknown node sends Location Request packet by its rf receiver and transmitter to i-th anchor node, i-th
After anchor node receives Location Request packet, multiple Distance estimation is carried out using distance estimating algorithm and unknown node, obtains i-th
The multiple measured value of distance between individual anchor node and unknown node, and statistics calculating is carried out, using the average statistical of measured value as distance
Estimated result di_ u, the uncertainty d using the SS difference of measured value as distance estimations resulti_ σ, i=i+1；
Step 6: judging whether i value is more than I, if so, then performing step 7, step 5 is otherwise performed；
Step 7: according to the distance estimations result { d between unknown node and I anchor node1_u,d2_u,d3_ u ..., di_ u ...,
dI_ u }, and uncertainty sequence { d corresponding to them1_σ,d2_σ,d3_ σ ..., di_ σ ..., dI_ σ } obtain error propagation sequence
Arrange Q={ d1_σ*d1_u,d2_σ*d2_u,d3_σ*d3_ u ..., di_σ*di_ u ..., dI_σ*dI_u}；
Step 8: error propagation sequence Q is ranked up, error propagation sequence Q '={ d ' after being sorted1_σ*d′1_u,
d′2_σ*d′2_u,d′3_σ*d′3_ u ..., d 'i_σ*d′i_ u ..., d 'I_σ*d′I_ u }, and take K wherein minimum value：{d′1_
σ*d′1_u,d′2_σ*d′2_u,d′3_σ*d′3_ u ..., d 'k_σ*d′k_ u ..., d 'K_σ*d′K_ u }, so that it is determined that K value is corresponding
Distance estimations result { d '1_u,d′2_u,d′3_ u ..., d 'k_ u ..., d 'K_ u }, wherein K is positive integer, and 3<K<I；
Step 9: the distance estimations result { d ' with reference to corresponding to K value1_u,d′2_u,d′3_ u ..., d 'k_ u ..., d 'K_ u }, with
And coordinate information (the x ' of K anchor node coordinate corresponding to K distance estimations result1, y '1), (x '2, y '2), (x '3, y '3) ...,
(x′k, y 'k) ..., (x 'K, y 'K), according to criterion of least squares and maximum likelihood location algorithm, calculate the coordinate of unknown node.
2. the maximum likelihood localization method according to claim 1 propagated based on minimal error with optimum choice, its feature
It is, 5≤I≤12 in step 1.
3. the maximum likelihood localization method according to claim 2 propagated based on minimal error with optimum choice, its feature
It is, I values are 10 in step 1.
4. the maximum likelihood localization method according to claim 3 propagated based on minimal error with optimum choice, its feature
It is, K values are 8 in step 8.
5. the maximum likelihood localization method according to claim 1 propagated based on minimal error with optimum choice, its feature
It is, the number of multiple Distance estimation is 60~180 in step 5.
6. the maximum likelihood localization method according to claim 5 propagated based on minimal error with optimum choice, its feature
It is, the number of multiple Distance estimation is 150 in step 5.
7. the maximum likelihood localization method according to claim 1 propagated based on minimal error with optimum choice, its feature
It is, is bubble sort to the mode that error propagation sequence Q is ranked up in the step 8.
8. a kind of propagated based on minimal error according to claim 1 is determined with the maximum likelihood of bubble sort method optimum choice
Position method, it is characterised in that the distance estimating algorithm in the step 5 is SDS-TWR, RSSI, TOA, TDOA or AOA.
9. a kind of propagated based on minimal error according to claim 1 is determined with the maximum likelihood of bubble sort method optimum choice
Position method, it is characterised in that the calculation formula of unknown node coordinate (x, y) is in step 9：
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2
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Application publication date: 20171208 |