CN107479026A - A kind of weighted mass center localization method for the anchor node optimum choice propagated based on minimal error - Google Patents

A kind of weighted mass center localization method for the anchor node optimum choice propagated based on minimal error Download PDF

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CN107479026A
CN107479026A CN201710732831.1A CN201710732831A CN107479026A CN 107479026 A CN107479026 A CN 107479026A CN 201710732831 A CN201710732831 A CN 201710732831A CN 107479026 A CN107479026 A CN 107479026A
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mrow
anchor node
node
mass center
weighted mass
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焉晓贞
罗清华
韩志豪
周鹏太
杨鹏
杨一鹏
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Harbin Institute of Technology Weihai
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Harbin Institute of Technology Weihai
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/14—Determining absolute distances from a plurality of spaced points of known location
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

A kind of weighted mass center localization method for the anchor node optimum choice propagated based on minimal error, the weighted mass center 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 weighted mass center localization method of anchor node optimum choice propagated based on minimal error 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 distance estimations average statistical and several range estimations of SS difference product value minimum, and anchor node construction weighted mass center positioning equation group corresponding to selection are obtained using dynamic sliding window and the method for single pass；Finally obtain high-precision positioning result.

Description

A kind of weighted mass center positioning of the anchor node optimum choice propagated based on minimal error Method
Technical field
The present invention relates to high-precision distance estimations and location technology.
Background technology
In actual wireless communication 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 weighted mass center 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 distance value and anchor node needed for being come during optimum choice weighted mass center positioning equation set constructor with this, are realized and reduced Influence of the distance estimations error to positioning result, so as to improve the purpose of weighted mass center positioning precision.
The content of the invention
The invention aims to solve the problems, such as that communication distance evaluated error causes positioning precision relatively low, there is provided a kind of The weighted mass center localization method for the anchor node optimum choice propagated based on minimal error.
A kind of weighted mass center localization method bag of anchor node optimum choice propagated based on minimal error of the present invention Include following steps：
Step 1: have I+1 wireless sensor node in system, the anchor node A={ A of respectively I positioning1,A2, A3,…,Ai,…,AIAnd 1 unknown node, they all have nanoLOC rf receiver and transmitters, and can use it is bilateral right The methods of measurement obtain the range estimation between any two node, wherein i is positive integer, and 1≤i≤I, I are user's setting Positive integer, and 4≤I≤15, 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, the foundation of RF transceiver scanning discovery unknown node is respectively adopted Wireless network, and network join request packet is sent by RF transceiver, application adds the wireless network, if adding net Network success, then perform 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 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 mark of measured value Accurate poor di_ σ is used as distance diThe uncertainty of estimated result, i=i+1, wherein j are positive integer, and 1≤j≤J, J set for user Fixed positive integer, and 50≤J≤150, in of the invention, J values are 100；
Step 6: judging whether i value is more than I, if so, then performing step 7, otherwise, step 5 is performed；
Step 7: system obtains 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 }, definition misses Difference propagates sequence Q={ d1_σ*d1_u,d2_σ*d2_u,d3_σ*d3_ u ..., di_σ*di_ u ..., dI_σ*dI_ u }, definition estimation Mass parameter sliding window w={ w1,w2,w3,…,wk,…,wK, wherein 1≤i≤I, 1≤k≤K, i and k initialization value are 1, Wherein K is user-defined positive integer, and 3≤K≤I, and in this patent, K values are 8, defined variable l, and 1≤l≤K, l are initial It is worth for K；
Step 8: system judges whether k is more than K, if it is, k values are set to 1, step 10 is performed, otherwise performs step 9；
Step 9: wkValue be set to Inf, wherein Inf is maximum real number, k=k+1, performs step 8；
Step 10: system judges whether i is more than I, if it is, performing step 15, step 11 is otherwise performed；
Step 11: system judges whether k is more than K, if it is, k values are set to 1, i=i+1, step 10 is performed, is otherwise held Row step 12；
Step 12: system judges diWhether _ σ is less than wk, if so, performing step 13, otherwise, k=k+1, perform step 11；
Step 13: system judges whether l is less than k, if it is, l value is set to K, wl=di_σ*di_ u, perform step 10 One, otherwise, perform step 14；
Step 14: wl=wl-1, l=l-1, perform step 13；
Step 15: system obtains sliding window sequence w={ w1,w2,w3,…,wk,…,wK, its corresponding distance is estimated Distance estimations result d'={ d' of the evaluation sequence as optimum choice1_u,d'2_u,d'3_ u ..., d'k_ u ..., d'K_ u }, will Anchor node A'={ A' corresponding to the distance estimations result of optimum choice1,A'2,A'3,…,A'k,…,A'KAs optimum choice Anchor node, perform step 10 six；
Step 16: system is according to distance estimations result { d'1_u,d'2_u,d'3_ u ..., d'k_ u ..., d'K_ u }, and Coordinate information (the x' of corresponding K anchor node coordinate1, y'1), (x'2, y'2), (x'3, y'3) ..., (x'k, y'k) ..., (x'K, y'K), and criterion of least squares is combined, the coordinate (x, y) of unknown node is calculated by formula (1) and formula (2) respectively：
Wherein k is positive integer, and 1≤k≤K；
Step 17: judge whether weighted mass center location tasks are completed, if it is, step 10 eight is performed, otherwise, next On individual anchor point, step 4 is performed；
Step 18: terminate the weighted mass center location tasks of anchor node optimum choice propagated based on minimal error.
Brief description of the drawings
Fig. 1 is a kind of flow chart of the weighted mass center localization method for the anchor node optimum choice propagated based on minimal error.
Embodiment
Embodiment one：Illustrate present embodiment with reference to Fig. 1, one kind described in present embodiment is based on minimal error The weighted mass center localization method of the anchor node optimum choice of propagation comprises the following steps：
Step 1: have I+1 wireless sensor node in system, the anchor node A={ A of respectively I positioning1,A2, A3,…,Ai,…,AIAnd 1 unknown node, they all have nanoLOC rf receiver and transmitters, and can use it is bilateral right The methods of measurement obtain the range estimation between any two node, wherein i is positive integer, and 1≤i≤I, I are user's setting Positive integer, and 4≤I≤15, 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, the foundation of RF transceiver scanning discovery unknown node is respectively adopted Wireless network, and network join request packet is sent by RF transceiver, application adds the wireless network, if adding net Network success, then perform 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 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 mark of measured value Accurate poor di_ σ is used as distance diThe uncertainty of estimated result, i=i+1, wherein j are positive integer, and 1≤j≤J, J set for user Fixed positive integer, and 50≤J≤150, in of the invention, J values are 100；
Step 6: judging whether i value is more than I, if so, then performing step 7, otherwise, step 5 is performed；
Step 7: system obtains 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 }, definition misses Difference propagates sequence Q={ d1_σ*d1_u,d2_σ*d2_u,d3_σ*d3_ u ..., di_σ*di_ u ..., dI_σ*dI_ u }, definition estimation Mass parameter sliding window w={ w1,w2,w3,…,wk,…,wK, wherein 1≤i≤I, 1≤k≤K, i and k initialization value are 1, Wherein K is user-defined positive integer, and 3≤K≤I, and in this patent, K values are 8, defined variable l, and 1≤l≤K, l are initial It is worth for K；
Step 8: system judges whether k is more than K, if it is, k values are set to 1, step 10 is performed, otherwise performs step 9；
Step 9: wkValue be set to Inf, wherein Inf is maximum real number, k=k+1, performs step 8；
Step 10: system judges whether i is more than I, if it is, performing step 15, step 11 is otherwise performed；
Step 11: system judges whether k is more than K, if it is, k values are set to 1, i=i+1, step 10 is performed, is otherwise held Row step 12；
Step 12: system judges diWhether _ σ is less than wk, if so, performing step 13, otherwise, k=k+1, perform step 11；
Step 13: system judges whether l is less than k, if it is, l value is set to K, wl=di_σ*di_ u, perform step 10 One, otherwise, perform step 14；
Step 14: wl=wl-1, l=l-1, perform step 13；
Step 15: system obtains sliding window sequence w={ w1,w2,w3,…,wk,…,wK, its corresponding distance is estimated Distance estimations result d'={ d' of the evaluation sequence as optimum choice1_u,d'2_u,d'3_ u ..., d'k_ u ..., d'K_ u }, will Anchor node A'={ A' corresponding to the distance estimations result of optimum choice1,A'2,A'3,…,A'k,…,A'KAs optimum choice Anchor node, perform step 10 six；
Step 16: system is according to distance estimations result { d'1_u,d'2_u,d'3_ u ..., d'k_ u ..., d'K_ u }, and Coordinate information (the x' of corresponding K anchor node coordinate1, y'1), (x'2, y'2), (x'3, y'3) ..., (x'k, y'k) ..., (x'K, y'K), and criterion of least squares is combined, the coordinate (x, y) of unknown node is calculated by formula (1) and formula (2) respectively：
Wherein k is positive integer, and 1≤k≤K；
Step 17: judge whether weighted mass center location tasks are completed, if it is, step 10 eight is performed, otherwise, next On individual anchor point, step 4 is performed；
Step 18: terminate the weighted mass center location tasks of anchor node optimum choice propagated based on minimal error.
Specific embodiment two, present embodiment are that one kind described in embodiment one is propagated based on minimal error The weighted mass center localization method of anchor node optimum choice be described further, in present embodiment, using dynamic sliding window With the method for single pass, the several of SS difference minimum can be expeditiously selected in distance estimations standard difference sequence It is individual, provide support for the optimum choice of anchor node.
Specific embodiment three, present embodiment are that one kind described in embodiment one is propagated based on minimal error The weighted mass center localization method of anchor node optimum choice be described further, in present embodiment, using based on minimum statistics The anchor node optimum choice of standard deviation, reduce the influence that distance estimations error positions to weighted mass center, realize high-precision weighting Center coordination.
Specific embodiment four, present embodiment are that one kind described in embodiment one is propagated based on minimal error The weighted mass center localization method of anchor node optimum choice be described further, in present embodiment, the distance in the present invention is estimated Meter method can also be used based on other method for estimating distance such as RSSI, TOA, TDOA and AOA.
Specific embodiment five, present embodiment are that one kind described in embodiment one is propagated based on minimal error The weighted mass center localization method of anchor node optimum choice be described further, in present embodiment, used localization method Weighted mass center localization method under three-dimensional situation is improved similarly effective.

Claims (5)

  1. A kind of 1. weighted mass center localization method for the anchor node optimum choice propagated based on minimal error, it is characterised in that the side Method comprises the following steps：
    Step 1: have I+1 wireless sensor node in system, the anchor node A={ A of respectively I positioning1,A2,A3,…, Ai,…,AIAnd 1 unknown node, they all have nanoLOC rf receiver and transmitters, and can use bilateral counterpart method Measurement obtains the range estimation between any two node, and wherein i is positive integer, and 1≤i≤I, I are the just whole of user's setting Number, and 4≤I≤15, 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 node Shens It please add network；
    Step 3: after I anchor node initializes successfully, the wireless of RF transceiver scanning discovery unknown node foundation is respectively adopted Network, and by RF transceiver send network join request packet, application add the wireless network, if add network into Work(, then step 4 is performed, 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 anchor node, i-th After anchor node receives Location Request packet, using bilateral reciprocity distance-finding method, pass through 4J packet between unknown node Interaction, obtain 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, the SS of measured value is poor 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's setting Positive integer, and 50≤J≤150, in of the invention, J values are 100；
    Step 6: judging whether i value is more than I, if so, then performing step 7, otherwise, step 5 is performed；
    Step 7: system obtains 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 }, definition estimation matter Measure parameter sliding window w={ w1,w2,w3,…,wk,…,wK, wherein 1≤i≤I, 1≤k≤K, i and k initialization value are 1, its Middle K is user-defined positive integer, and 3≤K≤I, and in this patent, K values are 8, defined variable l, and 1≤l≤K, l initial value For K；
    Step 8: system judges whether k is more than K, if it is, k values are set to 1, step 10 is performed, otherwise performs step 9；
    Step 9: wkValue be set to Inf, wherein Inf is maximum real number, k=k+1, performs step 8；
    Step 10: system judges whether i is more than I, if it is, performing step 15, step 11 is otherwise performed；
    Step 11: system judges whether k is more than K, if it is, k values are set to 1, i=i+1, step 10 is performed, otherwise performs step Rapid 12；
    Step 12: system judges diWhether _ σ is less than wk, if so, performing step 13, otherwise, k=k+1, perform step 10 One；
    Step 13: system judges whether l is less than k, if it is, l value is set to K, wl=di_σ*di_ u, step 11 is performed, Otherwise, step 14 is performed；
    Step 14: wl=wl-1, l=l-1, perform step 13；
    Step 15: system obtains sliding window sequence w={ w1,w2,w3,…,wk,…,wK, by its corresponding range estimation Distance estimations result d'={ d' of the sequence as optimum choice1_u,d'2_u,d'3_ u ..., d'k_ u ..., d'K_ u }, it will optimize Anchor node A'={ A' corresponding to the distance estimations result of selection1,A'2,A'3,…,A'k,…,A'KAnchor section as optimum choice Point, perform step 10 six；
    Step 16: system is according to distance estimations result { d'1_u,d'2_u,d'3_ u ..., d'k_ u ..., d'K_ u }, and correspondingly K anchor node coordinate coordinate information (x'1, y'1), (x'2, y'2), (x'3, y'3) ..., (x'k, y'k) ..., (x'K, y 'K), and criterion of least squares is combined, the coordinate (x, y) of unknown node is calculated by formula (1) and formula (2) respectively：
    <mrow> <mi>x</mi> <mo>=</mo> <mfrac> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>K</mi> </munderover> <mrow> <mo>(</mo> <msub> <msup> <mi>x</mi> <mo>&amp;prime;</mo> </msup> <mi>k</mi> </msub> <mo>/</mo> <msub> <msup> <mi>d</mi> <mo>&amp;prime;</mo> </msup> <mi>k</mi> </msub> <mo>_</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>K</mi> </munderover> <mrow> <mo>(</mo> <mn>1</mn> <mo>/</mo> <msub> <msup> <mi>d</mi> <mo>&amp;prime;</mo> </msup> <mi>k</mi> </msub> <mo>_</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <mi>y</mi> <mo>=</mo> <mfrac> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>K</mi> </munderover> <mrow> <mo>(</mo> <msub> <msup> <mi>y</mi> <mo>&amp;prime;</mo> </msup> <mi>k</mi> </msub> <mo>/</mo> <msub> <msup> <mi>d</mi> <mo>&amp;prime;</mo> </msup> <mi>k</mi> </msub> <mo>_</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>K</mi> </munderover> <mrow> <mo>(</mo> <mn>1</mn> <mo>/</mo> <msub> <msup> <mi>d</mi> <mo>&amp;prime;</mo> </msup> <mi>k</mi> </msub> <mo>_</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow>
    Wherein k is positive integer, and 1≤k≤K；
    Step 17: judge whether weighted mass center location tasks are completed, if it is, step 10 eight is performed, otherwise, next fixed On site, step 4 is performed；
    Step 18: terminate the weighted mass center location tasks of anchor node optimum choice propagated based on minimal error.
  2. A kind of 2. weighted mass center positioning side of anchor node optimum choice propagated based on minimal error according to claim 1 Method is described further, it is characterised in that, can be in distance estimations standard using dynamic sliding window and the method for single pass The several of SS difference minimum are expeditiously selected in difference sequence, support is provided for the optimum choice of anchor node.
  3. A kind of 3. weighted mass center positioning side of anchor node optimum choice propagated based on minimal error according to claim 1 Method is described further, it is characterised in that is used the anchor node optimum choice based on minimum statistics standard deviation, is reduced distance estimations The influence that error positions to weighted mass center, realize high-precision weighted mass center positioning.
  4. A kind of 4. weighted mass center positioning side of anchor node optimum choice propagated based on minimal error according to claim 1 Method is described further, it is characterised in that the method for estimating distance in invention, which can also use, is based on RSSI, TOA, TDOA and AOA Etc. other method for estimating distance.
  5. A kind of 5. weighted mass center positioning side of anchor node optimum choice propagated based on minimal error according to claim 1 Method is described further, it is characterised in that used localization method improves to the weighted mass center localization method under three-dimensional situation It is same effective.
CN201710732831.1A 2017-08-24 2017-08-24 A kind of weighted mass center localization method for the anchor node optimum choice propagated based on minimal error Pending CN107479026A (en)

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Application publication date: 20171215