CN114609583A - Wireless sensor network target positioning method based on RSS-AoA measurement - Google Patents

Wireless sensor network target positioning method based on RSS-AoA measurement Download PDF

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CN114609583A
CN114609583A CN202210069673.7A CN202210069673A CN114609583A CN 114609583 A CN114609583 A CN 114609583A CN 202210069673 A CN202210069673 A CN 202210069673A CN 114609583 A CN114609583 A CN 114609583A
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丁伟中
钟秋波
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Ningbo University of Technology
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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
    • G01S5/0257—Hybrid positioning
    • G01S5/0258—Hybrid positioning by combining or switching between measurements derived from different systems
    • G01S5/02585—Hybrid positioning by combining or switching between measurements derived from different systems at least one of the measurements being a non-radio measurement
    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention provides a wireless sensor network target positioning method based on RSS-AoA measurement, which relates to the field of target positioning methods and comprises the following steps: s1: constructing a measurement model, obtaining true values of RSS and AoA through a preset first calculation process, and obtaining actual measurement values of the RSS and the AoA through a preset second calculation process; s2: calculating an approximate value of the source node by a WLS method, calculating weight matrixes C and S according to the approximate value, and calculating the position of the source node by the weight matrixes C and S; s3: calculating the standard deviation of each anchor and each evaluation function according to the position of the source node, and calculating the noise value of the measured value; s4: the standard deviation and noise values are used as weights for each anchor and each evaluation function. The method can provide a wireless sensor network target positioning method with better performance under the condition of not increasing complexity.

Description

Wireless sensor network target positioning method based on RSS-AoA measurement
Technical Field
The invention relates to the field of target positioning methods, in particular to a wireless sensor network target positioning method based on RSS-AoA measurement.
Background
In recent years, positioning methods have played an increasingly important role in wireless sensor networks. The wireless sensor network is a wireless network consisting of sensors. A wireless sensor network consists of anchors at known locations and targets at unknown locations. The location of the target is determined by the location of the anchor and the radio signal. Common radio signals include time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA), and Received Signal Strength (RSS). Hybrid range-based positioning may be any combination of four radio signals. Which measurement method is used is determined by the sensor hardware. Since TOA and TDOA have strict requirements on precise timing synchronization in measurements, the cost is greatly increased. Thus, AOA/RSS hybrid localization provides an attractive solution for low cost measurements.
And accurately calculating the position of the source node according to the RSS/AoA measurement. However, the positioning problem becomes an optimization problem due to the RSS/AoA measurement error. Target position estimation based on RSS/AoA hybrid measurements is a non-convex system optimization problem with the difficulty of overcoming measurement noise. Semi-definite programming (SDP) and Second Order Cone Programming (SOCP) can effectively solve this problem, but their complexity is too great. Only the weight that varies with the distance between the source node and the anchor is not the optimal weight. The ECWLS method changes the weight by first calculating an approximate position of the target using a least square method and then calculating an approximate error covariance matrix from the approximate position. The approximation error covariance matrix is used as a weight. Due to the limited number of anchors, the estimation of the measurement noise variance has large errors. The method directly multiplies the inverse of the estimate by a weight, thereby reducing the error. The TELS method is based on AoA measurements. The approximate location of the source node is calculated using LS, and then the variance is calculated as a weight. Both of the above methods consider only the influence of the noise variance of the evaluation function term, but not the influence of the measurement noise value. When the noise standard deviation is the same, the smaller the noise value is, the larger the weight is.
At present, the algorithm complexity of the SR-WLS method is high. In the WLS method, only the weight related to the distance is not optimal. In the ECWLS method, because the number of anchor nodes is limited, the estimation of the measurement noise variance is inaccurate, and the final precision is influenced. In the TELS method, the influence of measurement noise on the weight is not considered, assuming that the variance of the measurement noise is the same.
Disclosure of Invention
The invention solves the problem of how to provide a wireless sensor network target positioning method with better performance under the condition of not increasing complexity.
In order to solve the above problems, the present invention provides a method for positioning a target of a wireless sensor network based on RSS-AoA measurement, comprising the steps of:
s1: constructing a measurement model, obtaining true values of RSS and AoA through a preset first calculation process, and obtaining actual measurement values of the RSS and the AoA through a preset second calculation process;
s2: calculating an approximate value of the source node by a WLS method, calculating weight matrixes C and S according to the approximate value, and calculating the position of the source node by the weight matrixes C and S;
s3: calculating the standard deviation of each anchor and each evaluation function according to the position of the source node, and calculating the noise value of the measured value;
s4: the standard deviation and noise values are used as weights for each anchor and each evaluation function.
Further, the step S1, where the presetting of the first calculation flow includes:
s11: the true value of RSS is expressed by the formula:
Figure BDA0003481618060000021
s12: the true value of AoA is expressed by the formula:
Figure BDA0003481618060000022
wherein s isi＝[si1,si2,si3]TN denotes N anchor nodes, i being 1, …; x ═ x1,x2,x3]TA source node representing a known location; i x-si| | represents the distance between the source node and the ith anchor node; phi is aiA true value representing the azimuth; alpha is alphaiRepresenting the true value of the pitch angle.
Further, the step S1, where the preset second calculation process includes:
s13: in the case of actual measurement errors, the actual measured values of RSS and AoA are expressed by the formulas:
Figure BDA0003481618060000031
wherein n isi,mi,viIndependent zero mean gaussian noise representing received power, azimuth and elevation angle, respectively.
Further, the weight matrix C is associated with the variance of each anchor and each evaluation function;
according to the spherical coordinate | | x-siI can be expressed as ui T(x-si) For i ═ 1, …, N; unit vector uiDefined by the actual measured RSS value; equations (1), (2) and (3) can be converted into:
Figure BDA0003481618060000032
suppose | ni|、|mi|、|viUsing a first order Taylor expansion,. epsilon. | -. 11i,ε2i,ε3iExpressed as:
Figure BDA0003481618060000033
the variance of equation (5) is:
Figure BDA0003481618060000034
further, the weight matrix C will reflect the standard deviation and the influence of noise values for each anchor and each evaluation function, and equation (6) is equivalently expressed as:
Figure BDA0003481618060000035
the weight of each anchor and each evaluation function term is inversely proportional to its variance, and the weight matrix C is then represented as:
Figure 1
further, the weight of each term of formula (8) is multiplied by σ2The final estimate is unchanged, and is expressed as:
Figure BDA0003481618060000042
further, the weight matrices S and ni,mi,vi(ii) related;
ni，mi，vican be prepared from
Figure BDA0003481618060000043
The estimate is expressed as:
Figure BDA0003481618060000044
the weight matrix S is then expressed as:
Figure BDA0003481618060000045
further, the position of the source node is calculated by the WLS method as follows:
Figure BDA0003481618060000051
wherein
Figure BDA0003481618060000052
W＝diag([w1,w1,w1])，
Figure BDA0003481618060000053
Figure BDA0003481618060000054
The final estimated values of the weight matrices C and S are calculated as:
Figure BDA0003481618060000055
the invention adopts the technical scheme at least comprising the following beneficial effects:
the invention provides an RSS/AoA hybrid positioning method based on error variance and measurement noise weighted least squares (ENWLS). The method has good calculation precision and greatly reduces the calculation complexity. The Weighted Least Squares (WLS) method improves computational accuracy without increasing complexity. After the measurement model is linearized, it is more practical to estimate the position using a Least Squares (LS) method. The method is based on a three-dimensional wireless sensor network, and high-precision positioning is realized under the condition of not increasing complexity. Linear WLS errors are approximated using a first order taylor approximation and the location of the target is estimated using the WLS method, and then a weight matrix is determined by estimating the linear WLS error variance and the measured noise values on the sensor nodes. The Root Mean Square Error (RMSE) of the method is superior to that of the existing RSS/AoA mixed positioning method.
Drawings
Fig. 1 is a flowchart of a target positioning method for a wireless sensor network based on RSS-AoA measurement according to an embodiment of the present invention;
fig. 2 is a schematic three-dimensional position diagram of an anchor node and a source node of the RSS-AoA measurement-based wireless sensor network target positioning method provided in the embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
The following are specific embodiments of the present invention and are further described with reference to the drawings, but the present invention is not limited to these embodiments.
Examples
The embodiment provides a target positioning method of a wireless sensor network based on RSS-AoA measurement, as shown in fig. 1, the method includes the steps of:
s1: constructing a measurement model, obtaining true values of RSS and AoA through a preset first calculation process, and obtaining actual measurement values of the RSS and the AoA through a preset second calculation process;
s2: calculating an approximate value of the source node by a WLS method, calculating weight matrixes C and S according to the approximate value, and calculating the position of the source node by the weight matrixes C and S;
s3: calculating the standard deviation of each anchor and each evaluation function according to the position of the source node, and calculating the noise value of the measured value;
s4: the standard deviation and noise values are used as weights for each anchor and each evaluation function.
Referring to fig. 2, the step S1 is preset with a first calculation process including:
s11: the true value of RSS is expressed by the formula:
Figure BDA0003481618060000061
s12: the true value of AoA is expressed by the formula:
Figure BDA0003481618060000062
wherein s isi＝[si1,si2,si3]TN denotes N anchor nodes, i being 1, …; x ═ x1,x2,x3]TA source node representing a known location; i x-si| | represents the distance between the source node and the ith anchor node; phi is a unit ofiA true value representing the azimuth; alpha is alphaiRepresenting the true value of the pitch angle.
In step S1, the step S1 of presetting the second calculation procedure includes:
s13: in the case of actual measurement errors, the actual measured values of RSS and AoA are expressed by the formulas:
Figure BDA0003481618060000071
wherein n isi,mi,viIndependent zero mean gaussian noise representing received power, azimuth and elevation, respectively.
Wherein the weight matrix C is associated with the variance of each anchor and each evaluation function;
according to the spherical coordinate | | x-siI can be expressed as ui T(x-si) For i ═ 1, …, N; unit vector uiDefined by the actual measured RSS value; equations (1), (2) and (3) can be converted into:
Figure BDA0003481618060000072
suppose | ni|、|mi|、|viUsing a first order Taylor expansion,. epsilon. | -. 11i,ε2i,ε3iExpressed as:
Figure BDA0003481618060000073
the variance of equation (5) is:
Figure BDA0003481618060000074
wherein the weight matrix C will reflect the standard deviation and the influence of noise values for each anchor and each evaluation function, and equation (6) is equivalently expressed as:
Figure BDA0003481618060000075
the weight of each anchor and each evaluation function term is inversely proportional to its variance, and the weight matrix C is then represented as:
Figure 2
multiplying the weight of each term of equation (8) by σ2The final estimate is unchanged, and is expressed as:
Figure BDA0003481618060000082
wherein the weight matrix S and ni,mi,vi(ii) related;
ni,mi,vican be prepared from
Figure BDA0003481618060000083
The estimate is expressed as:
Figure BDA0003481618060000084
the weight matrix S is then expressed as:
Figure BDA0003481618060000085
the position of the source node calculated by the WLS method is as follows:
Figure BDA0003481618060000086
wherein
Figure BDA0003481618060000091
W＝diag([w1,w1,w1])，
Figure BDA0003481618060000092
Figure BDA0003481618060000093
The final estimated values of the weight matrices C and S are calculated as:
Figure BDA0003481618060000094
due to advances in Radio Frequency (RF) and microelectromechanical systems, large-scale networks composed of a large number of sensor nodes are currently in use. The wireless sensor network has great application potential because of the autonomy in human-computer interaction and the relative cheapness of the sensor nodes. They are used in many different fields, such as monitoring (medical, industrial, environmental, agricultural), event detection (flood, hail, fire), exploration (outer space, deep water, underground), surveillance.
Forest fire detection is also one of the future development directions. The sensor nodes (source nodes) may be randomly dropped from the aircraft. They are used to measure the temperature in the vicinity to detect fires. Once one of them detects a high temperature (fire hazard), they can communicate their location and a valid warning message to the firefighter. And their positions are calculated by the algorithm of the present invention. The algorithm calculates the position of the source node through the signals sent to the source node by the anchor node with known position.
The RSS/AoA hybrid positioning method based on error variance and measurement noise weighted least squares (ENWLS) is provided. The method has good calculation precision and greatly reduces the calculation complexity. The Weighted Least Squares (WLS) method improves computational accuracy without increasing complexity. After the measurement model is linearized, it is more practical to estimate the position using a Least Squares (LS) method. The method is based on a three-dimensional wireless sensor network, and high-precision positioning is realized under the condition of not increasing complexity. A first order taylor approximation is used to approximate the linear WLS error and the WLS method is used to estimate the location of the target, and then a weight matrix is determined by estimating the linear WLS error variance and the measured noise values on the sensor nodes. The Root Mean Square Error (RMSE) of the method is superior to that of the existing RSS/AoA mixed positioning method.
Although the present disclosure has been described above, the scope of the present disclosure is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present disclosure, and such changes and modifications will fall within the scope of the present invention.

Claims (8)

1. A target positioning method of a wireless sensor network based on RSS-AoA measurement is characterized by comprising the following steps:
s1: constructing a measurement model, obtaining true values of RSS and AoA through a preset first calculation process, and obtaining actual measurement values of the RSS and the AoA through a preset second calculation process;
s2: calculating an approximate value of the source node by a WLS method, calculating weight matrixes C and S according to the approximate value, and calculating the position of the source node by the weight matrixes C and S;
s3: calculating the standard deviation of each anchor and each evaluation function according to the position of the source node, and calculating the noise value of the measured value;
s4: the standard deviation and noise values are used as weights for each anchor and each evaluation function.
2. The RSS-AoA measurement based target positioning method of claim 1, wherein the step S1 is configured to preset a first calculation procedure including:
s11: the true value of RSS is expressed by the formula:
Figure FDA0003481618050000011
s12: the true value of AoA is expressed by the formula:
Figure FDA0003481618050000012
wherein s isi＝[si1,si2,si3]TN denotes N anchor nodes, i being 1, …; x ═ x1,x2,x3]TA source node representing a known location; i x-si| | represents the distance between the source node and the ith anchor node; phi is aiA true value representing the azimuth; alpha is alphaiRepresenting the true value of the pitch angle.
3. The RSS-AoA measurement based target positioning method of claim 2, wherein the step S1 is configured to preset a second calculation procedure including:
s13: in the case of actual measurement errors, the actual measurements of RSS and AoA are expressed by the formulas:
Figure FDA0003481618050000021
wherein n isi,mi,viIndependent zero mean gaussian noise representing received power, azimuth and elevation, respectively.
4. The RSS-AoA measurement based wireless sensor network target positioning method of claim 3, wherein the weight matrix C is related to the variance of each anchor and each evaluation function;
according to the spherical coordinate | | x-siI can be expressed as ui T(x-si) For i ═ 1, …, N; unit vector uiDefined by the actual measured RSS value; equations (1), (2) and (3) can be converted into:
Figure FDA0003481618050000022
suppose | ni|、|mi|、|viUsing a first order Taylor expansion,. epsilon. | -. 11i,ε2i,ε3iExpressed as:
Figure FDA0003481618050000023
the variance of equation (5) is:
Figure FDA0003481618050000024
5. the RSS-AoA measurement based wireless sensor network target positioning method according to claim 4, wherein the weight matrix C will reflect the standard deviation of each anchor and each evaluation function and the influence of noise value, and the equation (6) is equivalently expressed as:
Figure FDA0003481618050000031
the weight of each anchor and each evaluation function term is inversely proportional to its variance, and the weight matrix C is then represented as:
Figure FDA0003481618050000032
6. the RSS-AoA measurement based wireless sensor network target positioning method according to claim 5, wherein the weight of each term of formula (8) is multiplied by σ2The final estimate is unchanged, and is expressed as:
Figure FDA0003481618050000033
7. the method of claim 3, wherein the weight matrices S and n arei,mi,vi(ii) related;
ni,mi,vican be prepared from
Figure FDA0003481618050000034
The estimate is expressed as:
Figure FDA0003481618050000035
the weight matrix S is then expressed as:
Figure FDA0003481618050000041
8. the RSS-AoA measurement based wireless sensor network target positioning method of claim 6, wherein the calculating the location of the source node by the WLS method is:
Figure FDA0003481618050000042
wherein
Figure FDA0003481618050000043
Figure FDA0003481618050000044
The final estimated values of the weight matrices C and S are calculated as:
Figure FDA0003481618050000045
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