CN111132053B - Positioning model sensor map-based backscatter signal positioning method - Google Patents
Positioning model sensor map-based backscatter signal positioning method Download PDFInfo
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- CN111132053B CN111132053B CN201911317589.7A CN201911317589A CN111132053B CN 111132053 B CN111132053 B CN 111132053B CN 201911317589 A CN201911317589 A CN 201911317589A CN 111132053 B CN111132053 B CN 111132053B
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Abstract
The invention provides a backscattering signal positioning method based on a positioning model sensor map, which comprises the following steps: performing position estimation by a region-based method through dividing fields to obtain a plurality of position nodes and reference nodes; combining the position nodes and the reference nodes to obtain a plurality of overlapped triangles, wherein the vertex of each overlapped triangle is an anchor, and the boundary triangle is obtained by any group of three reference nodes; dividing the overlapped triangle into four overlapped sub-areas, utilizing an APIT algorithm, firstly receiving position information from an anchor point by a sensor node, and secondly, checking whether the sensor node is in a boundary triangle by a point in a triangulation test; obtaining a positioning model sensor map by an APIT algorithm; and obtaining the backscattering signal positioning by combining a positioning model sensor map. The invention overcomes the problem of larger positioning error of the traditional single sensor. The problem of low indoor positioning accuracy is solved by using a high-precision positioning technology based on a backscattering signal.
Description
Technical Field
The invention relates to the technical field of a positioning model sensor map and a backscattering signal positioning method.
Background
At present, scatterers around a base station are positioned by utilizing GIS system information, and then the scatterers are used as virtual base stations to position a mobile station. Let the rectangular coordinate of the ith base station be (x)i,yi) I is 1, N is the base number, the cartesian coordinates of the mobile station are (x, y), and the cartesian coordinates of the scatterer nearest to the base station i are (B)xik,Byik) Known through the GIS system (B)xik,Byik) Specific values of (a). Measured parameters of base station (L)ik,θik) For the distance value and the arrival angle (if there are multiple multipaths for each base station, the shortest distance is taken), the rectangular coordinates (B) of the scatterer nearest to the base station i in the direction of the arrival angle can be known from θ ikxik,Byik) Therefore, the distance from the scatterer nearest to the base station i can be obtained,
by means of LikAnd pikValue determines whether the mobile station is inNon-direct wave environment:
l < p, in a direct wave environment;
Lik≥pikand in a non-direct wave environment. (2)
When the mobile station is in the direct wave environment, the distance and the arrival angle measurement value of the path are directly used to position the mobile station, i.e. the position of the mobile station can be calculated. When secondary scattering occurs in the actual environment, a larger positioning error occurs, and the algorithm is improved to adapt to the secondary scattering condition. The electric wave is emitted from the mobile station via the scatterer (M)xk,Myk) Scattered by the scatterer (B)xk,Byk) When the mobile station reaches different base stations, the distance value, the arrival angle and the Doppler frequency shift of the path measured by the base station are (if a plurality of multi-paths exist in each base station, the shortest path is taken), and the distance between the two scatterers is
Distance of scatterer to mobile station is
Further derivation can obtain the following expression
Thus (B) can bexik,Byik) N as a virtual base station, the above equation as a distance difference measurement, using TDOA algorithm to locate the scatterer, the (M) will be solvedxk,Myk) The substitute formula (4) can be solved into a scatterer (M)xk,Myk) Distance h to mobile stationkThen, the same method is used to solve for the other two scatteringsThe position of the body, and the distance to the mobile station, and finally the 3 scatterers (M)xk,Myk) K is 1,2,3 is a virtual base station, hkAnd k is 1,2 and 3 are distance measurement values, and the position of the mobile station is solved by using an LLOP method.
X＝[x,y]T,AX＝B,
Wherein
Solved to X ═ ATA)-1ATB
From the above positioning process, it can be seen that radio waves are emitted from the mobile station via the scatterer (M)xk,Myk) Scattered by the scatterer (B)xik,Byik) To different base stations, and thus determines at the base station side which multipath signal is passing through the scatterer (M)xk,Myk) Scatter becomes the key to the algorithm. The main idea of multipath signal matching is to use doppler shift, which easily proves that the doppler shifts of signals arriving at different base stations through the same scatterer are the same, and thus. Can pass through (B)xik,Byik) This characteristic matches multipath signals, and it is assumed that the same Doppler shift is passed (B)xik,Byik) Is diffuse.
En＝(L1k-P1k-r1k)2-(L2k-P2k-r2k)2-(L3k-P3k-r3k)2
All combinations of measured values of the same Doppler shift measured at the base station side are substituted into the above formula, EnThe smallest pair of combinations is the combination that matches correctly. It is noted that accurate positioning of intelligent driving can be facilitated when the scatterers are only located on two special straight lines.
Disclosure of Invention
The method is combined with the RFID fusion algorithm to complete the map construction of the indoor vehicle RFID positioning model sensor, and the problem that the positioning error of the traditional single sensor is large is solved; the problem of low indoor positioning accuracy is solved by using a high-precision positioning technology based on a backscattering signal.
The backscatter signal positioning method based on the positioning model sensor map is characterized by comprising the following steps of:
step 1) carrying out position estimation by a region-based method through a segmentation field to obtain a plurality of position nodes and reference nodes;
step 2) combining the position nodes and the reference nodes in the step 1) to obtain a plurality of overlapped triangles, wherein the vertex of each overlapped triangle is an anchor, and the boundary triangle is obtained by any group of three reference nodes;
step 3) dividing the overlapped triangle obtained in step 2) into four overlapped sub-areas, using an APIT algorithm, firstly receiving position information from anchor points by sensor nodes, secondly, checking whether the sensor nodes are in a boundary triangle by points in a triangulation test, wherein the boundary triangle is formed by connecting three anchor points for receiving signals; the APIT algorithm is combined with the grid scanning algorithm to aggregate results; the APIT algorithm determines the positions of the nodes by calculating the centers of gravity of the intersection points of all the overlapped triangles; obtaining a positioning model sensor map;
and 4) obtaining the backscattering signal positioning by combining a positioning model sensor map.
The RFID fusion algorithm completes the map construction of the indoor vehicle RFID positioning model sensor, and overcomes the problem of larger positioning error of the traditional single sensor. The problem of low indoor positioning accuracy is solved by using a high-precision positioning technology based on a backscattering signal.
Drawings
FIG. 1 is a schematic diagram of an indoor RFID vehicle positioning model sensor mapping concept.
FIG. 2 is a schematic diagram of an indoor RFID vehicle location improvement APIT algorithm.
FIG. 3a is an initial trajectory of the RIM model for tag backscatter along the RFID at a DOI of 0.015.
FIG. 3b is an initial trajectory of the RIM model for tag backscatter along the RFID at a DOI of 0.02.
FIG. 4 is a schematic illustration of bias in pseudo-measurement causing non-linear distortion to the estimated trajectory.
Detailed Description
The backscatter signal positioning method based on the positioning model sensor map comprises the following steps:
step 1) carrying out position estimation by a region-based method through a segmentation field to obtain a plurality of position nodes and reference nodes;
step 2) combining the position nodes and the reference nodes in the step 1) to obtain a plurality of overlapped triangles, wherein the vertex of each overlapped triangle is an anchor, and the boundary triangle is obtained by any group of three reference nodes;
step 3) dividing the overlapped triangle obtained in step 2) into four overlapped sub-areas, using an APIT algorithm, firstly receiving position information from anchor points by sensor nodes, secondly, checking whether the sensor nodes are in a boundary triangle by points in a triangulation test, wherein the boundary triangle is formed by connecting three anchor points for receiving signals; the APIT algorithm is combined with the grid scanning algorithm to aggregate results; the APIT algorithm determines the positions of the nodes by calculating the centers of gravity of the intersection points of all the overlapped triangles; obtaining a positioning model sensor map;
and 4) obtaining the backscattering signal positioning by combining a positioning model sensor map.
1.1 indoor vehicle RFID location model sensor map construction
The sensor map construction of the positioning model uses an APIT algorithm, the algorithm needs a small number of anchor points, and a new region-based method is adopted to carry out position estimation by dividing fields. Furthermore, the nodes may be equipped with high power radio transmitters. The main idea of APIT node location is to consider overlapping triangles. The vertices of these triangles are anchors. The boundary triangles are obtained using any set of three reference nodes, rather than using the coverage area nodes of a single reference node. In the APIT algorithm, sensor nodes first receive position information from nearby anchor points, and secondly, a point in a triangulation (PIT) test checks whether the sensor node is in a virtual triangle formed by connecting three anchor points that receive signals. After the test is finished, the APIT algorithm carries out aggregation result and is realized through a grid scanning algorithm. The APIT algorithm determines the location of a node by calculating the center of gravity (COG) of the intersection of all overlapping triangles in which the node is located. Using the APIT algorithm for positioning leads to two main problems: (1) foundation pit testing problems, (2) anchor point selection problems, leading to increased time requirements. To solve these problems, we modify the APIT algorithm and refer to it as a modified APIT algorithm. In this modified APIT algorithm, we reduce the pit test error (edge effect and uneven placement of neighbors) by selecting the appropriate anchor pitch triangle. In order to reduce the calculation amount and avoid the selection of useless anchor points, the new anchor point selection method is proposed to divide the application range into four non-overlapping and four overlapping sub-areas, and the four overlapping areas are used as the main parts of the positioning model sensor map.
As shown in fig. 1, while node M is inside the triangle, APIT decides that it is outside the triangle. The edges near the node and outside some neighbor triangles further from all anchor nodes and M. Thus, node M erroneously believes that this is due to the edge effect that although triangular, the outer node M is outside the triangle but because neighbors are close to or far from all anchors while node M assumes it is an inside triangle. Three possible anchor points are selected for each node and a pit trial is performed. When M is a node within Δ ABC, if M is shifted in any direction, the new location must be close to or far from at least one of the anchors, B or C, while when a node M is outside Δ ABC, M is shifted, there must be a direction where M is close to or far from all three anchors, B and C, and when there is a direction where a point neighbor node M is close to (or further from) an anchor, B and C, then M is outside Δ ABC. Otherwise, M is Δ ABC internal. It can correctly determine if node M is inside Δ ABC. In order to realize a pit algorithm in a wireless sensor network under the condition that nodes do not need to move, a method for carrying out pit approximate test by utilizing the characteristic of high node density in the wireless sensor network is provided. To simulate the movement of a node in PPIT, the node uses neighbor information exchanged through beacons. If there are no neighboring nodes close to or far from all three anchors, B and C at the same time, assume that M is inside Δ ABC. Otherwise, M is outside this triangle. Such as the algorithm idea shown in fig. 2.
After an indoor RFID vehicle positioning center is obtained, a neighbor of each central neighborhood point is obtained according to the thought, and for each APIT internal decision (decision that the APIT test determines that the node is located in a specific area), the value of the grid area where the corresponding triangle is located is increased progressively. For external decisions, the grid area is also decremented. After all the triangular areas are calculated, the maximum overlapping area is found by using the obtained information, and then the gravity center is calculated by using the maximum overlapping area to carry out position estimation.
1.2 neighbor Algorithm simulation results
The neighbor simulation shows that: the wireless distances (R) of the RFID are respectively 1R, 1.25R, 1.5R, 1.75R and 2R, and the average connection levels are respectively 5.9, 8.9, 12.2, 16.2 and 20.7. 3, 4, 6 and 10 random anchors were used. With only 4 anchor nodes, with an average connectivity of 8.9 or higher, the position average error of the MDS-MAP estimate is less than 100% R. When the connectivity level is 12.2 or higher, the error of using only 3 anchors is quite good, approaching or exceeding 50%.
In the isotropic radio model, the received signal strength is usually expressed by the formula, i.e., the received signal strength is the transmission power-path loss + fading. The transmit power of a node is determined by the battery status and the type of transmitter, amplifier and antenna. The edge model proposed herein enhances the isotropic radio model by approximating the three main properties of the radio signal, anisotropy, continuous variation and heterogeneity, the path loss describing the energy loss of the signal as it reaches the receiver. These properties are generally ignored by previous isotropic radio models.
To represent the irregularity of the radiogram, the parameter DOI (irregularity) is introduced in the edge model. The DOI parameter is defined as the maximum percentage change in path loss in unit degree change in the radio propagation direction. The RIM model is a generic radio model that can default to an isotropic model when the DOI value is 0. A RIM model is built based on actual sensor data. It is a hybrid method that introduces real data (DOI values) into the simulation to better approximate the actual radio irregular pattern. Statistical analysis of our experimental data shows that the variance of the received signal strength in different directions (mainly due to the variation of path loss, because fig. 3a shows that the fading is very small, and the distribution conforms to Weibull [ Devore 1982 ]. fig. 3a is the initial trajectory of the RIM model of the tag along the RFID backscatter when the DOI is 0.015. fig. 3b is the initial trajectory of the RIM model of the tag along the RFID backscatter when the DOI is 0.02. Weibull distribution can be used to simulate natural phenomena such as wind speed variation, radiation scattering, etc. rayleigh distribution is a special case of Weibull distribution, and is a common multipath fading modeling method in wireless communications.
1.3 RIM model localization experiment of tag along RFID backscatter
The construction of the simulation experiment environment complies with the following steps: before being installed in an indoor butterfly cage, the position sensing system based on the ultrahigh frequency RFID is tested in an anechoic room. Four reader antennas are located at the corners of a square 3 meters long. The antennas are above 1.5 meters and they are directed towards the centre of the square. The crawler RFID tag is mounted on a movable support 1.5 meters high. The movement of the tag is achieved by manually sliding the carriage on the floor along a predetermined trajectory.
In this experiment, the tag was moved along the coordinate line of the RIM model of RFID backscatter. The trace represented by the solid blue line in the lower graph is obtained by simple point to least squares trilateration with the correct initial position. The dashed line indicates that the trajectory is the same for unknown tag initial positions, assumed in the middle of the measurement area. As can be seen from fig. 4, the bias in the pseudo-measurement produces significant nonlinear distortion to the estimated trajectory, although the initial position is only 1 meter away.
Claims (2)
1. The backscatter signal positioning method based on the positioning model sensor map is characterized by comprising the following steps of:
step 1) carrying out position estimation by a region-based method through a segmentation field to obtain a plurality of position nodes and reference nodes;
step 2) combining the position nodes and the reference nodes in the step 1) to obtain a plurality of overlapped triangles, wherein the vertex of each overlapped triangle is an anchor, and the boundary triangle is obtained by any group of three reference nodes;
step 3) dividing the overlapped triangle obtained in step 2) into four overlapped sub-areas, using an APIT algorithm, firstly receiving position information from anchor points by sensor nodes, secondly, checking whether the sensor nodes are in a boundary triangle by points in a triangulation test, wherein the boundary triangle is formed by connecting three anchor points for receiving signals; the APIT algorithm is combined with the grid scanning algorithm to aggregate results; the APIT algorithm determines the positions of the nodes by calculating the centers of gravity of the intersection points of all the overlapped triangles; obtaining a positioning model sensor map;
and 4) obtaining the backscattering signal positioning by combining a positioning model sensor map.
2. The method of claim 1, wherein each location node obtained in step 1) is equipped with a high power radio transmitter.
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| CN106412828A (en) * | 2016-09-14 | 2017-02-15 | 扬州大学 | Approximate point-in-triangulation test (APIT)-based wireless sensor network node positioning method |
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