CN108761391B - Model type equipment-free target positioning method - Google Patents
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Abstract
The invention discloses a model type equipment-free target positioning method, which aims at the defects of an equal-weight elliptical model commonly adopted by the current model type equipment-free target positioning technology, provides a layered elliptical shadow weight model according to the spatial relation of the influence of a target on a wireless link to describe the influence degree of the target on the wireless link so as to achieve the aim of more accurately describing the relation between the change of Received Signal Strength (RSS) and the target position and improve the model type equipment-free target positioning performance; meanwhile, when positioning is realized, the target position is automatically selected by utilizing the cross target automatic search technology, so that the accuracy of a positioning result is improved, and the influence of background noise and a false target image is overcome.
Description
Technical Field
The invention relates to an improved model type equipment-free target positioning method, and belongs to the technical field of wireless positioning.
Background
Different from the traditional positioning mode that a positioning target needs to carry positioning equipment (such as a GPS receiver, a mobile phone and the like) matched with a positioning system, the Device-free localization (DFL) does not need to carry any positioning Device on the positioning target and actively participate in the positioning process, so that the DFL can play an important role in the positioning field which cannot be realized by the traditional positioning method such as personnel search and rescue, illegal intrusion detection, old people care under special conditions and the like. Compared with the existing portable-equipment-free positioning based on the technologies such as a camera, an ultra-wideband radar, infrared and ultrasonic waves, the DFL technology based on the wireless sensor network has the advantages of low cost, good universality, capability of penetrating through walls and smoke for positioning and the like, so that the DFL technology becomes a research hotspot in the field of the current DFL.
The current DFL methods based on wireless sensor networks can be roughly divided into 3 types, namely, fingerprint-based DFL, geometry-based DFL, model-based DFL, and the like. The DFL of the fingerprint base requires that a fingerprint database is established in advance, and the database needs to be updated along with the change of the environment, so that the requirements on manpower and material resource investment are high. The DFL of the geometric basis expresses the link connection as a straight line segment and carries out positioning by utilizing the geometric relation among the links; although the method does not need to establish a fingerprint database, the method is easily influenced by multipath and the like. The model-based DFL establishes a relationship between a target position and a signal intensity change using a shadow weight model, and performs a position image by a regularization method by using the idea of medical CT, which is also called as a Radio Tomography (RTI) technique. Due to the intuitive nature of RTI positioning, it is receiving a great deal of attention. One of the keys to achieving RTI is the need to use a shadow weight model to establish the relationship between target location and signal strength variation. In the initial RTI, this relationship is constructed using an elliptical shading weight model that states that the weight of all grid points within an ellipse made with a pair of wireless nodes as the focus of the ellipse is inversely proportional to the distance of the pair of nodes, and the weight of all grid points outside the ellipse is zero. Although the model has certain rationality, the same weight of all grid points in the ellipse is not practical, and the length of the short axis of the ellipse of the model is selected by experience and also lacks of theoretical basis. Therefore, the RTI imaging result based on the elliptical shadow weight model is often low in imaging quality, and false targets are easy to appear, so that the DFL precision is influenced.
Disclosure of Invention
The invention aims to solve the problems in the prior art and provides a device-free target positioning method which can improve the accuracy of a positioning result and overcome the influence of background noise and a false target image.
In order to achieve the purpose, the technical scheme provided by the invention is as follows: a model type equipment-free target positioning method comprises the following steps:
step one, establishing a wireless positioning system, wherein the positioning system comprises a plurality of wireless receiving and transmitting nodes, and the wireless receiving and transmitting nodes are communicated with each other to form a plurality of wireless links;
step two, establishing a hierarchical ellipse weight model according to the spatial relationship of the wireless link affected by the equipment-free target;
step three, respectively measuring RSS values of a wireless link when no target exists and when a target exists;
step four, calculating a radio frequency tomography result based on the hierarchical ellipse weight model to obtain an imaging graph;
and step five, acquiring the target position on the imaging graph by using a cross target automatic searching method.
The technical scheme is further designed as follows: the positioning system comprises M +1 wireless receiving and transmitting nodes which are networked on the basis of a wireless communication protocol, wherein the M wireless receiving and transmitting nodes form a measuring network and are uniformly distributed on the periphery of a positioning area of the positioning system, and the M wireless receiving and transmitting nodes are communicated with each other pairwise to form an L (M x (M-1)/2 wireless links; the M +1 th node is a data acquisition node and is responsible for collecting data; the area located by the locating system is evenly divided into N pixel points.
The formula of the gradient shadow weight model corresponding to the ith (i ═ 1,2, …, L) link in the hierarchical elliptical weight model is as follows:
wherein, wijA weight value d representing the influence on the ith link when the target is located at the jth pixel pointiIs the ith link length, dij1，dij2Respectively the distance from the jth pixel point to two nodes forming the ith link, aiThe length of the major axis of the ellipse corresponding to the ith link is shown.
Is the maximum 1 st fresnel zone radius corresponding to the ith link, where λ represents the wavelength of the electromagnetic wave.
The radio frequency tomography comprises the following steps:
step 4.1, calculating the RSS variation of the L effective links respectively, recording the result as delta Y, and obtaining the following results according to the radio frequency tomography principle:
ΔY＝Wx+v
wherein x is [ x ]1,x2,…,xN]TPixel vector, x, representing the division of the localization areaiI is 1,2, …, N represents the value of each pixel point, v represents the noise vector, and W is the weight matrix;
step 4.2, introducing a regularization constraint term to obtain an objective function as follows:
wherein, α represents the regularization coefficient, Q represents the regular matrix, | | | | | represents the 2 norm, the above equation is solved, and the following is obtained:
x＝(WTW+αQTQ)-1WTΔY。
the cross target automatic searching method comprises the following steps:
step 5.1, eliminating noise points with smaller brightness values by using an averaging method; averaging the brightness of all pixel points on the imaging graph, and setting the brightness value of the pixel point lower than the average value as 0;
step 5.2, recalculating the brightness values of all pixel points on the imaging graph, and calculating the product of the brightness values of all pixel points in a cross-shaped neighborhood taking each pixel point as the center and having the arm length of r, wherein the calculation formula is as follows:
wherein, pi represents the multiplication operation, x (i, j) represents the original brightness value of the pixel point with the coordinate (i, j),
recalculating the brightness value of a pixel point with coordinates (i, j), wherein when m is 0, n is-r, -r +1, … -1,1, … r-1, r, and when n is 0, m is-r, -r-1, … -1,1, … r-1, r; r represents the cross arm length;
and 5.3, taking the coordinate of the brightest pixel as the coordinate of the target, wherein the non-zero pixel area in the imaging graph is the position of the target.
The invention achieves the following beneficial effects:
(1) the method of the invention uses the layered ellipse weight model to replace the prior ellipse model with fixed weight to realize the radio frequency tomography, simultaneously overcomes the defect that the length of the minor axis of the ellipse depends on the value by experience, can effectively reduce the model error and improve the imaging quality and the positioning performance.
(2) The method of the invention automatically selects the target position through the cross target automatic search technology, overcomes the influence of background noise and false target images, and improves the positioning precision and robustness.
Drawings
FIG. 1 is a schematic view of a positioning system of the present invention;
FIG. 2 is a schematic diagram of a hierarchical elliptical weight model parameter relationship;
FIG. 3 is a schematic diagram of a cross-shaped neighborhood;
FIG. 4 is a graph of imaging results based on a prior art elliptical weight model in an embodiment of the present invention;
FIG. 5 is a graph of imaging results based on a hierarchical elliptical weight model in an embodiment of the present invention;
fig. 6 is a diagram of a positioning result after the target automatic search method is performed in the embodiment of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and the specific embodiments.
The invention discloses an improved model type equipment-free target positioning method, which comprises the following steps:
step one, establishing a positioning system;
the positioning system comprises M +1 wireless transceiving nodes, networking is carried out on the basis of an IEEE802.15.4 wireless communication protocol, wherein the M wireless transceiving nodes form a measurement network and are uniformly distributed on the periphery of a positioning area, and the M +1 node is a data acquisition node and is responsible for collecting data; the M wireless receiving and transmitting nodes are communicated with each other to form an L (M x (M-1)/2 wireless links; the positioning area is evenly divided into N pixel points, and the structure of the positioning system is shown in figure 1.
Step two, constructing a hierarchical ellipse weight model according to the spatial relationship of the wireless link influenced by the equipment-free target;
according to Fresnel theory, most of the energy is propagated in the first Fresnel zone. Thus, a target can be considered to be effectively occluded when it occludes the first Fresnel zone of the link. When the target is outside the first fresnel zone, the influence on the link measurement value is considered to be small, and the weight value is set to 0 as in the existing elliptical model. Meanwhile, the closer the position to the link has the greater influence on the RSS value, the higher weight value is given, and thus a new weight model is obtained. According to the above analysis, the formula of the gradient shadow weight model corresponding to the ith (i ═ 1,2, …, L) link is as follows:
wherein, wijA weight value d representing the influence on the ith link when the target is located at the jth pixel pointiIs the ith link length, dij1，dij2Are respectively jthDistance, a, from pixel to two nodes forming the ith linkiThe length of the major axis of the ellipse corresponding to the ith link is shown.
Is the maximum 1 st fresnel zone radius corresponding to the ith link, where λ represents the wavelength of the electromagnetic wave. Examples of the above quantities are shown in figure 2.
Step three, measuring RSS values of a wireless link when no target exists and when a target exists;
according to the communication theory, the Received Signal Strength (RSS) value of the receiving end in the ith link can be expressed as
yi(t)＝Pi-Li-Si(t)-Fi(t)-vi(t) (2)
Wherein P isiIndicating the transmit power at the transmit end, generally assuming a fixed transmit power, LiRepresenting static losses, S, related to transmission distance, antenna pattern, etci(t) represents shading loss, Fi(t) represents fading loss, vi(t) represents noise. Respectively measuring the RSS measurement values of the ith link when no target exists and the ith link when the target exists, and then the RSS variation delta y of the ith link at the moment ti(t) can be represented as
Wherein y isi(0)＝Pi-Li-Fi(0)-vi(0) Indicating a background RSS measurement for the ith link when no target is present,
since the noise is much smaller than the shadow fading, Δ yi(t) is mainly determined by the shadow fading at time t. In the same measurement mode, the measurement values of all L links can be represented by a vector Y (t) ═ y1(t)y2(t)…yL(t)]TIs represented by (1), wherein]TRepresenting a transpose operation. Accordingly, the backThe scene measurement vector can be Y (0) ═ Y1(0)y2(0)…yL(0)]TTo indicate. By calculating the difference between the measurement vector Y (t) and the background measurement vector Y (0), the RSS variation vector Δ Y (t) at time t can be obtained as abs [ Y (t) -Y (0)]＝[Δy1(t)Δy2(t)…ΔyL(t)]Wherein abs [ 2 ]]Representing an absolute value operation.
Step four, radio frequency tomography imaging is carried out to form an imaging graph;
according to the radio frequency tomography principle, it is possible to obtain:
ΔY＝Wx+v (4)
wherein x is [ x ]1,x2,…,xN]TPixel vector, x, representing the division of the localization areaiN represents a value on each pixel point, v represents a noise vector, and the weight matrix W is calculated according to the formula (1);
introducing a regularization constraint term to obtain an objective function as follows:
wherein, α represents the regularization coefficient, Q represents the regular matrix, | | · | | | represents the 2 norm, and equation (5) is solved to obtain:
x＝(WTW+αQTQ)-1WTΔY (6)
fifthly, determining the target position based on a cross target automatic searching method;
step 5.1, eliminating noise points with smaller brightness values by using an averaging method: averaging the brightness of all pixel points on the imaging graph, and setting the brightness value of the pixel point lower than the average value as 0;
step 5.2, only the high-brightness noise pixels remain after the averaging method eliminates the background, and the surrounding pixels cannot be all high-brightness pixels necessarily because of the randomness of the noise. According to the characteristics and considering the calculated amount, the brightness values of all pixel points on the imaging graph are recalculated according to the following rules: calculating the product of the brightness values of all pixel points in a cross-shaped neighborhood which takes each pixel point as the center and has the arm length of r, wherein the calculation formula is as follows:
wherein, pi represents the multiplication operation, x (i, j) represents the original brightness value of the pixel point with the coordinate (i, j),
recalculating the brightness value of a pixel point with coordinates (i, j), wherein when m is 0, n is-r, -r + 1, … -1,1, … r-1, r, and when n is 0, m is-r, -r-1, … -1,1, … r-1, r; r represents the cross half-arm length; as shown in fig. 3;
step 5.3, because the brightness value of a part of the background is already set to 0 in the process of eliminating the background by the averaging method, as long as the brightness value of one pixel in the cross-shaped neighborhood of a certain pixel is 0, the brightness value of the pixel becomes zero. For the target pixel, the pixel points with higher brightness values are arranged in the cross-shaped neighborhood of the target pixel, and the pixel points cannot become zero after multiplication, so that after the step is finished, the remaining non-zero pixel area is the position of the target, and the brightest pixel coordinate is the coordinate of the target.
Examples
In this embodiment, a wireless transceiving node is autonomously developed based on a CC2530 chip conforming to the Zigbee protocol. The positioning area is a square area of 5 meters multiplied by 5 meters, 1 wireless transceiving node is arranged every 1 meter, 20 wireless transceiving nodes in total form a positioning network, and the other 1 wireless node is used as a data acquisition node and is responsible for transmitting measurement data to a computer. Each positioning node is placed on a support with a height of 1 meter. In terms of software protocol, the present embodiment is based on the wireless communication protocol of ieee802.15.4, and utilizes Z-stack protocol stack software to autonomously develop program codes for polling measurement and reading of received signal strength values. The 20 positioning nodes are sequentially coded with ID numbers from 1 to 20, and different modules are distinguished by the difference of the ID numbers. When one node sends the positioning data, the data packet can carry the ID number of the sending module, and after the next node receives the ID number, the sending of the positioning data of the node can be triggered, so that the polling measurement is established. After a sending node sends positioning data, other positioning nodes generate an intensity value RSSI when receiving the data, immediately store the data, then sequentially send the data to a data acquisition node, and transmit the data to a computer through the data acquisition node. Once the data is acquired, imaging and object extraction are performed using the methods described above. As shown in fig. 4, it is a graph of the experimental result of imaging of a single target obtained by RTI technology using the prior constant-weight elliptical model, where the target to be located is at the (1,2) meter position, and fig. 4 is a graph of the experimental result of imaging of a single target obtained by the present invention using the hierarchical elliptical model under the same conditions, where the target to be located is also at the (1,2) meter position. In fig. 4, due to the adoption of the ellipse model with fixed weight, target bright spots on the image are not clear enough, and a large amount of background shadows exist, while the target image in fig. 5 adopting the hierarchical ellipse model becomes obvious, but the background interference is still strong, and the target image is easily interfered by a false target. As shown in fig. 6, after the target automatic search technique using the method of the present invention is adopted, the background noise is significantly suppressed, and the false target no longer appears.
The technical solutions of the present invention are not limited to the above embodiments, and all technical solutions obtained by using equivalent substitution modes fall within the scope of the present invention.
Claims (3)
1. A model type equipment-free target positioning method is characterized by comprising the following steps:
step one, establishing a wireless positioning system, wherein the positioning system comprises a plurality of wireless receiving and transmitting nodes, and the wireless receiving and transmitting nodes are communicated with each other to form a plurality of wireless links;
step two, establishing a hierarchical ellipse weight model according to the spatial relationship of the wireless link affected by the equipment-free target;
step three, respectively measuring RSS values of a wireless link when no target exists and when a target exists;
step four, calculating a radio frequency tomography result based on the hierarchical ellipse weight model to obtain an imaging graph;
the formula of the gradient shadow weight model corresponding to the ith (i ═ 1,2, …, L) link in the hierarchical elliptical weight model is as follows:
wherein, wijA weight value d representing the influence on the ith link when the target is located at the jth pixel pointiIs the ith link length, dij1，dij2Respectively the distance from the jth pixel point to two nodes forming the ith link, aiIndicating the length of the major axis of the ellipse corresponding to the ith link,
the radius of the maximum No. 1 Fresnel zone corresponding to the ith link, wherein lambda represents the wavelength of the electromagnetic wave;
acquiring a target position by using a cross target automatic searching method;
the cross target automatic searching method comprises the following steps:
step 5.1, eliminating noise points with smaller brightness values by using an averaging method; averaging the brightness of all pixel points on the imaging graph, wherein the brightness value of the pixel point lower than the average value is set as 0;
step 5.2, recalculating the brightness values of all pixel points on the imaging graph, and calculating the product of the brightness values of all pixel points in a cross-shaped neighborhood taking each pixel point as the center and having the arm length of r, wherein the calculation formula is as follows:
wherein, pi represents the multiplication operation, x (i, j) represents the original brightness value of the pixel point with the coordinate (i, j),
the pixel point with coordinate (i, j) is recalculated to have brightness value, and when m is 0, n is-r, -r +1, …-1,1, … r-1, r, when n is 0, m-r, -r-1, … -1,1, … r-1, r; r represents the cross arm length;
and 5.3, taking the coordinate of the brightest pixel as the coordinate of the target, wherein the non-zero pixel area in the imaging graph is the position of the target.
2. The model-class equipment-free target positioning method according to claim 1, characterized in that: the positioning system comprises M +1 wireless receiving and transmitting nodes which are networked on the basis of a wireless communication protocol, wherein the M wireless receiving and transmitting nodes form a measuring network and are uniformly distributed on the periphery of a positioning area of the positioning system, and the M wireless receiving and transmitting nodes are communicated with each other pairwise to form an L (M x (M-1)/2 wireless links; the M +1 th node is a data acquisition node and is responsible for collecting data; the positioning area is evenly divided into N pixel points.
3. The model-class equipment-free target positioning method according to claim 1, characterized in that: the radio frequency tomography comprises the following steps:
step 4.1, calculating the RSS variation of the L effective links respectively, recording the result as delta Y, and obtaining the following results according to the radio frequency tomography principle:
ΔY＝Wx+v
wherein x is [ x ]1,x2,…,xN]TPixel vector, x, representing the division of the localization areaiI is 1,2, …, N represents the value of each pixel point, v represents the noise vector, and W is the weight matrix;
step 4.2, introducing a regularization constraint term to obtain an objective function as follows:
wherein, α represents the regularization coefficient, Q represents the regular matrix, | | | | | represents the 2 norm, the above equation is solved, and the following is obtained:
x＝(WTW+αQTQ)-1WTΔY。
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