A Method for Estimating the Target Viewpoint K-Coverage Rate in a Wireless Visual Sensor Network

Abstract

The present invention discloses a method for estimating the target view angle K-coverage rate of a wireless visual sensor network. By constructing a system model of the wireless visual sensor network for complex environments; according to the system model of the wireless visual sensor network, the target view angle K-coverage estimator for randomly deployed WVSNs in complex environments is derived to predict the target view angle K-coverage rate; Matlab is used to simulate the application scenarios under study, and the fitting situation between the theoretical numerical results of the target view angle K-coverage rate and the experimental simulation results is verified through simulation experiments. The present invention assumes that all nodes are randomly deployed in complex environments to achieve target coverage. At the same time, in order to better identify and track targets, WVSNs need to effectively monitor the surface direction of the targets; through simulation experiments, the theoretical numerical results of the model are compared and analyzed with the experimental simulation results, and the results show that the root mean square error between them basically remains within 6%.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless visual sensor networks, and particularly discloses a method for estimating the target view K-coverage rate of a wireless visual sensor network for complex environments. Background Art

[0002] The wide application of Wireless Visual Sensor Networks (WVSNs) in fields such as wireless intelligent traffic monitoring, anti-terrorism monitoring, wildlife protection, marine biology monitoring, and intelligent battlefield monitoring has attracted a large number of scholars to engage in related research. Different from traditional Wireless Sensor Networks (WSNs), WVSNs are composed of a large number of visual sensor nodes with adjustable sensing directions (referred to as nodes), and have characteristics such as real-time intelligent image processing, adaptive adjustment of sensing directions, target recognition, and tracking. In practical applications, small WVSNs generally adopt deterministic deployment schemes, such as indoor monitoring and urban traffic monitoring. However, for some large WVSN application scenarios (such as wildlife monitoring, intelligent battlefield monitoring, and marine biology monitoring), since the region of interest (FoI) of this application scenario is often inaccessible and the cost of using a deterministic deployment is relatively high, therefore, a random deployment scheme is often adopted in the prior art. However, the prior art has studied the K-coverage problem of WVSNs with boundary deployment and derived a K-coverage estimation model, but has not considered the target view coverage and obstacle occlusion problems. Summary of the Invention

[0003] The present invention provides a method for estimating the target view K-coverage rate of a wireless visual sensor network, aiming to study the problem of estimating the target view K-coverage of randomly deployed WVSNs in complex environments.

[0004] The present invention relates to a method for estimating the target view K-coverage rate of a wireless visual sensor network, including the following steps:

[0005] Construct a wireless visual sensor network system model for complex environments;

[0006] According to the wireless visual sensor network system model, deduce the target view K-coverage estimator of randomly deployed WVSNs in complex environments, and predict the target view K-coverage rate;

[0007] Use Matlab to simulate the studied application scenario, and verify the fitting situation between the theoretical numerical results and the experimental simulation results of the target view K-coverage rate through simulation experiments.

[0008] Further, in the steps of constructing a wireless visual sensor network system model for a complex environment, the wireless visual sensor network system model includes a node perception model, which is described by a quadruple to describe the node perception model, and the node perception model is:

[0009]

[0010] where s(x s , y s ) represents the node and its coordinate information; r, and respectively represent the sensing radius, sensing angle of view, and sensing direction of the node.

[0011] Further, in the steps of constructing a wireless visual sensor network system model for a complex environment, the wireless visual sensor network system model includes an obstacle model, which is described by a quadruple <o(x o , y o ), a, b, β>, and the obstacle model is:

[0012] <o(x o , y o ), a, b, β>

[0013] where o(x o , y o ) represents the obstacle and its center point coordinate information; a and b respectively represent the length and width of the obstacle, satisfying a ≥ b; β represents the rotation angle of the obstacle.

[0014] Further, in the steps of constructing a wireless visual sensor network system model for a complex environment, the wireless visual sensor network system model includes an application scenario model. In the application scenario model, all nodes are randomly deployed within the extended region of interest to achieve perspective coverage monitoring of the targets within the extended region of interest; specifically, the heterogeneous nodes are divided into u ∈ Z + classes, and is used to represent the set of all nodes located within the extended region of interest, and S i represents the set of the i-th class of nodes; is used to represent the total number of all nodes, where u represents the number of heterogeneous node types, and Z + represents positive integers; N i represents the number of the i-th class of nodes.

[0015] Further, in the steps of constructing a wireless visual sensor network system model for a complex environment, for nodes s i ∈ S i and s j ∈ S j , if i ≠ j, then there is ri ≠r j and If i ≠ j, then there is r i = r j and where s i represents any node in the set of nodes of the i-th class, and S i represents the set of nodes of the i-th class; s j represents any node in the set of nodes of the j-th class, and S j represents the set of nodes of the j-th class; r i represents the sensing radius of the nodes of the i-th class, and r j represents the sensing radius of the nodes of the j-th class, represents the sensing angle of view of the nodes of the i-th class, represents the sensing angle of view of the nodes of the j-th class.

[0016] Furthermore, in the steps of constructing a wireless visual sensor network system model for a complex environment, the obstacles are divided into v ∈ Z + , and use to represent the set of all obstacles located in the region of interest, and O j represents the j-th class of obstacles; use to represent the number of all obstacles, where W j is the number of obstacles of the O j class, and the lengths and widths of different types of obstacles are different; use T = {t i |1 ≤ i ≤ M} to represent the set of targets located in the region of interest, and t i represents any target, and M represents the total number of targets in the region of interest.

[0017] Furthermore, in the steps of constructing a wireless visual sensor network system model for a complex environment, the side length and area of the extended region of interest are respectively expressed as:

[0018]

[0019] ||Ω′|| = (L′) 2

[0020] where L′ represents the side length of the extended region of interest, L represents the side length of the region of interest, r i represents the sensing radius of the S i class of nodes, u represents the number of classifications of heterogeneous nodes, ||Ω′|| represents the area of the extended region of interest, and Ω′ represents the extended region of interest.

[0021] Furthermore, according to the wireless visual sensor network system model, in the process of deriving the target perspective K-coverage estimator for randomly deployed WVSNs in a complex environment and predicting the target perspective K-coverage rate, the target perspective K-coverage rate is as follows:

[0022]

[0023] Among them, P{X≥K} represents the target perspective K-coverage rate, represents the probability that the target is exactly covered in perspective by k different types of nodes. X represents the event that the target is covered in perspective by at least k different types of nodes. K represents the perspective coverage degree, k represents the cumulative parameter of the perspective coverage degree variable, and λ represents the density of each type of node; D(t) i represents the detection area of the target t relative to the i-th type of node, represents the sensing perspective of the i-th type of node, θ represents the effective perspective, f(r i ) represents the mathematical expectation of the probability that the target in the sensing area of the i-th type of node is not blocked by any obstacles, and π represents the pi.

[0024] Furthermore, in the process of constructing the wireless visual sensor network system model for a complex environment, use A i to represent the event that the target t located in the area of interest is covered by the node s∈S i . Then the probability of the event A i occurring is:

[0025] P(A i ) = q i ×p i

[0026] Among them, P(A i ) represents the probability of the event A i occurring, represents the probability that the node s falls within the detection area D(t) i of the target t; represents the probability that the target t is located within the angle with the node s as the vertex and as the angle bisector; π represents the pi, r i represents the sensing radius of the S i type of node, ||Ω′|| represents the area of the extended area of interest, W j is the number of obstacles of the O j type, a j represents the length of the obstacle of the O j type, b j represents the width of the obstacle of the O j type, and v represents the number of obstacle classifications.

[0027] Further, in the steps of constructing the wireless visual sensor network system model for complex environments, use B i to represent the face direction of target t The event that the angle between the face direction of t and the visible direction from t to node s is not greater than the effective viewing angle θ, then event B i The probability of occurrence is:

[0028]

[0029] where, P(B i ) represents the probability of event B i occurring, θ represents the effective viewing angle, and π represents pi.

[0030] The beneficial effects achieved by the present invention are as follows:

[0031] The present invention provides a method for estimating the target view angle K-coverage rate of a wireless visual sensor network. By constructing a wireless visual sensor network system model for complex environments; according to the wireless visual sensor network system model, deriving a target view angle K-coverage estimator for randomly deployed WVSNs in complex environments to predict the target view angle K-coverage rate; using Matlab to simulate the application scenarios studied, and verifying the fitting situation between the theoretical numerical results of the target view angle K-coverage rate and the experimental simulation results through simulation experiments. The method for estimating the target view angle K-coverage rate of the wireless visual sensor network provided by the present invention assumes that all nodes are randomly deployed in a complex environment (with obstacles) to achieve target coverage. At the same time, in order to better identify and track the target, WVSNs need to effectively monitor the face direction of the target; for this application scenario, the present invention proposes the concept of target view angle coverage and derives a target view angle K-coverage estimator. By comparing and analyzing the model theoretical numerical results with the experimental simulation results through simulation experiments, the results show that the root mean square error between them basically remains within 6%; the research of the present invention has important guiding significance for predicting the target coverage rate of WVSNs deployed in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 is a schematic flow chart of the method for estimating the target view angle K-coverage rate of the wireless visual sensor network of the present invention;

[0033] Figure 2 is a schematic diagram of the node sensing model of the present invention;

[0034] Figure 3 is a schematic diagram of the obstacle model of the present invention;

[0035] Figure 4 is a schematic diagram of the application scenario of the present invention;

[0036] Figure 5This is a schematic diagram of the detection area of the present invention. Detailed implementation manners

[0037] To better understand the above technical solution, the following will describe the above technical solution in detail in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0038] As Figures 1 to 5 shown, a method for estimating the target view K-coverage rate of a wireless visual sensor network according to the first embodiment of the present invention includes the following steps:

[0039] Step S100: Construct a wireless visual sensor network system model for a complex environment.

[0040] Construct a wireless visual sensor network system model for a complex environment. The wireless visual sensor network system model includes a node perception model, an obstacle model, and an application scenario model.

[0041] Step S200: According to the wireless visual sensor network system model, derive the target view K-coverage estimator for randomly deployed WVSNs in a complex environment, and predict the target view K-coverage rate.

[0042] According to the constructed wireless visual sensor network system model, derive the target view K-coverage estimator for randomly deployed WVSNs in a complex environment, and predict the target view K-coverage rate. The target view K-coverage rate is the ratio of the number of targets covered by view K in the target set T to the total number of targets. It is expressed as P = |T'| / M, where T' represents the set of targets covered by view K, |T'| represents the number of elements in the set T', and M represents the total number of targets in the region of interest.

[0043] Step S300: Use Matlab to simulate the application scenario under study, and verify the fitting situation between the theoretical numerical results and the experimental simulation results of the target view K-coverage rate through simulation experiments.

[0044] Use Matlab 8a to simulate the application scenario under study, and verify the fitting situation between the theoretical numerical results and the experimental simulation results of the target view K-coverage rate through a series of simulation experiments. Due to the randomness of the application scenario deployment, in order to obtain more accurate simulation results, the Monte Carlo method is used to run each group of simulation scenarios m = 200 times repeatedly, and finally the average value is taken as the final experimental simulation result.

[0045] Furthermore, please refer to Figures 1 to 5 In the method for estimating the target view K-coverage rate of the wireless visual sensor network provided in this embodiment, in step S100, please refer to Figure 2 , the wireless visual sensor network system model includes a node perception model, which adopts a quadruple Describe the node perception model, and the node perception model is as follows:

[0046]

[0047] In Equation (1), s(x s , y s ) represents the node and its coordinate information; r, and respectively represent the perception radius, field of view (FoV), and perception direction of the node. It can be seen that the perception area of the node is a sector with a central angle of and a radius of r. is the angular bisector vector of the perception area.

[0048] Preferably, referring to Figures 1 to 5 , for the method for estimating the target view angle K-coverage rate of the wireless visual sensor network provided in this embodiment, the wireless visual sensor network system model includes an obstacle model, and a quadruple <o(x o , y o ) is used to describe the obstacle model, and the obstacle model is as follows:

[0049] <o(x o , y o ), a, b, β> (2)

[0050] In Equation (2), o(x o , y o ) represents the obstacle and its central point coordinate information; a and b respectively represent the length and width of the obstacle, satisfying a ≥ b; β represents the rotation angle of the obstacle.

[0051] Furthermore, referring to Figures 1 to 5 , for the method for estimating the target view angle K-coverage rate of the wireless visual sensor network provided in this embodiment, the wireless visual sensor network system model includes an application scenario model. In the application scenario model, visual nodes are randomly and independently deployed in a certain obstacle area to achieve view angle coverage of the target. The shaded square area is the FoI, denoted by Ω, and its side length is L. Since the nodes may be located in the boundary area of the FoI, the perception area of the nodes may be deployed in the area outside the FoI (i.e., there is a boundary effect). To eliminate the boundary effect, the FoI is now extended, which is called the Extended Field of Interest (EFoI). The dashed square area is the EFoI, denoted by Ω′, and its side length is L′. This embodiment assumes that all nodes are randomly deployed within the EFoI to achieve view angle coverage monitoring of the targets therein.

[0052] Divide the heterogeneous nodes into u ∈ Z+ Class, denoted by as the set of all nodes within EFoI, S i denotes the set of nodes of the i-th class; denoted by as the total number of all nodes. For node s i ∈S i and s j ∈S j , if i≠j, then r i ≠r j and Conversely, then r i =r j and Meanwhile, the obstacles are divided into v∈Z + , denoted by as the set of all obstacles within FoI, O j denotes the j-th class of obstacles; denoted by as the total number of all obstacles, where, W j is the number of the j-th class of obstacles, and the lengths and widths of different types of obstacles are different. In addition, let T={t j |1≤i≤M} denote the set of targets within FoI. According to the above mathematical description, the side length and area of EFoI can be expressed as: i |1≤i≤M} represents the set of targets within FoI. According to the above mathematical description, it can be known that the side length and area of EFoI can be expressed as:

[0053]

[0054] In formula (3), u represents the number of heterogeneous node types, Z + represents positive integers; N i represents the number of nodes of the i-th class; s i represents any node in the set of nodes of the i-th class, S i represents the set of nodes of the i-th class; s j represents any node in the set of nodes of the j-th class, S j represents the set of nodes of the j-th class; r i represents the sensing radius of the i-th class of nodes, r j represents the sensing radius of the j-th class of nodes, represents the sensing angle of the i-th class of nodes, represents the sensing angle of the j-th class of nodes; t i represents any target within the region of interest, M represents the total number of targets within the region of interest; L′ represents the side length of the extended region of interest, L represents the side length of the region of interest, r i represents the sensing radius of the S i class of nodes, u represents the number of classifications of heterogeneous nodes, ||Ω′|| represents the area of the extended region of interest, and Ω′ represents the extended region of interest.

[0055] Preferably, see Figures 1 to 5 , for the method for estimating the target perspective K-coverage of the wireless vision sensor network provided in this embodiment, in step S200, the relevant definition theorems and the target perspective K-coverage estimation method are set as follows:

[0056] I. Relevant definition theorems

[0057] 1. Definition 1: Target perspective coverage

[0058] If the target t is located within the sensing area of the node s, and the angle between its surface direction (i.e., the front-facing direction) and the visible vector (abbreviated as the visible direction) is not greater than θ ∈ [0, π / 2) (referred to as the effective perspective), and it is not blocked by obstacles, then it is said that the target t is covered by the node s in terms of perspective.

[0059] 2. Definition 2: Target perspective K-coverage and coverage degree

[0060] If the target t is covered by at least K nodes in terms of perspective, then this target is said to be covered by perspective K, where K is the target perspective coverage degree.

[0061] 3. Definition 3: Target perspective K-coverage rate

[0062] The ratio of the number of targets in the target set T that are covered by perspective K to the total number of targets is called the target perspective K-coverage rate, denoted as P = |T′| / M, where T′ represents the set of targets covered by perspective K, and |T′| represents the number of elements in the set T′.

[0063] 4. Definition 4: Detection area

[0064] The detection area of the target t is defined as a circle with it as the center and the node sensing radius r as the radius, as Figure 5 shown. For different types of nodes, the detection area of the target t is different, and D(t) i is used to represent the detection area of the target t relative to the S i type of node. It can be known that its area is ||D(t) i || = πr i 2 .

[0065] 5. Theorem 1: Target perspective coverage determination theorem

[0066] To enable the target t to be covered by the node s in terms of perspective, the following four conditions must be met:

[0067] (1) dist(s, t) ≤ r: That is, the distance between the target t and the node s is less than the sensing radius.

[0068] (2) That is, the target t is located within the angle with the node s as the vertex and as the angular bisector.

[0069] (3) That is, the node s is located within the angle 2θ with the target t as the vertex and as the angular bisector.

[0070] (4) For If it must satisfy (x s - x i1 )(x t - x i1 ) > 0 and (x s - x i2 )(x t - x i2 ) > 0: That is, the visual direction from the target t to the node s is not blocked by any obstacle.

[0071] II. Problem Description

[0072] To simplify the problem, the following assumptions are made in this embodiment:

[0073] (1) All targets and obstacles are located within the FoI. The targets and obstacles are independent of each other. The lengths and widths of heterogeneous obstacles are different from each other; the lengths and widths of homogeneous obstacles are the same.

[0074] (2) To eliminate the boundary effect, all nodes are randomly deployed in the eFoI. The nodes are independent of the targets and obstacles, and the nodes are immovable. The sensing radii and sensing perspectives of heterogeneous nodes are different from each other; the sensing radii and sensing perspectives of homogeneous nodes are the same.

[0075] III. Target Perspective K-Coverage Estimation Method

[0076] For the application scenario model studied in this embodiment, in this subsection, the target perspective K-coverage estimator for randomly deployed WVSNs in a complex environment will be derived. We randomly select a node s ∈ S i belonging to class S i and any target t in the FoI, and derive the probability model of the target being covered by the node s in terms of perspective.

[0077] Let A i denote the event that the target t located within the FoI is covered by the node s ∈ S i . This event must satisfy two conditions: 1) The node s is located within the detection area D(t) of the target t i (i.e., satisfying condition (1) of Theorem 1); 2) The target t is located within the angle with the node s as the vertex and The angle bisector Inside (i.e., satisfying condition (2) of Theorem 1). This event A i The probability of occurrence is:

[0078] P(A i ) = q i ×p i (4)

[0079] In equation (4), Indicates the probability that the node s falls within the detection area D(t) of the target t i Inside; Indicates that the target t is located at the vertex with the node s, The angle bisector Inside the angle.

[0080] Use B i To represent the event that the angle between the surface direction of the target t And the visible direction from t to the node s is not greater than the effective viewing angle θ. The probability of this event occurring is equal to the probability that the node s is located within the angle 2θ with t as the vertex and As the angle bisector (i.e., satisfying condition (3) of Theorem 1), which can be expressed as:

[0081]

[0082] It can be seen from equation (5) that the probability of event B i Occurs is a function of θ and is independent of the type of node.

[0083] Lemma 1: A large number of points are randomly distributed in the region R with density λ. For any sub-region Use E to represent the event that the point is located in R'. It can be known that the number of occurrences i of this event follows a Poisson distribution with intensity λ||R'||, that is Among them, ||R'|| represents the area of R'.

[0084] Since heterogeneous obstacles are randomly distributed in the FoI, visual occlusion may occur. Now assume that the target t is located within the sensing area of the node s, and the distance between them is represented by x ∈ (0, r i . For the obstacle o ∈ O j , when it is tangent to the line segment st, use l j To represent the distance from the center point of this obstacle to the line segment st. It can be known that When l j = b j / 2, the formed occlusion band is the smallest. When The formed occlusion band is the largest. Since the rotation angle of the obstacle is random and follows a uniform distribution, it can be known that l jAlso follows a uniform distribution, and its expected value can be expressed as To accurately describe the visual occlusion contribution of this obstacle, use Construct an occlusion zone, denoted by Γ j Its area To ensure that the target t is not occluded by the obstacle o, there must be no such obstacles within the occlusion zone Γ j (i.e., the central point coordinates of such obstacles are all located outside Γ j ). Denote this event (i.e., satisfying condition (4) of Theorem 1) by c j . According to Lemma 1, the probability of this event occurring is:

[0085]

[0086] In Equation (6), Represents the density of obstacles of type O j , W j Represents the number of obstacles of type O j , ||Ω|| represents the area of the region of interest; P(c j ) represents the probability that there are no obstacles of type O j in the occlusion zone Γ j , μ j Represents the density of obstacles of type O j , ||Γ j || represents the area of the occlusion zone formed by the node and obstacles of type O j , Represents the mathematical expectation of the distance between the node and obstacles of type O j , and x represents the distance between the node and obstacles of type O j .

[0087] Denote the event that the target t within the sensing region of the i-th type of node s is not occluded by any type of obstacle by C i . Since the obstacles are independent of each other, the probability of this event is known to be:

[0088]

[0089] In Equation (7), P(C i ) represents the probability of the occurrence of event C i , μ j Represents the density of obstacles of type O j , ||Γ j || represents the area of the occlusion zone formed by the node and obstacles of type O j , Represents the mathematical expectation of the distance between node s and obstacles of type O j , and x represents the distance between the node and obstacles of type O j .

[0090] This probability value is a random value with respect to the distance x. To more accurately describe the probability of event C i occurring, find its mathematical expectation (denoted by f(r i ), that is:

[0091]

[0092] In Equation (8), f(r i ) represents the mathematical expectation of the probability that the target within the sensing area of the i-th type of node s is not blocked by any obstacles. P(C i ) represents the probability that the target t within the sensing area of the i-th type of node is not blocked by any type of obstacle, and r i represents the sensing radius of the i-th type of node.

[0093] Use F i to represent the event that the target t is covered by the perspective of the node s. For the complex environment application scenario studied in this embodiment, according to Equations (4), (5), and (8), it can be known that the probability of event F i occurring is:

[0094]

[0095] In Equation (9), P(F i ) represents the probability of event F i occurring, P(A i ) represents the probability that the target within the region of interest is covered by the i-th type of node, P(B i ) represents the probability that the angle between the direction of the target surface within the region of interest and the visible direction from this target to the node is not greater than the effective viewing angle, f(r i ) represents f(r i ) represents the mathematical expectation of the probability that the target within the sensing area of the i-th type of node s is not blocked by any obstacles, r i represents the sensing radius of the i-th type of node, represents the sensing viewing angle of the i-th type of node, ||Ω′|| represents the area of the extended region of interest, W represents the total number of heterogeneous obstacles, a j and b j respectively represent the length and width of the j-th type of obstacle.

[0096] Use to represent the event that the target t is exactly covered by the perspective of k i S i type of nodes. This event follows a binomial distribution It can be known that the probability of this event occurring is:

[0097]

[0098] In formula (10), represents the probability that the target t is exactly covered by the perspective of k i S i class nodes, N i represents the number of i S i class nodes, k i represents the perspective coverage contributed by the

[0099] Inference 1: When a large number of i S-class nodes are randomly deployed in the EFoI, it approximately follows a Poisson distribution with an intensity of where represents the density of the S i class nodes.

[0100] Lemma 2: Given then we can obtain where k i ∈[0, K]∩Z.

[0101] Now, let G K represent the event that the target t is exactly covered by the perspective of K different types of nodes, where represents the perspective coverage of the target t; k i represents the perspective coverage contributed by the i S-class nodes to the target t. According to Lemma 2 and Inference 1, the probability of this event occurring is:

[0102]

[0103] In formula (11), P(G K ) represents the probability that the target t is exactly covered by the perspective of K different types of nodes, represents the event that the target t is exactly covered by the perspective of k i type-i nodes, λ i represents the density of the type-i nodes, D(t) i represents the detection area of the target t with respect to the type-i nodes, represents the sensing perspective of the type-i nodes, θ represents the effective perspective, f(r i ) represents the mathematical expectation of the probability that the target within the sensing area of the type-i nodes is not blocked by any obstacles, and π represents the pi.

[0104] Let X represent the event random variable that the target t is covered by at least K different types of nodes. According to formula (11) and probability knowledge, the probability of the event {X≥K} occurring is:

[0105]

[0106] In Equation (12), P{X≥K} represents the target perspective K-coverage rate, and P(G j ) represents the probability that the target t is exactly covered by j different types of nodes in terms of perspective. λ i represents the density of the i-th type of node, and D(t) i represents the detection area of the target t with respect to the i-th type of node. represents the sensing perspective of the i-th type of node, θ represents the effective perspective, and f(r i ) represents the mathematical expectation of the probability that the target within the sensing area of the i-th type of node is not blocked by any obstacles. π represents the pi.

[0107] Conclusion 1: Now assume that the densities of heterogeneous nodes randomly deployed within the EFoI are the same, that is λ i = λ j . Use λ to represent the density of each type of node, that is, λ = λ i = λ j . According to Equation (12), the target perspective K-coverage rate is as follows:

[0108]

[0109] In Equation (13), P{X≥K} represents the target perspective K-coverage rate, represents the probability that the target is exactly covered by k different types of nodes in terms of perspective, X represents the event that the target is covered by at least k different types of nodes in terms of perspective, K represents the perspective coverage degree, k represents the cumulative parameter of the perspective coverage degree variable, and λ represents the density of each type of node; D(t) i represents the detection area of the target t with respect to the i-th type of node, represents the sensing perspective of the i-th type of node, θ represents the effective perspective, and f(r i ) represents the mathematical expectation of the probability that the target within the sensing area of the i-th type of node is not blocked by any obstacles. π represents the pi.

[0110] Conclusion 1 describes the relationship between the node density and the target perspective K-coverage rate, the node sensing radius, the sensing perspective, the effective perspective, and the obstacles. Given parameters such as the node critical density, the sensing radius, the sensing perspective, the effective and obstacle radii, etc., this model can be used to predict the target perspective K-coverage rate. Meanwhile, given the target perspective K-coverage rate, the sensing radius, the sensing perspective, the effective perspective, and the obstacle parameters, this model can be used to predict the node density.

[0111] Proposition 1: When the perspective coverage of the target in the FoI is infinite, the target perspective K-coverage rate is approximately 0, that is, when K→∞, P{X≥K}→0.

[0112] Example 1: Consider that S1 and S2 type nodes with the same density are randomly deployed in the EFoI, and 50 targets, 5 O1 type obstacles, and 5 O2 type obstacles are all located in the square region of interest FoI with a side length of 100m. The node related parameters are set as: r1 = 20m, and r2 = 15m, The obstacle related parameters are set as: a1 = 10m, b1 = 5m and a2 = 6m, b2 = 3m. To make the target 1-perspective coverage rate reach at least 90%, by using the exhaustive method for Equation (13), we can get λ1 = λ2 = 199×10 -4 / m 2 , so it can be known that at least 356 S1 type nodes and 356 S2 type nodes need to be deployed simultaneously.

[0113] Preferably, see Figures 1 to 5 , in the method for estimating the target perspective K-coverage rate of the wireless visual sensor network provided in this embodiment, in step S300, Matlab 8a is used to simulate the studied application scenario, and the fitting situation between the theoretical numerical results and the experimental simulation results of Conclusion 1 is verified through a series of simulation experiments. Due to the randomness of the application scenario deployment, in order to obtain more accurate simulation results, the Monte carlo method is used to repeatedly run each group of simulation scenarios m = 200 times, and finally the mean value is taken as the final experimental simulation result.

[0114] In order to better reflect the deviation between the theoretical numerical results and the experimental simulation results, the root mean squared error (RMSE) is used to measure the accuracy of the model. The root mean squared error is defined as:

[0115]

[0116] In Equation (14), RMSE represents the root mean squared error, P i is the experimental simulation result of the i-th time for each group of scenarios, P is the theoretical numerical result of each group of scenarios; m is the number of times each group of simulation scenarios is repeatedly run.

[0117] Analyze the influence of factors such as node density, sensing radius, and sensing perspective on the actual simulation results and theoretical numerical results. In this embodiment, it is assumed that 80 targets, 5 O1, and 5 O2 two types of heterogeneous obstacles are located in the square FoI with a side length of 100m. At the same time, it is assumed that S1 and S2 two types of heterogeneous nodes with the same node density are randomly and independently distributed in the EFoI.

[0118] Compared with the prior art, the method for estimating the target view angle K-coverage rate of the wireless vision sensor network provided in this embodiment constructs a system model of the wireless vision sensor network for complex environments; according to the system model of the wireless vision sensor network, it derives the target view angle K-coverage estimator for randomly deployed WVSNs in complex environments to predict the target view angle K-coverage rate; uses Matlab to simulate the application scenarios under study, and verifies the fitting situation between the theoretical numerical results of the target view angle K-coverage rate and the experimental simulation results through simulation experiments. The method for estimating the target view angle K-coverage rate of the wireless vision sensor network provided by the present invention assumes that all nodes are randomly deployed in a complex environment (with obstacles) to achieve target coverage. At the same time, to better identify and track the target, WVSNs need to effectively monitor the surface direction of the target; for this application scenario, this embodiment proposes the concept of target view angle coverage and derives the target view angle K-coverage estimator. By comparing and analyzing the theoretical numerical results of the model with the experimental simulation results through simulation experiments, the results show that the root mean square error between them basically remains within 6%; the research of this embodiment has important guiding significance for predicting the target coverage rate of WVSNs deployed in complex environments.

[0119] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present invention. Obviously, those skilled in the art can make various changes and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these modifications and variations.

Claims

1. A method for estimating target viewing angle K-coverage in a wireless visual sensor network, characterized in that: The following steps are involved: Construct a wireless visual sensor network system model for complex environments; According to the wireless visual sensor network system model, a target view K-coverage estimator of randomly deployed WVSNs in complex environments is derived to predict the target view K-coverage rate; Matlab is used to simulate the application scenario under study, and the fitting between the theoretical numerical results of the target viewing angle K-coverage and the experimental simulation results is verified through simulation experiments; In the step of deriving a target perspective K-coverage estimator for randomly deployed WVSNs in a complex environment according to the wireless visual sensor network system model and predicting the target perspective K-coverage, the target perspective K-coverage is: Among them, P{X≥K} represents the target viewing angle K-coverage, represents the probability that the target is covered by k nodes of different types, X represents the event that the target is covered by at least k nodes of different types, K represents the view coverage, k represents the cumulative parameter of the view coverage variable, and λ represents the density of each type of node; D(t) i represents the detection area of ​​target t relative to the i-th type node, represents the perception angle of the i-th node, θ represents the effective angle, and f(r i ) represents the mathematical expectation of the probability that the target in the perception area of ​​the i-th node is not blocked by any obstacle, and π represents pi; In the step of constructing a wireless visual sensor network system model for complex environments, A i Indicates that the target t in the region of interest is represented by the node s∈S i Covered events, then event A i The probability of occurrence is: P(A i )=q i ×p i Among them, P(A i ) represents event A i The probability of occurrence, Indicates that node s falls within the detection area D(t) of target t i Probability within Indicates that the target t is located at the vertex of node s and r i Indicates S i The node-like perception radius, ||Ω′|| represents the area of ​​the extended region of interest, W j O j The number of obstacles of this type, a j Indicates O j The length of the obstacle, b j Indicates O j The width of the obstacle class, v represents the number of obstacle classifications; In the step of constructing a wireless visual sensor network system model for complex environments, event B i The probability of occurrence is: Among them, P(B i ) indicates event B i Probability of occurrence; Using C i represents the event that the target t in the perception area of ​​the i-th node s is not blocked by any type of obstacle. Since the obstacles are independent of each other, we know that the event C i The probability is: Among them, P(C i ) represents event C i The probability of occurrence, μ j Indicates O j The density of class obstacles, Represents the node s and O j The mathematical expectation of the distance between the obstacles, x represents the distance between the node and O j The distance between obstacles; Use F i Indicates the event that target t is covered by node s, event F i The probability of occurrence is: Among them, P(F i ) represents event F i The probability of occurrence; use It means that the target t is exactly k i S i Class nodes implement events covered by perspective, events The probability of occurrence is: in, It means that the target t is exactly k i S i The probability that a class node achieves view coverage, N i Indicates S i The number of class nodes, k i Indicates S i The visual coverage contributed by the class node.

2. The method for estimating target viewing angle K-coverage of a wireless visual sensor network according to claim 1, characterized in that: In the step of constructing a wireless visual sensor network system model for complex environments, the wireless visual sensor network system model includes a node perception model using a four-tuple To describe the node perception model, the node perception model is: Among them, s(x s ,y s ) represents the node and its coordinate information; r, and They represent the node's perception radius, perception viewing angle, and perception direction respectively.

3. The method for estimating target viewing angle K-coverage of a wireless visual sensor network according to claim 2, characterized in that: In the step of constructing a wireless visual sensor network system model for complex environments, the wireless visual sensor network system model includes an obstacle model using a four-tuple <o(x o ,y o ),a,b,β> describes the obstacle model, and the obstacle model is: <o(x o ,y o ),a,b,β> Among them, o(x o ,y o ) represents the coordinate information of the obstacle and its center point; a and b represent the length and width of the obstacle respectively, satisfying a≥b; β represents the rotation angle of the obstacle.

4. The method for estimating target viewing angle K-coverage of a wireless visual sensor network according to claim 3, characterized in that: In the step of constructing a wireless visual sensor network system model for complex environments, the wireless visual sensor network system model includes an application scenario model, in which all nodes are randomly deployed in an extended area of ​​interest to achieve visual coverage monitoring of targets in the extended area of ​​interest; specifically, the heterogeneous nodes are divided into u∈Z + Class, use represents the set of all nodes located in the extended area of ​​interest, S i Represents the set of nodes of the i-th category; represents the number of all nodes, where u represents the number of heterogeneous node types, and Z + Represents a positive integer; N i Represents the number of nodes in the i-th category.

5. The method for estimating target viewing angle K-coverage of a wireless visual sensor network according to claim 4, characterized in that: In the step of constructing a wireless visual sensor network system model for complex environments, for node s i ∈S i and j ∈S j , if i≠j, then r i ≠r j and If i = j, then r i =r j and Among them, s i represents any node in the i-th type of node set, S i represents the set of nodes of the i-th category; s j represents any node in the j-th node set, S j represents the set of nodes of the jth type; r i represents the perception radius of the i-th type of node, r j represents the perception radius of the j-th type of node, represents the perception perspective of the i-th type of node, Represents the perception perspective of the j-th node.

6. The method for estimating target viewing angle K-coverage of a wireless visual sensor network according to claim 5, characterized in that: In the step of constructing a wireless visual sensor network system model for complex environments, the obstacles are divided into v∈Z + ,use represents the set of all obstacles within the area of ​​interest, O j Indicates the jth type of obstacle; represents the number of all obstacles, where W j O j The number of obstacles of different types, the length and width of different types of obstacles are different; use T = {t i |1≤i≤M} represents the set of targets located in the region of interest, t i represents any target, and M represents the total number of targets in the region of interest.

7. The method for estimating target viewing angle K-coverage rate in a wireless visual sensor network according to claim 6, characterized in that: In the step of constructing a wireless visual sensor network system model for complex environments, the side length and area of ​​the extended region of interest are respectively expressed as: ||Ω′||=(L′) 2 Where L′ represents the side length of the extended region of interest, L represents the side length of the region of interest, and r i Indicates S i Class node perception radius, u represents the number of heterogeneous node classifications, ||Ω′|| represents the area of ​​the extended region of interest, and Ω′ represents the extended region of interest.

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