An unmanned aerial vehicle safety perception and communication joint optimization method based on information age

Abstract

The application discloses a kind of unmanned plane safety perception and communication joint optimization method based on information age, method includes: establishing a system model;According to system model, define task scheduling model, task unmanned plane trajectory model, probability perception model, communication model and average peak information age model;According to task scheduling model, task unmanned plane trajectory model, probability perception model, communication model and average peak information age model, construct a joint optimization problem with the minimum average peak information age as objective function;By constructing the unmanned plane perception system model of fusion information age and physical layer security, introduce PAoI as core performance index, the freshness of perception data is quantified.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and specifically to a method for joint optimization of UAV security perception and communication based on information age. Background Technology

[0002] With the development of wireless networks, drones have been widely applied in various fields. Drones can be deployed as wireless communication platforms to perform critical tasks related to information gathering. However, collecting new information is challenging in mission-critical scenarios where monitoring equipment cannot be deployed due to budget constraints, logistical challenges, or adverse environmental conditions. To address this issue, drones can be equipped with sensing and communication modules to perform sensing tasks in many critical temporal scenarios. In these applications, drones can perceive information of interest from targets and transmit the perception results to a ground controller (GC) for environmental analysis, scene reconstruction, life-saving operations, channel modeling, and more. Compared to on-site observation and traditional spaceborne and airborne remote sensing, drone-based sensing and communication can collect high-resolution spatiotemporal information and enable highly mobile and low-operating-cost operations in inaccessible areas. Furthermore, the freshness of information depends on the timeliness of sensing results during sensing and transmission, which has a critical impact on GC processing or decision-making. Due to the dynamic changes in target state or environment, outdated information may lead to incorrect decisions by the GC. Therefore, drones need to frequently perceive target information and transmit it to the GC in a timely and reliable manner to improve the freshness of the perceived information.

[0003] Information Age (AOI) is an emerging performance metric for quantifying the freshness of information. AOI is defined as the time elapsed since the latest data update was generated, and this data was initially used in Internet of Things (IoT) applications involving remote monitoring. Peak AOI (PAOI) is defined as the maximum value of the AOI before receiving a new update.

[0004] However, current work mostly aims to maximize throughput or minimize energy consumption. In scenarios such as real-time monitoring or emergency response, the timeliness of perceived data is crucial; focusing solely on transmission rate while ignoring the time lag between data generation and reception may result in base stations receiving high-quality but outdated data, reducing the system's actual utility. Furthermore, existing work often assumes a static channel environment or only considers simple point-to-point communication, failing to adequately discuss how to ensure security through the synergy of trajectory design and artificial interference in highly dynamic flight scenarios involving malicious eavesdropping drones. Summary of the Invention

[0005] This invention proposes a joint optimization method for UAV safety perception and communication based on information age, in order to solve the technical problems mentioned in the background.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a joint optimization method for UAV security perception and communication based on information age, comprising the following steps: S1. Establish a system model; S2. Based on the system model, define the task scheduling model, the task UAV trajectory model, the probability perception model, the communication model, and the average peak information age model. S3. Based on the task scheduling model, the task UAV trajectory model, the probability perception model, the communication model, and the average peak information age model, construct a joint optimization problem with minimizing the average peak information age as the objective function; S4. Decompose the joint optimization problem into a task scheduling subproblem, a sensing frequency subproblem, and a trajectory planning and resource allocation subproblem, and solve them; S5. Alternately optimize the trajectory planning and resource allocation subproblems until the change in the objective function is less than a preset threshold, and obtain the minimum peak information age of the system.

[0007] Preferably, S1 includes: Establish a system model that includes K mission targets, one mission drone, one eavesdropping drone, and one ground base station.

[0008] Preferably, step S2 includes the following steps: S21. Define the task scheduling model: Define the initial label for each task objective, represented as... The task scheduling sequence of the task scheduling model is composed of This indicates that the sequence is mapped as ,Right now , , indicating the task objective The mission drone in The task objective of this visit is to introduce a binary matrix. To represent the task scheduling sequence, a binary matrix elements in If the mission drone visits the first The task objective is labeled as The task objective is... ;otherwise, ;No. The planar location of each access task target is represented as: , ,in The set of locations for all mission objectives. Representing a binary matrix The In this scenario, the mission drone can only sense one mission target at a time, and each mission target is served only once in each update cycle. ; S22. Define the trajectory model of the mission drone: For the first... The first in the cycle The planar coordinate position of the mission objective, the mission drone, during perception is represented as: The planar coordinate position during communication transmission is represented as The time taken to fly from the previous task point or initial position to the perception point is defined as... The time for each perception is Communication time is For the first The first in the cycle Task target service time , is represented as: = ,in, Indicates the number of perceptions; The trajectory of the mission drone is approximated by its position over a series of time intervals, with a maximum flight speed of [missing information]. The resulting distance constraints at consecutive locations are shown below:

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[0012] in, Indicates the first Start time of the next cycle. Indicates the first Duration of the next cycle. This is the maximum endurance of the mission drone; S23. Define the probability perception model: Within the [number] update cycle, the [number]th ... The single-perception success probability model for each task objective is as follows:

[0013] in, Representing sensor points and tasks The distance between them Indicates the altitude of the mission drone. It is a positive parameter representing the sensing quality; At the same time, the target must be within the sensor's sensing range, as expressed by the formula:

[0014] in, Due to the maximum sensing range limitation, This is the maximum sensing angle of the sensor; No. The first task objective is in the The overall success rate of perception in each update cycle is:

[0015] The overall perceived success rate must meet the following requirements:

[0016] in, The minimum required threshold for the overall perceived success probability; S23. Define the communication model: When the links from the mission drone to the ground base station and the eavesdropping drone are both line-of-sight links, let... The channel power gain per unit distance, from the mission drone to the base station and the eavesdropping drone, follows a free-space path loss model and is expressed as:

[0017]

[0018] in, and These are the planar coordinates of the ground base station and the eavesdropping drone, respectively. To eavesdrop on the altitude of drones; S24. Define the average peak information age model: All results detected by the mission UAV for each mission target will be aggregated into a packet and sent to the ground base station. Let the first peak information age model be... The start time of each update cycle is:

[0019] in, Represented as the first The duration of each update cycle; The first In the first update cycle The service start time of each task objective is represented as follows: ; No. In the first update cycle The average peak information age of the task objectives is as follows:

[0020]

[0021] in, For the ( +1) update cycles from the first task objective to the... The sum of the service times for each task objective. For the first In the update cycle from the first +1 mission objective to the The sum of the service times for each task objective. For the first The first task objective is in the Communication time within each update cycle; The average peak information age of all groups sensed from all task objectives across all update cycles is as follows: .

[0022] Preferably, the mission drone will use a portion of its power to generate artificial interference noise; The transmit power is divided into two parts: transmission power. and artificial interference power Due to limited radio frequency output power, mission drones have maximum power limitations. Therefore, the power satisfies: ; The achievable transmission rate of communication bandwidth B Approximate location of the transmission end Given the instantaneous rate at the point, the achievable rate of the mission drone from the transmission point to the base station is:

[0023] in, This represents the power of additive white Gaussian noise. ; The achievable speed from the mission drone to the eavesdropping drone at the transmission point is:

[0024]

[0025]

[0026] The secure transmission rate from the mission drone to the base station can be expressed as: .

[0027] Preferably, when the onboard sensors equipped on the mission drone generate sensing data at a fixed rate R Mbit / s, the size of the data packet carrying the sensing results of a mission target is expressed as follows: ; The information capacity transmitted to the base station should be greater than The size of the data packets generated by the first sensing event, assuming all sensing data packets are securely received by the base station, is expressed as:

[0028] Preferably, the optimization variables of the joint optimization problem include the task scheduling order { , , Number of perceptions for each task objective Hovering position of mission-specific UAVs for perception and communication { , Service time , , } and communication power allocation { , }; The joint optimization problem is represented by the optimization variables as follows:

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[0037] Among them, constraints Because The resulting set of distance constraints, constraints 2 is the sensing range limitation, constraint 3 indicates a constraint on the probability of successful perception. Imposing restrictions on the launch power of mission-specific UAVs, constraining Ensure reliable transmission.

[0038] Preferably, step S4 includes the following steps: S41. Solve the task scheduling subproblem: S411, the mission drone departs from the starting point; S412. From all unvisited task sets, select the task closest to the current location for sensing and communication. The communication location is directly above the ground base station. S413. Map the initial mission label to the scheduling sequence and update the current position of the mission drone; S414. Repeat steps S412 and S413 until all nodes are visited to obtain the final task schedule. S42. Solve the task scheduling subproblem; S43. Solve the trajectory planning and resource allocation subproblem.

[0039] Preferably, step S42 includes the following steps: S421. When the mission drone hovers above the mission target to perform a perception task, the maximum success probability of a single perception is... = Obtain, get A lower bound, namely ; S422. The target is perceived to be at the edge of the mission drone's perception range. At this point, the distance between the mission drone and the target reaches its maximum value. To obtain the minimum probability of successful perception. = Therefore, the upper bound of the maximum number of senses is ; S423, due to Integer and range of values ∈[ A one-dimensional exhaustive search strategy is used to determine the optimal number of sensing iterations. .

[0040] Preferably, step S5 includes the following steps: S51. Given the number of perception attempts and task scheduling, the optimization problem is simplified to:

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[0049] S52. The optimization variables are divided into two parts: power, hovering position and service time, and then optimized alternately. S53. For a fixed hovering position and service time, the optimization problem is transformed into:

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[0052] The optimal solution usually satisfies the total power constraint equality, that is, allocating all remaining power to the interference power. = ; Bivariate optimization simplifies to univariate optimization in the interval [0, ] Search on the best The optimal power allocation is obtained by using the golden ratio search.

[0053] Preferably, step S5 further includes the following step: S54. For the perceptual probability constraint, by introducing a logarithmic transformation, the original constraint has the following nonlinear form:

[0054] By introducing a logarithmic transformation, this constraint is equivalently transformed into a convex second-order cone constraint:

[0055] Among them, the maximum distance threshold required to meet the perception requirements. Defined as: ; S55. For secure transmission constraints, introduce auxiliary variables. As a lower bound for the safe rate, the original constraint Replace with:

[0056]

[0057]

[0058] Among them, product constraint Equivalent representation as a rotational second-order cone constraint:

[0059] Define the squared distance variable ,but

[0060] At the r-th iteration point Place, order , remember A single variable function and its relation to Taking the derivative, we get:

[0061] remember ,but exist The global affine lower bound at a point can be written as:

[0062] This expression satisfies ; Therefore, we construct an iterative lower bound for the safe rate: ( This convexes the rate difference constraint. ; because Let f be an affine function, and It is a convex function, and its negative value is a concave function, therefore For concave functions, the set {( , )∣ } is a convex set; S44. Transform the subproblem into a convex optimization subproblem and solve it. The convex optimization subproblem is as follows:

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[0069] As can be seen from the above technical solution, this invention provides a joint optimization method for UAV security perception and communication based on information age. Compared with the prior art, this invention has the following advantages: 1. This invention constructs a UAV perception system model that integrates information age and physical layer security. Unlike traditional energy efficiency or rate optimization, it introduces PAoI as a core performance indicator to quantify the freshness of perception data. At the same time, it establishes a physical layer security model that includes power allocation for artificial interference. This ensures that the data of each perceived target can be safely transmitted back to the base station within the cycle, while effectively resisting attacks from eavesdroppers and solving the problems of insufficient freshness of perception data and low communication security.

[0070] 2. This invention employs a multi-level joint optimization algorithm architecture to decouple the complexity of the mixed-integer non-convex programming problem in the original problem into three sub-problems: task scheduling, sensing count search, and hovering position and resource allocation. It utilizes the nearest neighbor algorithm to solve the discrete task scheduling problem, determines the search boundary for the sensing count through theoretical derivation, and designs an iterative optimization algorithm for hovering position and resource allocation based on block coordinate descent and continuous convex approximation to optimize the power allocation, hovering position, and service time of the task UAV. This yields the minimum peak information age of the system, reducing the information age of the data while ensuring sensing success rate and communication security, thereby improving the real-time performance of data acquisition and the robustness of the system. Attached Figure Description

[0071] Figure 1 This is a flowchart illustrating a method for joint optimization of UAV safety perception and communication based on information age, according to the present invention. Figure 2 This is a schematic diagram of a model for a joint optimization method of UAV safety perception and communication based on information age according to the present invention. Figure 3 This is a diagram illustrating the evolution of information age in this invention. Detailed Implementation

[0072] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0073] like Figure 1 As shown in this embodiment, a method for joint optimization of UAV safety perception and communication based on information age includes the following steps: S1. Establish a system model; S2. Based on the system model, define the task scheduling model, the task UAV trajectory model, the probability perception model, the communication model, and the average peak information age model. S3. Based on the task scheduling model, the task UAV trajectory model, the probability perception model, the communication model, and the average peak information age model, construct a joint optimization problem with minimizing the average peak information age as the objective function. S4. Decompose the joint optimization problem into a task scheduling subproblem, a sensing frequency subproblem, and a trajectory planning and resource allocation subproblem, and solve them; S5. Alternately optimize the trajectory planning and resource allocation subproblems until the change in the objective function is less than a preset threshold, and obtain the minimum peak information age of the system.

[0074] Furthermore, S1 includes: like Figure 2 As shown, a system model is established that includes K mission targets, one mission drone, one eavesdropping drone, and one ground base station. Without losing generality, the mission drone and the eavesdropping drone maintain a fixed flight altitude throughout the entire operation, and the position of the eavesdropping drone is fixed. It is assumed that the altitude of the ground base station is negligible compared to the drone's flight altitude. The mission drone performs the following actions during a mission: (1) The mission drone starts from its initial position and moves towards the first task. (2) The mission drone selects a suitable sensing position within its sensing range and performs S sensing operations on the target data. During the sensing process, the sensing position remains unchanged to ensure stability and quality. (3) After performing S sensing operations, all sensing results are aggregated into a single data packet and transmitted to the BS. (4) The mission drone moves towards the next task. Steps (2)-(3) are repeated until all tasks are completed. (5) Finally, the mission drone returns to its final position. The process of the mission drone completing S sensing operations and one transmission for K tasks is defined as one update cycle. In this scenario, a total of N update cycles are considered.

[0075] Furthermore, S2 includes the following steps: S21. Define the task scheduling model: Define an initial label for each task objective, denoted as... The task scheduling sequence of the task scheduling model is composed of This indicates that the sequence is mapped as ,Right now , , indicating the task objective The mission drone in To address the task objective of this visit and to transform task scheduling into a computable form, a binary matrix is ​​introduced. To represent a task scheduling sequence, where elements If the mission drone visits the first The task objective is labeled as The task objective is... ;otherwise, This matrix shows that for each target, the access order is determined by the column number of the "1" element in the nth row of the matrix. No. The planar location of each access task target is represented as: , ,in The set of locations for all mission objectives. Representation matrix The In this scenario, the mission drone can only sense one mission target at a time, and each mission target is served only once in each update cycle. ; S22. Define the mission drone trajectory model: Specifically, during the perception process, sensing quality is crucial to the BS's decision-making. Due to limited sensing range and accuracy, the UAV may fail to capture critical information. Therefore, this invention uses a probabilistic perception model to characterize the probability of successful perception. A successful perception event is a random variable whose probability is negatively correlated with the sensing distance. To evaluate sensing quality, for the... The first in the cycle The planar coordinate position of the mission objective, the mission drone, during perception is represented as: The planar coordinate position during communication transmission is represented as The time taken to fly from the previous task point or initial position to the perception point is defined as... The time for each perception is Communication time is For the first The first in the cycle The service time for each task objective is denoted as... , is represented as: = ,in, The number of sensing operations indicates that the trajectory of the mission drone is approximated by its position over a series of time intervals, and because the maximum flight speed is... The resulting distance constraints at consecutive locations are shown below:

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[0079] in, Indicates the first Start time of the next cycle. Indicates the first Duration of the next cycle. This is the maximum battery life of a UAV; S23. Define the probability-aware model: No. Within the [number] update cycle, the [number]th ... The single-perception success probability of each task objective is modeled as follows:

[0080] in, Representing sensor points and tasks The distance between them Indicates the altitude of the mission drone. It is a positive parameter representing the sensing quality; At the same time, the target of the mission must be within the sensing range of the sensor, which can be expressed by the formula:

[0081] in, Due to the maximum sensing range limitation, This is the maximum sensing angle of the sensor; No. The first task objective is in the The overall success rate of perception in each update cycle is:

[0082] To ensure the performance of radar sensing, the overall success probability of sensing must meet the following requirements:

[0083] in, The minimum required threshold for overall success probability is given according to the actual requirements of the application scenario. S23. Define the communication model: Based on field measurements of the air-to-ground (A2G) channel, when the drone is at a sufficiently high altitude, the line-of-sight (LOS) link dominates the A2G channel. Therefore, during communication transmission, when the links from the mission drone to the ground base station and the eavesdropping drone are both line-of-sight links, The channel power gain per unit distance, from the mission drone to the base station and the eavesdropping drone, follows a free-space path loss model and is expressed as:

[0084]

[0085] in, and These are the planar coordinates of the ground base station and the eavesdropping drone, respectively. To eavesdrop on the altitude of drones; Furthermore, to prevent eavesdropping, the mission drone uses some of its power to generate artificial interference noise; The transmit power is divided into two parts: transmission power. and artificial interference power Due to limited radio frequency output power, mission drones have maximum power limitations. Therefore, the power satisfies: ; Ground base stations can eliminate artificial interference signals through technologies such as physical layer key distribution, while eavesdropping drones cannot eliminate this artificial interference; therefore, according to Shannon's formula, the achievable transmission rate of the communication bandwidth B is... Approximate location of the transmission end Given the instantaneous rate at the point, the achievable rate of the mission drone from the transmission point to the base station is:

[0086] in, This represents the power of additive white Gaussian noise. ; The achievable speed from the mission drone to the eavesdropping drone at the transmission point is:

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[0089] The secure transmission rate from the mission drone to the base station can be expressed as: ,in, In fact, It is always non-negative; therefore, without sacrificing optimality, we will omit the operator in the following analysis. .

[0090] S24. Define the average peak information age model: like Figure 3As shown, all results detected by the mission-oriented UAV for each mission target are aggregated into a single packet and sent to the ground base station. The Area of ​​Interest (AoI) is used as a performance metric to measure the freshness of information sent to the ground base station, defined as the time elapsed since the generation of the sensing data packet. When the mission-oriented UAV generates a sensing data packet, the AoI begins to increase until the next data packet is successfully updated, at which point the AoI reaches its maximum value, or PAoI. Each target generates one data packet for each update, and each data packet has a PAoI value. Let the first The start time of each update cycle is:

[0091] in, Represented as the first The duration of each update cycle; The first In the first update cycle The service start time of each task objective is represented as follows: ; No. In the first update cycle The average peak information age of the task objectives is as follows:

[0092]

[0093] in, For the ( +1) update cycles from the first task objective to the... The sum of the service times for each task objective. For the first In the update cycle from the first +1 mission objective to the The sum of the service times for each task objective. For the first The first task objective is in the Communication time within each update cycle; To measure the overall performance of the system, the average peak information age of all groups sensed from all task objectives across all update cycles is as follows: .

[0094] Furthermore, when the onboard sensors equipped on the mission drone generate sensing data at a fixed rate R Mbit / s, the size of the data packet carrying the sensing results of a mission target is expressed as: ; The information capacity transmitted to the base station should be greater than The size of the data packets generated by this sensing operation, and the secure reception of all sensing data packets by the base station, should be such that, to ensure the base station receives complete sensing data from the mission UAV, the information capacity transmitted to the base station should be greater than [a certain value]. The size of the data packets generated by the secondary sensing; therefore, the inequality constraint is expressed as: ; In an environment with the risk of eavesdropping, to ensure that all sensed data packets are securely received by the base station, the constraint is further expressed as:

[0095] Furthermore, the optimization variables in the joint optimization problem include the task scheduling order { , , Number of perceptions for each task objective Hovering position of mission-specific UAVs for perception and communication { , Service time , , } and communication power allocation { , }; The joint optimization problem is represented by the optimization variables as follows:

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[0104] Among them, constraints Because The resulting set of distance constraints, constraints 2 is the sensing range limitation, constraint 3 indicates a constraint on the probability of successful perception. Imposing restrictions on the launch power of mission-specific UAVs, constraining Ensure reliable transmission.

[0105] Furthermore, the optimization problem obtained in S3 has NP-hard complexity when solved directly. To effectively address this issue, this invention proposes a multi-level joint optimization algorithm. This algorithm decomposes the joint optimization problem into three sub-problems: a task scheduling sub-problem, a sensing frequency sub-problem, and a trajectory planning and resource allocation joint optimization sub-problem, which are then solved iteratively. S4 includes the following steps: S41. Solve the task scheduling subproblem: Specifically, the task scheduling subproblem is a variant of the Traveling Salesman Problem (TSP). To reduce computational complexity and considering that the time of the task UAV is mainly determined by the path length and that the accumulation of PAoI is positively correlated with time, we use the Nearest Neighbor (NN) algorithm to determine the task scheduling. S411, the mission drone departs from the starting point; S412. From all unvisited task sets, select the task closest to the current location for sensing and communication. The communication location is directly above the ground base station. S413. Map the initial mission label to the scheduling sequence and update the current position of the mission drone; S414. Repeat steps S412 and S413 until all nodes are visited to obtain the final task schedule. S42. Solve the task scheduling subproblem; S43. Solve the trajectory planning and resource allocation subproblem.

[0106] Furthermore, S42 includes the following steps: S421. When the mission drone hovers above the mission target to perform a perception task, the maximum success probability of a single perception is... = Obtain, get A lower bound, namely ; S422. The target is perceived to be at the edge of the mission drone's perception range. At this point, the distance between the mission drone and the target reaches its maximum value. To obtain the minimum probability of successful perception. = Therefore, the upper bound of the maximum number of senses is ; S423, due to Integer and range of values ∈[ A one-dimensional exhaustive search strategy is used to determine the optimal number of sensing iterations. .

[0107] Furthermore, S5 includes the following steps: Specifically, in the power allocation optimization section, with a fixed hovering position and service time, although the PAoI objective function does not directly include power, power determines the safe rate. This determines the minimum transmission time required to complete data transmission; the higher the security rate, the more constraints need to be met. The smaller the value, the lower the PAoI (Power over Interference). Therefore, the power optimization subproblem can be understood as: maximizing the safe rate under the total power constraint, making it easier to find a smaller transmission time through trajectory and time optimization; S51. Given the number of perception attempts and task scheduling, the optimization problem is simplified to:

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[0116] S52. The optimization variables are divided into two parts: power, hovering position and service time, and then optimized alternately. S53. For a fixed hovering position and service time, the optimization problem is transformed into:

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[0119] From the observed expression, it can be seen that for any fixed value, increasing the value will decrease the eavesdropping link rate, thus increasing the value; therefore, the optimal solution usually satisfies the total power constraint equality, that is, allocating the remaining power to the interference power. = ; Bivariate optimization simplifies to univariate optimization in the interval [0, ] Search on the best The optimal power allocation is obtained by using the golden ratio search.

[0120] Furthermore, S5 also includes the following steps: S54. For the perceptual probability constraint, by introducing a logarithmic transformation, the original constraint has the following nonlinear form:

[0121] By introducing a logarithmic transformation, this constraint is equivalently transformed into a convex second-order cone constraint:

[0122] Among them, the maximum distance threshold required to meet the perception requirements. Defined as: ; S55. For secure transmission constraints, introduce auxiliary variables. As a lower bound for the safe rate, the original constraint Replace with:

[0123]

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[0125] Among them, product constraint Equivalent representation as a rotational second-order cone constraint:

[0126] Define the squared distance variable ,but

[0127] At the r-th iteration point Place, order , remember A single variable function and its relation to Taking the derivative, we get:

[0128] remember ,but exist The global affine lower bound at a point can be written as:

[0129] This expression satisfies ; Therefore, we construct an iterative lower bound for the safe rate: ( This convexes the rate difference constraint. ; because Let f be an affine function, and It is a convex function, and its negative value is a concave function, therefore For concave functions, the set {( , )∣ } is a convex set; S44. Transform the subproblem into a convex optimization subproblem and solve it. The convex optimization subproblem is as follows:

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[0136] In summary, this invention utilizes the nearest neighbor algorithm to solve discrete task scheduling problems. Through theoretical derivation, the search boundary for the number of perceptions is determined. An iterative optimization algorithm based on block coordinate descent and continuous convex approximation for hovering position and resource allocation is designed to optimize the power allocation, hovering position, and service time of the task UAV. This yields the minimum peak information age of the system, which can reduce the information age of the data and improve the real-time performance of data acquisition and the robustness of the system while ensuring the success rate of perception and communication security.

[0137] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk).

[0138] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0139] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0140] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A joint optimization method for UAV security perception and communication based on information age, characterized in that, Includes the following steps: S1. Establish a system model; S2. Based on the system model, define the task scheduling model, the task UAV trajectory model, the probability perception model, the communication model, and the average peak information age model. S3. Based on the task scheduling model, the task UAV trajectory model, the probability perception model, the communication model, and the average peak information age model, construct a joint optimization problem with minimizing the average peak information age as the objective function; S4. Decompose the joint optimization problem into a task scheduling subproblem, a sensing frequency subproblem, and a trajectory planning and resource allocation subproblem, and solve them; S5. Alternately optimize the trajectory planning and resource allocation subproblems until the change in the objective function is less than a preset threshold, and obtain the minimum peak information age of the system.

2. The method for joint optimization of UAV security perception and communication based on information age as described in claim 1, characterized in that: S1 includes: Establish a system model that includes K mission targets, one mission drone, one eavesdropping drone, and one ground base station.

3. The method for joint optimization of UAV security perception and communication based on information age as described in claim 2, characterized in that: S2 includes the following steps: S21. Define the task scheduling model: Define the initial label for each task objective, represented as... The task scheduling sequence of the task scheduling model is composed of This indicates that the sequence is mapped as ,Right now , , indicating the task objective The mission drone in The task objective of this visit is to introduce a binary matrix. To represent the task scheduling sequence, a binary matrix elements in If the mission drone visits the first The task objective is labeled as The task objective is... ;otherwise, ;No. The planar location of each access task target is represented as: , ,in The set of locations for all mission objectives. Representing a binary matrix The In this scenario, the mission drone can only sense one mission target at a time, and each mission target is served only once in each update cycle. ; S22. Define the mission drone trajectory model: For the first... The first in the cycle The planar coordinate position of the mission objective, the mission drone, during perception is represented as: The planar coordinate position during communication transmission is represented as The time taken to fly from the previous task point or initial position to the perception point is defined as... The time for each perception is Communication time is For the first The first in the cycle Task target service time , is represented as: = ,in, Indicates the number of perceptions; The trajectory of the mission drone is approximated by its position over a series of time intervals, with a maximum flight speed of [missing information]. The resulting distance constraints at consecutive locations are shown below: in, Indicates the first Start time of the next cycle. Indicates the first Duration of the next cycle. This is the maximum endurance of the mission drone; S23. Define the probability perception model: Within the [number] update cycle, the [number]th ... The single-perception success probability model for each task objective is as follows: in, Representing sensor points and tasks The distance between them Indicates the altitude of the mission drone. It is a positive parameter representing the sensing quality; At the same time, the target must be within the sensor's sensing range, as expressed by the formula: in, Due to the maximum sensing range limitation, This is the maximum sensing angle of the sensor; No. The first task objective is in the The overall success rate of perception in each update cycle is: The overall perceived success rate must meet the following requirements: in, The minimum required threshold for the overall perceived success probability; S23. Define the communication model: When the links from the mission drone to the ground base station and the eavesdropping drone are both line-of-sight links, let... The channel power gain per unit distance, from the mission drone to the base station and the eavesdropping drone, follows a free-space path loss model and is expressed as: in, and These are the planar coordinates of the ground base station and the eavesdropping drone, respectively. To eavesdrop on the altitude of drones; S24. Define the average peak information age model: All results detected by the mission UAV for each mission target will be aggregated into a packet and sent to the ground base station. Let the first peak information age model be... The start time of each update cycle is: in, Represented as the first The duration of each update cycle; The first In the first update cycle The service start time of each task objective is represented as follows: ; No. In the first update cycle The average peak information age of the task objectives is as follows: in, For the ( +1) update cycles from the first task objective to the... The sum of the service times for each task objective. For the first In the update cycle from the _ ... +1 mission objective to the The sum of the service times for each task objective. For the first The first task objective is in the Communication time within each update cycle; The average peak information age of all groups sensed from all task objectives across all update cycles is as follows:

4. The method for joint optimization of UAV security perception and communication based on information age as described in claim 3, characterized in that: The mission drone will use some of its power to generate artificial interference noise. That is, the transmit power is divided into two parts: the transmission power. and artificial interference power Due to limited radio frequency output power, mission drones have maximum power limitations. Therefore, the power satisfies: ; The achievable transmission rate of communication bandwidth B Approximate location of the transmission end Given the instantaneous rate at the point, the achievable rate of the mission drone from the transmission point to the base station is: in, This represents the power of additive white Gaussian noise. ; The achievable speed from the mission drone to the eavesdropping drone at the transmission point is: The secure transmission rate from the mission drone to the base station can be expressed as: .

5. The method for joint optimization of UAV security perception and communication based on information age as described in claim 4, characterized in that: When the onboard sensors equipped on a mission drone generate sensing data at a fixed rate R, the size of the data packet carrying the sensing results of a mission target is expressed as: ; The information capacity transmitted to the base station should be greater than The size of the data packets generated by the first sensing event, assuming all sensing data packets are securely received by the base station, is expressed as:

6. The method for joint optimization of UAV security perception and communication based on information age as described in claim 5, characterized in that: The optimization variables of the joint optimization problem include the task scheduling order. , , Number of perceptions for each task objective Hovering position of mission-specific UAVs for perception and communication { , Service time , , } and communication power allocation { , }; The joint optimization problem is represented by the optimization variables as follows: Among them, constraints Because The resulting set of distance constraints, constraints 2 is the sensing range limitation, constraint 3 indicates a constraint on the probability of successful perception. Imposing restrictions on the launch power of mission-specific UAVs, constraining Ensure reliable transmission.

7. The method for joint optimization of UAV security perception and communication based on information age as described in claim 6, characterized in that: S4 includes the following steps: S41. Solve the task scheduling subproblem: S411, the mission drone departs from the starting point; S412. From all unvisited task sets, select the task closest to the current location for sensing and communication. The communication location is directly above the ground base station. S413. Map the initial mission label to the scheduling sequence and update the current position of the mission drone; S414. Repeat steps S412 and S413 until all nodes are visited to obtain the final task schedule. S42. Solve the task scheduling subproblem; S43. Solve the trajectory planning and resource allocation subproblem.

8. The method for joint optimization of UAV security perception and communication based on information age as described in claim 7, characterized in that: S42 includes the following steps: S421. When the mission drone hovers above the mission target to perform a perception task, the maximum success probability of a single perception is... = Obtain, get A lower bound, namely ; S422. The target is perceived to be at the edge of the mission drone's perception range. At this point, the distance between the mission drone and the target reaches its maximum value. To obtain the minimum probability of successful perception. = Therefore, the upper bound of the maximum number of senses is ; S423, due to Integer and range of values ∈[ A one-dimensional exhaustive search strategy is used to determine the optimal number of sensing iterations. .

9. The method for joint optimization of UAV security perception and communication based on information age as described in claim 8, characterized in that: S5 includes the following steps: S51. Given the number of perception attempts and task scheduling, the optimization problem is simplified to: S52. The optimization variables are divided into two parts: power, hovering position and service time, and then optimized alternately. S53. For a fixed hovering position and service time, the optimization problem is transformed into: The optimal solution usually satisfies the total power constraint equality, that is, allocating the remaining power to the interference power. = ; Bivariate optimization simplifies to univariate optimization in the interval [0, ] Search on the best The optimal power allocation is obtained by using the golden ratio search.

10. The method for joint optimization of UAV security perception and communication based on information age as described in claim 9, characterized in that: The S5 also Includes the following steps: S54. For the perceptual probability constraint, by introducing a logarithmic transformation, the original constraint has the following nonlinear form: By introducing a logarithmic transformation, this constraint is equivalently transformed into a convex second-order cone constraint: Among them, the maximum distance threshold required to meet the perception requirements. Defined as: ; S55. For secure transmission constraints, introduce auxiliary variables. As a lower bound for the safe rate, the original constraint Replace with: Among them, product constraint Equivalent representation as a rotational second-order cone constraint: Define the squared distance variable ,but At the r-th iteration point Place, order , remember A single variable function and its relation to Taking the derivative, we get: remember ,but exist The global affine lower bound at a point can be written as: This expression satisfies ; Therefore, we construct an iterative lower bound for the safe rate: ( This convexes the rate difference constraint. ; because Let f be an affine function, and It is a convex function, and its negative value is a concave function, therefore For concave functions, the set {( , )∣ } is a convex set; S44. Transform the subproblem into a convex optimization subproblem and solve it. The convex optimization subproblem is as follows: 。

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