Unmanned aerial vehicle sensing task allocation method based on communication and sensing integrated base station

Abstract

The invention belongs to the technical field of signal processing, and discloses an unmanned aerial vehicle sensing task allocation method based on a communication and sensing integrated base station, which comprises the following steps: acquiring a macro base station sensing situation; the macro base station evaluates and calculates the sensing time resource consumption of the sensing integrated base station; the macro base station completes task allocation scheme characterization and problem modeling; the macro base station optimizes an unmanned aerial vehicle perception task allocation scheme; the sensing integrated base station senses the unmanned aerial vehicle and obtains the low-altitude situation of the unmanned aerial vehicle. According to the method, the communication perception common front end and time-division working integrated base station is utilized, the unmanned aerial vehicle perception task allocation method with multi-base-station cooperation is optimally designed, the low-altitude unmanned aerial vehicle perception problem is effectively solved, the time resource utilization rate is greatly improved, and meanwhile, compared with an offline optimal scheme with long time consumption, the method has the advantage that the method is easy to implement. Under the condition that performance is hardly lost, algorithm complexity is greatly reduced, and the requirement for real-time task allocation is met.

Description

Technical Field

[0001] The present invention mainly relates to the field of signal processing technology, and in particular to a method for allocating perception tasks of unmanned aerial vehicles based on a telepathic base station. Background Art

[0002] The low-altitude economy is a comprehensive economic form that is driven by various low-altitude flight activities of various manned and unmanned aircraft, radiating and driving the integrated development of related fields. It has the characteristics of a long industrial chain, a wide radiation area, strong growth and driving force. The development of the global low-altitude economy has gone through the early application exploration stage and the standardized development stage. In the early days, due to the immaturity of low-altitude technology, low-altitude tourism and exploration and application in agriculture and industry were the main focus. After 2010, with the increasing maturity of low-altitude flight technology and the diversification of applications, standardized supervision has become the main task of the development of low-altitude economies in various countries during this period. At present, the global low-altitude economy is in the stage of further application and popularization.

[0003] With the booming development of the low-altitude economy, drones, as an important subject in the low-altitude economy, are also growing rapidly in number. Figure 1 As shown in Figure 2, the global investment in drones has increased from US$121 million in 2013 to US$4.806 billion in 2022. As the number of drones increases, existing dedicated drone monitoring equipment and radars are difficult to deploy on a large scale due to the lack of available sites and high installation and maintenance costs. Therefore, it is urgent to develop low-cost, wide-coverage and effective detection methods to accurately perceive and efficiently control massive drones.

[0004] Based on the simple transformation of 5G base stations, the perception function is added on the basis of the communication function, the inter-sensory sharing hardware is realized, and part of the communication time resources are allocated to the perception function, which can effectively solve the problem of detecting low-altitude drones and improve the time utilization efficiency of base stations. Small base stations perceive drones through frequency-modulated continuous waves and send the perceived information to macro base stations through wireless communication. The macro base station monitors and controls drones based on the received perception information. A core difficulty of this problem is how to effectively allocate the perception tasks of multiple drones to each small base station, so that each base station can efficiently coordinate the scheduling of communication and perception time resources, and improve the overall time utilization of multiple base stations.

[0005] Currently, there is no research on the task allocation scheme for low-altitude UAV perception by integrated synaesthesia base stations. Summary of the invention

[0006] The purpose of the present invention is to provide a method for allocating UAV perception tasks based on a telepathic base station. Aiming at future low-altitude UAV perception scenarios, the present invention designs a method for allocating UAV perception tasks based on a telepathic base station, and collaboratively optimizes the time resources of base station perception and communication, thereby greatly improving the time resource utilization rate of the base station.

[0007] To achieve the above object, the present invention provides a method for allocating perception tasks of unmanned aerial vehicles based on a telepathic base station, comprising the following steps: Step 1: Macro base station sensing situation acquisition: The macro base station exchanges information with the telepathic base station to obtain situation information including the telepathic base station and the drone, and completes parameter collection; Step 2: The macro base station evaluates and calculates the perception time resource consumption of the telepathic base station: The telepathic base station adopts a time-division working mode, and the acquisition of each drone's situation information is divided into two stages, namely, the perception stage using frequency modulated continuous waves and the communication stage using wireless channels to transmit the perception information to the macro base station; Step 3: The macro base station completes the task allocation scheme representation and problem modeling: The macro base station optimizes the perception task allocation scheme and models the optimization problem based on the time resource constraints of the telepathic base station and the perception benefit parameters of the drone, so as to maximize the perception benefit of the drone under the constraint of the limited time resources of the telepathic base station; Step 4: The macro base station optimizes the drone perception task allocation plan: The macro base station obtains the drone perception task allocation plan by solving the optimization problem, and sends the plan to each integrated base station for execution; Step 5: The integrated base station senses the drone and obtains its low-altitude situation.

[0008] Furthermore, the macro base station sensing situation acquisition in step 1 includes the following steps: S1: The macro base station receives the historical perception information of the UAV from the synaesthesia base station and obtains the synaesthesia base station set Collection with drone , and determine any base station With drones The distance between ; S2: The macro base station updates each drone based on the distance between the telepathic base station and the drone. The set of sensing base stations , when the synaesthesia base station With drones The distance between Does not exceed the upper limit of the sensing distance of the telepathic base station When the intersensory base station Perception drone ability; S3: The macro base station evaluates each drone based on historical information Perceived benefit parameters .

[0009] Furthermore, in step 2, the macro base station evaluates and calculates the time resource consumption of the telepathic base station, including the following steps: S4: In the perception stage using FMCW, the telepathic base station uses FMCW to sense the objects within its maximum perception range. The drone inside can sense the intersensory base station Perception drone The perceived time resource consumed is , where the perception waveform consists of multiple chirp signals, and the duration of each chirp signal is , For a given detection signal-to-noise ratio The minimum coherent processing time under Expressed as ,in is the Boltzmann constant, is the antenna temperature, is the noise factor, For drones The radar cross section area, To sense the transmit power, is the transmitting antenna gain, is the receiving antenna gain, To sense the signal wavelength; S5: In the communication stage of transmitting the perception information to the macro base station using the wireless channel, the telepathic base station transmits the perception information of the drone to the macro base station using the 5G signal. Transmission sensing drone The perceived time resource consumed by the information is ,in is the duration of a single communication frame, A base station for interaesthesia Perception drone The amount of information A base station for interaesthesia In the The data transmission rate between each communication frame and the macro base station, Expressed as ,in For communication bandwidth, is the communication transmission power, is the small-scale fading that obeys the standard normal distribution, The distance between the telesensing base station and the macro base station, is the path fading coefficient, is the noise spectral density; S6: Synaesthesia Base Station Get a drone The time resources consumed by the situation are expressed as .

[0010] Furthermore, in step 3, the macro base station completes the task allocation scheme characterization and problem modeling, including the following steps: S7: Define the total set of task allocation schemes , where the elements Indicates drone The sensing task is assigned to the synaesthesia base station implement; S8: Total set of task allocation schemes Can be divided into non-intersecting subsets ,in Indicates that it can be assigned to the synaesthesia base station All perception tasks of ; S9: Given a perception task allocation scheme , UAV perception benefits Expressed as , that is, when the drone When the sensing task is assigned to at least one synaesthesia base station, the corresponding sensing benefit can be obtained. ,in, is the indicator function, when the event Established ,otherwise ; S10: Under the time resource constraints of the telepathic base station, the macro base station optimizes the task allocation scheme to maximize , The objective function is the perception benefit, and the constraint is that the telepathic base station must be within a scheduling cycle. Complete the assigned drone perception task within 30 seconds.

[0011] Furthermore, in step 4, the macro base station optimizes the drone sensing task allocation scheme, including the following steps: S11: Initialize task allocation plan , a set of feasible task allocation solutions , the remaining time resources of each telepathic base station ; S12: For each feasible task assignment object , calculate the increase in perceived benefit after adding it to the task allocation plan ; S13: Assign objects to feasible tasks In the above example, we select the maximum perceived benefit increase per unit time resource. , and add it to the task allocation plan, update ; S14: After the task allocation plan is updated, the synaesthesia base station is updated accordingly Remaining time resources ; S15: Update the set of feasible task allocation solutions , that is, delete the assigned drone For relevant perception tasks, only the elements with sufficient time resources to complete the corresponding perception tasks are retained; S16: If it is feasible to allocate a set of task solutions , then output the task allocation plan Otherwise, return to step S12; S17: Optimized task allocation plan It is sent to each integrated base station for execution to complete the perception of the drone and the transmission of perception information.

[0012] Beneficial effect: The present invention provides a method for allocating UAV perception tasks based on an integrated base station for communication perception. It utilizes an integrated base station with a common front end for communication perception and time division, and optimizes the design of a method for allocating UAV perception tasks for multi-base station collaboration. It effectively solves the problem of low-altitude UAV perception and greatly improves the utilization rate of time resources. At the same time, compared with the time-consuming offline optimal solution, it can greatly reduce the algorithm complexity with almost no performance loss, thus meeting the needs of real-time task allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a development chart of the investment scale of the global drone industry; Figure 2 It is an overall workflow diagram involved in the embodiment of the present invention; Figure 3 is a system model diagram involved in an embodiment of the present invention; Figure 4 It is a performance comparison chart of the method involved in the embodiment of the present invention and three common task allocation methods under different scheduling cycle parameters. DETAILED DESCRIPTION

[0014] The preferred mechanism and implementation method of the present invention are further described below in conjunction with the accompanying drawings and specific implementation methods. Example

[0015] like Figures 1 to 4As shown, an embodiment of the present invention discloses a technical solution for a method for allocating perception tasks of a UAV based on a synaesthesia integrated base station. Figure 1 This is a graph showing the development of global drone industry investment scale. Figure 2 The present invention relates to a method for allocating perception tasks of unmanned aerial vehicles based on a synaesthesia integrated base station, comprising the following steps: Step 1: Macro base station sensing situation acquisition: The macro base station exchanges information with the telepathic base station to obtain situation information including the telepathic base station and the drone, and completes parameter collection; its system model is as follows Figure 3 As shown; S1: The macro base station receives the historical perception information of the UAV from the synaesthesia base station and obtains the synaesthesia base station set Collection with low-altitude drones , and determine any base station With drones The distance between .

[0016] S2: The macro base station updates each drone based on the distance between the telepathic base station and the drone. The set of sensing base stations , that is, the integrated base station With drones The distance between Do not exceed the upper limit of the sensing distance of the base station When the base station Perception drone ability.

[0017] S3: The macro base station evaluates each drone based on historical information Perceived benefit parameters .

[0018] Step 2: The macro base station evaluates and calculates the perception time resource consumption of the telepathic base station: The telepathic base station adopts a time-division working mode and divides the acquisition of each drone's situation information into two stages, namely the perception stage using frequency modulated continuous waves and the communication stage using wireless channels to transmit the perception information to the macro base station.

[0019] S4: In the perception stage using FMCW, the telepathic base station uses FMCW to sense the objects within its maximum perception range. The drone inside senses the base station Perception drone The perceived time resources consumed are: , The perception waveform consists of multiple chirp signals, and the duration of each chirp signal is , For a given detection signal-to-noise ratio The minimum coherent processing time under , that is: , in is the Boltzmann constant, is the antenna temperature, is the noise factor, For drones The radar cross section area, To sense the transmit power, is the transmitting antenna gain, is the receiving antenna gain, To sense the signal wavelength.

[0020] S5: In the communication stage of transmitting the perception information to the macro base station using the wireless channel, the telepathic base station uses the 5G signal to transmit the perception information of the drone to the macro base station. Transmission sensing drone The perceived time resources consumed by the information are: , in is the duration of a single communication frame, For base station Perception drone The amount of information For base station In the The data transmission rate between a communication frame and the macro base station can be expressed as: , in For communication bandwidth, is the communication transmission power, is the small-scale fading that obeys the standard normal distribution, The distance between the telesensing base station and the macro base station, is the path fading coefficient, is the noise spectral density.

[0021] S6: Synaesthesia Base Station Get a drone The time resources consumed by the situation can be expressed as: .

[0022] Step 3: The macro base station completes the task allocation scheme representation and problem modeling: The macro base station optimizes the perception task allocation scheme and models the optimization problem based on the time resource constraints of the telepathic base station and the perception benefit parameters of the drone, so as to maximize the perception benefit of the drone under the constraint of the limited time resources of the telepathic base station; S7: Define the total set of task allocation schemes , where the elements Indicates drone The sensing task is assigned to the synaesthesia base station implement.

[0023] S8: Total set of solutions Can be divided into non-intersecting subsets ,in Indicates that it can be assigned to a base station All perception tasks of .

[0024] S9: Given a perception task allocation scheme , UAV perception benefits It can be expressed as: , When the drone When the sensing task is assigned to at least one synaesthesia base station, the corresponding sensing benefit can be obtained. .in, is the indicator function, when the event Established ,otherwise .

[0025] S10: Under the time resource constraints of the telepathic base station, the macro base station optimizes the task allocation scheme to maximize

[0026] The objective function is the perception benefit, and the constraint is that the telepathic base station must be within a scheduling cycle. Complete the assigned drone perception task within 30 seconds.

[0027] Step 4: The macro base station optimizes the drone perception task allocation plan: The macro base station obtains the drone perception task allocation plan by solving the optimization problem, and sends the plan to each integrated base station for execution; S11: Initialize task allocation plan , a set of feasible task allocation solutions , the remaining time resources of each telepathic base station .

[0028] S12: For each feasible task assignment object , calculate the increase in perceived benefit after adding it to the task allocation plan .

[0029] S13: Assign objects to feasible tasks In the above example, we select the maximum perceived benefit increase per unit time resource. , and add it to the task allocation plan, update .

[0030] S14: After the task allocation plan is updated, the synaesthesia base station is updated accordingly Remaining time resources .

[0031] S15: Update the set of feasible task allocation solutions , that is, delete the assigned drone For relevant perception tasks, only elements with sufficient time resources to complete the corresponding perception tasks are retained.

[0032] S16: If it is feasible to allocate a set of task solutions , then output the task allocation plan Otherwise, return to step S12.

[0033] S17: Optimized task allocation plan It is sent to each integrated base station for execution to complete the perception of the drone and the transmission of perception information.

[0034] Step 5: The integrated base station senses the drone and obtains its low-altitude situation.

[0035] The UAV perception task allocation method proposed in the present invention can significantly improve the perception benefit and the time resource utilization rate of the synaesthesia base station compared with the random task allocation method and the independent greedy task allocation method, and can approach the offline optimal allocation scheme. Among them, the random task allocation method refers to the random allocation of perception tasks, the independent greedy task allocation method refers to each base station independently using a greedy algorithm to allocate tasks, and the offline optimal allocation scheme refers to the use of an exhaustive method to obtain the optimal allocation strategy offline.

[0036] Figure 4 The performance of the drone perception task allocation method proposed in the present invention is compared with the performance of the three existing algorithms under different scheduling cycle lengths. It can be seen that the performance of the method proposed in the present invention is significantly better than the random task allocation method and the independent greedy task allocation method. In addition, the performance of the algorithm proposed in the present invention is always close to the offline optimal allocation solution. At the same time, through the algorithm complexity analysis, it can be found that the scheme optimization time of the method of the present invention is much lower than the offline optimal allocation solution, which can better adapt to the actual working environment.

[0037] The present invention provides a method for allocating UAV perception tasks based on a telepathic base station. The method utilizes an integrated base station with a common front end for communication perception and time division, and optimizes the design of a multi-base station collaborative UAV perception task allocation method. The problem of low-altitude UAV perception is effectively solved, and the time resource utilization rate is greatly improved. Compared with the time-consuming offline optimal solution, the algorithm complexity can be greatly reduced with almost no performance loss, thereby meeting the needs of real-time task allocation.

[0038] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. However, any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for allocating perception tasks of unmanned aerial vehicles based on a telepathic base station, characterized in that: The following steps are involved: Step 1: Macro base station sensing situation acquisition: The macro base station exchanges information with the telepathic base station to obtain situation information including the telepathic base station and the drone, and completes parameter collection; Step 2: The macro base station evaluates and calculates the perception time resource consumption of the telepathic base station: The telepathic base station adopts a time-division working mode, and the acquisition of each drone's situation information is divided into two stages, namely, the perception stage using frequency modulated continuous waves and the communication stage using wireless channels to transmit the perception information to the macro base station; Step 3: The macro base station completes the task allocation scheme representation and problem modeling: The macro base station optimizes the perception task allocation scheme and models the optimization problem based on the time resource constraints of the telepathic base station and the perception benefit parameters of the drone, so as to maximize the perception benefit of the drone under the constraint of the limited time resources of the telepathic base station; Step 4: The macro base station optimizes the drone perception task allocation plan: The macro base station obtains the drone perception task allocation plan by solving the optimization problem, and sends the plan to each integrated base station for execution; Step 5: The integrated base station senses the drone and obtains its low-altitude situation.

2. The method for allocating UAV perception tasks based on a telepathic base station according to claim 1 is characterized in that: In step 1, the macro base station senses the situation acquisition, including the following steps: S1: The macro base station receives the historical perception information of the UAV from the synaesthesia base station and obtains the synaesthesia base station set Collection with drone , and determine any base station With drones The distance between ; S2: The macro base station updates each drone based on the distance between the telepathic base station and the drone. The set of sensing base stations , when the synaesthesia base station With drones The distance between Does not exceed the upper limit of the sensing distance of the telepathic base station When the intersensory base station Perception drone ability; S3: The macro base station evaluates each drone based on historical information Perceived benefit parameters .

3. The method for allocating UAV perception tasks based on a telepathic base station according to claim 1 is characterized in that: In step 2, the macro base station evaluates and calculates the time resource consumption of the telepathic base station perception, including the following steps: S4: In the perception stage using FMCW, the telepathic base station uses FMCW to sense the objects within its maximum perception range. The drone inside can sense the intersensory base station Perception drone The perceived time resource consumed is , where the perception waveform consists of multiple chirp signals, and the duration of each chirp signal is , For a given detection signal-to-noise ratio The minimum coherent processing time under Expressed as ,in is the Boltzmann constant, is the antenna temperature, is the noise factor, For drones The radar cross section area, To sense the transmit power, is the transmitting antenna gain, is the receiving antenna gain, To sense the signal wavelength; S5: In the communication stage of transmitting the perception information to the macro base station using the wireless channel, the telepathic base station transmits the perception information of the drone to the macro base station using the 5G signal. Transmission sensing drone The perceived time resource consumed by the information is ,in is the duration of a single communication frame, A base station for interaesthesia Perception drone The amount of information A base station for interaesthesia In the The data transmission rate between each communication frame and the macro base station, Expressed as ,in For communication bandwidth, is the communication transmission power, is the small-scale fading that obeys the standard normal distribution, The distance between the telesensing base station and the macro base station, is the path fading coefficient, is the noise spectral density; S6: Synaesthesia Base Station Get a drone The time resources consumed by the situation are expressed as .

4. The method for allocating UAV perception tasks based on a telepathic base station according to claim 1 is characterized in that: In step 3, the macro base station completes the task allocation scheme characterization and problem modeling, including the following steps: S7: Define the total set of task allocation schemes , where the elements Indicates drone The sensing task is assigned to the synaesthesia base station implement; S8: Total set of task allocation schemes Can be divided into non-intersecting subsets ,in Indicates that it can be assigned to the synaesthesia base station All perception tasks of ; S9: Given a perception task allocation scheme , UAV perception benefits Expressed as , that is, when the drone When the sensing task is assigned to at least one synaesthesia base station, the corresponding sensing benefit can be obtained. ,in, is the indicator function, when the event Established ,otherwise ; S10: Under the time resource constraints of the telepathic base station, the macro base station optimizes the task allocation scheme to maximize ， The objective function is the perception benefit, and the constraint is that the telepathic base station must be within a scheduling cycle. Complete the assigned drone perception task within 30 seconds.

5. The method for allocating UAV perception tasks based on a telepathic base station according to claim 1 is characterized in that: In step 4, the macro base station optimizes the drone perception task allocation scheme, including the following steps: S11: Initialize task allocation plan , a set of feasible task allocation solutions , the remaining time resources of each telepathic base station ; S12: For each feasible task assignment object , calculate the increase in perceived benefit after adding it to the task allocation plan ; S13: Assign objects to feasible tasks In the above example, we select the maximum perceived benefit increase per unit time resource. , and add it to the task allocation plan, update ; S14: After the task allocation plan is updated, the synaesthesia base station is updated accordingly Remaining time resources ; S15: Update the set of feasible task allocation solutions , that is, delete the assigned drone For relevant perception tasks, only elements with sufficient time resources to complete the corresponding perception tasks are retained; S16: If it is feasible to allocate a set of task solutions , then output the task allocation plan Otherwise, return to step S12; S17: Optimized task allocation plan It is sent to each integrated base station for execution to complete the perception of the drone and the transmission of perception information.

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