An Efficient Communication and Sensing Resource Allocation Method for Low-Earth-Orbit Communication Satellites

Abstract

The present invention belongs to the field of satellite communication technology, and discloses an efficient communication and sensing resource allocation method for low-earth orbit communication satellites, including: (1) arranging ground wave positions according to the satellite altitude and coverage area; (2) estimating the satellite service time and hopping beam period according to the satellite altitude and service distance; (3) estimating the time resources required for sensing according to the ground wave position distribution and the type of sensing target; (4) estimating the resources required for communication according to the geographical location and development status of the ground wave positions; (5) for a specific scenario, jointly optimizing the time slot allocation according to the sensing and communication requirements; (6) using the time slot allocation result to generate a hopping beam pattern according to the characteristics of communication and sensing. The present invention introduces a sensing module, jointly optimizes the hopping beam time slot allocation, generates a hopping beam pattern, realizes the integrated design of communication and sensing without destroying the original communication system, maximizes the system efficiency, and is more suitable for the communication and sensing scenarios of future low-earth orbit satellites.

Description

Technical Field

[0001] The present invention belongs to the technical field of satellite communication, and specifically relates to an efficient communication and sensing resource allocation method for low-orbit communication satellites. Background Art

[0002] Communication and sensing integration, that is, communication and sensing integration, is one of the important new capabilities and innovative directions for future communication. By integrating communication and sensing systems, this technology has characteristics such as multi-sensory, natural, interactive, and intelligent, and has broad application prospects in fields such as low-altitude economy, intelligent transportation, and intelligent manufacturing.

[0003] Currently, common communication and sensing integration platforms are found in ground base stations and drones, with limited coverage and limited sensing targets. In contrast, low-orbit satellite platforms have significant advantages over ground terminals in terms of operating cost, coverage, working ability, anti-interference ability, etc., and are excellent carriers for communication and sensing integration.

[0004] However, there is currently little research on communication and sensing integration based on low-orbit satellites, especially in terms of demand analysis, system design, and resource allocation on the communication and sensing sides. Existing satellite sensing technologies are commonly found in space-based early warning satellites, which directly utilize the working mechanism of ground radars, with high power, high cost, obvious targets, and usage scenarios that are outside the civilian scope. Existing literature proposes using low-orbit communication satellites to complete sensing tasks. With a low orbital altitude, high ground receiving power, large quantity, and wide coverage area, compared with high-orbit satellites, the possibility of achieving sensing is higher. However, its functional design is completely based on existing radar sensing, sacrificing all communication capabilities to complete sensing, without considering the actual working requirements and characteristics of communication satellites, which is not realistic for civilian communication satellites.

[0005] To utilize limited platform resources, existing satellite communication often adopts a hopping beam mechanism, which realizes coverage of more regions by hopping the beam between different wave positions, maximizing the utilization of system resources. Under this system, the resource allocation system is relatively perfect, aiming to maximize user satisfaction. However, under this system, resource allocation is completely based on communication considerations, leaving no spare resources for the sensing side, and lacking demand analysis at the sensing level, making it unable to directly accommodate sensing modules. Therefore, how to correctly estimate sensing requirements, achieve joint communication and sensing design, and maximize system efficiency while being compatible with existing satellite communication systems is a problem that needs to be solved. Summary of the Invention

[0006] To overcome the deficiencies in the existing technology, the present invention introduces perception demand analysis and joint optimization design, and proposes an efficient communication and sensing resource allocation method for low-earth orbit communication satellites. Combining the hopping beam working system, under the condition that the platform resources are limited and no spare sensing resources can be allocated, the joint optimization of communication and sensing time resource allocation is carried out to generate a hopping beam pattern, maximizing the utilization of system resources and realizing the integration of communication and sensing.

[0007] To achieve the above object, the present invention is realized through the following technical solutions:

[0008] The present invention is an efficient communication and sensing resource allocation method for low-earth orbit communication satellites. The communication and sensing resource allocation method specifically includes the following steps:

[0009] Step 1: According to the satellite orbit altitude and the beam width , estimate the radius of a single ground wave position . After expanding from the central wave position, obtain the size and shape of the cluster, and perform ground wave position arrangement to obtain the ground wave position distribution;

[0010] Step 2: According to the satellite orbit altitude and the maximum service distance between the satellite and the ground , estimate the maximum service time of the satellite , determine the number of time slots within the hopping beam period and the length of a single time slot ;

[0011] Step 3: Utilize the ground wave position distribution obtained in Step 1, combined with the perception target type and the radar link propagation equation, to estimate the perception demand time of each ground wave position ;

[0012] Step 4: Utilize the ground wave position distribution obtained in Step 1, according to the geographical location weighting factor and the economic weighting factor or the ship traffic weighting factor , estimate the communication resource demand of each wave position ;

[0013] Step 5: Utilize the perception resource demand obtained in Step 3 and the communication resource demand obtained in Step 4 , perform joint optimization of time slot allocation with the second-order difference function as the objective to obtain the number of time slots required for each wave position ;

[0014] Step 6: Utilize the number of time slots required for each wave position obtained in Step 5 , combined with the maximum holding time of the user terminal and the maximum target perception interruption time , determine the dwell time and working order of the satellite beam at each wave position, and generate a hopping beam pattern.

[0015] A further improvement of the present invention lies in that: in the step 1, the ground wave position arrangement specifically includes the following steps:

[0016] Step 1.1: Obtain the ground wave position radius covered by a single beam according to the satellite orbital altitude and beam width:

[0017]

[0018] Among them, is the working wavelength, is the antenna aperture, is the beam width, is the satellite orbital altitude, is the ground wave position radius;

[0019] Step 1.2: Perform ground wave position arrangement on the basis of a single ground wave position design: the wave position shape is set to a regular hexagon, and it is arranged outward from the central wave position to form a cluster composed of multiple wave positions. Each wave position is numbered respectively to facilitate the analysis of the communication and sensing requirements of each wave position. From the ground wave position radius of the wave position, the distance between the centers of adjacent wave positions is obtained as , and the satellite coverage area is divided into a set of multiple clusters. Since the resource scheduling methods for each cluster are the same, the following demand analysis and optimization processes are all based on a single cluster and a single beam.

[0020] A further improvement of the present invention lies in that: the step 2 specifically includes the following steps:

[0021] Step 2.1: Calculate the running speed of the satellite according to the satellite altitude:

[0022]

[0023] Among them, is the satellite running speed, is the gravitational constant, is the mass of the earth, is the satellite orbital altitude, is the radius of the earth. The maximum receiving elevation angle between the satellite and the ground receiving end is set to 60°. When the earth curvature is not considered, the maximum service distance between the satellite and the ground receiving end is approximately twice the satellite orbital altitude , that is = ;

[0024] Step 2.2: The maximum service time Determined by the satellite overflight distance and the operating speed The satellite overflight distance is determined by the satellite orbital altitude , the Earth radius and the maximum satellite-to-ground service distance as follows:

[0025] θ r = a cos( r e 2 + ( r e + h ) 2 − R max 2 ) / ( 2 × r e × ( r e + h ))]

[0026]

[0027]

[0028] Among them, is the geocentric angle between the satellite and the ground receiving end when the satellite operates to the farthest service segment, is the overflight distance that the satellite can provide services, is the maximum service time of the satellite. The hopping beam period length is less than the maximum service time and is set to an integer number of seconds. The number of hopping beam time slots is 128 or 64. After determining the hopping beam period and the number of time slots, the single time slot length is obtained. The single time slot length is equal to the hopping beam period length divided by the number of time slots .

[0029] A further improvement of the present invention is that: Step 3 specifically includes the following steps:

[0030] Step 3.1: Obtain the distance between the satellite and each ground beam position center from the distance between adjacent beam position centers and the satellite orbital altitude . Assuming that the initial position of the satellite is directly above the center beam position of the cluster, the distance between the satellite and the center of this beam position , and the distances between the satellite and other beam position centers are obtained from the satellite altitude and the number of adjacent beam position intervals , where , is the number of interval beam positions between this beam position and the center beam position;

[0031] Step 3.2: Calculate the perceived demand time in the static scenario and the perceived demand time in the dynamic service scenario according to the obtained distance between the satellite and the ground beam position center .

[0032] A further improvement of the present invention is that: In the static scenario, the link propagation equation of the radar is:

[0033]

[0034] Among them, is the maximum transmission power, is the transmission gain, is the transmission aperture, is the perceived target size, is the equivalent number of integrated pulses, is the pulse integration efficiency, is the Boltzmann constant, is the noise temperature, is the noise factor, is the noise bandwidth, is the farthest sensing distance.

[0035] The signal-to-noise ratio required for single-pulse detection is obtained from the false alarm probability and the detection probability as follows:

[0036] ( S / N ) 1 = A + 0 . 12 AB + 1 . 7 B A = ln[ 0 . 62 / P fa ] B = ln[ P d / ( 1 − P d )]

[0037] The average transmission power is converted from the maximum peak power as follows:

[0038]

[0039] wherein, is the pulse duration, is the pulse width. Using the receiver half-power bandwidth to represent the noise bandwidth , the peak power is converted to the average power , and the product of the half-power bandwidth and the pulse width is equal to 1 in a non-modulated simple pulse radar system. Finally, the sensing required time expressed in can be obtained:

[0040]

[0041] wherein, is the receiving gain.

[0042] A further improvement of the present invention is that in a dynamic service scenario, on the basis of determining the initial position and movement direction of the satellite, the distance between satellite targets is related to the movement time of the satellite during sensing. Assuming that the perceived target is stationary at the center of the wave position, the initial position of the satellite is directly above the center of the wave position, and the movement direction is away from the center of the wave position, then the distance between the satellite and the center of the wave position can be expressed as:

[0043] ​

[0044] Among them, is the satellite movement time, is the distance between the satellite and the ground wave position center, i.e., the target.

[0045] The entire continuous sensing process in a dynamic scenario can be regarded as the accumulation of signal-to-noise ratios. The satellite's movement trajectory is divided into arcs of equal length. When the arcs are divided small enough, it can be considered that the sensing distance between the satellite and the target is the same within this period of movement, and the signal-to-noise ratio accumulated within each period of time is calculated accordingly.

[0046] By calculating the accumulated signal-to-noise ratio until the minimum signal-to-noise ratio required for sensing is met, the time required to complete the sensing task can be obtained:

[0047]

[0048] In the formula, is the signal-to-noise ratio accumulated within the th arc segment, is the movement time of the th arc segment, is the distance between the satellite and the target within the th arc segment, and thus the sensing required time of the satellite is obtained.

[0049] A further improvement of the present invention lies in: in the said step 4, each ground wave position is divided into grids in terms of longitude and latitude, and the communication resource requirements of the ground wave position are determined by calculating the sum of the traffic volumes of all grids , specifically including:

[0050] Step 4.1, the geographical location weighting factor of land is , and the gross domestic product per unit area is used to measure the economic development status of a region. The economic weighting factor is calculated as follows:

[0051]

[0052]

[0053] Among them, is the total value of the area where the grid is located, is the size of the grid, is the maximum gross domestic product per unit area among all grids;

[0054] Step 4.2, the geographical location weighting factor of the ocean or lake is, and the ship flow weighting factor is used to measure the flow difference between grids:

[0055]

[0056] Among them, is the number of ships within the grid obtained by querying the AIS ship positioning, is the maximum number of ships in all grids, and the traffic intensity factor of each grid is expressed as:

[0057]

[0058] Step 4.3: Combine the basic communication rate of this area , and count the grid traffic intensity within the ground wave position to obtain the communication traffic demand of this ground wave position :

[0059] .

[0060] A further improvement of the present invention lies in that: Step 5 specifically includes the following steps:

[0061] Step 5.1: Select to use the integrated communication and sensing time slots to realize the multiplexing of time slot resources within the hopping beam period, achieving the basic communication and sensing functions. To ensure the basic communication ability, select to use the second-order difference objective function as the optimization objective:

[0062]

[0063] Among them, is the communication traffic provided by the th wave position, is the communication traffic required by the th wave position, is the number of wave positions within the cluster;

[0064] Step 5.2: After the optimization objective is determined, the time slot joint optimization needs to meet the following constraint conditions:

[0065] a. Communication constraint conditions:

[0066]

[0067] Among them, is the wave position with both communication and sensing requirements, is the wave position with only communication requirements, is the number of time slots allocated to the th wave position, is the total number of time slots within the hopping beam period, is the maximum number of beams that can work simultaneously within the cluster;

[0068] The communication traffic provided by the beam Relationship with the number of time slots is as follows:

[0069]

[0070] wherein, represents the communication loss coefficient, is the total communication capacity provided by the communication and sensing integrated system;

[0071] b. Sensing constraint conditions:

[0072]

[0073] wherein, is the sensing performance loss coefficient, is the single time slot length, the shortest required sensing time is equal to the sensing demand time ;

[0074] Step 5.3. Convert the solution process of optimizing the second-order difference function in Step 5.1 under the constraint conditions into a convex optimization problem, and use the convex optimization toolbox to solve the integer solution of time slot allocation .

[0075] A further improvement of the present invention lies in that: the specific steps of the said Step 6 are as follows:

[0076] According to the integer solutions of time slot allocation of each wave position obtained in Step 5, arrange them in ascending order of the number of time slots, so that the wave positions with fewer time slots are preferentially allocated;

[0077] Step 6.2. Considering all wave positions, within the maximum holding time of synchronization of the communication user terminal, ensure that all wave positions have appeared, except for the wave positions that have been allocated. Move the time slots of the wave positions that have not appeared to the front, so that the beam revisit time and the maximum holding time of synchronization of the communication user terminal satisfy ;

[0078] Step 6.3. Considering a single wave position, the time slot intervals of all wave positions cannot exceed the maximum discontinuous time of the target sensing. Move the time slots that exceed to the front, so that the beam revisit time and the maximum discontinuous time of the target sensing satisfy ;

[0079] Step 6.4. After the time slot quantity arrangement in Step 6.1 and the time slot movement in Step 6.2 and Step 6.3, a hopping beam pattern is obtained.

[0080] The beneficial effects of the present invention are:

[0081] The present invention introduces the concept of integrated communication and sensing time slots, enabling the provision of basic sensing resources to the target even when there are limited resources on the satellite platform and no spare resources can be allocated, thus enabling integrated communication and sensing transmission in the low-earth orbit satellite scenario.

[0082] The present invention utilizes the joint optimization of communication and sensing time slot allocation and the integrated design of hopping beam patterns, making it compatible with the existing hopping beam communication system and maximizing the utilization of existing platform resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 It is a flow block diagram of the optimized allocation of communication and sensing resources of the method of the present invention.

[0084] Figure 2 It is a schematic diagram of the ground wave position arrangement in the present invention.

[0085] Figure 3 It is the time slot allocation result after the joint optimization of the method of the present invention.

[0086] Figure 4 It is the integrated hopping beam pattern obtained by the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0087] The embodiments of the present invention will be disclosed below with reference to the drawings. For the sake of clarity, many practical details will be described together in the following description. However, it should be understood that these practical details are not intended to limit the present invention. That is to say, in some embodiments of the present invention, these practical details are not necessary.

[0088] As Figure 1 shown, the present invention is an efficient communication and sensing resource allocation method for low-earth orbit communication satellites. The communication and sensing resource allocation method specifically includes the following steps:

[0089] Step 1. Estimate the radius of a single ground wave position according to the satellite orbital altitude and the beam width , and after expanding outward from the center of the wave position, obtain the size and shape of the cluster, and perform ground wave position arrangement to obtain the ground wave position distribution.

[0090] Among them, the specific steps of performing ground wave position arrangement include the following:

[0091] Step 1.1. Obtain the radius of the ground wave position covered by a single beam according to the satellite orbital altitude and the beam width:

[0092]

[0093] Among them, is the operating wavelength, is the antenna aperture, is the beam width, is the satellite orbital altitude, and is the ground wave position radius;

[0094] Step 1.2. Arrange the ground wave positions based on the design of a single ground wave: Set the wave position shape as a regular hexagon, and expand from the central wave position to arrange a cluster including 7 wave positions, numbered from 1 to 7. From the ground wave position radius of the wave position, the distance between the centers of adjacent wave positions is obtained as , and the satellite coverage area is divided into a set of multiple clusters. Since the resource scheduling method for each cluster is the same, the following demand analysis and optimization processes are all based on single cluster and single beam.

[0095] Step 2. According to the satellite orbital altitude and the maximum satellite-ground service distance , estimate the maximum service time of the satellite, and determine the number of time slots in the hopping beam period and the length of a single time slot. Specifically, it includes the following steps:

[0096] Step 2.1. Calculate the running speed of the satellite according to the satellite altitude:

[0097]

[0098] where is the running speed of the satellite, is the gravitational constant, is the mass of the earth, is the satellite orbital altitude, is the radius of the earth. The maximum receiving elevation angle between the satellite and the ground receiving end is set to 60°. Without considering the earth's curvature, the maximum satellite-ground service distance is approximately twice the satellite orbital altitude , that is = ;

[0099] Step 2.2. The maximum service time of the satellite is determined by the satellite overflight distance and the running speed . The satellite overflight distance is determined by the satellite orbital altitude , the radius of the earth and the maximum satellite-ground service distance :

[0100] θ r = a cos( r e 2 + ( r e + h ) 2 − R max 2 ) / ( 2 × r e × ( r e + h ))]

[0101]

[0102]

[0103] Among them, is the geocentric angle between the satellite and the ground receiving end when the satellite runs to the farthest service segment, is the over-the-horizon distance that the satellite can provide services, is the maximum service time of the satellite. The hopping beam period length is less than the maximum service time and is set to an integer number of seconds. The number of hopping beam time slots is 128 or 64. After determining the hopping beam period and the number of time slots, the length of a single time slot is obtained , and the single time slot length is equal to the hopping beam period length divided by the number of time slots .

[0104] Step 3: Using the ground wave position distribution obtained in Step 1, combined with the perceived target type and the radar link propagation equation, estimate the perceived demand time required for each ground wave position , including the following steps:

[0105] Step 3.1: From the distance between the centers of adjacent wave positions and the satellite orbital altitude , the distance between the satellite and the center of each ground wave position is obtained. Assuming that the initial position of the satellite is directly above the center wave position of the cluster, the distance between the satellite and the center of this wave position, and the distances between the centers of other wave positions and the satellite are obtained from the satellite altitude and the number of adjacent wave position intervals, where is the number of interval wave positions between this wave position and the center wave position. In this embodiment, the distance between the satellite and the No. 1 wave position in the cluster: .

[0106] Step 3.2: Calculate the perceived demand time in the static scenario and the perceived demand time in the dynamic service scenario according to the obtained distance between the satellite and the center of the ground wave position .

[0107] In the static scenario, the radar sensing link propagation equation is:

[0108]

[0109] Among them, is the maximum transmit power, is the transmit gain, is the transmit aperture, is the size of the perceived target, is the equivalent number of accumulated pulses, is the pulse accumulation efficiency, is the Boltzmann constant, is the noise temperature, is the noise figure, is the noise bandwidth, is the farthest sensing distance.

[0110] is the signal-to-noise ratio required for monopulse detection, which is obtained from the false alarm probability and the detection probability as follows:

[0111] ( S / N ) 1 = A + 0 . 12 AB + 1 . 7 B A = ln[ 0 . 62 / P fa ] B = ln[ P d / ( 1 − P d )]

[0112] is the average transmit power, which is converted from the maximum peak power as follows:

[0113]

[0114] wherein, is the pulse duration, is the pulse width. Using the receiver half-power bandwidth to replace the noise bandwidth , the peak power is converted to the average power , and the product of the half-power bandwidth and the pulse width is equal to 1 in a non-modulated simple pulse radar system. Finally, the sensing required time expressed in can be obtained: wherein,

[0115]

[0116] wherein, is the receive gain.

[0117] In a dynamic service scenario, based on determining the initial position and movement direction of the satellite, the distance between satellite targets is related to the satellite movement time during the sensing process. Assuming that the sensing target is stationary at the center of the wave position, the initial position of the satellite is directly above the center of the wave position, and the movement direction is away from the center of the wave position, then the distance between the satellite and the center of the wave position can be expressed as:

[0118]

[0119] wherein, is the satellite movement time, is the distance between the satellite and the ground wave position center, i.e., the target, during the satellite movement;

[0120] In a dynamic scenario, the entire continuous sensing process can be regarded as the accumulation of the signal-to-noise ratio. The satellite's motion trajectory is divided into arcs of equal length. When the arcs are divided small enough, it can be considered that the sensing distance between the satellite and the target is the same within this period of motion, and the signal-to-noise ratio accumulated within each period of time is calculated accordingly.

[0121] By calculating the accumulated signal-to-noise ratio until the minimum signal-to-noise ratio required for sensing is met, the time required to complete the sensing task can be obtained:

[0122]

[0123] In the formula, is the signal-to-noise ratio accumulated within the th arc, is the time of the satellite's motion within the th arc, is the distance between the satellite and the target within the th arc. Thus, the sensing required time of the satellite is obtained.

[0124] Step 4: Using the ground wave position distribution obtained in Step 1, according to the geographical location weighting factor and the economic weighting factor or the ship traffic weighting factor , estimate the communication resource requirements for each wave position .

[0125] In this step, each ground wave position is divided into grids in terms of longitude and latitude, and the communication resource requirements for this ground wave position are determined by calculating the sum of the traffic volumes of all grids , specifically including:

[0126] Step 4.1: The geographical location weighting factor for land is set to 0.9. The gross regional product per unit area is used to measure the economic development status of a region. The calculation formula for the economic weighting factor is as follows:

[0127]

[0128]

[0129] Among them, is the total value of the region where the grid is located, is the size of the grid, is the maximum gross regional product per unit area among all grids;

[0130] Step 4.2: The geographical location weighting factor for the ocean or lake is Set it to 0.1 and use the ship traffic weighting factor to measure the traffic difference between grids:

[0131]

[0132] Among them, is the number of ships within the grid obtained by querying the AIS ship positioning, is the maximum number of ships in all grids, and the traffic intensity factor of each grid is expressed as:

[0133]

[0134] Step 4.3. Combine the basic communication rate of this area , and count the grid traffic intensity within the ground wave position to obtain the communication traffic demand of this ground wave position :

[0135] .

[0136] The communication capacity that the system can provide is determined by the beam itself. The total communication capacity within the cluster is:

[0137]

[0138] Among them, is the full-frequency reuse bandwidth, is the signal-to-interference-plus-noise ratio of the wave position, is the attenuation factor, is the equivalent downlink power, is the noise power, is the interference power received. Here, only one beam works within a single cluster, and the interference power can be set to 0.

[0139] The equivalent downlink power is the received power of the ground terminal is:

[0140]

[0141] Among them is equal to the satellite altitude in the calculation .

[0142] Step 5. Use the sensing demand obtained in Step 3 and the communication resource demand obtained in Step 4 , and perform joint optimization of time slot allocation with the second-order difference function as the objective to obtain the number of time slots required for each wave position .

[0143] Specifically, it includes the following steps:

[0144] Step 5.1. Select to use the integrated synesthesia time slot to achieve the multiplexing of time slot resources within the hopping beam period, to achieve the basic synesthesia function. To ensure the basic communication ability, select to use the second-order difference objective function as the optimization objective:

[0145]

[0146] where, is the communication traffic provided by the th wave position, is the communication traffic required by the th wave position, is the number of wave positions within the cluster;

[0147] Step 5.2. After the optimization objective is determined, the time slot joint optimization needs to meet the following constraint conditions:

[0148] a. Communication constraint conditions:

[0149]

[0150] where, is the wave position with both communication and sensing requirements, is the wave position with only communication requirements, is the number of time slots allocated to the th wave position, is the total number of time slots within the hopping beam period, is the maximum number of beams working simultaneously within the cluster;

[0151] The relationship between the communication traffic provided by the beam and the number of time slots is:

[0152]

[0153] where, represents the communication loss coefficient, is the total communication capacity provided by the integrated synesthesia system;

[0154] b. Sensing constraint conditions:

[0155]

[0156] where, is the sensing performance loss coefficient, is the single time slot length, is the shortest required sensing time equal to the sensing demand time ;

[0157] Step 5.3: Transform the solution process of optimizing the second-order difference function in Step 5.1 under the constraint conditions into a convex optimization problem, and use the convex optimization toolbox to solve the integer solution of time slot allocation. 。

[0158] Step 6: Use the number of time slots required for each wave position obtained in Step 5 , combined with the maximum holding time of the user terminal and the target sensing maximum interruption time , determine the dwell time and working order of the satellite beam at each wave position, and generate a hopping beam pattern, which specifically includes the following steps:

[0159] According to the integer solution of time slot allocation for each wave position obtained in Step 5, arrange them in ascending order of the number of time slots, so that the wave positions with fewer time slots are allocated first.

[0160] Step 6.2: Considering all wave positions, within the maximum holding time of communication user terminal synchronization, ensure that all wave positions have appeared, except for the wave positions that have been allocated. Move the time slots of the wave positions that have not appeared to the front, so that the beam revisit time and the maximum holding time of communication user terminal synchronization satisfy ;

[0161] Step 6.3: Considering a single wave position, the interval of all time slots of the wave position cannot exceed the target sensing maximum interruption time. Move the exceeded time slots to the front, so that the beam revisit time and the target sensing maximum interruption time satisfy ;

[0162] Step 6.4: After the time slot quantity arrangement in Step 6.1 and the time slot movement in Step 6.2 and Step 6.3, obtain the hopping beam pattern.

[0163] To verify the beneficial effects of the present invention, the present invention provides simulation experiments to further verify the present invention.

[0164] 1. Experimental scenario:

[0165] The scenario is set as a common low-earth orbit communication satellite. The specific satellite parameters are an altitude of 500 km, a working frequency of 6 GHz, a beam width of 10.83 degrees, a transmit power of 80 W, a transmit gain of 23.3 dB, and a bandwidth of 40 MHz.

[0166] 2. Experimental content and results:

[0167] Experiment 1: Arrange the ground wave positions with reference to the satellite conditions. According to the satellite altitude and beam width, estimate the radius of a single ground wave position, expand to obtain the size and shape of the cluster, and arrange the ground wave positions. The results are asFigure 2 as shown

[0168] Experiment 2: Perform joint optimization of time slot allocation in combination with requirements and design a hopping beam pattern. Figure 3 This is a comparison of the time slot allocation between conventional hopping beam time slot allocation and the time slot allocation after joint optimization. Figure 4 This is the hopping beam pattern designed in combination with the characteristics of communication and sensing. From Figure 3 It can be seen that joint optimization can compensate a certain time slot for the wave positions with sensing requirements, enabling them to complete sensing while ensuring the original communication function. Compared with the existing solutions, the proposed solution can reasonably balance system resources. From Figure 4 It can be seen that the generated hopping beam pattern can meet both communication needs and sensing needs, achieving compatibility with the conventional hopping beam system.

[0169] The above is only the implementation manner of the present invention and is not used to limit the present invention. For those skilled in the art, various changes and modifications can be made to the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the scope of the claims of the present invention.

Claims

1. An efficient synaesthesia resource allocation method for a low-orbit communication satellite, characterized in that: The synaesthesia resource allocation method specifically comprises the following steps: Step 1: Estimate the radius r of a single ground wave position according to the satellite orbit height h and the beam width θ, expand outward from the central wave position to obtain the size and shape of the cluster, arrange the ground wave positions, and obtain the ground wave position distribution; Step 2: According to the satellite orbit height h and the maximum satellite-to-ground service distance R max , estimate the maximum service time T of the satellite max , determine the number of time slots W and the length of a single time slot T in the beam hopping period sot ; Step 3: Using the ground wave position distribution obtained in step 1, combined with the perceived target type and radar link propagation equation, estimate the dynamic perception requirement time T for each ground wave position. sense ; Step 4: Using the ground wave position distribution obtained in step 1, the weighting factor α is used according to the geographical location. i and the economic weighting factor β i or the ship traffic weighting factor γ i , estimate the communication resource requirement R for each wave ci ; Step 5: Use the dynamic perception demand time T obtained in step 3 sense The communication resource requirement R obtained in step 4 ci , the time slot allocation is jointly optimized with the second-order difference function as the target, and the number of time slots N required for each wave position is obtained i ; Step 6: Use the number of time slots N required for each wave position obtained in step 5 i , combined with the maximum holding time T of the user terminal D Maximum interruption time T with target perception B , determine the satellite beam’s dwell time and working order at each wave position, and generate a beam hopping pattern.

2. The method for efficiently allocating synaesthesia resources for a low-orbit communication satellite according to claim 1, characterized in that: In step 1, the ground wave position arrangement specifically includes the following steps: Step 1.1: Obtain the ground wave radius covered by a single beam based on the satellite orbit height and beam width: Where, λ is the operating wavelength, D is the antenna aperture, θ is the beam width, h is the satellite orbit height, and r is the ground wave position radius; Step 1.2: Arrange the ground wave positions based on the design of a single ground wave position: the wave position shape is set to a regular hexagon, and the arrangement is expanded outward from the central wave position to form a cluster consisting of multiple wave positions. The wave positions are numbered separately to facilitate the analysis of the communication and perception requirements of each wave position. The distance between the centers of adjacent wave positions is obtained from the ground wave position radius r of the wave position: The satellite coverage area is divided into a collection of multiple clusters.

3. The method for efficiently allocating synaesthesia resources for a low-orbit communication satellite according to claim 1, characterized in that: The step 2 specifically includes the following steps: Step 2.1: Calculate the satellite's speed based on the satellite's orbital altitude: Where v is the satellite speed, G is the gravitational constant, M is the mass of the earth, h is the satellite orbit height, r e is the radius of the earth, the maximum receiving elevation angle between the satellite and the ground receiving end is set to 60°, and when the curvature of the earth is not considered, the maximum satellite-to-ground service distance between the satellite and the ground receiving end is R max is twice the satellite orbit height h, that is, R max =2h; Step 2.2: Maximum service time of satellite T max The satellite overhead distance L is determined by the satellite overhead distance and the running speed v. r The satellite orbit height h and the earth radius r e and the maximum satellite-to-ground service distance R max Sure: L r =2×θ r ×(r e +h) Among them, θ r L is the angle between the satellite and the earth's center when the satellite is at the farthest point it can serve. r The overhead distance at which the satellite can provide service, T max is the maximum service time of the satellite. The beam hopping period length should be less than the maximum service time and set to an integer second. The number of beam hopping time slots W is 128 or 64. The length of a single time slot T sot It is equal to the beam hopping period length divided by the number of time slots W.

4. The method for efficiently allocating synaesthesia resources for a low-orbit communication satellite according to claim 1, characterized in that: The step 3 specifically comprises the following steps: Step 3.1: The distance between the centers of adjacent wave positions And the satellite orbit height h to get the distance R between the satellite and each ground wave position center i , assuming that the initial position of the satellite is directly above the center wave position of the cluster, then the distance between the satellite and the center of the wave position is R i =h, the distances between other wave position centers and the satellite are obtained from the satellite height and the number of adjacent wave position intervals. Among them, k is the number of wave positions between the wave position and the central wave position; Step 3.2: According to the distance R between the satellite and the ground wave position center i Calculate the perception demand time T in static scenes sense0 and the perceived demand time T in dynamic scenarios sense .

5. The method for efficiently allocating synaesthesia resources for a low-orbit communication satellite according to claim 4, characterized in that: In a static scenario, the radar link propagation equation is: Among them, P t is the maximum transmission power, G t is the transmission gain, A e is the transmit aperture, σ is the perceived target size, nE i (n) is the equivalent accumulated pulse number, E i (n) is the pulse accumulation efficiency, k is the Boltzmann constant, T noise is the noise temperature, F n is the noise factor, B n is the noise bandwidth, R LDmax is the farthest sensing distance, (S / N)1 is the signal-to-noise ratio required for single pulse detection, and the false alarm probability P fa and the detection probability P d get: (S / N)1=A+0.12AB+1.7B A=ln[0.62 / P fa ] B=ln[P d / (1-P d )] P av is the average transmission power, which is determined by the maximum transmission power P t Convert to: in, is the pulse duration, T p is the pulse width, and the noise bandwidth B is expressed by the receiver half-power bandwidth B n , maximum transmission power P t Converted to average power P av , half-power bandwidth B and pulse width The product of the two is B In a simple unmodulated pulse radar system, it is equal to 1, and finally T sense0 The static perception demand time represented by: Among them, G r For receiving gain.

6. The method for efficiently allocating synaesthesia resources for a low-orbit communication satellite according to claim 5, characterized in that: In the dynamic service scenario, based on the initial position and movement direction of the satellite, the distance between satellite targets is related to the satellite movement time during the perception process. Assuming that the perception target is stationary at the center of the wave position, the initial position of the satellite is directly above the center of the wave position, and the movement direction is away from the center of the wave position, the distance between the satellite and the center of the wave position can be expressed as: Where t is the satellite motion time, R i It is the distance between the satellite and the wave position center, i.e. the target, during the satellite movement; The entire continuous perception process in a dynamic scene is the accumulation of the signal-to-noise ratio. The satellite's trajectory is divided into arcs of equal length. The length of each arc is no more than 0.5 km. The distance between the satellite and the target in this arc uses the same value R. i To indicate that R i It is calculated from the time the satellite has moved before reaching the arc, thereby converting the satellite's dynamic accumulation process into the superposition of multiple static accumulation processes; By calculating the accumulated signal-to-noise ratio until the minimum signal-to-noise ratio required for perception is met, the dynamic accumulation time required to complete the perception task is obtained: Among them, SNR i is the accumulated signal-to-noise ratio in the i-th arc, T i is the time of satellite motion in the i-th arc, R i is the satellite target distance within the i-th arc, and the satellite's dynamic perception requirement time is expressed as: T sense =T0+T1+...+T i .

7. The method for efficiently allocating synaesthesia resources for a low-orbit communication satellite according to claim 1, characterized in that: In step 4, each ground wave position is divided into grids based on longitude and latitude, and the communication resource requirement R of the ground wave position is determined by calculating the sum of the traffic of all grids. ci , specifically including: Step 4.1: The geographical location weighting factor of land is α i , using the gross regional product per unit area g i To measure the GDP status of a region, the economic weighting factor β i The calculation formula is as follows: Among them, GDP i is the total GDP value of the area where the grid is located, S i is the size of the grid, g max is the maximum regional GDP per unit area in all grids; Step 4.2: The weighting factor for the geographical location of an ocean or lake is α i , using the ship traffic weighting factor γ i To measure the traffic differences between grids: Among them, J i is the number of ships in the grid obtained by querying the ship positioning, J max is the maximum number of ships in all grids, and the traffic intensity factor E of each grid i It is expressed as: Step 4.3: Combined with the basic communication rate D of the area, the grid traffic intensity within the ground wave position is counted to obtain the communication resource demand R of the ground wave position. ci : R ci =D.∑E i 。 8. The method for efficiently allocating synaesthesia resources for a low-orbit communication satellite according to claim 7, characterized in that: The step 5 specifically comprises the following steps: Step 5.1, choose to use the interawareness integrated time slot to realize the reuse of time slot resources within the beam hopping period to achieve basic interawareness function. In order to ensure basic communication capabilities, choose to use the second-order difference objective function as the optimization target: Among them, T ci The communication traffic provided for the i-th wave position, R ci is the communication resource requirement of the ith wave position, k is the number of waves in the cluster; Step 5.2: After the optimization target is determined, the time slot joint optimization must meet the following constraints: a. Communication constraints: Among them, N a For the wave position that has both communication and perception requirements, N b For the wave position that only needs communication, The communication traffic provided by the beam is T ci With the number of time slots N i The relationship is: Where α is the communication loss coefficient, T c Total communication capacity provided for the synaesthesia integration system; N i is the number of time slots allocated to the ith beam position, W is the total number of time slots in the beam hopping period, N max is the maximum number of beams working simultaneously in the cluster; b. Perception constraints: Among them, β sense is the perceived performance loss coefficient, T sot is the length of a single time slot, T imin The shortest required perception time for the i-th wave position is equal to the dynamic perception demand time T sense ; Step 5.3: Convert the second-order difference function optimization under constraints in step 5.1 into a convex optimization problem, and use the convex optimization toolbox to solve the integer solution N for time slot allocation. i .

9. The method for efficiently allocating synaesthesia resources for a low-orbit communication satellite according to claim 8, characterized in that: The step 6 specifically includes the following steps: Step 6.1, according to the time slot allocation solutions of each wave position obtained in step 5, arrange them in order of the number of time slots from small to large, so that the wave position with a small number of time slots is allocated first; Step 6.2: Consider all beam positions and ensure that the time slots of all beam positions are allocated within the maximum synchronization retention time of the communication user terminal, except for the already allocated beam positions. Move the time slots of the non-appearing beam positions to the front, so that the beam revisit time T RV The maximum time T required to synchronize with the communication user terminal user D Between, satisfying T RV ≤T D ; Step 6.3: Consider a single beam position. The intervals of all time slots of this beam position cannot exceed the maximum interruption time of target perception. The time slots exceeding the interruption time are moved to the front, making the beam revisit time T RV The maximum interruption time T from target perception B , satisfying T RV ≤T B ; Step 6.4: After the time slot number arrangement in step 6.1 and the time slot shifting in steps 6.2 and 6.3, a beam hopping pattern is obtained.

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