Multi-aperture antenna array combination system and optimization design and control method thereof

Abstract

The application discloses a multi-aperture antenna array combination system and an optimization design and control method thereof, and belongs to the technical field of deep space exploration TT&C communication; the optimization design method comprises the following steps: obtaining the single cost, physical aperture size and equivalent conversion coefficient of a plurality of alternative parabolic antennas based on a unified reference aperture; setting the lower limit of the total equivalent aperture constraint and the lower limit of the total equivalent antenna quantity constraint of the antenna array after overall synthesis; taking the configuration quantity of each physical aperture alternative parabolic antenna as a decision variable, constructing a planning model containing the total equivalent aperture constraint lower limit and the total equivalent antenna quantity constraint lower limit, and configuring a penalty weight parameter in the planning model, which converts the constraint condition into a penalty value; using an evolutionary algorithm with integer constraint to evaluate the aperture and cost of the iterative combination generation by generation in the planning model, and calculating the optimal antenna quantity combination configuration scheme with the optimization target of minimizing the total construction cost; and configuring and building a heterogeneous multi-aperture antenna array composed of a plurality of parabolic antennas with different physical apertures according to the optimal antenna quantity combination configuration scheme.

Description

Technical Field

[0001] This invention belongs to the field of deep space exploration telemetry, tracking and communication technology, and specifically relates to a multi-aperture antenna array combination system and its optimization design and control method. Background Technology

[0002] As deep space exploration missions expand from the Moon and Mars to the Jupiter system and even the edge of the solar system, communication distances have increased from hundreds of thousands of kilometers to billions of kilometers. In this context, ground receiving systems require extremely high receiving gain. While constructing a single large-aperture parabolic antenna (such as a 70-meter or 100-meter antenna) can improve receiving capabilities, it has the following drawbacks: 1) High construction costs and time: The cost of a single large-aperture antenna can easily reach hundreds of millions of yuan, and the construction period can take more than five years; 2) Poor scheduling flexibility: A single antenna can only support one deep space mission at a time, making it difficult to run multiple missions in parallel; 3) Reliability and maintenance issues are prominent: key equipment is concentrated at a single point, and a failure will lead to the interruption of detection.

[0003] Smaller aperture antenna arrays (such as the 34 m antenna array of DSN) provide a distributed parallel approach, but most existing arrays are homogeneous arrays of equal aperture, without considering the comprehensive impact of multi-aperture combined heterogeneous structures on performance, efficiency and cost.

[0004] Existing publicly available technologies mainly focus on array signal processing algorithms (coherent synthesis, interferometry, etc.), while at the system architecture level—how to scientifically combine different aperture units and how to perform resource allocation and dynamic optimization within the array—there is still a lack of system design and engineering implementation solutions.

[0005] Therefore, there is an urgent need for a new array system architecture and corresponding design method to achieve the optimal balance between receiving capability, cost and scheduling flexibility. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the present invention aims to provide a multi-aperture antenna array combination system and its optimized design and control method, thereby solving the problems in the prior art.

[0007] The objective of this invention can be achieved through the following technical solutions: An optimized design method for multi-aperture antenna arrays, used for the physical construction and selection of multi-aperture antennas for deep space exploration, includes the following steps: Obtain the unit cost, physical aperture size, and equivalent conversion factor calculated based on a unified reference aperture for multiple candidate parabolic antennas; Set lower limits for the total equivalent aperture and the total equivalent antenna number of the system after the overall antenna array is synthesized; The number of candidate parabolic antennas of each physical aperture is used as a decision variable to construct a planning model that includes the lower limit of the total equivalent aperture constraint and the lower limit of the total equivalent antenna number constraint of the system. In the planning model, a penalty weight parameter is configured to convert the degree of constraint non-compliance into a penalty value. An evolutionary algorithm with integer constraints is used to evaluate the caliber and cost of iterative combinations in the planning model generation by generation, and the optimal antenna number combination configuration scheme is calculated and output with the goal of minimizing the total construction cost. Based on the optimal antenna quantity combination configuration scheme, a heterogeneous multi-aperture antenna array is configured and constructed, which is composed of a variety of parabolic antennas with different physical apertures in a physically discrete combination.

[0008] Furthermore, the calculation logic for the total equivalent aperture of the system is as follows: the total equivalent aperture of the combined system is calculated by taking the square root of the sum of the products of the squares of the physical apertures of each candidate parabolic antenna and their corresponding configuration quantities. The calculation logic for the total equivalent antenna number is as follows: the total equivalent antenna number after combination is calculated by multiplying the equivalent conversion coefficient of each candidate parabolic antenna by the corresponding configuration number and summing the results.

[0009] Furthermore, the calculation steps of the comprehensive fitness index in the evolutionary algorithm with integer constraints include: When the total equivalent diameter of the system calculated by iterative combination is lower than the lower limit of the total equivalent diameter constraint of the system, the first severe penalty coefficient is accumulated; When the total number of equivalent antennas calculated by iterative combination is lower than the lower limit of the total number of equivalent antennas constraint, the second moderate penalty coefficient is accumulated; The total construction cost corresponding to the scheme is summed with the accumulated penalty coefficient and the negative value is used as the comprehensive fitness index of the iterative scheme for evolutionary calculation.

[0010] A dynamic control method for multi-aperture antenna arrays, applied to heterogeneous arrays composed of parabolic antennas with various physical apertures, includes the following steps: Collect parameters of deep space communication missions that are conducted in parallel with multiple tasks, and obtain the task priority level, target bit error rate, data volume requirement, visible time window and minimum reception duration requirement for each task. Using time periods as the basic unit, a usable time slice matrix is ​​generated for each parabolic antenna in the heterogeneous array; Based on the communication link budget requirements and the target bit error rate of each task, the task requirements are quantized and mapped to the minimum equivalent receiving aperture required to support the corresponding task in completing signal reception. Based on the data volume requirements and communication rate parameters, estimate the minimum reception time period for each task; Based on the extracted task comprehensive weight ranking results, an interval scheduling algorithm is used to allocate physical antenna resource combinations that simultaneously meet the minimum equivalent receiving aperture requirement and the minimum receiving time period within the available window of the available time slice matrix to the tasks ranked first. Generate a work schedule containing antenna combination mapping relationships and send it to the underlying antenna control equipment to establish signal aggregation data channels for the corresponding subarrays.

[0011] Furthermore, before performing the interval scheduling algorithm, the system extracts three indicators for each task: task priority level, target bit error rate, and data volume requirement. After calculation, the system outputs the comprehensive weight of the task, which is then used to prioritize filling and arranging tasks in the available time slice matrix.

[0012] Furthermore, the calculation steps for estimating the minimum reception time period for each task include: The theoretical time consumption value is obtained by dividing the data volume requirement by the product of the communication rate and the set communication efficiency factor. Extract the maximum value between the theoretical time consumption value and the hard-set minimum reception time requirement, and output it as the minimum reception time period.

[0013] Furthermore, the dynamic control method also includes a dynamic rescheduling step based on state monitoring: When an external event such as a new task insertion, physical antenna equipment failure, or channel fading causing the actual link bit error rate to exceed the limit is detected during system operation, a local reallocation trigger rescheduling mechanism is initiated; the rescheduling mechanism includes executing at least one of the following action strategies: When a new high-priority task is received, the execution task with a lower overall task weight is paused, the parabolic antenna it occupies is released to allow the new task to access, and the paused task is placed in the timeline queue for compensation and reconnection after a delay. When the actual link bit error rate is detected to exceed the limit, one or more parabolic antennas in the idle time slice matrix are added to dynamically increase the subarray to complete the link compensation, and the physical antenna resources are automatically recovered after the link is restored. When one or more physical antenna devices fail, the current task resource allocation table of the failed antenna is unloaded, and the remaining antenna resources in the available time slice matrix are reorganized and allocated to cover the remaining visible time window.

[0014] A multi-aperture antenna array combination system, comprising: Multi-aperture antenna array module: It is composed of multiple large parabolic subarrays with different physical apertures, which are physically connected in parallel through overall configuration using the above method; The array overall control center module is connected to the multi-aperture antenna array module and is equipped with a memory and a task scheduling processor. The task scheduling processor is configured to execute the logical operation steps in the dynamic control method described above to output and issue scheduling control instructions. High-sensitivity signal receiving and compensation module: Distributed and connected to the ends of each of the parabolic subarrays, it has a built-in low-noise amplifier, digital delay corrector, phase synchronization beacon receiving unit and multi-channel amplitude and phase weighted synthesizer. It is used to receive radio frequency signals from parabolic subarrays with different physical apertures according to the scheduling control command, eliminate system distance difference and phase difference caused by geographical location dispersion and physical aperture difference through hardware signal, and output a single data stream with high signal-to-noise ratio through amplitude and phase weighted combining.

[0015] Furthermore, each of the parabolic subarrays with different physical apertures in the multi-aperture antenna array module is independently configured with a power amplifier link, a local subarray phase correction system, and a time reference synchronization device for providing a unified clock reference for the heterogeneous array.

[0016] Furthermore, a task data processing and management module is cascaded at the output of the high-sensitivity signal receiving and compensation module. The task data processing and management module includes a link decoding unit, a signal locking unit, and a bit error rate feedback calculation unit. It is configured to transmit link signal-to-noise ratio and actual bit error rate status data back to the array overall control center module in real time in a closed loop, so as to trigger the task scheduling processor to perform local antenna rebalancing scheduling operation based on channel fading.

[0017] The beneficial effects of this invention are: 1. This invention innovatively combines antennas of different apertures (e.g., 20m, 40m, 70m) heterogeneously at the physical level and introduces a comprehensive optimization model based on equivalent conversion coefficients during the planning stage. By setting dual hard constraints of "total equivalent aperture" and "total equivalent antenna number," it uses an evolutionary algorithm with a penalty mechanism to search for the minimum cost solution. This feature combination breaks through the limitations of traditional fixed-aperture array structures and high construction costs. It enables deep space ground receiving systems to effectively avoid the extremely high construction costs and ultra-long construction cycles of a single ultra-giant antenna while ensuring overall communication gain (meeting link budget) and concurrent task scale, significantly reducing overall construction investment and providing a low-cost, scalable configuration template for deep space infrastructure.

[0018] 2. The control system of this invention introduces a scheduling mechanism based on task requirement quantification and time-slice matching. This mechanism calculates a comprehensive weight by integrating task priority, target bit error rate (BER), and data volume, and quantitatively converts these indicators into the "minimum equivalent receiving aperture" and "minimum receiving time" required to support the task. This allows for priority scheduling within the available antenna time-slice matrix. This feature fundamentally changes the serial operation limitation of a single large-aperture antenna, which "can only support one deep-space mission at a time, making it difficult to perform multiple missions in parallel." Through software algorithms, the system dynamically allocates and combines heterogeneous subarrays on demand, maximizing overall utilization with limited hardware resources and ensuring continuous and stable customized communication support for multiple high-priority deep-space missions.

[0019] 3. This invention incorporates an event-driven local dynamic rescheduling mechanism within its dynamic control flow. Through dynamic monitoring, the system automatically triggers local recalculation when a sudden high-priority task is inserted, antenna equipment malfunctions and stops, or the actual link bit error rate exceeds the limit due to channel fading. This triggers a strategy of "pausing low-priority tasks, adding available antennas to compensate for the link, or reorganizing the remaining visible window." This dynamic response feature completely solves the fatal risk of traditional single-node large-aperture antennas where "critical equipment is concentrated at a single point, and a failure will lead to detection interruption." It endows the antenna array with intelligent characteristics of "task-driven, state-adaptive," ensuring rapid system response in harsh spatial channel environments or under sudden hardware failures, providing extremely high fault tolerance and task continuity guarantees.

[0020] 4. In the underlying physical link, the system of this invention is equipped with a high-sensitivity signal receiving module and a multi-channel signal synthesis module specifically configured for each parabolic subarray. Utilizing the aforementioned specific signal processing hardware features, it specifically addresses and eliminates system distance and phase differences caused by the spatial dispersion and aperture variations of antennas with different physical diameters. This allows for the precise alignment of weak radio frequency signals captured by subarrays of different specifications, thereby supporting perfect coherent synthesis of heterogeneous array elements and ultimately outputting a single high signal-to-noise ratio data stream, ensuring effective reception of deep-space signals at the billions of kilometers level from the underlying hardware level. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of the functional modules of the multi-aperture antenna array combination system of the present invention; Figure 2 This is a schematic diagram of the operation of the multi-channel signal synthesis module provided in Embodiment 1 of the present invention; Figure 3 This is a flowchart illustrating the design process of a ground-based deep-space antenna array assembly provided in Embodiment 2 of the present invention. Figure 4 This is a flowchart illustrating the operation optimization process of the multi-aperture antenna array combination system provided in Embodiment 3 of the present invention. Detailed Implementation

[0023] 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 only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Example 1 like Figure 1 As shown, a multi-aperture antenna array system includes: a multi-aperture antenna array module, a high-sensitivity signal receiving module, a multi-channel signal synthesis module, a mission data processing and management module, and a mission overall control center module. Each antenna in the multi-aperture antenna array module receives downlink signals from the deep spacecraft. The high-sensitivity signal receiving module amplifies, down-converts, and digitally samples each received downlink signal. The multi-channel signal synthesis module estimates the errors of the multiple signals and performs coherent signal synthesis. The mission data processing and management module performs baseband processing on the received signals and monitors the communication link status of each antenna. The mission overall control center module dynamically adjusts the association between the antennas and the mission based on external mission requirements and the communication link status of each antenna.

[0025] Specifically: 1. Multi-aperture antenna array module It includes several parabolic antennas of different apertures, such as 20m subarray, 40m subarray, and 70m subarray. Each subarray consists of several antennas of the same aperture. Each antenna includes a reflector, feed, and rotation mechanism, and independently completes the convergence and reception of downlink signals from deep spacecraft.

[0026] 2. High-sensitivity signal receiving module It includes several signal receiving channels. Each channel consists of a low-noise amplifier, a downconverter, and a digital sampling section. It performs low-noise amplification, downconversion, and digital sampling on the downlink signals received by each antenna, and outputs multiple digital intermediate frequency signals.

[0027] 3. Multi-channel signal synthesis module Composed of a digital delay corrector, a phase synchronization beacon receiving and tracking module, an amplitude and phase weighted synthesizer, etc., it performs time delay, frequency and phase error estimation and signal-to-noise ratio estimation on each digital intermediate frequency signal participating in the synthesis, corrects the signal error based on the error estimation results, and performs weighted synthesis on the corrected signal based on the signal-to-noise ratio estimation, and outputs a single high signal-to-noise ratio data stream.

[0028] To facilitate implementation by those skilled in the art, the above implementation mechanism is further described below. This embodiment is applicable to heterogeneous arrays composed of antennas with different physical apertures (e.g., 20 m and 70 m), and performs coherent weighted synthesis of downlink signals from the same detector under extremely low signal-to-noise ratio conditions in deep space.

[0029] (1) System composition and signal interface like Figure 2 As shown, the multi-channel signal synthesis module is further divided into a digital delay corrector, a phase synchronization beacon receiving and tracking module, and an amplitude-phase weighted synthesizer. Their functions are as follows: Digital delay corrector: realizes digital intermediate frequency signal x i The delay is estimated for [n] and integer sampling delay and fractional delay compensation are performed (fractional delay FIR / Farrow structure can be used).

[0030] Phase synchronization beacon receiving and tracking module: performs frequency offset and phase tracking on the downlink carrier / pilot (referred to as "beacon" in this paper), outputs phase / frequency offset estimate and performs correction.

[0031] Amplitude-phase weighted synthesizer: performs signal-to-noise ratio estimation, weight calculation and normalization, and weighted superposition on each complex baseband after error correction, and outputs a single data stream z[n] for subsequent demodulation and decoding.

[0032] (2) Acquisition and distribution of phase synchronization beacons A. Public reference sources (internal references based on the same source) All receiving channels share a unified frequency and time reference (e.g., 10 MHz reference and 1 PPS) for: Local oscillator / frequency synthesizer phase-locked loop; ADC sampling clock synchronization; Digital processing timing alignment.

[0033] This co-source reference is used to eliminate the long-term drift introduced by the independent local oscillators of each channel, so that the remaining differences mainly come from propagation path differences, equipment group delay differences, phase disturbances caused by antenna structure / servo, etc., which can be estimated and corrected by the algorithm described later.

[0034] B. Methods for distributing beacon phase information Select a high signal-to-noise ratio channel (typically the largest aperture antenna) as the reference channel. The reference channel outputs the following in each update cycle: Carrier frequency offset estimation; Phase estimation; Or the equivalent NCO phase accumulation value.

[0035] The above estimates are distributed to each synthesis channel via the station network / high-speed bus as a reference for phase tracking and relative correction of each channel.

[0036] (3) Accuracy target and compensation algorithm of digital delay correction Heterogeneous array coherent synthesis requires aligning each channel to the same symbol time / phase reference. Therefore, delay compensation employs a two-stage strategy of "coarse alignment + fine alignment".

[0037] A. Coarse delay estimation (sampling level) For each channel of digitized intermediate frequency signal x i [n] Perform the following processing: Obtain initial values ​​based on geometric predictions (station-detector distance, antenna coordinates). ;exist Perform a correlation search in the vicinity or locate the location based on pilot correlation peaks to obtain the integer sample delay. The coarse alignment accuracy should be sufficient to achieve "one sampling period".

[0038] B. Fractional delay compensation (subsampling level) To achieve coherent superposition, fractional time delay is further estimated and compensated. (Unit: sampling period). Possible methods: Fractional delay (FIR) implementation (e.g., Farrow structure or Lagrange interpolation) ; Alternatively, fractional delay compensation can be achieved by applying a linear phase slope in the frequency domain.

[0039] (4) Frequency / phase error estimation and compensation For each channel, after delay compensation, the result is... Using a digital phase-locked loop (PLL / Costas) or an auxiliary phase estimator, the residual frequency offset is obtained. and residual phase .

[0040] Then perform complex rotation compensation: in, The sampling period.

[0041] The above , Updates can be done in blocks (e.g., per block). The update cycle is performed in seconds; for faster phase disturbances caused by servo tracking, the update cycle can be appropriately reduced or the loop bandwidth can be increased (as a configurable parameter).

[0042] (5) Signal-to-noise ratio estimation and dynamic weighted synthesis of heterogeneous array elements Because the received signal amplitude and signal-to-noise ratio differ significantly between 20 m and 70 m, the synthesizer needs to assign weights to each channel in real time to avoid noise or phase jitter introduced by low-quality channels, which would lead to a decrease in synthesis gain.

[0043] A. Signal-to-noise ratio estimation After calibration for each channel Based on pilot / decision error estimation in each update cycle (Any linear value or dB value is acceptable). For example: Pilot signals are known: Noise is estimated using pilot error power; No explicit pilot: Noise measurement is obtained using a carrier-to-noise ratio estimator or a decision pilot error estimator.

[0044] Simultaneously maintain channel status flags: locked / unlocked, synchronization normal / abnormal. When locked or synchronization abnormal, the channel weight is directly set to zero.

[0045] B. Weight Calculation To achieve robust coherent synthesis of heterogeneous array elements, the weights are allocated according to SNR: If linearity is adopted (Not dB): in This is the channel gain calibration coefficient. ≥1 is the configurable index ( =1 indicates linear weighting; >1 can enhance the dominance of high SNR channels and suppress phase noise introduced by low SNR channels.

[0046] If the estimated output is : To prevent excessively large weights or weight fluctuations in individual large-aperture channels, further amplitude limiting, normalization, and smoothing are implemented: When a channel loses lock / synchronization is abnormal or When the value is below the threshold, the weight of the channel is set to zero or rapidly decayed to zero to avoid the low-quality channel reducing the quality of the synthesized output.

[0047] C. Weighted coherent superposition The final synthesized output is: Output It is a single-channel high signal-to-noise ratio data stream for subsequent demodulation, decoding, error / frame error monitoring, and store-and-forward.

[0048] 4. Task Data Processing and Management Module It includes modules such as baseband processing and link monitoring, performs baseband frequency conversion processing on the received signal in the digital domain, provides support for deep space data transmission and telemetry reception, and monitors the link status such as signal-to-noise ratio and bit error rate of each antenna.

[0049] 5. Overall Task Control Center Module It includes a task scheduling unit, an array structure reconfiguration controller, and an optimization algorithm operation module. The task scheduling unit dynamically updates the antenna task schedule, the array structure reconfiguration controller configures antenna combinations of different apertures according to task requirements, and the optimization algorithm operation module dynamically determines the array combination strategy based on task priority and channel conditions.

[0050] The core of this invention lies in achieving synergy between static planning optimization and dynamic operation optimization at the system level, forming an intelligent antenna array system that can both generate the optimal combined system and adaptively adjust during task execution.

[0051] 1. Planning Optimization: Array Structure Design Optimization Based on Cost and Aperture Constraints This invention proposes a comprehensive optimization method for the antenna array planning and design phase. By simultaneously constraining the total equivalent aperture and the total equivalent antenna quantity, it minimizes the overall construction cost while meeting communication performance and array scale requirements. The method uses the cost, performance, and conversion ratio of antennas with different apertures as input to establish a multi-type antenna combination model. The model comprehensively considers system capability requirements (i.e., the equivalent receiving capability after array synthesis) and construction scale requirements (the number of equivalent antennas converted to a unified reference aperture), automatically searching for the optimal configuration scheme that meets the conditions. The results can provide a quantitative basis for antenna model selection, quantity planning, and investment budgeting during the array construction phase. The core steps are as follows: (1) Basic Modeling. In the early stages of array design, the different types of antennas that can be selected (such as 20-meter, 40-meter, and 70-meter antennas) and their main specifications are first identified, including aperture size, unit cost, and equivalent coefficient. The equivalent coefficient is calculated based on a unified reference aperture (such as a 30-meter antenna) and is used to reflect the "reduced value" of antennas with different apertures in the array. For example, the equivalent value corresponding to a large aperture antenna is greater than 1, while that for a small aperture antenna is less than 1.

[0052] (2) Determine optimization constraints. Then, based on the system communication task requirements, determine two types of constraints: total equivalent aperture constraint, the equivalent aperture after the overall synthesis of the antenna array must not be lower than the system design requirements to ensure communication gain and link budget; total equivalent antenna quantity constraint, the "reconstructed quantity" of various types of antennas in the array is statistically calculated with equivalent conversion coefficients as weights, and its total quantity must not be less than the design target value to ensure multi-task support capability.

[0053] (3) Constructing an optimization model. Under the above constraints, a planning model is established with system construction cost as the optimization objective. The model uses the number of antennas as the decision variable and continuously adjusts the combination of various antennas according to the preset constraints to seek the minimum cost solution that meets the performance requirements. Among them, there are penalty coefficients between the constraints to balance the contradictory relationship between aperture capability, number of antennas and cost.

[0054] (4) Perform optimization calculations. Using an evolutionary algorithm with integer constraints (such as an improved genetic algorithm), the optimal combination that satisfies the constraints is automatically searched. The algorithm uses the number of each type of antenna as the basic encoding, evaluates its performance and cost generation by generation, and gradually approaches the optimal solution through selection, crossover, and mutation operations. The process finally outputs the minimum cost scheme that meets the double constraint requirements.

[0055] 2. Operational optimization: Array scheduling and adaptive weight optimization based on task load and link status. This invention proposes a long-term pre-allocation and dynamic adjustment method for antenna array resources in multi-task deep space communication scenarios. It enables array resource planning at the weekly / monthly scale and real-time rescheduling at the minute level under multi-task parallel conditions. This method comprehensively considers task importance, link quality prediction, and minimum reception duration requirements. By constructing a bidirectional matching model between task requirements and antenna resources, it achieves time occupancy, priority guarantee, and dynamic rescheduling of each antenna within the array. This method maximizes the overall array utilization under limited resources, ensures continuous and stable communication support for high-priority tasks, and improves task response speed and system reliability. The core steps are as follows: (1) Task Information Collection and Weight Evaluation. The system periodically compiles a task list, recording the basic information of each task, including task identifier, task type (such as scientific data download, telemetry, ranging, etc.), priority level, target bit error rate (BER), data volume requirement, visible time window, and minimum reception duration requirement (e.g., no less than 4 hours per day). Based on the above indicators, the tasks are weighted and prioritized to reflect their importance in the overall resource allocation. This ranking result provides a basis for subsequent resource allocation.

[0056] (2) Antenna resource modeling and time slice generation. Resource modeling is performed on all antennas in the array, including aperture type, geographical location, viewing window, maintenance plan, and available time periods. The system establishes a complete time axis with days or hours as the basic time unit, generates an available time slice matrix for each antenna, and forms an array available resource pool for subsequent scheduling calculations.

[0057] (3) Task requirement quantification. For each task, the system calculates the minimum ground receiving capability (measured in equivalent aperture) required to complete the communication based on the link budget and target BER requirement, thereby determining the type of antenna combination required to support task reception. At the same time, it estimates the minimum reception time by combining the task data volume and reception rate, forming a task resource requirement description file for subsequent overall scheduling optimization.

[0058] (4) Long-term scheduling and antenna allocation. Based on the existing task weights and resource matrix, the system adopts an improved interval scheduling algorithm or integer programming model for antenna arrangement. During the scheduling process, the time and capacity requirements of high-priority tasks are given priority, and antenna resources that meet the equivalent aperture requirements are allocated first within the available window; secondary tasks are filled sequentially in the remaining time slices. After the algorithm runs, it outputs the initial scheduling table for each antenna, which can cover a time range of one week or one month, providing a planning basis for project execution.

[0059] (5) Dynamic Adjustment and Adaptive Correction. During system operation, if a sudden event occurs (such as the insertion of a new high-priority task, antenna equipment failure, changes in maintenance plans, etc.), or if channel fading causes continuous degradation of link quality and reaches a quantitative trigger threshold, the system will automatically trigger a local rescheduling mechanism. The "channel fading" trigger can be defined by the link monitoring quantity: when the measured SNR (or Eb / N0) is lower than the task's required threshold minus the margin, and fails to recover for N consecutive monitoring cycles, it is triggered (for example, if the monitoring refresh cycle is 10 s, the trigger duration is T=10Ns; in engineering, the margin can be 1~3dB, and N=2~6). Simultaneously, if receive lockout / frame synchronization loss / continuous frame loss occurs and continues for at least one monitoring cycle, this can also be used as a strong trigger condition to directly initiate the local rescheduling mechanism. This mechanism recalculates the resource allocation relationship of the affected tasks and quickly generates a new scheduling scheme by pausing low-priority tasks, recombining available antennas, or extending the working window. The updated work schedule will be sent to the antenna control system in real time, achieving system-level immediate response and task continuity assurance.

[0060] Through the above two-level optimization mechanism, the present invention achieves optimal configuration at the structural level during the construction phase and adaptive optimization of real-time performance during the operation phase, thereby constructing a multi-aperture antenna array system with intelligent features throughout its entire life cycle.

[0061] Furthermore, the pseudocode for the relevant optimization algorithm of this invention is as follows: 1. Cost- and performance-based planning optimization def optimize_structure(antenna_types, cost, scale_factor, target_equiv_diameter, target_equiv_count): """ antenna_types: list of available antenna diameters cost: estimated cost for each type of antenna scale_factor: equivalent scale factor (relative to 30 m antenna) """ # Initialization population = random_initialize(antenna_types) # Use the median cost of the initial population as C_ref to avoid extreme values. This ensures that the penalty term and cost are of the same dimension and order of magnitude, thus avoiding "too small a penalty makes the constraint ineffective" or "too large a penalty causes the population to quickly fall into a local optimum".

[0062] C_ref = median(sum(cost[i] comb[i] for i in range(len(antenna_types))) (for comb in population) for iteration in range(max_iter): for combination in population: # Calculate the total equivalent aperture and total equivalent number of antennas of the system t = iteration / max_iter # Normalized iteration progress in [0,1] # Dynamic penalty weight (smaller in the early stages, larger in the later stages) # Caliper Constraint Weight w_d = C_ref (w_d0 + (w_d1 - w_d0) (t 2)) # Quantity Constraint Weights w_n = C_ref (w_n0 + (w_n1 - w_n0) (t 2)) equiv_diameter = combine_diameter(combination, antenna_types) equiv_count = sum(scale_factor[i] combination[i] for i inrange(len(antenna_types))) total_cost = sum(cost[i] combination[i] for i in range(len(antenna_types))) # Constraint Violation v_d = max(0.0, (target_equiv_diameter - equiv_diameter) / target_equiv_diameter) v_n = max(0.0, (target_equiv_count - equiv_count) / target_equiv_count) # Penalty function: Square penalty (the larger the violation, the faster the penalty increases) penalty = w_d (v_d 2) + w_n (v_n 2) # Fitness: Minimize (cost + penalty) => Maximize its negative value combination.fitness = -(total_cost + penalty) # Population update based on fitness population = evolve_population(population) return select_best_solution(population) 2. Operational optimization based on link status and task load def schedule_tasks(tasks, antennas, planning_period): """ tasks: [{'id':1, 'priority':5, 'BER':1e-6, 'data_volume':200, 'bitrate':12, 'visible_window':[(8,16)], 'min_time':4}] antennas: [{'id':'A1','diameter':70,'availability':[(0,24)]}] planning_period: scheduling period in days""" # Step 1. Sorting by weight (priority, BER, data volume) – Normalize first, then weight. # Pre-statistics: Used to logarithmically normalize the data volume. V_list = [t['data_volume'] for t in tasks] V_min, V_max = min(V_list), max(V_list) # Preliminary statistics: BER working range BER_min_cfg = 1e-6 BER_max_cfg = 1e-3 # Weighting coefficient w_p, w_ber, w_v = 0.50, 0.30, 0.20 for t in tasks: t['weight'] = calculate_task_weight( priority=t['priority'], ber=t['BER'], data_volume=t['data_volume'], V_min=V_min, V_max=V_max, BER_min=BER_min_cfg, BER_max=BER_max_cfg, w_p=w_p, w_ber=w_ber, w_v=w_v ) tasks_sorted = sorted(tasks, key=lambda x: x['weight'], reverse=True) def calculate_task_weight(priority, ber, data_volume, V_min, V_max, BER_min, BER_max, w_p=0.50, w_ber=0.30, w_v=0.20): # (1) Priority normalized to [0,1] p_norm = (priority - 1) / (5 - 1) # 1..5 ->0..1 # (2) BER normalization: Map 10^-6..10^-3 to [0,1] using a logarithmic scale. # Reliability is "more critical the more stringent": the smaller the BER, the greater the weight should be. # ber_clamp to avoid out-of-bounds access ber_clamp = min(max(ber, BER_min), BER_max) ber_norm = (log10(BER_max) - log10(ber_clamp)) / (log10(BER_max)- log10(BER_min)) # 1e-3->0, 1e-6->1 # (3) Data volume normalization: Log normalization suppresses the magnitude of the data volume. # If V_min == V_max, set it to 0.5 to avoid division by zero. if V_max == V_min: v_norm = 0.5 else: v_norm = (log10(data_volume) - log10(V_min)) / (log10(V_max)- log10(V_min)) # (4) Obtain the comprehensive weight by linear weighting weight = w_p p_norm + w_ber ber_norm + w_v v_norm return weight # Step 2. Antenna Resource Modeling for a in antennas: a['time_slots'] = build_time_matrix(planning_period, a['availability']) # Step 3. Quantify task requirements (engineering simplification: E_b / N0 threshold + reference link calibration) for t in tasks_sorted: # Threshold obtained from BER and coding scheme (lookup table / fitting), unit dB t['ebn0_req_db'] = lookup_ebn0_threshold_db(t['BER'], t.get('modcod', 'LDPC_r12')) # Obtain propagation and system parameters from mission geometry and frequency band (configurable / provided by external modules) link_ctx = { 'freq_hz': t.get('freq_hz', default_freq_hz), 'range_m': t.get('range_m', default_range_m), # Deep space distance 'tx_eirp_dbw': t.get('tx_eirp_dbw', default_tx_eirp), # Equivalent isotropic radiated power 'sys_losses_db': t.get('sys_losses_db', default_losses_db), # Pointer to / polarization / implementation loss, etc. 't_sys_k': t.get('t_sys_k', default_tsys_k), # or directly give the G / T to the station. } # Estimate the minimum receiving capacity required to meet the threshold (preferably using G / T; then convert to equivalent aperture). t['required_gt_dbk'] = estimate_required_gt_dbk( ebn0_req_db=t['ebn0_req_db'], bitrate_bps=t['bitrate'] 1e6, # Adjust according to your bitrate units link_ctx=link_ctx ) t['required_aperture'] = gt_to_equiv_diameter( required_gt_dbk=t['required_gt_dbk'], freq_hz=link_ctx['freq_hz'], eta=default_efficiency ) t['required_time'] = max(t['data_volume'] / t['bitrate'] / efficiency_factor, t['min_time']) # Step 4. Long-term scheduling (high-priority tasks are assigned first) for t in tasks_sorted: success = False for a in sorted(antennas, key=lambda x: -x['diameter']): if check_free_slot(a, t['visible_window'], t['required_time']): assign_task(a, t) success = True break If not successful: log_unassigned(t) # Step 5. Dynamic Adjustment Mechanism while system_operating: event = detect_event() # Such as task insertion, antenna failure, link fading, etc. if not event: continue if event['type'] == 'new_task': insert_and_reschedule(tasks, antennas, event) elif event['type'] == 'antenna_fault': remove_fault_antenna(antennas, event) reschedule_related_tasks(tasks, antennas) elif event['type'] == 'link_drop': adjust_link_budget(tasks, antennas, event) update_work_schedule(antennas) Example 2 This embodiment uses a specific example to illustrate the design process of a ground-based deep-space antenna array combination; such as... Figure 3 As shown, the details are as follows: The input conditions are shown in Table 1 below: Table 1. Input for Ground-based Deep Space Antenna Assembly Design Optimization calculation: The optimal combination is obtained through algorithm iteration. N 20 = 8, N 40 = 5, N 70 = 2 Calculation verification: D eq = sqrt( 8×20 2 + 5×40 2 + 2×70 2 = 156.3 m, N eq = 8×0.44 + 5×1.78 + 2×5.44 = 30.1, all of which satisfy the constraints.

[0063] Total cost: C = 8×2600 + 5×5200 + 2×13000 = 86,600 million yuan.

[0064] Engineering evaluation: The equivalent aperture margin is about 4.2%; the number of equivalent antennas meets the design requirements; compared with the full 30m solution (30 units, unit price of about RMB 40 million, total price of RMB 1.2 billion), the cost is reduced by about 28%.

[0065] This solution significantly reduces investment costs while ensuring system communication capabilities, and forms an adjustable equipment configuration template suitable for configuration planning in different deep space exploration phases (such as planetary tracking and control period and cruise period).

[0066] Example 3 This embodiment uses specific examples to illustrate the operation optimization process of a multi-aperture antenna array combination system; such as... Figure 4 As shown, the details are as follows: Assuming the array system needs to support multiple deep space exploration missions simultaneously during a 7-day observation period, the antenna and mission information is as follows: Antenna resources are shown in Table 2 below: Table 2 Antenna Availability Input for Operational Optimization Design of Multi-Aperture Antenna Array Combination System The task information is shown in Table 3 below: Table 3 Task Requirements Input for Operation Optimization Design of Multi-Aperture Antenna Array Combination System The system calculates the overall weight of tasks based on priority, bit error rate requirements, and data volume. High-priority task T1 first acquires resources from two 70m antennas and one 40m antenna, satisfying 5 hours of communication time per day. Secondary task T2 utilizes the remaining 40m antenna within its visible window for allocation. T3 and T4 sequentially fill the array's idle time slots, forming a continuous 7-day schedule. Based on these principles, the work schedule for each antenna is automatically generated and sent to the control terminal.

[0067] In actual operation of antenna arrays, the handling of emergencies is as follows: Sudden Task Insertion: When the system receives an urgent task T5 (priority 6, communication window 14:00–16:00), the scheduling module automatically pauses T4 and releases the A6 antenna. The new task is directly inserted into the idle period and compensates for the remaining reception time of T4 in the next scheduling cycle.

[0068] Link anomaly recovery: If a decrease in the signal-to-noise ratio of the T1 link is detected, an additional 40-meter antenna will be automatically added, and the system will automatically recover it in the next time period.

[0069] Equipment failure handling: If antenna A2 is temporarily shut down for maintenance, the system will reallocate the remaining reception window of task T1 to cover A3 and A4, ensuring that high-priority tasks are not interrupted.

[0070] Through the aforementioned operational optimization mechanism, the antenna array achieved the following within a 7-day cycle: 100% continuous coverage of high-priority tasks; an overall array resource utilization rate increase of approximately 20%; and an average task scheduling adjustment time of less than 2 minutes under conditions of sudden task insertion and equipment failure. This method realizes the transformation of antenna array operation from "fixed allocation" to "task-driven, state-adaptive," significantly improving the real-time performance and mission support capabilities of the deep space telemetry and control system.

[0071] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0072] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. An optimized design method for a multi-aperture antenna array, characterized in that, The physical construction and selection of multi-aperture antennas for deep space exploration includes the following steps: Obtain the unit cost, physical aperture size, and equivalent conversion factor calculated based on a unified reference aperture for multiple candidate parabolic antennas; Set lower limits for the total equivalent aperture and the total equivalent antenna number of the system after the overall antenna array is synthesized; The number of candidate parabolic antennas of each physical aperture is used as a decision variable to construct a planning model that includes the lower limit of the total equivalent aperture constraint and the lower limit of the total equivalent antenna number constraint of the system. In the planning model, a penalty weight parameter is configured to convert the degree of constraint non-compliance into a penalty value. An evolutionary algorithm with integer constraints is used to evaluate the caliber and cost of iterative combinations in the planning model generation by generation, and the optimal antenna number combination configuration scheme is calculated and output with the goal of minimizing the total construction cost. Based on the optimal antenna quantity combination configuration scheme, a heterogeneous multi-aperture antenna array is configured and constructed, which is composed of a variety of parabolic antennas with different physical apertures in a physically discrete combination.

2. The optimized design method for a multi-aperture antenna array according to claim 1, characterized in that, The calculation logic for the total equivalent aperture of the system is as follows: the total equivalent aperture of the combined system is calculated by taking the square root of the sum of the squares of the physical apertures of each candidate parabolic antenna and the corresponding number of antennas configured. The calculation logic for the total equivalent antenna number is as follows: the total equivalent antenna number after combination is calculated by multiplying the equivalent conversion coefficient of each candidate parabolic antenna by the corresponding configuration number and summing the results.

3. The optimized design method for a multi-aperture antenna array according to claim 1, characterized in that, The steps for calculating the comprehensive fitness index in the evolutionary algorithm with integer constraints include: When the total equivalent diameter of the system calculated by iterative combination is lower than the lower limit of the total equivalent diameter constraint of the system, the first severe penalty coefficient is accumulated; When the total number of equivalent antennas calculated by iterative combination is lower than the lower limit of the total number of equivalent antennas constraint, the second moderate penalty coefficient is accumulated; The total construction cost corresponding to the scheme is summed with the accumulated penalty coefficient and the negative value is used as the comprehensive fitness index of the iterative scheme for evolutionary calculation.

4. A dynamic control method for a multi-aperture antenna array, characterized in that, The method is applied to heterogeneous arrays composed of parabolic antennas with various physical apertures, and includes the following steps: Collect parameters of deep space communication missions that are conducted in parallel with multiple tasks, and obtain the task priority level, target bit error rate, data volume requirement, visible time window and minimum reception duration requirement for each task. Using time periods as the basic unit, a usable time slice matrix is ​​generated for each parabolic antenna in the heterogeneous array; Based on the communication link budget requirements and the target bit error rate of each task, the task requirements are quantized and mapped to the minimum equivalent receiving aperture required to support the corresponding task in completing signal reception. Based on the data volume requirements and communication rate parameters, estimate the minimum reception time period for each task; Based on the extracted task comprehensive weight ranking results, an interval scheduling algorithm is used to allocate physical antenna resource combinations that simultaneously meet the minimum equivalent receiving aperture requirement and the minimum receiving time period within the available window of the available time slice matrix to the tasks ranked first. Generate a work schedule containing antenna combination mapping relationships and send it to the underlying antenna control equipment to establish signal aggregation data channels for the corresponding subarrays.

5. The dynamic control method for a multi-aperture antenna array according to claim 4, characterized in that, Before performing the interval scheduling algorithm, the system extracts three indicators for each task: task priority level, target bit error rate, and data volume requirement. After calculation, the system outputs the comprehensive weight of the task, which is then used to prioritize filling and arranging tasks in the available time slice matrix.

6. The dynamic control method for a multi-aperture antenna array according to claim 4, characterized in that, The calculation steps for estimating the minimum reception time period for each task include: The theoretical time consumption value is obtained by dividing the data volume requirement by the product of the communication rate and the set communication efficiency factor. Extract the maximum value between the theoretical time consumption value and the hard-set minimum reception time requirement, and output it as the minimum reception time period.

7. The dynamic control method for a multi-aperture antenna array according to claim 4, characterized in that, The dynamic control method also includes a dynamic rescheduling step based on state monitoring: When an external event such as a new task insertion, physical antenna equipment failure, or channel fading causing the actual link bit error rate to exceed the limit is detected during system operation, a local reallocation trigger rescheduling mechanism is initiated; the rescheduling mechanism includes executing at least one of the following action strategies: When a new high-priority task is received, the execution task with a lower overall task weight is paused, the parabolic antenna it occupies is released to allow the new task to access, and the paused task is placed in the timeline queue for compensation and reconnection after a delay. When the actual link bit error rate is detected to exceed the limit, one or more parabolic antennas in the idle time slice matrix are added to dynamically increase the subarray to complete the link compensation, and the physical antenna resources are automatically recovered after the link is restored. When one or more physical antenna devices fail, the current task resource allocation table of the failed antenna is unloaded, and the remaining antenna resources in the available time slice matrix are reorganized and allocated to cover the remaining visible time window.

8. A multi-aperture antenna array combination system, characterized in that, include: Multi-aperture antenna array module: physically connected in parallel by multiple large parabolic subarrays with different physical apertures obtained by overall configuration using the method described in any one of claims 1 to 3; Array overall control center module: connected to the multi-aperture antenna array module, internally equipped with a memory and a task scheduling processor, the task scheduling processor being configured to execute the logical operation steps in the dynamic control method as described in any one of claims 4 to 7 to output and issue scheduling control instructions; High-sensitivity signal receiving and compensation module: Distributed and connected to the ends of each of the parabolic subarrays, it has a built-in low-noise amplifier, digital delay corrector, phase synchronization beacon receiving unit and multi-channel amplitude and phase weighted synthesizer. It is used to receive radio frequency signals from parabolic subarrays with different physical apertures according to the scheduling control command, eliminate system distance difference and phase difference caused by geographical location dispersion and physical aperture difference through hardware signal, and output a single data stream with high signal-to-noise ratio through amplitude and phase weighted combining.

9. The multi-aperture antenna array combination system according to claim 8, characterized in that, The parabolic subarrays with different physical apertures in the multi-aperture antenna array module are each independently configured with a power amplifier link, a local subarray phase correction system, and a time reference synchronization device for providing a unified clock reference for the heterogeneous array.

10. The multi-aperture antenna array combination system according to claim 8, characterized in that: A task data processing and management module is cascaded at the output of the high-sensitivity signal receiving and compensation module. The task data processing and management module includes a link decoding unit, a signal locking unit, and a bit error rate feedback calculation unit. It is configured to transmit link signal-to-noise ratio and actual bit error rate status data back to the array overall control center module in real time in a closed loop, so as to trigger the task scheduling processor to perform local antenna weighting scheduling operation based on channel fading.

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