Spectrum monitoring and interference impact prediction method and device, storage medium and product

Abstract

The present disclosure relates to the technical field of radio astronomy, and particularly relates to a spectrum monitoring and interference influence prediction method and device, a storage medium and a product. The method comprises: acquiring electromagnetic spectrum measured data of a target area, the electromagnetic spectrum measured data being used to indicate electromagnetic signal spectrum characteristics of multiple spatial points in the target area; generating a three-dimensional spectrum situation map covering the target area based on the electromagnetic spectrum measured data, the three-dimensional spectrum situation map being used to indicate intensity distribution of electromagnetic signals of different frequencies in space; and performing fusion analysis on the three-dimensional spectrum situation map and antenna directional diagram data of a radio telescope in different spatial directions to predict a performance change degree of the radio telescope affected by interference. Through fusion analysis on the three-dimensional spectrum situation map and the antenna directional diagram data of the radio telescope, the performance change degree of the radio telescope affected by interference is accurately predicted, the evaluation accuracy is improved, and scientific support is provided for radio telescope observation.

Description

Technical Field

[0001] This disclosure relates to the field of radio astronomy technology, and in particular to a method, device, storage medium and product for spectrum monitoring and interference effect prediction. Background Technology

[0002] Radio telescopes are crucial instruments for astronomical observation, studying astrophysical phenomena by receiving weak electromagnetic signals from celestial bodies. However, with the rapid development of radio communication, broadcasting, radar, and other services, the electromagnetic environment has become increasingly complex. Radio telescopes are frequently affected by various forms of man-made electromagnetic interference, severely degrading the quality of observational data and scientific output. Therefore, monitoring the electromagnetic environment around radio telescopes and assessing the impact of interference on telescope performance is of significant practical importance.

[0003] In related technologies, the assessment methods for electromagnetic interference (EMI) from radio telescopes typically include: first, setting up monitoring points around the site and collecting electromagnetic signal data in specific frequency bands using spectrum analyzers or receivers; then, determining whether the interference signal exceeds the protection threshold through simple field strength measurements or power calculations; and finally, making a qualitative or semi-quantitative assessment of the impact based on the permissible interference levels given by the International Telecommunication Union (ITU) or relevant standards. For example, some methods, based on the measurement results of a single monitoring point, treat the interference source as an equivalent point source and, combined with a free-space propagation model, estimate the level of the interference signal when it reaches the telescope. However, traditional spectrum monitoring is usually limited to measurements at a single point or a few discrete points. The obtained electromagnetic data is difficult to comprehensively reflect the signal distribution characteristics in the three-dimensional space of the entire site area, especially in cases of complex terrain or building obstruction. The propagation path and intensity distribution of electromagnetic signals exhibit significant spatial differences, which single-point monitoring cannot capture, leading to a large deviation between the assessment results of the interference impact and the actual observations.

[0004] Therefore, how to accurately predict the impact of interference on radio telescopes is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0005] In view of this, this disclosure proposes a method, apparatus, storage medium, and product for spectrum monitoring and interference impact prediction.

[0006] According to one aspect of this disclosure, a method for spectrum monitoring and interference impact prediction is provided, the method comprising:

[0007] Acquire measured electromagnetic spectrum data of the target area, wherein the measured electromagnetic spectrum data is used to indicate the electromagnetic signal spectrum characteristics of multiple spatial points within the target area;

[0008] Based on the measured electromagnetic spectrum data, a three-dimensional spectrum situation map covering the target area is generated. The three-dimensional spectrum situation map is used to indicate the intensity distribution of electromagnetic signals of different frequencies in space.

[0009] Acquire antenna pattern data of a radio telescope, the antenna pattern data being used to indicate the characteristics of antenna gain variation with spatial direction;

[0010] By fusing and analyzing the three-dimensional spectral situation map and the antenna pattern data in different spatial directions, the extent of performance changes of the radio telescope affected by interference can be predicted.

[0011] In one possible implementation, acquiring measured electromagnetic spectrum data of the target region includes:

[0012] The electromagnetic spectrum measurement data is obtained by using a drone detection platform to perform gridded or path-scanning flights within the target area. The electromagnetic spectrum measurement data includes frequency information, location information, and signal strength information of multiple spatial points within the target area.

[0013] In another possible implementation, generating a three-dimensional spectral situation map covering the target region based on the measured electromagnetic spectrum data includes:

[0014] Based on the measured electromagnetic spectrum data, combined with the site topography data of the target area and the preset radio wave propagation model, propagation loss correction is performed to obtain the corrected discrete point spectrum data.

[0015] The discrete point spectral data is expanded into continuously distributed gridded data through a preset spatial interpolation or matrix filling algorithm.

[0016] Based on the gridded data, a three-dimensional spectral situation map covering the target area is generated.

[0017] In another possible implementation, the fusion analysis of the three-dimensional spectral situation map and the antenna pattern data in different spatial directions to predict the degree of performance change of the radio telescope due to interference includes:

[0018] The three-dimensional spectrum situation map and the antenna pattern data are fused and analyzed in different spatial directions to calculate the total interference power of the antenna feed coupled into the radio telescope from different spatial directions;

[0019] Based on the total interference power, predict the degree of performance change of the radio telescope affected by the interference.

[0020] In another possible implementation, the step of fusing and analyzing the three-dimensional spectral situation map and the antenna pattern data in different spatial directions to calculate the total interference power coupled into the antenna feed of the radio telescope from different spatial directions includes:

[0021] At least one interference source is identified from the three-dimensional spectrum situation map, and the transmission power, location information and transmission antenna pattern of each interference source are obtained;

[0022] Based on the location information of each interference source and the geographical location of the radio telescope, the signal propagation path loss of each interference source reaching the feed aperture of the radio telescope is determined by a preset radio wave propagation model.

[0023] Based on the observation direction of the radio telescope, the receiving antenna gain corresponding to the direction of each interference source is obtained from the antenna pattern data;

[0024] Based on the transmit power of each interference source, the transmit antenna pattern of the interference source, the signal propagation path loss, and the corresponding receive antenna gain, calculate the interference power component coupled to the antenna feed for each interference source.

[0025] The total interference power is obtained by summing the interference power components of each interference source.

[0026] In another possible implementation, predicting the degree of performance change of the radio telescope affected by the interference based on the total interference power includes:

[0027] Based on the total interference power, the parameter loss of the radio telescope affected by the interference is predicted by a preset parameter calculation model. The parameter loss includes sensitivity loss and / or signal-to-noise ratio reduction.

[0028] In another possible implementation, the method further includes:

[0029] Based on the three-dimensional spectrum situation map, a preset interference threshold algorithm is used to identify occasional interference signals, which are interference signals that exhibit random characteristics in the time domain.

[0030] The Monte Carlo method is used to perform statistical analysis on the identified sporadic interference signals to predict the observable time window within the target observation frequency band. The observable time window is a continuous period of time during which the signal strength of the sporadic interference signal is lower than a preset threshold.

[0031] According to another aspect of this disclosure, a spectrum monitoring and interference impact prediction device is provided, the device comprising:

[0032] The first acquisition module is used to acquire measured electromagnetic spectrum data of the target area, wherein the measured electromagnetic spectrum data is used to indicate the electromagnetic signal spectrum characteristics of multiple spatial points within the target area.

[0033] The generation module is used to generate a three-dimensional spectrum situation map covering the target area based on the measured electromagnetic spectrum data. The three-dimensional spectrum situation map is used to indicate the intensity distribution of electromagnetic signals of different frequencies in space.

[0034] The second acquisition module is used to acquire antenna pattern data of the radio telescope, the antenna pattern data being used to indicate the characteristics of antenna gain variation with spatial direction;

[0035] The prediction module is used to fuse and analyze the three-dimensional spectrum situation map and the antenna pattern data in different spatial directions to predict the degree of performance change of the radio telescope affected by interference.

[0036] In one possible implementation, the first acquisition module is further configured to:

[0037] The electromagnetic spectrum measurement data is obtained by using a drone detection platform to perform gridded or path-scanning flights within the target area. The electromagnetic spectrum measurement data includes frequency information, location information, and signal strength information of multiple spatial points within the target area.

[0038] In another possible implementation, the generation module is further configured to:

[0039] Based on the measured electromagnetic spectrum data, combined with the site topography data of the target area and the preset radio wave propagation model, propagation loss correction is performed to obtain the corrected discrete point spectrum data.

[0040] The discrete point spectral data is expanded into continuously distributed gridded data through a preset spatial interpolation or matrix filling algorithm.

[0041] Based on the gridded data, a three-dimensional spectral situation map covering the target area is generated.

[0042] In another possible implementation, the prediction module is further configured to:

[0043] The three-dimensional spectrum situation map and the antenna pattern data are fused and analyzed in different spatial directions to calculate the total interference power of the antenna feed coupled into the radio telescope from different spatial directions;

[0044] Based on the total interference power, predict the degree of performance change of the radio telescope affected by the interference.

[0045] In another possible implementation, the prediction module is further configured to:

[0046] At least one interference source is identified from the three-dimensional spectrum situation map, and the transmission power, location information and transmission antenna pattern of each interference source are obtained;

[0047] Based on the location information of each interference source and the geographical location of the radio telescope, the signal propagation path loss of each interference source reaching the feed aperture of the radio telescope is determined by a preset radio wave propagation model.

[0048] Based on the observation direction of the radio telescope, the receiving antenna gain corresponding to the direction of each interference source is obtained from the antenna pattern data;

[0049] Based on the transmit power of each interference source, the transmit antenna pattern of the interference source, the signal propagation path loss, and the corresponding receive antenna gain, calculate the interference power component coupled to the antenna feed for each interference source.

[0050] The total interference power is obtained by summing the interference power components of each interference source.

[0051] In another possible implementation, the prediction module is further configured to:

[0052] Based on the total interference power, the parameter loss of the radio telescope affected by the interference is predicted by a preset parameter calculation model. The parameter loss includes sensitivity loss and / or signal-to-noise ratio reduction.

[0053] In another possible implementation, the apparatus further includes: an analysis module, for:

[0054] Based on the three-dimensional spectrum situation map, a preset interference threshold algorithm is used to identify occasional interference signals, which are interference signals that exhibit random characteristics in the time domain.

[0055] The Monte Carlo method is used to perform statistical analysis on the identified sporadic interference signals to predict the observable time window within the target observation frequency band. The observable time window is a continuous period of time during which the signal strength of the sporadic interference signal is lower than a preset threshold.

[0056] According to another aspect of this disclosure, a spectrum monitoring and interference impact prediction apparatus is provided, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above method.

[0057] According to another aspect of this disclosure, a non-volatile computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the above-described method.

[0058] According to another aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the above-described method.

[0059] This disclosure provides a method for spectrum monitoring and interference impact prediction. By acquiring measured electromagnetic spectrum data from multiple spatial points in a target area, a three-dimensional spectrum situation map covering the target area is generated. This comprehensively and meticulously depicts the intensity distribution characteristics of electromagnetic signals of different frequencies in three-dimensional space, effectively overcoming the limitations of traditional single-point monitoring in reflecting spatial differences in signals under complex terrain or obstructions. Based on this, the three-dimensional spectrum situation map is fused with the antenna pattern data of a radio telescope for spatial directional analysis. This not only fully considers the variation characteristics of the telescope antenna gain with the observation direction, accurately calculating the actual equivalent power of the interference signal entering the receiving system through the antenna sidelobes or main lobes, significantly improving the accuracy of interference assessment and avoiding missed or incorrect judgments due to neglecting antenna directivity; it also dynamically predicts the degree of performance change of the radio telescope affected by interference, providing a scientific basis for the formulation of astronomical observation plans, avoidance of interference sources, and optimization of electromagnetic environmental protection measures. Furthermore, the embodiments disclosed herein organically integrate three-dimensional electromagnetic environment data with equipment characteristic data, constructing a complete technical chain from spectrum monitoring to impact prediction, significantly improving the automation and intelligence level of interference assessment, and providing strong support for the high-quality observation and operation of radio telescopes.

[0060] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0061] The accompanying drawings, which are included in and form part of this specification, illustrate exemplary embodiments, features, and aspects of this disclosure together with the specification and serve to explain the principles of this disclosure.

[0062] Figure 1 A flowchart of a spectrum monitoring and interference impact prediction method provided in an exemplary embodiment of this disclosure is shown.

[0063] Figure 2 This illustration shows a schematic diagram of classifying observed data according to an exemplary embodiment of the present disclosure.

[0064] Figure 3 A schematic diagram illustrating the construction process of an electromagnetic spectrum map provided in an exemplary embodiment of this disclosure is shown.

[0065] Figure 4 A schematic diagram of a data analysis framework based on the Monte Carlo method provided in an exemplary embodiment of this disclosure is shown.

[0066] Figure 5 A schematic diagram of the architecture of an experimental platform provided in an exemplary embodiment of this disclosure is shown.

[0067] Figure 6 A schematic diagram of the system architecture of a radio environment monitoring platform provided in an exemplary embodiment of this disclosure is shown.

[0068] Figure 7 A functional block diagram of automated spectrum monitoring software provided in an exemplary embodiment of this disclosure is shown.

[0069] Figure 8 A schematic diagram of the main interface of the host computer software provided in an exemplary embodiment of this disclosure is shown.

[0070] Figure 9 A schematic diagram illustrating the structure of an EMC probe kit provided in an exemplary embodiment of this disclosure and its user interface with host computer spectrum monitoring software is shown.

[0071] Figure 10 A schematic diagram of the internal module of an unmanned aerial vehicle platform provided in an exemplary embodiment of this disclosure is shown.

[0072] Figure 11 A schematic diagram of a threshold-based interference signal separation process provided in an exemplary embodiment of this disclosure is shown.

[0073] Figure 12 A schematic diagram showing the electromagnetic situation map test results provided by an exemplary embodiment of this disclosure is illustrated.

[0074] Figure 13 A schematic diagram of the structure of a spectrum monitoring and interference impact prediction device provided in an exemplary embodiment of the present disclosure is shown.

[0075] Figure 14 This is a block diagram illustrating an apparatus according to an exemplary embodiment. Detailed Implementation

[0076] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0077] As used herein, the terms “comprising,” “including,” “having,” or variations thereof are open-ended and include one or more of the stated features, integrals, elements, steps, components, or functions, but do not exclude the presence or addition of one or more other features, integrals, elements, steps, components, functions, or groups thereof.

[0078] When an element is referred to as “connected,” “coupled,” “responding,” or a variation thereof relative to another element, it may be directly connected, coupled, or responding to another element, or there may be an intermediate element present.

[0079] Although the terms first, second, third, etc., may be used herein to describe various elements / operations, these elements / operations should not be limited by these terms. These terms are only used to distinguish one element / operation from another. Therefore, without departing from the teachings of the inventive concept, a first element / operation in some embodiments may be referred to as a second element / operation in other embodiments.

[0080] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0081] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0082] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, data stored, data displayed, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant regions.

[0083] This disclosure provides a method for spectrum monitoring and interference impact prediction. By fusing and analyzing a three-dimensional spectrum situation map with the antenna pattern data of a radio telescope, it accurately predicts the degree of performance change of a radio telescope affected by interference, improves the accuracy of the assessment, and constructs a complete chain from monitoring to prediction, providing scientific support for radio telescope observation.

[0084] The spectrum monitoring and interference impact prediction method provided in this disclosure will now be described using several exemplary embodiments.

[0085] Please refer to Figure 1This document illustrates a flowchart of a spectrum monitoring and interference impact prediction method provided in an exemplary embodiment of the present disclosure. This embodiment uses the method in a computing device as an example for illustration. The method includes the following steps.

[0086] Step 101: Obtain the measured electromagnetic spectrum data of the target area. The measured electromagnetic spectrum data is used to indicate the electromagnetic signal spectrum characteristics of multiple spatial points within the target area.

[0087] The target area refers to the specific geographic space where electromagnetic environment monitoring and interference assessment are required. It is usually centered on the radio telescope site and covers a protected area or geographic boundary of interest with a certain radius.

[0088] Electromagnetic spectrum measurement data refers to a set of data obtained through physical measurement methods that reflects the state of the electromagnetic environment within a target area. This data contains information in at least three dimensions: frequency information (i.e., the carrier frequency or frequency band of the signal), location information (i.e., the latitude, longitude, and altitude of the measurement point), and signal strength information (i.e., field strength value or power spectral density).

[0089] In some embodiments, the electromagnetic spectrum measurement data can be acquired in any of the following ways: 1. Direct measurement method: The UAV detection platform is controlled to fly to various preset spatial points within the target area, and the electromagnetic signals at those points are received using an airborne spectrum analyzer or software-defined radio equipment. The signal frequency, amplitude, and corresponding latitude, longitude, and altitude coordinates are recorded. 2. Data import method: Historical measurement data files collected and stored through previous flight missions are read from external storage devices or remote servers. 3. Real-time transmission and reception: The spectrum data collected during flight is transmitted back to the ground control station in real time via the UAV data link, where it is received and parsed to form a dataset.

[0090] Step 102: Based on the measured electromagnetic spectrum data, generate a three-dimensional spectrum situation map covering the target area. The three-dimensional spectrum situation map is used to indicate the intensity distribution of electromagnetic signals of different frequencies in space.

[0091] A 3D spectrum situation map is a multi-dimensional data visualization model used to comprehensively display the distribution of electromagnetic signals in a three-dimensional spatial coordinate system. This map not only includes spatial coordinate axes (longitude, latitude, and altitude), but also visually maps the signal strength distribution characteristics at specific frequencies or frequency bands through variations in color intensity, color temperature, or contour surfaces.

[0092] Intensity distribution refers to the energy distribution pattern of electromagnetic signals in space. It is usually manifested as a gradient change where the signal is strong in the near-field source region and weak in the far-field region, as well as the distribution of shadow areas formed by buildings and terrain obstruction.

[0093] In some embodiments, the generation of the three-dimensional spectral situation map includes any of the following methods: 1. Spatial interpolation algorithm: Based on discrete measured data points, the signal intensity values ​​of non-sampling points are calculated using the inverse distance weighting method, Kriging interpolation method, or radial basis function method, and filled into a three-dimensional spatial grid to form a continuous data field. 2. Volume rendering technology: Using ray casting algorithms or texture mapping techniques in computer graphics, the interpolated three-dimensional data field is converted into a visualized stereoscopic image. 3. Frequency domain slice synthesis: Three-dimensional intensity maps are generated for multiple discrete frequency points, and then multi-layer three-dimensional situation maps covering a wide frequency band are constructed by stacking or fusion.

[0094] Step 103: Obtain the antenna pattern data of the radio telescope. The antenna pattern data is used to indicate the characteristics of antenna gain variation with spatial direction.

[0095] A radio telescope is an astronomical observation device used to receive radio waves from celestial bodies. It typically has extremely high receiving sensitivity and extremely low noise temperature, and is extremely sensitive to external electromagnetic interference.

[0096] Antenna pattern data is a function describing the radiation or reception characteristics of a radio telescope antenna in the far field as a function of spatial direction. It is usually expressed quantitatively in polar coordinates or three-dimensional spherical coordinates, showing the antenna's gain, main lobe width, side lobe level, and pointing accuracy at different azimuth and elevation angles.

[0097] Antenna gain is the ratio of the power density of an antenna in the direction of maximum radiation to the power density of an ideal point source antenna in the same direction, under the condition of equal input power. It is used to characterize the antenna's ability to concentrate energy in a specific direction.

[0098] In some embodiments, the acquisition of radio telescope antenna pattern data includes any of the following methods: 1. Theoretical model import: Based on the physical parameters of the radio telescope, such as aperture, operating frequency, and feed type, theoretical pattern data is calculated using electromagnetic simulation software and imported into the system. 2. Actual calibration data reading: The measured pattern database file obtained during installation or periodic maintenance of the radio telescope through actual measurements (such as the radio source method or satellite beacon method) is read. 3. Parametric modeling: Based on the technical specifications provided by the antenna manufacturer, the antenna pattern is simplified into a mathematical model composed of key parameters such as main lobe width, side lobe level, and pointing accuracy.

[0099] It should be noted that step 103 can be executed before step 102 or in parallel with step 102. This embodiment of the present disclosure does not limit this, as long as step 103 is executed before step 104.

[0100] Step 104: The three-dimensional spectrum situation map and antenna pattern data are fused and analyzed in different spatial directions to predict the degree of performance change of the radio telescope affected by interference.

[0101] Spatial orientation refers to the azimuth and elevation angles in a spherical coordinate system with the phase center of the radio telescope antenna as the origin. Fusion analysis refers to the process of associating, superimposing, and comprehensively calculating multidimensional heterogeneous data (three-dimensional spectral situation diagram and antenna radiation pattern) in a unified spatial coordinate system. Its core is to achieve a quantitative assessment of the impact of the external electromagnetic environment on a specific receiving system through data-level integration and computation.

[0102] In some embodiments, the fusion analysis includes: fusing and analyzing three-dimensional spectral situation data and antenna pattern data in different spatial directions to calculate the total interference power of the antenna feed coupled into the radio telescope from different spatial directions; and predicting the degree of performance change of the radio telescope affected by interference based on the total interference power.

[0103] The antenna feed is the core component in a radio telescope antenna system responsible for collecting and guiding electromagnetic waves to the receiver front end; it is typically located at the antenna's focal point. The feed converts electromagnetic energy in space into guided waves in the transmission line and serves as the entry point for the entire receiving link.

[0104] The total interference power refers to the sum of the effective electromagnetic energy coupled into the antenna feed from all external interference sources through the spatial filtering effect of the radio telescope antenna. This power value is a fundamental physical quantity for measuring the overall impact of interference on the receiving system. In some embodiments, the total interference power is calculated in any of the following ways: 1. Spatial integration method: The entire three-dimensional space is divided into countless differential solid angle elements. At each angle element, the corresponding interference field strength in the spectrum situation diagram is multiplied by the gain value in the antenna pattern, and then the integral is performed over the entire spatial sphere to obtain the total coupled power. 2. Discrete source accumulation method: The discrete interference sources identified in the three-dimensional spectrum situation diagram are processed one by one, and the coupled power of each source is calculated and then summed, which is the method detailed in the subsequent steps of the embodiments of this disclosure. It should be noted that the relevant details can be referred to the relevant descriptions in the embodiments below, and will not be introduced here.

[0105] The degree of performance change refers to the degradation or shift of a radio telescope's key observational performance indicators compared to a non-interference baseline state under the influence of external electromagnetic interference. This degree is a quantitative description of the interference's impact, used to characterize the severity of the interference and the level of loss in the telescope's observational capabilities. The degree of performance change is usually quantified from one or more of the following dimensions: 1. Sensitivity loss: This refers to the degree of deterioration in the intensity of the weakest celestial object signal that the telescope can detect. Sensitivity loss is usually reflected in the increase in the system's equivalent noise temperature or the percentage increase in the minimum detectable signal flux density. This indicator directly affects the telescope's detection depth and weak source observation capabilities. 2. Signal-to-noise ratio (SNR) reduction: This refers to the reduction in the ratio of signal to noise power at the receiver system's output relative to the non-interference state, assuming the observed target signal strength remains constant. The SNR reduction is usually expressed in decibels and directly affects the quality of observational data and the feasibility of subsequent scientific processing.

[0106] Predicting the degree of performance change refers to extrapolating and quantifying the impact of external electromagnetic interference on the telescope's observation performance based on the calculated total power of the interference, combined with the system parameters of the radio telescope and a pre-set mathematical model. The purpose of the prediction is to assess the consequences of interference in advance, either before it actually occurs or during real-time monitoring, providing a basis for decision-making regarding interference avoidance, observation plan adjustments, or interference source handling.

[0107] In summary, the embodiments of this disclosure provide a spectrum monitoring and interference impact prediction method. By acquiring measured electromagnetic spectrum data from multiple spatial points in a target area, a three-dimensional spectrum situation map covering the target area is generated. This comprehensively and meticulously depicts the intensity distribution characteristics of electromagnetic signals of different frequencies in three-dimensional space, effectively overcoming the limitations of traditional single-point monitoring in reflecting spatial differences in signals under complex terrain or obstructions. Furthermore, by fusing the three-dimensional spectrum situation map with the antenna pattern data of the radio telescope in spatial direction, it not only fully considers the variation characteristics of the telescope antenna gain with the observation direction, accurately calculates the actual equivalent power of the interference signal entering the receiving system through the antenna sidelobes or main lobes, significantly improving the accuracy of interference assessment and avoiding missed or misjudged cases due to neglecting antenna directivity; it also dynamically predicts the degree of performance change of the radio telescope affected by interference, providing a scientific basis for the formulation of astronomical observation plans, avoidance of interference sources, and optimization of electromagnetic environmental protection measures. Furthermore, the embodiments disclosed herein organically integrate three-dimensional electromagnetic environment data with equipment characteristic data, constructing a complete technical chain from spectrum monitoring to impact prediction, significantly improving the automation and intelligence level of interference assessment, and providing strong support for the high-quality observation and operation of radio telescopes.

[0108] In some embodiments, the method of acquiring electromagnetic spectrum measurement data may include performing gridded or path-scanning flight in a target area using an unmanned aerial vehicle (UAV) detection platform to obtain electromagnetic spectrum measurement data, which includes frequency information, location information, and signal strength information of multiple spatial points in the target area.

[0109] A drone detection platform refers to a mobile monitoring system that uses drones as aerial carriers and carries electromagnetic spectrum monitoring equipment. This platform possesses maneuverability and can collect data within a target area according to a preset flight path. Gridded flight refers to a path planning method where the drone hovers or traverses point-to-point according to a preset latitude and longitude grid, aiming to achieve spatially discrete sampling of the target area. Path scanning flight refers to a continuous movement data collection method where the drone flies at a constant speed along a specific flight path (such as a serpentine line, concentric circles, or radial lines), continuously recording the spectrum data corresponding to its location during flight.

[0110] In some embodiments, the generation of a three-dimensional spectrum situation map may include: based on measured electromagnetic spectrum data, combining the site topography data of the target area and a preset radio wave propagation model to correct propagation loss, obtaining corrected discrete point spectrum data; expanding the discrete point spectrum data into continuously distributed gridded data through a preset spatial interpolation or matrix filling algorithm; and generating a three-dimensional spectrum situation map covering the target area based on the gridded data.

[0111] Site topographic data refers to a digital description of the surface morphology of the target area (especially the area surrounding the radio telescope site). It typically includes a digital elevation model, used to accurately reflect the blocking, reflection, and diffraction effects of man-made or natural features such as mountains, gullies, and buildings on radio wave propagation.

[0112] Electromagnetic wave propagation models are mathematical models used to quantitatively describe the signal attenuation of electromagnetic waves as they propagate from a source to a receiver in space, due to factors such as path loss, atmospheric absorption, obstruction, and multipath effects. Common types include free-space propagation models, two-ray models, diffraction models (such as the knife-edge diffraction model), and deterministic models based on geographic information systems.

[0113] Propagation loss correction refers to the process of correcting the original measured signal strength by calculating the path attenuation of radio waves propagating from the spatial point where the interference source is located to the receiving antenna of the radio telescope. Its purpose is to eliminate the influence of terrain features on the measured values ​​and restore or deduce the radiation characteristics of the interference source itself or the true field strength at a specific location.

[0114] Discrete-point spectral data refers to a set of spatially discontinuous data points that exist only along the flight path of an unmanned aerial vehicle (UAV). Each data point corresponds to a specific set of three-dimensional coordinates and its measured or corrected spectral information at those coordinates.

[0115] Spatial interpolation is a method that uses known discrete point values ​​to estimate the values ​​of unknown points within the same region using mathematical functions. Its core assumption is that spatially close points have similar values. Commonly used algorithms include the inverse distance weighting method, Kriging, and spline function methods.

[0116] Matrix imputation algorithms are mathematical methods based on low-rank matrix completion theory, used to recover complete data matrices from sparsely sampled observation data. They are suitable for imputing "missing values" in electromagnetic spectrum data due to the limited number of sampling points, and are particularly applicable to scenarios where the data has a low-dimensional structure or spatiotemporal correlation.

[0117] Gridded data refers to dividing a target region into regularly arranged grid cells in a spatial dimension. Each grid cell is assigned a corresponding spectral characteristic value, thereby forming a spatially continuous and structurally regular data field.

[0118] Furthermore, the generation of the three-dimensional spectrum situation map includes the following steps: First, based on the measured electromagnetic spectrum data, combined with the site topographic data of the target area and a preset radio wave propagation model, propagation loss correction is performed to obtain the corrected discrete-point spectrum data. Since the measured electromagnetic spectrum data directly reflects the electromagnetic environment of the measurement point but does not consider the influence of terrain features on radio wave propagation, it is necessary to introduce site topographic data and a radio wave propagation model for correction. Specifically, the site topographic data uses a digital elevation model to accurately describe the surface morphology within the target area; the radio wave propagation model is selected based on factors such as the line-of-sight conditions, terrain obstruction, and atmospheric environment of the target area, including free-space propagation models, two-ray models, or diffraction models. During the correction process, for each measured discrete point, the theoretical propagation loss from that point to the radio telescope receiving antenna is calculated using the radio wave propagation model combined with topographic profile data. This corrects the measured signal strength, thereby eliminating the influence of terrain obstruction, diffraction, and multipath effects on the measured values, resulting in corrected discrete-point spectrum data that reflects the radiation characteristics of the interference source itself or the true field strength at a specific location. Secondly, discrete point spectral data is expanded into continuously distributed gridded data using pre-defined spatial interpolation or matrix filling algorithms. Due to the limitations of the UAV's flight path, the corrected discrete point data only exists on the measurement track and cannot directly cover the continuous space of the entire target area. To solve this problem, spatial interpolation or matrix filling algorithms are used to expand the discrete data into a regular grid form. For spatial interpolation algorithms, inverse distance weighting, kriging, or radial basis function methods can be used to estimate the values ​​of unknown grid points within the region using known discrete point values. The core assumption is that points that are spatially close have similar values. For matrix filling algorithms, the discrete point data is arranged into a sparse matrix according to spatial coordinates. Taking advantage of the low-rank characteristic of electromagnetic spectrum data, which typically exhibits smooth spatial changes, the missing grid point data is iteratively solved using low-rank matrix completion or tensor completion algorithms. Through the above expansion processing, the originally sparse and discrete measurement data is transformed into a spatially continuous and structurally regular gridded data field covering the target area. Finally, based on the gridded data, a three-dimensional spectral situation map covering the target area is generated. The expanded gridded data is input into a visualization rendering engine to construct an electromagnetic spectrum situation model in a three-dimensional spatial coordinate system. Specific implementation methods include: using voxel rendering, treating each three-dimensional grid unit as a voxel, assigning different colors and transparency based on its signal strength value, and generating a three-dimensional cloud map with a sense of depth through a ray casting algorithm; or using isosurface extraction, employing a moving cube algorithm to extract isosurfaces corresponding to specific signal strength thresholds from the gridded data, highlighting the boundary contours of strong interference areas in the form of semi-transparent or multi-layered surfaces; or using a three-dimensional terrain overlay method, using the site topography data as a base... Figure 3A three-dimensional surface is used to map spectral intensity information onto or around it using methods such as heatmaps and bar charts, forming a comprehensive situational map that integrates terrain and electromagnetic environment. The generated three-dimensional spectral situational map is used to indicate the intensity distribution of electromagnetic signals of different frequencies in space, providing basic data support for subsequent fusion analysis with antenna patterns.

[0119] In some embodiments, the fusion analysis of three-dimensional spectrum situation map and antenna pattern data may include: fusing the three-dimensional spectrum situation map and antenna pattern data in different spatial directions to calculate the total interference power of the antenna feed coupled into the radio telescope from different spatial directions; and predicting the degree of performance change of the radio telescope affected by interference based on the total interference power.

[0120] Furthermore, the calculation of the total interference power may include: identifying at least one interference source from the three-dimensional spectrum situation map, obtaining the transmit power, location information, and transmit antenna pattern of each interference source; determining the signal propagation path loss of each interference source to the feed surface of the radio telescope using a preset radio wave propagation model based on the location information of each interference source and the geographical location of the radio telescope; obtaining the corresponding receiving antenna gain in the direction of each interference source from the antenna pattern data according to the observation direction of the radio telescope; calculating the interference power component coupled to the antenna feed of each interference source based on the transmit power, transmit antenna pattern, signal propagation path loss, and corresponding receiving antenna gain of each interference source; and summing the interference power components of each interference source to obtain the total interference power.

[0121] An interference source is a man-made or natural radiator that can radiate electromagnetic energy within a target area and potentially affect radio telescope observations. In an electromagnetic spectrum situation diagram, an interference source typically appears as a localized spatial region where the signal strength is significantly higher than the background noise.

[0122] Transmit power refers to the amount of electromagnetic energy output by the interference source at its radiating port, usually measured in watts or decibels / milliwatts. This parameter can be deduced from measured data or obtained from the specifications of the interference source equipment.

[0123] The radiation pattern of an interfering source's transmitting antenna is a function describing the energy distribution characteristics of the interfering source's antenna in various directions in space. It is used to correct the equivalent radiated power of the interfering source in different directions, and typically includes two modes: omnidirectional radiation or directional radiation.

[0124] Signal propagation path loss is the total energy attenuation that occurs when electromagnetic waves radiate from the transmitting antenna of the interference source, undergo physical processes such as free space attenuation, terrain diffraction, atmospheric absorption, and multipath effects, and finally reach the feed surface of the radio telescope.

[0125] The receiving antenna gain is a characterization of a radio telescope antenna's ability to receive electromagnetic waves in a specific spatial direction (i.e., the direction of the interference source). This value is obtained directly from the antenna pattern data by spatial angle indexing and reflects the degree to which the antenna amplifies or suppresses signals from that direction.

[0126] Interference power components refer to the effective interference energy of a single interference source that, after passing through multiple stages such as transmission radiation, spatial propagation, and reception focusing, is finally coupled to the antenna feed. The power components of each interference source are independent of each other, but together they constitute the total interference power.

[0127] Furthermore, predicting the degree of performance change of the radio telescope affected by the interference based on the total interference power can include: predicting the amount of parameter loss of the radio telescope affected by the interference based on the total interference power using a preset parameter calculation model. The amount of parameter loss includes the amount of sensitivity loss and / or the decrease in signal-to-noise ratio.

[0128] A parameter calculation model is a mathematical function or empirical formula used to establish a quantitative relationship between the total interference power and key performance indicators of a radio telescope. This model is typically constructed based on inherent parameters of the radio telescope, such as system noise temperature, receiver linearity, and observation bandwidth.

[0129] Sensitivity loss refers to the degree of degradation in the intensity of the weakest celestial object signal that a radio telescope can detect under interference conditions compared to an interference-free state. Sensitivity loss typically manifests as an increase in the system's equivalent noise temperature or an increase in the minimum detectable signal flux density.

[0130] The signal-to-noise ratio (SNR) decrease refers to the reduction in the signal-to-noise ratio at the receiver system's output due to the introduction of total interference power, assuming the target signal strength remains constant. SNR decrease directly impacts the quality of the observed data and the efficiency of integration time.

[0131] In some embodiments, the specific process of calculating the total interference power and predicting the performance loss of the radio telescope is as follows: 1. Interference source identification and parameter acquisition: First, at least one interference source is identified from the three-dimensional spectrum situation map. The three-dimensional spectrum situation map contains the electromagnetic signal intensity distribution information of each spatial point in the target area. Specific methods for identifying interference sources include: using the threshold segmentation method, setting a signal intensity threshold in the three-dimensional spectrum situation map, marking continuous voxel regions above the threshold as potential interference sources, and extracting the spatial range of each interference source through connected component analysis; or using the peak detection method, performing local extremum search on the spectrum situation map in the spatial domain, locating the local maximum signal intensity point, and regarding it as an independent interference source center; or using a density-based spatial clustering algorithm, clustering discrete high-intensity data points into multiple interference source groups, and calculating the centroid of each group as the interference source location. After identifying the interference source, the transmission power, location information, and transmission antenna pattern of each interference source are obtained. The transmission power and location information of the interference sources are directly extracted from the three-dimensional spectrum situation map: the location information corresponds to the latitude, longitude, and altitude coordinates of the spatial point where the interference source is located, and the transmission power is obtained by inverting the signal strength value at that location point, or by obtaining the equivalent transmission power after correction for propagation loss. The methods for obtaining the transmission antenna pattern of the interference source include: matching it with a pre-built electromagnetic interference source database, retrieving the equipment model and transmission antenna pattern parameters of the interference source based on its geographical location or frequency characteristics; or, when specific parameters cannot be obtained, approximating it using the default pattern of an omnidirectional antenna. 2. Signal propagation path loss determination: Based on the location information of each interference source and the geographical location of the radio telescope, the signal propagation path loss from each interference source to the feed surface of the radio telescope is determined using a preset radio wave propagation model. The specific implementation includes: First, based on the latitude, longitude, and altitude coordinates of the interference source and the radio telescope, the straight-line distance, azimuth, and elevation angle between the two points are calculated; second, combined with the site topographic data, the topographic profile along the path connecting the interference source to the radio telescope is extracted to identify whether there is line-of-sight obstruction; finally, based on whether there is line-of-sight obstruction, a suitable radio wave propagation model is selected for calculation. Under line-of-sight conditions, a free-space propagation model is used to calculate the path loss; under non-line-of-sight conditions, a diffraction model (such as a knife-edge diffraction model) combined with topographic profile data is used to calculate the diffraction loss, while considering the atmospheric absorption factor, to finally obtain the total propagation path loss. 3. Obtaining the receiving antenna gain: Based on the observation direction of the radio telescope, the receiving antenna gain corresponding to the direction of each interference source is obtained from the antenna pattern data.The specific implementation includes: First, converting the spatial orientation (azimuth and elevation) of the interference source relative to the radio telescope into the coordinate system used by the antenna pattern data; second, locating the gain value corresponding to the direction of the interference source in the three-dimensional spherical data of the antenna pattern based on the current observation direction of the radio telescope; if the antenna pattern data consists of discrete sampling points, using bilinear interpolation or spherical spline interpolation to obtain the gain value in the non-sampling direction; if the radio telescope is in a tracking observation state, dynamically updating the observation direction according to the time series and retrieving the receiving antenna gain at the corresponding moment in real time. 4. Interference power component calculation and accumulation: Based on the transmit power of each interference source, the transmission antenna pattern of the interference source, the signal propagation path loss, and the corresponding receiving antenna gain, calculate the interference power component coupled to the antenna feed of each interference source. After calculating the interference power component of each interference source, accumulate the power components of all interference sources to obtain the total interference power. The accumulation method uses linear summation, that is, under incoherent conditions, the power components of each interference source are directly added together. 5. Based on the total interference power, predict the parameter loss of the radio telescope affected by the interference through a preset parameter calculation model. The parameter loss includes the sensitivity loss and / or the signal-to-noise ratio decrease.

[0132] In this embodiment of the disclosure, after generating a three-dimensional spectral situation map covering the target area, the three-dimensional spectral situation map can be further used for time-frequency domain analysis to identify occasional interference signals and predict secure communication windows. In some embodiments, based on the three-dimensional spectral situation map, a preset interference threshold algorithm is used to identify occasional interference signals, which are interference signals with random characteristics in the time domain; the Monte Carlo method is used to perform statistical analysis on the identified occasional interference signals to predict observable time windows within the target observation frequency band, where the observable time window is a continuous period of time during which the signal strength of the occasional interference signal is lower than a preset threshold.

[0133] Intermittent interference signals refer to electromagnetic interference signals that exhibit intermittent, random, and sudden characteristics in the time domain. This type of interference does not have a periodic or continuous stable pattern; its occurrence time, duration, and signal strength are all uncertain. It usually originates from non-continuously operating electronic devices, mobile communication terminals, frequency hopping devices, or other random radiation sources.

[0134] Because sporadic interference signals exhibit significant randomness, non-periodicity, and transient characteristics in the time domain, traditional detection methods based on fixed thresholds or average power are prone to missed detections or false alarms. This embodiment employs a preset interference threshold algorithm, combined with spatiotemporal data from a three-dimensional spectral situation map, for dynamic identification. An interference threshold algorithm is a mathematical decision-making method used to distinguish normal background signals from abnormal interference signals in electromagnetic spectrum data. This algorithm, based on preset signal strength thresholds, statistical characteristics, or machine learning models, classifies signals exceeding or conforming to specific criteria as interference. For sporadic interference signals, this algorithm typically incorporates abrupt change detection or outlier analysis in the time dimension.

[0135] Given the random nature of sporadic interference signals, deterministic models struggle to accurately predict their future behavior. This embodiment introduces the Monte Carlo method, employing large-scale random sampling and statistical simulation of a historical set of sporadic interference events. The Monte Carlo method is a numerical computation method based on random sampling that approximates the statistical characteristics of complex systems by repeatedly simulating the possible values ​​of random variables. In this scheme, it is used to model and simulate the random arrival time, duration, and intensity distribution of sporadic interference signals, thereby predicting their statistically significant behavior.

[0136] Based on various evolution scenarios obtained from Monte Carlo simulations, channel availability within the target observation frequency band is statistically analyzed to predict the observable time window. The observable time window refers to a continuous period within the target observation frequency band where the intensity of occasional interference signals is below a preset threshold. This window represents the time interval during which the radio telescope can conduct effective observations without being affected by interference, and is a crucial basis for observation plan scheduling.

[0137] The preset threshold refers to the threshold for the strength of interference signals that is set in advance based on the interference protection requirements of the radio telescope or the signal-to-noise ratio requirements of the observation mission. When the interference signal strength is higher than the threshold, the interference is considered to affect the observation quality; when it is lower than the threshold, the interference is considered acceptable or negligible.

[0138] In summary, the embodiments disclosed herein aim to address the following key technical issues: how to achieve fusion analysis of spectral situation and antenna pattern data under the combined effects of broadband, multi-frequency, multi-interference sources, and far-field sidelobe levels, and to predict their negative impact on telescope sensitivity. Far-field sidelobe noise (FSSN) is a potential key factor affecting the sensitivity of radio telescope systems. The total power of interference signals coupled into the receiving system from the far-field sidelobe constitutes a dynamically affecting factor on sensitivity, which is closely related to the real-time changing far-field sidelobe gain and the anisotropic electromagnetic field background. It is an important component that still needs improvement in current sensitivity theoretical calculations. Especially for large-aperture radio telescopes in the hundreds of meters range, their far-field sidelobe gain is high, the introduced interference is stronger, and the resulting sensitivity changes are more significant. It is necessary to introduce background electromagnetic field data for dynamic evaluation.

[0139] To this end, this disclosure presents a three-dimensional spectral situation and telescope pattern joint analysis system. Under the three-dimensional electromagnetic field background formed by the combined effects of multiple interference sources, this system predicts when potential interference exceeds a preset threshold and analyzes the potential decrease in telescope sensitivity based on the sensitivity of the observed target to the interference signal. This provides decision support for the observation planning of newly constructed telescopes, aiming to achieve the optimal scientific input-output ratio. Furthermore, this disclosure combines spectral situation data with the radiation pattern function of the radio telescope antenna to construct a three-dimensional variation equation covering the telescope's observation frequency, tracking trajectory, and the influence of the electromagnetic background environment. This allows for the study of potential signal-to-noise ratio or sensitivity loss during observation and tracking. The fusion analysis of spectral situation and antenna pattern in different directions is the most important step in predicting signal-to-noise ratio changes and improving scientific output. Therefore, this technological breakthrough is expected to ultimately provide standard analysis services for all future large radio telescopes.

[0140] In some embodiments, during the data acquisition phase, a commercial spectrum analyzer is first used to capture broadband radio signals during broadband monitoring. Then, a software radio terminal and a radio frequency analyzer are used to collect data in the frequency band to obtain information on its duration of existence. Finally, the power density information of the interference signal is calculated by taking advantage of the high sensitivity of the commercial spectrum analyzer.

[0141] From the perspective of radio astronomy observation, relevant frequency bands can be divided into four categories: The first category is radio astronomy protected frequency bands, which are frequency bands planned for radio astronomy use by the International Telecommunication Union (ITU) and the State Radio Regulatory Commission (SART), such as the neutral hydrogen 1420MHz band. It is necessary to investigate whether there are interfering signals occupying these bands, and the interfering sources must be removed through law enforcement. The second category is the main radio astronomy observation frequency bands, which are frequency bands that are not explicitly allocated for radio astronomy use but have important scientific research value, such as the L-band and C-band. Their available bandwidth should be examined, and their sensitivity and other indicators should be analyzed in conjunction with other indicators of the 120-meter radio telescope. The third category is the main interference or already used frequency bands, which are the existing interference sources such as WiFi and 5G around the site. The corresponding interference thresholds given in ITU-R Recommendation RA.769 should be evaluated. The fourth category is unused frequency bands, which are blank frequency bands discovered during spectrum measurement. The feasibility of their use should be considered by using the sporadic signal detection method.

[0142] Please refer to Figure 2 This diagram illustrates a classification of observed data according to an exemplary embodiment of this disclosure. As shown, frequency bands are divided into four categories: guard bands, primary observation bands, interfering or occupied bands, and idle bands. For guard bands, channel occupancy is examined, and illegal interference sources are removed. For primary observation bands, available bandwidth and sensitivity analysis are performed, time-frequency distribution analysis of occasional interference is conducted, and interference source location is implemented. For interfering or occupied bands, impact analysis is performed, strong interference sources are removed according to relevant standards, and available bandwidth analysis is conducted. For idle bands, available bandwidth analysis is performed, time-frequency distribution analysis of occasional interference is conducted, and frequency band availability is considered.

[0143] In some embodiments, in terms of constructing a spectrum situation map, based on UAV detection data, three-dimensional spatial electromagnetic field data within the target area is acquired, continuous three-dimensional spatial spectrum situation data is constructed through interpolation methods, and the propagation data is corrected by integrating factors such as terrain information and propagation correction factors, ultimately forming a three-dimensional spectrum situation map that integrates interference frequency, terrain and spatial information.

[0144] In terms of detection methods, a UAV equipped with multiple broadband antennas and a software-defined radio terminal is used to achieve precise location of interference sources and detection of spatial electromagnetic fields. It possesses functions such as rapid direction finding and positioning during movement, spatial electromagnetic field sensing and recording, and real-time data transmission. During flight, the UAV platform records the following data in real time: real-time spectrum data, real-time altitude data (accuracy up to 0.1 meters), and real-time position coordinates (accuracy up to 1 meter). These data are aggregated to form a spatial three-dimensional measured sparse dataset. Based on this sparse dataset, missing spatial points are interpolated and filled in, thus constructing a uniformly spaced spatial spectrum dataset.

[0145] Please refer to Figure 3 The figure illustrates a schematic diagram of the electromagnetic spectrum map construction process provided by an exemplary embodiment of this disclosure. As shown, the electromagnetic spectrum map construction process includes: acquiring sensing data from multiple spatial points within a target area through node sensing; combining terrain information and node location information; performing interpolation estimation and data fusion processing on the acquired sensing data; and finally generating an electromagnetic spectrum map at time t. This process realizes the construction of a continuous three-dimensional spectrum situation from discrete monitoring data, providing a dynamic and complete spatial electromagnetic environment characterization for subsequent interference analysis.

[0146] In some embodiments, for electromagnetic compatibility (EMC) testing of equipment within the station, a detection scheme combining multiple electric field probes and an EMC spectrum analyzer is employed. The electric field probes cover the entire observation frequency band of the 120-meter telescope. The testing standard refers to the electromagnetic radiation limits for electronic equipment specified in GJB-151A. According to the RE102 ground equipment testing requirements, the applicable frequency range for this project is 2MHz to 18GHz, covering the observation frequency band from 1GHz to 18GHz, referencing the naval and army limits in the RE102-3 limits applicable to ground equipment. The equipment electromagnetic radiation test is primarily conducted at a distance of 1 meter between the radiating equipment and the receiving equipment.

[0147] In some embodiments, interference sources are analyzed and processed based on relevant International Telecommunication Union (ITU) standards. Using the interference threshold given in ITU-R RA.769 Recommendation as a benchmark, the spectrum data is processed: various automatic search algorithms are studied to search for sporadic signals exceeding the interference threshold, aiming to quickly analyze sporadic interference in different frequency bands within a large amount of collected data and statistically analyze its time-frequency distribution characteristics. After obtaining the time-frequency distribution of the interference signal, the Monte Carlo method is applied to data quality assessment. Through data grouping and inverse probability theory, the confidence level of the probability of interference signal occurrence is calculated to determine whether the frequency band meets the minimum observation requirement of less than 5% interference time proportion in the ITU-R RA.1513 standard. Furthermore, electromagnetic compatibility test data is analyzed to assess whether the interference level to the telescope after shielding by the observation building and path attenuation meets the corresponding interference threshold given in ITU-R RA.769 Recommendation.

[0148] Interference threshold estimation is a key technology in radio environment quality assessment, used to determine the acceptable interference level in a radio system to ensure the reliability of communication links and the effective utilization of spectrum resources. Traditional interference threshold estimation algorithms are mainly based on mathematical models and empirical formulas, determining the threshold by analyzing the statistical characteristics of interference signals. These algorithms are simple to implement and have low computational complexity, but their accuracy and adaptability are insufficient when facing complex and dynamic radio environments. In recent years, with the development of artificial intelligence technology, machine learning methods have been widely used in interference threshold estimation. By learning the complex nonlinear relationship between interference and system performance through training data, they can adapt to dynamically changing radio environments. For example, interference threshold estimation methods based on deep neural networks can effectively handle high-dimensional data and improve estimation accuracy. They use the K-means clustering algorithm to classify spectrum data and dynamically adjust the interference threshold based on communication system performance requirements and historical data. For radio astronomy monitoring data, the frequency band to be estimated is first determined, and the observation data is randomly sampled to obtain the original data sample. Then, the sample variance and expectation are estimated, and the sample threshold is used as the interference threshold for the radio environment. Since radio astronomy data is stored in logarithmic form, logarithmic restoration is required to preserve the correlation of the original data, thereby improving the accuracy of interference threshold estimation and enabling rapid traversal of observation data based on this threshold algorithm.

[0149] In the analysis and prediction of observable time, the Monte Carlo method, as a numerical computation method based on probability and statistics theory, solves mathematical, physical, and engineering problems through random sampling and statistical simulation. Its core idea lies in constructing a probabilistic model corresponding to the actual problem and approximating the solution through random experiments and statistical estimation.

[0150] Please refer to Figure 4This illustration shows a schematic diagram of a data analysis framework based on the Monte Carlo method provided in an exemplary embodiment of this disclosure. The data analysis framework mainly includes three core steps. First, a probabilistic model is constructed, establishing a probabilistic statistical model corresponding to the actual evaluation problem, laying the foundation for subsequent stochastic simulation. Second, random sampling and experimental simulation are performed, randomly selecting relevant parameters according to a predetermined probability distribution, and generating simulated sample data through multiple independent trials. Finally, statistical estimation and result analysis are performed, statistically analyzing the simulation results to solve for the probability distribution, expected value, and confidence interval of the target parameters. This framework provides a complete numerical calculation path for assessing the probability of interference signal occurrence and predicting observable time. Monte Carlo analysis is a statistical experiment involving multiple independent trials. In each trial, all parameters are randomly selected according to a fixed, predetermined probability distribution. The data in the experimental results that do not meet the requirements reflect the probability of unqualified experiments occurring in actual situations. For radio astronomy monitoring data, the raw data is first randomly grouped, and the interference ratio of each group is statistically analyzed based on the interference threshold estimation results to obtain the expected probability of interference occurrence. Then, the relationship function between the interference probability and its confidence level is established through inverse probability theory. In conjunction with ITU-RRA.1513 recommendation, it is assessed whether the pre-observation frequency band meets the observation index requirements.

[0151] In the analysis of radio telescope sensitivity loss based on spectral situation maps, the antenna pattern can be represented as: with respect to the azimuth angle θ and elevation angle θ The gain function. Furthermore, by fusing the observation frequency f and time t dimensions, the static radiation pattern is extended into a comprehensive response function containing multi-dimensional spatiotemporal information. . Spectrum situation matrix and During the fusion analysis, an antenna pointing model is established to fuse the spatial positional relationships of the antenna under different pointing angles θ and ψ. Simplified to Construct an antenna pointing model in the spectrum situation matrix The electromagnetic background level intensity in each direction of the antenna is further calculated by interpolation and fusion at the location of the antenna.

[0152] Combined radio telescope antenna patterns, interference received by the radio telescope It is a function of observation time t and frequency f. Furthermore, t can be simplified to the sum of the power from different interference sources coupled into the antenna feed through different antenna sidelobes and lobes after path attenuation when the antenna direction is fixed. The total interference power is... The expression is as follows:

[0153]

[0154] in, The interference power received by the radio telescope from the i-th interference source is... Let be the transmission power of the i-th interference source. The radiation pattern of the transmitting antenna of the i-th interference source at frequency f is shown. This is the radiation pattern of the receiving antenna (i.e., the radio telescope). Let be the signal propagation path loss from the i-th interference source to the antenna feed port. The final signal level produced by the interference signal on the telescope depends on the following three factors: 1. The transmission power of the interference source and the radiation pattern of the transmitting antenna; 2. The relative position between the interference source and the telescope antenna. and The accurate value depends on the movement of the antenna. It is variable; 3. The propagation attenuation of interference signals at different frequencies is related to parameters such as propagation model, scattering, diffraction, and direct attenuation caused by terrain factors.

[0155] Based on the total interference power mentioned above, and referring to the relationship model between far-field sidelobe noise and sensitivity loss, the formula for the relationship between sensitivity loss and the influence of sidelobe level can be obtained:

[0156]

[0157] in, This is the normalized gain value (in dB) of the radio telescope antenna pattern at the incident angle of the interference source. Its value is usually negative and represents the degree of attenuation of the interference signal by the antenna sidelobes. The calculated sensitivity loss rate (in %) indicates the percentage decrease in the telescope's ability to detect weak cosmic signals under current interference conditions. This formula allows for the calculation and analysis of the sensitivity loss of a radio telescope under given interference conditions. In actual measurements, the calculation results can be verified and analyzed by observing standard sources (such as high-powered radio sources 3C295 and 3C286).

[0158] In some embodiments, the method provided in this disclosure relies on four platforms working collaboratively, such as... Figure 5 As shown, the four platforms include a radio environment monitoring platform, an EMC detection platform, a UAV detection platform, and a data analysis platform. The radio monitoring platform is used for space electromagnetic monitoring, the EMC detection platform for on-site electromagnetic detection, the UAV detection platform for three-dimensional spectrum situational awareness, and the data analysis platform for data processing and evaluation based on relevant criteria. These four platforms work collaboratively from the perspectives of space electromagnetic environment monitoring, on-site equipment electromagnetic radiation detection, regional three-dimensional spectrum situational awareness, and comprehensive data analysis, providing complete platform support for spectrum monitoring and interference impact prediction. The four platforms will be introduced in turn below.

[0159] In some embodiments, the radio environment monitoring platform mainly consists of a test antenna, a preamplifier, a filter, a spectrum analyzer, a controllable turntable, a noise source, and a computer. A schematic diagram of the system structure of the radio environment monitoring platform is shown below. Figure 6 As shown in the diagram. The power supply modules provide +12V, +24V, and +5V voltages to power the various components within the platform. The radio signal received by the test antenna is selected by a microwave switch, pre-amplified by a low-noise amplifier, and then processed by a filter before being input to a spectrum analyzer for spectrum analysis. A controllable turntable receives antenna control signals, enabling precise control of the antenna's pointing. The noise source is controlled by a switch and can be used for system calibration and performance testing. The computer sends control signals to each component of the platform and collects test data output from the spectrum analyzer, achieving automated control and data processing throughout the entire monitoring process.

[0160] In some embodiments, the functional block diagram of the automated spectrum monitoring software is as follows: Figure 7 As shown in the diagram, the automated spectrum monitoring software mainly includes a preset information input module, a file writing module, a data processing module, and a data display and storage module. The preset information input module is used to configure the spectrum analyzer settings, and the file writing module is used to record test instruction files. The spectrum analyzer receives control commands from the front panel and inputs the spectrum analyzer data into the data processing module. The data processing module performs data integration on the received data, generating maximum and average value data, and supports both waterfall chart and instantaneous spectrum graph displays. The data display and storage module stores the processed test data to the hard drive, achieving persistent management of the test data. This functional block diagram realizes a fully automated spectrum monitoring function from parameter configuration, data acquisition, data processing to data display and storage.

[0161] In some embodiments, the main interface diagram of the host computer software is as follows: Figure 8 As shown. The main interface of the host computer software is divided into three parts from top to bottom. Basic Parameter Setting Area (top right): Used to configure the core parameters of the test task, including observation frequency band, direction, polarization mode, integration time, and frequency resolution. Real-time Waterfall Plot Display Area (middle): Displays two modes of spectrum waterfall plots side-by-side; the left side shows the average value waterfall plot, and the right side shows the maximum value waterfall plot, visually presenting the signal's changes over time in a three-dimensional (frequency-time-amplitude) format. Real-time Spectrum Plot Display Area (bottom): Synchronously displays the spectrum curve at the current moment, where the red spectral line represents the maximum value spectrum, and the blue spectral line represents the average value spectrum, used for precise analysis of the instantaneous and average intensity of the signal.

[0162] A detection scheme combining multiple electric field probes and an EMC spectrum analyzer was employed. Firstly, five broadband near-field probes covered the entire range of the 120-meter radio telescope. The performance specifications of the electric field probes are shown in Table 1. The main parameters of the EMC receiver are shown in Table 2.

[0163] Table 1

[0164]

[0165] Table 2

[0166]

[0167] A schematic diagram of the structure of the EMC probe kit and its user interface with the host computer spectrum monitoring software is shown below. Figure 9 As shown, five different probes with different functions are displayed: P1 (Large Loop Probe): for comprehensive EMC testing of large equipment (such as new energy vehicles and medical devices); P2 (Large-Range Radiation Probe): for rapid scanning of large-range radiated interference at the PCBA board level; P3 (Small-Range Radiation Probe): for precise location of radiation sources in specific unit circuits on a circuit board; P4 (Component Leakage Probe): for detecting electromagnetic leakage in individual electronic components; and P5 (Precision Positioning Probe): for precisely locating EMC leakage points within a 1mm range (such as component pins). This kit enables full-scale EMC fault diagnosis from the system level to the component level. The user interface of the host computer spectrum monitoring software accompanying the EMC probes mainly includes: Parameter Setting Area (left): provides configuration functions for key test parameters such as center frequency, sweep width, and resolution bandwidth; Spectrum Display Area (center): displays the spectrum of the tested signal in real time in a graphical manner, where the green curve is the trace of signal amplitude changing with frequency, and the vertical cursor is used to accurately read the frequency and amplitude values ​​of specific interference signals; and Data List Area (right): displays the peak signal data in the spectrum graph in list form. The software enables visualization, parameterized control, and quantitative analysis of the signals acquired by the probe.

[0168] The UAV detection platform includes monitoring and direction-finding equipment, a UAV platform, a remote control, and a monitoring PC. The monitoring equipment includes an antenna array, a monitoring and direction-finding module, and a digital signal processing board. After interference signals are collected by the antenna, the interference source is located using relevant interferometric direction-finding techniques and intersection positioning methods. A schematic diagram of the internal modules of the UAV platform is shown below. Figure 10As shown in the diagram, the platform is powered by a central power module and divided into four core functional areas: 1. Detection Module: Responsible for collecting flight status and environmental data, it contains an inertial measurement unit, barometer, gyroscope, and GPS unit. 2. Control Unit: As the "brain" of the UAV, it receives data from the detection module, processes flight attitude through its internal flight control subunit, and exchanges data with the outside world through the communication unit (including a data transmission module). 3. External Mounted Unit: Used to carry mission payloads; the diagram shows a camera and a monitoring and direction-finding device. The monitoring and direction-finding device is the core component for performing interference source localization tasks. 4. Drive Module: Responsible for providing flight power, consisting of four independent motors (motors 1 to 4). The arrows in the diagram clearly show the control and data flow between the modules: data from the detection module is input to the control unit; the control unit sends power control commands to the externally mounted monitoring and direction-finding device and receives its feedback; simultaneously, the control unit also directly drives the four motors in the module to achieve precise control of the UAV.

[0169] The UAV detection platform primarily has two functions: one is interference source direction finding: after the interference signal is collected by the antenna, the interference source is located using relevant interferometric direction finding techniques and intersection positioning methods. The other is electromagnetic situation analysis: utilizing the flexibility of UAVs and integrating geolocation capabilities, the interference signal levels within the flight area are tested and stored, and the flight trajectory is fused to form a spatial electromagnetic situation map, thereby determining the influence range of the interference source.

[0170] The data analysis platform integrates various radio frequency interference (RFI) identification algorithms, primarily including interference search threshold algorithms and Monte Carlo statistical evaluation models. Among these, threshold analysis is widely used in engineering practice due to its simplicity and high detection efficiency. Its theoretical basis lies in the fact that in radio astronomy observations, the intensity of man-made radio frequency interference signals from Earth is usually much higher than that of natural celestial radiation signals; therefore, effective identification of interference can be achieved by setting a reasonable intensity threshold. Currently, the mainstream threshold detection methods can be divided into three categories: Cumulative Sum (CUSUM), Simple Thresholding, and Combinatorial Thresholding.

[0171] The CUSUM algorithm dynamically estimates the discrimination threshold based on the cumulative statistical characteristics of the sample sequence (such as changes in mean or variance), and classifies data segments exceeding the threshold as RFI. This algorithm has low computational complexity and strong real-time performance, making it suitable for quickly screening suspicious time periods; however, its temporal resolution is limited, and it can only locate the approximate range of interference, making it difficult to accurately capture the start and end times of interference. Therefore, it is often used as a preprocessing module, in conjunction with subsequent high-precision algorithms for secondary confirmation.

[0172] Simple thresholding methods use the median of each row or column of data as a dynamic threshold, offering excellent computational efficiency. However, because they rely on instantaneous amplitude judgment, they are prone to missing low-amplitude components such as rising or falling edges when dealing with transient interference, leading to missed detections of adjacent sampling points. Given the spatial correlation of RFI, this type of "peak-sensitive" detection mechanism may affect the overall integrity of the identification.

[0173] The combined thresholding method introduces a sliding window and a multi-level decision mechanism, which improves the robustness of identification by combining the statistical features of multiple sampling points, effectively mitigating the false alarm problem of the simple thresholding method. However, the superposition of multiple conditions also brings the risk of increased false alarm rate, that is, some normal signals may be misjudged as interference.

[0174] In summary, the core idea of ​​various threshold algorithms is consistent: compare the received signal strength with a preset or dynamically generated threshold, and consider signals exceeding the threshold as interference signals. The key challenge lies in how to scientifically and adaptively determine the optimal threshold, thereby controlling the false alarm rate while ensuring the detection rate, and ultimately achieving effective separation of radiofrequency inversions (RFIs).

[0175] In practical applications, the threshold estimation algorithm first outputs the interference threshold for the target frequency band, which is then fed into the Monte Carlo statistical model as an input parameter to quantitatively evaluate the suitability of the spectrum data. Furthermore, each spectrum is treated as an independent experiment, assuming each spectrum contains j frequency points. If the proportion of frequency points with amplitudes exceeding the interference threshold is less than 5%, the spectrum is deemed to meet the radio astronomy observation quality requirements and is marked as "qualified"; otherwise, it is marked as "unqualified." By performing such Monte Carlo experiments on a large amount of historical or real-time spectrum data, it can be statistically determined that there are n unqualified samples out of N samples, thereby assessing the purity of the current electromagnetic environment or the system's anti-interference capability.

[0176] Figure 11 This diagram illustrates a threshold-based interference signal separation process provided in an exemplary embodiment of this disclosure. The diagram demonstrates a complete processing flow for automatically filtering and classifying time-series observation data using interference thresholds. This process separates continuous observation timelines (…). to The data is divided into N independent data sample groups. The data processing steps are as follows: 1. Data input: The system receives N sets of raw observation data, each set containing multiple frequency points ( to 1. Intensity value of ). 2. Threshold comparison: The preset or dynamically calculated interference threshold sequence ( to The following steps are applied to each data set: The logical judgment function `ans` compares the intensity value of each frequency point with the corresponding threshold: if the intensity value does not exceed the threshold, it is marked as "0" (representing a valid signal); if it exceeds the threshold, it is marked as "1" (representing an interference signal). 3. Result Statistics and Classification: The percentage of frequency points marked as "1" in each data set is counted. If the percentage of interference frequency points is greater than 5%, the data set is determined to be a "non-compliant sample" and classified as interfered data (n sets in total). If the percentage of interference frequency points is less than or equal to 5%, the data set is determined to be a "successful sample" and classified as usable data (Nn sets in total).

[0177] In some embodiments, the process of constructing a three-dimensional spectral situation map includes: first, acquiring a measured sparse dataset A. i (f i ,h i ,p i ), where f i Represents frequency, h i p represents height. i This represents geographic location coordinates. For missing data points not covered in the spatial data, an interpolation algorithm is used to fill in the gaps, thus constructing a uniformly spaced spatial spectrum dataset. .

[0178] From a mathematical perspective, this process is equivalent to the matrix filling problem, which involves reconstructing a complete matrix from a low-rank matrix with missing elements. Theoretically, matrix filling is an unsteady problem, meaning that without additional constraints, there are infinitely many solutions to recover the original matrix using only a small number of sampled elements. However, when the target matrix has low-rank or near-low-rank properties, accurate recovery can be achieved through optimization algorithms. Based on the above theory, a frequency point f is constructed. i Corresponding spectral situation matrix The calculation formula is shown below:

[0179]

[0180] Where M represents the number of rows in the matrix, corresponding to the number of sampling points divided in one dimension; N represents the number of columns in the matrix, corresponding to the number of sampling points divided in another dimension; M and N together form a uniform spatial grid, with each grid cell corresponding to a discrete sampling point. This represents the spectral data corresponding to the sampling point located at the M-th row and N-th column of the matrix; This represents the height of the sampling point located at the M-th row and N-th column of the matrix; This represents the geographical coordinates of the sampling point located at the M-th row and N-th column of the matrix.

[0181] Based on a preset spectral resolution, a series of spectral situation matrices at different frequencies can be constructed sequentially, and then stacked to form a three-dimensional "space-frequency" situation map. It should be noted that electromagnetic propagation exhibits significant local characteristics in areas with complex terrain. Therefore, a terrain correction factor must be introduced during the construction process, or interpolation calculations must be re-performed in local areas to ensure the accuracy of the situation map in complex environments.

[0182] In an illustrative example, a schematic diagram of the electromagnetic situation map test results is shown below. Figure 12 As shown in the figure, this map illustrates a two-dimensional geospatial spectral distribution map reconstructed from sparse measured data. The map uses interpolation and matrix filling algorithms to reconstruct the spectral data A obtained from discrete sampling points (such as fixed monitoring stations and drones shown on the left side of the map). i (f i ,h i ,p i This is expanded into a continuous rasterized dataset P(f) covering the entire target area. i The figure uses a heatmap format to visually represent a specific frequency point f. i The spatial distribution of signal strength is shown below: The horizontal and vertical axes represent east longitude and north latitude, respectively, precisely defining the geographical range. Color mapping: The color bars on the right display signal strength in dBμV; blue areas represent low-field-strength background noise, while yellow to red areas represent high-field-strength interference sources or radiation hotspots. Spatial characteristics: Multiple localized high-intensity core areas (orange-red areas) are visible in the map, indicating the presence of significant ground-based radiation sources; these are accompanied by irregular diffusion patterns, reflecting the influence of topography on electromagnetic wave propagation and localized situational characteristics. This map serves as a fundamental slice for constructing a three-dimensional "space-frequency" electromagnetic situational awareness map, supporting the generation of multi-layer matrices according to different frequency sequence, thereby achieving comprehensive perception and dynamic visualization assessment of the electromagnetic environment under complex conditions.

[0183] The calculation of electromagnetic shielding effectiveness is used to quantitatively evaluate the effectiveness of electromagnetic shielding measures and guide the optimized design of shielding parameters to ensure that the radiation of equipment within the site meets the radio astronomy protection thresholds stipulated by the International Telecommunication Union. The propagation loss of electromagnetic waves in free space is calculated as follows:

[0184]

[0185] in, Propagation loss is expressed in decibels (dB). The operating frequency is expressed in megahertz (MHz). The distance to be transmitted is measured in kilometers (km).

[0186] Electromagnetic waves attenuate when passing through a shield. To address electromagnetic radiation from equipment within a station, attenuation is achieved by implementing attenuation measures along the propagation path (such as shielded rooms) to reduce the signal strength below a threshold. This is achieved by increasing shielding to improve attenuation. The absorption attenuation is first calculated, and it primarily depends on the thickness and electromagnetic properties of the shielding material. The calculation formula is as follows:

[0187]

[0188]

[0189] in, The absorption attenuation is expressed in decibels (dB). The thickness of the metal shield is expressed in millimeters (mm). The operating frequency is expressed in megahertz (MHz). It is the relative magnetic permeability of the material. Electrical conductivity relative to copper.

[0190] Then, the reflection attenuation is calculated. Reflection attenuation mainly occurs at the interface between the air and the shield. The calculation formula is as follows:

[0191]

[0192] in, This represents the reflection attenuation, measured in decibels (dB).

[0193] Suppose that the interference radiated power measured at a certain frequency point on the EMC platform is... The power after path attenuation and absorption / reflection attenuation is :

[0194]

[0195] By iteratively adjusting the shielding parameters, namely changing the relative magnetic permeability and conductivity of the material, the final received power can be adjusted. The data is kept below the interference protection threshold for radio astronomy observations specified in ITU-R Recommendation RA.769 to ensure the purity of astronomical observation data.

[0196] In summary, this embodiment of the invention also utilizes an unmanned aerial vehicle (UAV) detection platform to perform gridded or path-scanning flights within the target area. This fully leverages the high maneuverability and autonomous flight capabilities of UAVs, overcoming the limitations of traditional ground-based monitoring methods in complex terrain conditions, and achieving comprehensive, systematic, and refined electromagnetic spectrum data acquisition of the target area. During this process, the acquired measured data not only covers key parameters such as frequency and signal strength but also precisely correlates the spatial location information of each acquisition point, thereby transforming traditional discrete and fragmented monitoring data into a multidimensional dataset with three-dimensional distribution characteristics. This approach significantly improves the efficiency and security of data acquisition in wide-area or high-risk environments, ensuring the consistency and repeatability of the acquisition process, and provides a complete and reliable data foundation for subsequently constructing a high-precision three-dimensional spectrum situation.

[0197] This embodiment of the disclosure also effectively eliminates the actual impact of factors such as terrain obstruction and multipath effects on signal strength by introducing site topographic data of the target area and combining it with a preset radio wave propagation model to correct the propagation loss of the measured discrete point data during the generation of the three-dimensional spectrum situation map. This makes the corrected data more realistically reflect the intrinsic distribution of the electromagnetic environment. On this basis, spatial interpolation or matrix filling algorithms are further used to expand the discrete point data into continuously distributed gridded data, thereby solving the contradiction between the sparseness of measured points and the requirement for continuity in visualization, and realizing an effective estimation of the electromagnetic distribution in unmeasured areas. The final generated three-dimensional spectrum situation map covering the entire target area not only realizes an intuitive and three-dimensional presentation of the electromagnetic environment, but also provides high-precision decision support for subsequent frequency conflict analysis, site selection optimization, and dynamic scheduling of spectrum resources.

[0198] This disclosure also overcomes the limitations of traditional electromagnetic interference assessments, which only consider the presence or simple superposition of interference sources, by fusing and analyzing three-dimensional spectral situation maps and antenna pattern data in different spatial directions. This achieves refined modeling of the interference coupling process. Furthermore, by calculating the total interference power coupled into the radio telescope antenna feed from different spatial directions, the spatial filtering characteristics of the antenna pattern can be fully considered, i.e., the physical fact that different directions of incoming waves have different gain responses. This ensures that the interference assessment results are highly consistent with the actual electromagnetic environment of the receiver. Based on this, the performance variation of the radio telescope is predicted based on the total interference power, directly linking the external electromagnetic environment assessment with the telescope's own performance indicators. This provides a quantitative decision-making basis for the formulation of radio telescope observation plans, interference avoidance, and the selection of protective measures.

[0199] This disclosure further refines the calculation process for the total interference power, establishing a complete calculation link from interference source identification to interference component accumulation. At least one interference source is identified from the three-dimensional spectral situation map, and its transmit power, location, and transmit antenna pattern are obtained. The propagation loss of the signal reaching the feed aperture is determined using a radio wave propagation model. Then, based on the real-time observation pointing of the telescope, the receive gain in the corresponding direction is extracted from the antenna pattern data. Finally, the interference power components coupled to the feed from each interference source are calculated and accumulated to obtain the total power. This process fully considers key factors that significantly influence interference assessment, namely the transmission characteristics of the interference source, spatial propagation loss, terrain obstruction effects, and the telescope's own directional response. This ensures the high accuracy and reliability of the calculated total interference power, avoiding evaluation biases that may arise from traditional simplified models.

[0200] This disclosure also maps the total interference power to a parameter loss, transforming the abstract impact of electromagnetic interference into a quantifiable and comparable performance indicator. Furthermore, based on a pre-defined parameter calculation model, the sensitivity loss and / or signal-to-noise ratio (SNR) reduction of the radio telescope are predicted. These two parameters directly relate to the core observational capabilities of the radio telescope—sensitivity determines the weakness of detectable signals, while the SNR directly affects the quality and reliability of the observation data. By quantifying the degree of loss in these key performance indicators, the impact level of interference on actual astronomical observation tasks can be intuitively reflected. This provides a clear numerical basis for selecting observation windows, adjusting observation durations, and setting thresholds for interferometry suppression measures, thus elevating interference impact assessment from qualitative description to quantitative analysis.

[0201] This disclosure also provides effective analysis and countermeasures for sporadic interference in radio astronomy observations. By employing a preset interference threshold algorithm to identify sporadic interference signals with random temporal characteristics from a three-dimensional spectral situation map, highly uncertain random interference can be separated from the complex electromagnetic environment. Based on this, the Monte Carlo method is used to statistically analyze the identified sporadic interference signals. By simulating the probability distribution of interference occurrence through extensive random sampling, the observable time window when the interference signal intensity within the target observation frequency band is below the preset threshold can be predicted. This process transforms the random interference problem into a probabilistic statistical problem, enabling astronomical observers to determine suitable observation time intervals in advance, rationally arrange observation tasks, effectively reduce the impact of sporadic interference on the observation plan, and improve the actual observation efficiency and data utilization of radio telescopes.

[0202] The following are device embodiments of the present disclosure. For parts not described in detail in the device embodiments, please refer to the technical details disclosed in the above method embodiments.

[0203] Please refer to Figure 13 This illustration shows a schematic diagram of a spectrum monitoring and interference impact prediction device provided in an exemplary embodiment of the present disclosure. The device can be implemented, in whole or in part, through software, hardware, or a combination of both, as a computing device. The device includes: a first acquisition module 1310, a generation module 1320, a second acquisition module 1330, and a prediction module 1340.

[0204] The first acquisition module 1310 is used to acquire electromagnetic spectrum measured data of the target area. The electromagnetic spectrum measured data is used to indicate the electromagnetic signal spectrum characteristics of multiple spatial points within the target area.

[0205] The generation module 1320 is used to generate a three-dimensional spectrum situation map covering the target area based on the measured electromagnetic spectrum data. The three-dimensional spectrum situation map is used to indicate the intensity distribution of electromagnetic signals of different frequencies in space.

[0206] The second acquisition module 1330 is used to acquire the antenna pattern data of the radio telescope. The antenna pattern data is used to indicate the characteristics of the antenna gain as a function of spatial direction.

[0207] The prediction module 1340 is used to fuse and analyze the three-dimensional spectrum situation map and antenna pattern data in different spatial directions to predict the degree of performance change of the radio telescope affected by interference.

[0208] In one possible implementation, the first acquisition module 1310 is further configured to:

[0209] By using an unmanned aerial vehicle (UAV) detection platform to conduct gridded or path-scanning flights within the target area, electromagnetic spectrum measurement data is obtained. The electromagnetic spectrum measurement data includes frequency information, location information, and signal strength information of multiple spatial points within the target area.

[0210] In another possible implementation, the generation module 1320 is also used for:

[0211] Based on measured electromagnetic spectrum data, combined with the site topography data of the target area and the preset electromagnetic wave propagation model, propagation loss is corrected to obtain the corrected discrete point spectrum data.

[0212] Discrete point spectral data is expanded into continuously distributed gridded data through a preset spatial interpolation or matrix filling algorithm.

[0213] Based on gridded data, a three-dimensional spectral situation map covering the target area is generated.

[0214] In another possible implementation, the prediction module 1340 is also used for:

[0215] By fusing and analyzing the three-dimensional spectrum situation map and antenna pattern data in different spatial directions, the total interference power coupled into the antenna feed of the radio telescope from different spatial directions is calculated.

[0216] Based on the total interference power, predict the degree of performance change of the radio telescope affected by the interference.

[0217] In another possible implementation, the prediction module 1340 is also used for:

[0218] Identify at least one interference source from the three-dimensional spectrum situation map, and obtain the transmission power, location information and transmission antenna pattern of each interference source;

[0219] Based on the location information of each interference source and the geographical location of the radio telescope, the signal propagation path loss of each interference source to the feed aperture of the radio telescope is determined by a preset radio wave propagation model.

[0220] Based on the observation direction of the radio telescope, the receiving antenna gain corresponding to the direction of each interference source is obtained from the antenna pattern data;

[0221] Based on the transmit power of each interference source, the transmit antenna pattern of the interference source, the signal propagation path loss, and the corresponding receive antenna gain, calculate the interference power component coupled to the antenna feed for each interference source.

[0222] The total interference power is obtained by summing the interference power components of each interference source.

[0223] In another possible implementation, the prediction module 1340 is also used for:

[0224] Based on the total interference power, the parameter loss of the radio telescope due to interference is predicted by a preset parameter calculation model. The parameter loss includes sensitivity loss and / or signal-to-noise ratio reduction.

[0225] In another possible implementation, the device further includes: an analysis module for:

[0226] Based on the three-dimensional spectrum situation map, a preset interference threshold algorithm is used to identify occasional interference signals, which are interference signals that exhibit random characteristics in the time domain.

[0227] The Monte Carlo method is used to statistically analyze the identified sporadic interference signals and predict the observable time window within the target observation frequency band. The observable time window is a continuous period of time during which the signal strength of the sporadic interference signal is lower than a preset threshold.

[0228] It should be noted that the above embodiments only illustrate the division of the above functional modules when implementing the device. In actual applications, the above functions can be assigned to different functional modules according to actual needs, that is, the content structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0229] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0230] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to perform the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0231] This disclosure also provides a spectrum monitoring and interference impact prediction device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above method.

[0232] This disclosure also provides a non-volatile computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described method.

[0233] This disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method.

[0234] Figure 14 This is a block diagram illustrating an apparatus 1900 according to an exemplary embodiment. For example, apparatus 1900 may be provided as a server or terminal device. (Refer to...) Figure 14 The apparatus 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by memory 1932 for storing instructions, such as application programs, that can be executed by the processing component 1922. The application programs stored in memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1922 is configured to execute instructions to perform the methods described above.

[0235] Device 1900 may also include a power supply component 1926 configured to perform power management of device 1900, a wired or wireless network interface 1950 configured to connect device 1900 to a network, and an input / output interface 1958 (I / O interface). Device 1900 can operate on an operating system, such as Windows Server, stored in memory 1932. TMmacOS X TM Unix TM Linux TM FreeBSD TM Or similar.

[0236] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions that can be executed by a processing component 1922 of the device 1900 to perform the above-described method.

[0237] Computer-readable storage media can be tangible devices capable of holding and storing programs / instructions used by instruction execution devices. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0238] The computer program (or computer-readable program instructions) described herein can be downloaded from a computer-readable storage medium to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage medium in the respective computing / processing device.

[0239] The computer program (or computer program instructions) used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions to implement various aspects of this disclosure.

[0240] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0241] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0242] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0243] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0244] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for spectrum monitoring and interference impact prediction, characterized in that, The method includes: Acquire measured electromagnetic spectrum data of the target area, wherein the measured electromagnetic spectrum data is used to indicate the electromagnetic signal spectrum characteristics of multiple spatial points within the target area; Based on the measured electromagnetic spectrum data, a three-dimensional spectrum situation map covering the target area is generated. The three-dimensional spectrum situation map is used to indicate the intensity distribution of electromagnetic signals of different frequencies in space. Acquire antenna pattern data of a radio telescope, the antenna pattern data being used to indicate the characteristics of antenna gain variation with spatial direction; By fusing and analyzing the three-dimensional spectral situation map and the antenna pattern data in different spatial directions, the extent of performance changes of the radio telescope affected by interference can be predicted.

2. The method according to claim 1, characterized in that, The acquisition of measured electromagnetic spectrum data for the target region includes: The electromagnetic spectrum measurement data is obtained by using a drone detection platform to perform gridded or path-scanning flights within the target area. The electromagnetic spectrum measurement data includes frequency information, location information, and signal strength information of multiple spatial points within the target area.

3. The method according to claim 1, characterized in that, The step of generating a three-dimensional spectrum situation map covering the target area based on the measured electromagnetic spectrum data includes: Based on the measured electromagnetic spectrum data, combined with the site topography data of the target area and the preset radio wave propagation model, propagation loss correction is performed to obtain the corrected discrete point spectrum data. The discrete point spectral data is expanded into continuously distributed gridded data through a preset spatial interpolation or matrix filling algorithm. Based on the gridded data, a three-dimensional spectral situation map covering the target area is generated.

4. The method according to any one of claims 1 to 3, characterized in that, The step of fusing and analyzing the three-dimensional spectral situation map and the antenna pattern data in different spatial directions to predict the degree of performance change of the radio telescope due to interference includes: The three-dimensional spectrum situation map and the antenna pattern data are fused and analyzed in different spatial directions to calculate the total interference power of the antenna feed coupled into the radio telescope from different spatial directions; Based on the total interference power, predict the degree of performance change of the radio telescope affected by the interference.

5. The method according to claim 4, characterized in that, The step of fusing and analyzing the three-dimensional spectral situation map and the antenna pattern data in different spatial directions to calculate the total interference power coupled into the antenna feed of the radio telescope from different spatial directions includes: At least one interference source is identified from the three-dimensional spectrum situation map, and the transmission power, location information and transmission antenna pattern of each interference source are obtained; Based on the location information of each interference source and the geographical location of the radio telescope, the signal propagation path loss of each interference source reaching the feed aperture of the radio telescope is determined by a preset radio wave propagation model. Based on the observation direction of the radio telescope, the receiving antenna gain corresponding to the direction of each interference source is obtained from the antenna pattern data; Based on the transmit power of each interference source, the transmit antenna pattern of the interference source, the signal propagation path loss, and the corresponding receive antenna gain, calculate the interference power component coupled to the antenna feed for each interference source. The total interference power is obtained by summing the interference power components of each interference source.

6. The method according to claim 4, characterized in that, The prediction of the degree of performance change of the radio telescope affected by the interference based on the total interference power includes: Based on the total interference power, the parameter loss of the radio telescope affected by the interference is predicted by a preset parameter calculation model. The parameter loss includes sensitivity loss and / or signal-to-noise ratio reduction.

7. The method according to any one of claims 1 to 6, characterized in that, The method further includes: Based on the three-dimensional spectrum situation map, a preset interference threshold algorithm is used to identify occasional interference signals, which are interference signals that exhibit random characteristics in the time domain. The Monte Carlo method is used to perform statistical analysis on the identified sporadic interference signals to predict the observable time window within the target observation frequency band. The observable time window is a continuous period of time during which the signal strength of the sporadic interference signal is lower than a preset threshold.

8. A spectrum monitoring and interference impact prediction device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

9. A non-volatile computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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