Signal quality evaluation and interference early warning method for low-altitude electromagnetic environment

Abstract

This application belongs to the field of low-altitude electromagnetic safety and discloses a signal quality assessment and interference early warning method for low-altitude electromagnetic environments. The method includes: performing spatiotemporal alignment preprocessing on multi-source monitoring data; fusing spectral quality characteristics and interference energy characteristics to generate an LSQI (Last Level Quality Index) for assessing the ability of the current electromagnetic environment to support the safe flight of aircraft; comparing the power timing characteristics of the target signal with a preset normal fluctuation threshold; if interference is preliminarily determined, extracting the spectral characteristics of the interference signal to determine its type; generating an adaptive interference detection threshold based on historical interference detection results and current environmental parameters; and generating electromagnetic interference early warning information when interference is confirmed. This method achieves deep collaboration of multi-source data, rapidly detects and warns of interference signals, and can also assess the ability of the current electromagnetic environment to support the safe flight of aircraft.

Description

Technical Field

[0001] This application belongs to the field of low-altitude electromagnetic safety, and specifically relates to a method for signal quality assessment and interference early warning in low-altitude electromagnetic environments. Background Technology

[0002] The low-altitude electromagnetic environment typically refers to the distribution and propagation environment of electromagnetic signals in the airspace below 3000 meters above the ground. With the rapid development of the low-altitude economy, applications such as drone logistics, urban air traffic, and low-altitude patrols are increasing, leading to a continuous growth in the number and types of aircraft in low-altitude airspace. This has created an urgent need for safe monitoring of the low-altitude electromagnetic environment. Compared to the conventional ground-based electromagnetic environment, the low-altitude electromagnetic environment presents unique technical challenges, including highly heterogeneous signal types, mobile and sudden interference sources, and stringent signal quality requirements for flight safety.

[0003] Chinese patent CN120257215B discloses a method and system for multi-dimensional information fusion of unmanned aerial vehicles (UAVs) based on acoustic-optical-electric composite detection. This method generates acoustic, optical, and electrical feature streams by collecting acoustic frequency band signals, visible light image sequences, and electromagnetic field intensity change data. Based on the time deviation between low-frequency vibration modes and transient electromagnetic pulses, the method performs multi-channel dynamic calibration of image distortion regions in the optical feature stream and corrects the spatiotemporal coordinate parameters of each feature stream. After generating the calibrated feature stream, it performs anti-interference fusion processing, extracts multi-dimensional coupled features, and inputs them into the UAV flight path planning model to generate a three-dimensional spatial situation model. Its core technical path lies in achieving physical spatiotemporal consistency alignment and noise suppression of acoustic-optical-electric multimodal data, mainly serving UAV obstacle localization and dynamic trajectory prediction.

[0004] Chinese patent CN121278504B discloses a UAV radio detection system and device based on frequency hopping sequence sorting. The system includes an environmental perception and feature extraction module, an adaptive threshold decision module, a frequency hopping sequence reconstruction module, and a multi-dimensional feature fusion and recognition module. By extracting electromagnetic environment features in real time, dynamically adjusting the detection threshold, reconstructing the frequency hopping sequence, and fusing multi-dimensional features, it achieves highly reliable detection and recognition of UAV frequency hopping signals. Its technical approach focuses on the sequence reconstruction and multi-dimensional feature fusion and recognition of frequency hopping signals, and its core application is the detection and assessment of UAV frequency hopping signals.

[0005] Chinese patent CN118226474A discloses a fusion method for multiple interference monitoring strategies in GNSS / Low Earth Orbit (LEO) navigation. This method involves real-time acquisition of GNSS / LEO navigation observation data, ephemeris data, ADS-B, and AIS data, and deployment of dedicated interference monitoring equipment. Interference detection and location are performed based on GNSS / LEO navigation observation data and ADS-B / AIS data, generating interference alarm identifiers and location information. The three monitoring results are then fused to generate a fused interference monitoring result, achieving adaptive fusion processing of multiple interference monitoring strategies. The technical approach lies in the post-hoc fusion of interference monitoring results from GNSS / LEO navigation observation data and ADS-B / AIS data. Its core application is in real-time PNT (Positioning, Navigation, and Timing) environmental interference monitoring and evaluation for GNSS / LEO navigation services.

[0006] The above-mentioned existing technical solutions have the following shortcomings: (1) Cross-type signal functional coupling anomalies cannot be captured Low-altitude airspace contains various signal types, including GNSS signals, ADS-B signals, UAV remote control signals, and image transmission link signals. These different signal types are functionally coupled. For example, GNSS signals provide a time reference for ADS-B messages; if GNSS signals are interfered with, the timestamp information of ADS-B messages will also be deviated. The data types collected in CN120257215B are limited to acoustic signals, visible light images, and electromagnetic field intensity variation data, and do not cover GNSS signal quality monitoring data and ADS-B parsing data. Therefore, it cannot comprehensively assess the reliability of navigation signals and the integrity of surveillance signals in the low-altitude electromagnetic environment. Although CN118226474A collects GNSS and ADS-B data simultaneously, it uses a decision-level post-hoc fusion method, that is, interference detection and positioning are performed independently based on each data source before fusion processing, rather than achieving deep joint analysis of multi-source data at the feature level. This makes it difficult to fully capture the functional coupling anomalies of cross-type signals.

[0007] (2) Mobility and sudden interference are difficult to detect effectively. Low-altitude aircraft, especially drones, are themselves potential carriers of mobile interference. The uncertainty of their flight trajectories leads to time-varying spatial distribution of interference signals. Furthermore, the switching on and off of unlicensed radio transmitting equipment in low-altitude environments is typically abrupt, short-lived, and involves large power fluctuations. CN121278504B's detection targets are limited to drone frequency-hopping signals, failing to consider the functional coupling between GNSS navigation signals, ADS-B surveillance signals, and drone communication signals. CN120257215B and CN118226474A both employ long-period statistical analysis methods, making it difficult to capture such transient interference in a short time.

[0008] In summary, existing technologies have shortcomings such as the inability to capture cross-type signal functional coupling anomalies and the difficulty in detecting mobility and sudden interference. Summary of the Invention

[0009] To address the aforementioned issues, this application provides a signal quality assessment and interference early warning method for low-altitude electromagnetic environments. This method enables deep collaboration of multi-source data, rapid detection and early warning of interference signals, and can also assess the ability of the current electromagnetic environment to support the safe flight of aircraft through the Low Altitude Environmental Signal Quality Index (LSQI).

[0010] The technical solution is as follows: A method for signal quality assessment and interference early warning in low-altitude electromagnetic environments includes: Acquire multi-source monitoring data, including at least spectrum monitoring data, GNSS signal quality monitoring data, ADS-B parsing data of Automatic Dependent Surveillance-Broadcast signals, and UAV remote identification signal data; The multi-source monitoring data is preprocessed with spatiotemporal alignment and spectral quality features are extracted. The background noise level is estimated and interference energy features are extracted from it. The spectral quality features and interference energy features are fused to generate the Low Altitude Environment Signal Quality Index (LSQI). The LSQI is used to evaluate the ability of the current electromagnetic environment to support the safe flight of the aircraft. Extract the power time-series features of the target signal and compare them with a preset normal fluctuation threshold. Based on the comparison results, make a preliminary judgment on whether there is an interference signal. If so, extract the spectral features of the interference signal and match the spectral features with a known interference type feature library to determine the interference type. Based on historical interference detection results and current environmental parameters, an adaptive interference detection threshold is generated. When the existence of the interference signal is confirmed based on the adaptive interference detection threshold, an electromagnetic interference warning message is generated.

[0011] Compared with the prior art, this application has the following advantages: (1) By collaboratively collecting spectrum monitoring data, GNSS signal quality monitoring data, ADS-B parsing data and UAV remote identification data, and performing high-precision spatiotemporal alignment and then performing fusion analysis at the feature level, the correlation between GNSS quality anomalies and ADS-B timestamp deviations can be captured simultaneously, solving the problem that single-source independent monitoring or decision-level a posteriori fusion cannot capture cross-type signal functional coupling anomalies in the existing technology. (2) The Low Altitude Environment Signal Quality Index (LSQI) was first created to comprehensively reflect the reliability of navigation signals, the integrity of surveillance signals, the cleanliness of the spectrum environment and the degree of interference. It can intuitively and quantitatively assess the ability of the current electromagnetic environment to support the safe operation of aircraft and fill the gap in the availability assessment index of dedicated signals in the low altitude electromagnetic environment. (3) By extracting the time-series features of the three dimensions of power time-series change slope, power fluctuation variance and power sudden change gradient, and using a short time window for rapid comparison, it can effectively capture the time-varying signal features caused by mobile interference sources and the power jump caused by sudden interference, and realize the rapid detection of mobile and sudden interference. (4) An adaptive interference detection threshold dynamic adjustment mechanism is adopted. The first layer is based on the historical detection results to count the false alarm rate and the missed alarm rate. The threshold is adjusted according to the deviation from the target value at different times, with flight safety as the priority. When the false alarm and missed alarm exceed the standard at the same time, the missed alarm rate is reduced first. The second layer introduces environmental parameters such as signal density level and spectrum congestion degree for dynamic correction, so that the interference detection threshold can adaptively track the changes in the complex electromagnetic environment and provide accurate early warning of interference signals.

[0012] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 The flowchart of the signal quality assessment and interference early warning method for low-altitude electromagnetic environments in this embodiment is shown. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0016] like Figure 1As shown in the figure, this application provides a method for signal quality assessment and interference early warning in low-altitude electromagnetic environments, including: S1. Acquire multi-source monitoring data, including at least spectrum monitoring data, GNSS signal quality monitoring data, ADS-B parsing data of Automatic Dependent Surveillance-Broadcast (ADS-B) signals, and UAV remote identification signal data; Multi-source monitoring data is acquired through distributed monitoring nodes. The spectrum monitoring data reflects the power distribution of radio signals at different frequencies in the low-altitude airspace. GNSS signal quality monitoring data is used to assess the reliability and integrity of navigation signals. GNSS signal quality monitoring data includes at least one or more of the following: carrier-to-noise ratio, pseudorange residual, positioning accuracy, satellite visibility number, and position accuracy attenuation factor of the GNSS signals received by each monitoring node. The GNSS system includes at least one or a combination of GPS, BeiDou, GLONASS, and Galileo.

[0017] ADS-B is the primary means of monitoring aircraft autonomously broadcasting their position, speed, and identity information in low-altitude airspace. ADS-B resolved data includes at least the aircraft's identification identifier (ICAO 24-bit address code), latitude and longitude coordinates, altitude, speed, heading, and the timestamp information of the ADS-B message. ADS-B resolved data is obtained by decoding the 1090ES or UAT data link signal broadcast by the aircraft using an ADS-B receiver.

[0018] A drone's remote ID is a signal that broadcasts its identity and location information to the outside world, enabling remote identification and tracking of the drone. The drone's remote ID signal data includes at least the drone's unique identifier (such as a serial number or session ID), the drone's current location (latitude, longitude, altitude, and speed), and its takeoff point.

[0019] S2. Perform spatiotemporal alignment preprocessing on the multi-source monitoring data, extract spectral quality features, estimate the background noise level and extract interference energy features from it, and fuse the spectral quality features and the interference energy features to generate the Low Altitude Environment Signal Quality Index (LSQI). Since multi-source monitoring data comes from different sensors and devices, the data streams differ in terms of time and space references. Direct fusion analysis would lead to causal confusion. Therefore, it is necessary to perform spatiotemporal alignment preprocessing on the multi-source monitoring data, which includes time synchronization correction and spatial coordinate alignment.

[0020] In this embodiment, the Low-altitude environment Signal Quality Index (LSQI) is used to assess the ability of the current electromagnetic environment to support the safe flight of the aircraft.

[0021] S3. Extract the power time-series features of the target signal and compare them with the preset normal fluctuation threshold. Based on the comparison results, preliminarily determine whether there is an interference signal. If so, extract the spectral features of the interference signal and match the spectral features with a known interference type feature library to determine the interference type. S4. Based on historical interference detection results and current environmental parameters, generate an adaptive interference detection threshold. When the existence of the interference signal is confirmed based on the adaptive interference detection threshold, generate electromagnetic interference warning information.

[0022] In this embodiment of the invention, by collaboratively collecting spectrum monitoring data, GNSS signal quality monitoring data, ADS-B parsing data, and UAV remote identification data, and performing fusion analysis at the feature level after high-precision spatiotemporal alignment, the correlation between GNSS quality anomalies and ADS-B timestamp deviations can be captured simultaneously. This solves the problem that existing technologies, such as single-source independent monitoring or decision-level a posteriori fusion, cannot capture cross-type signal functional coupling anomalies. It also pioneers the Low Altitude Environment Signal Quality Index (LSQI), filling the gap in usability assessment indicators for dedicated signals in low-altitude electromagnetic environments. Furthermore, the adaptive interference detection threshold can track changes in complex electromagnetic environments, providing complete decision support data for low-altitude airspace management, UAV safety supervision, and civil aviation flight protection.

[0023] According to this embodiment, the spectrum monitoring data is acquired as follows: a preset low-altitude electromagnetic frequency band is scanned, and the frequency band is divided into several discrete frequency intervals according to a preset frequency interval; power spectral density is sampled for each frequency interval to obtain the signal power sample value within that frequency interval; the power sample values ​​within several consecutive sampling periods are processed by moving average to obtain the steady-state power estimate of that frequency interval; the steady-state power estimates of all frequency intervals are arranged in order from low to high frequency to generate a spectrum monitoring data vector.

[0024] It should be noted that there is a functional coupling between GNSS signal quality data and ADS-B parsed data: the location information in the ADS-B message originates from the positioning result of the airborne GNSS receiver, and the timestamp in the message is also based on GNSS system time. Therefore, when the GNSS signal is interfered with, the location information and timestamp information in the ADS-B parsed data will deviate synchronously. This embodiment, through the coordinated acquisition and joint analysis of the two types of data, can capture this functional coupling anomaly across signal types. That is, when the degradation of GNSS signal quality and the anomaly in ADS-B data are spatiotemporally correlated, an interference event is determined to exist.

[0025] The spatiotemporal alignment preprocessing includes time synchronization correction and spatial coordinate alignment.

[0026] The time synchronization correction includes: reading the timestamp information of the built-in clock of each distributed monitoring node and calculating the clock deviation between each monitoring node and the unified time server; using the clock deviation to correct the original timestamp of the collected data to obtain a standard timestamp; using the standard timestamp as a reference, associating and grouping the multi-source monitoring data within the same preset time window, and sorting them in ascending order of timestamp.

[0027] According to a specific embodiment, each distributed monitoring node has a built-in high-precision clock (such as a GPS / BeiDou timing module) that synchronously records a local timestamp when collecting data. The central processing platform reads the timestamp information of each node and calculates the clock deviation between each node and the unified time server. To eliminate deviation anomalies caused by network transmission jitter, preferably, the median of multiple clock deviation values ​​measured within multiple sampling periods is taken as the final deviation estimate. The original timestamps of the data collected by each node are added to the corresponding clock deviations for time correction to obtain the corrected standard timestamps. A time window length is set (e.g., 100ms), and based on the standard timestamps, the four types of multi-source data (spectrum data, GNSS data, ADS-B data, and remote ID data) falling within the same time window are associated and grouped. The multi-source data within the same time window are sorted in ascending order according to the collection time to ensure data consistency in the time dimension and provide accurate time sequence relationships for subsequent cross-type signal correlation analysis.

[0028] Each monitoring node pre-stores precise geographical location information (longitude, latitude, and altitude). The spatial coordinate alignment includes associating and binding the spectral data of each monitoring node with its geographical location information. Spatial coordinate alignment associates and binds the spectral data, GNSS data, and ADS-B data collected by each node with the geographical location information of the collecting node, forming a standardized dataset with spatial coordinate labels. This provides a correct spatial reference for subsequent interference source direction calculation (based on the Time Difference of Arrival (TDOA) of multiple nodes).

[0029] Furthermore, spectral quality features are extracted and background noise levels are estimated based on the preprocessed spectral monitoring data. The spectral quality features are a multi-dimensional set of parameters characterizing the power level, signal integrity, and spectral occupancy status of signals at each frequency point within the monitoring band, and include at least the following three types of features: (1) Signal strength characteristics Signal strength characteristics are used to reflect the power energy level of the received signal and are a basic indicator for judging whether there is abnormal attenuation or enhancement of the signal.

[0030] In this embodiment, the power peak value of each frequency range is extracted as the original signal strength value. After normalizing the original signal strength value, the average value and root mean square value are calculated to obtain the signal strength characteristics.

[0031] The specific process is as follows: extract the power peak value of each frequency interval from the spatiotemporally aligned preprocessed spectrum monitoring data as the original signal strength value; map the original signal strength value to the [0,1] interval; calculate the arithmetic mean of the normalized signal strength value as the first signal strength statistic (reflecting the overall energy level); calculate the root mean square value of the normalized signal strength value as the second signal strength statistic (reflecting the degree of signal fluctuation); and concatenate the first signal strength statistic and the second signal strength statistic to generate a signal strength feature vector.

[0032] (2) Signal quality characteristics Signal quality characteristics are used to reflect the modulation integrity and decoding reliability of received signals, and are key indicators for judging whether navigation and surveillance signals can be correctly received and demodulated.

[0033] In this embodiment, signal quality characteristics are generated based on the carrier-to-noise ratio (C / N0) of the GNSS signal and the decoding success rate of the ADS-B signal. The GNSS carrier-to-noise ratio reflects the reception quality of the satellite signal; a lower C / N0 indicates more severe signal interference or attenuation. The ADS-B decoding success rate reflects the resolution integrity of the surveillance signal; a lower success rate indicates greater interference or environmental noise affecting the ADS-B signal. Since the GNSS signal provides a time reference for ADS-B messages, there is functional coupling between the two signal quality types, requiring joint analysis.

[0034] (3) Spectrum occupancy characteristics Spectrum occupancy characteristics are used to reflect the occupancy density and free distribution of each frequency point within the monitoring band, and serve as the basis for judging whether the spectrum is crowded or whether there is abnormal occupancy.

[0035] In this embodiment, spectrum occupancy features are generated based on the occupancy status and idle frequency distribution of each frequency interval within the monitoring frequency band. Frequency points with power spectral density higher than a preset occupancy threshold are marked as occupied, and those below the threshold are marked as idle. The occupancy / idle distribution pattern is statistically analyzed as the spectrum occupancy features.

[0036] Background noise is an important reference benchmark for distinguishing normal signals from abnormal signals. In this embodiment, the power values ​​of unoccupied idle frequency points or frequency band edges are selected and statistically analyzed to obtain an estimate of the background noise level under the current spectrum environment.

[0037] Extracting interference energy features from background noise level estimates specifically includes: 1. The sum of the estimated background noise level and the preset interference margin is used as the interference detection reference threshold. The preset interference margin is set in advance according to the system sensitivity requirements and false alarm rate tolerance, for example, it can be set to 5dBm or 10dBm. 2. Mark the frequency points in the preprocessed spectrum monitoring data that exceed the interference detection reference threshold as suspected interference frequency points; 3. The ratio of the number of suspected interference frequency points to the total number of frequency points is used as the proportion of interference frequency points. This ratio reflects the coverage range of the interference in the frequency domain. 4. Calculate the sum of the amplitudes by which the power values ​​of each suspected interference frequency point exceed the interference detection reference threshold as the interference energy intensity value, reflecting the severity of the interference power; 5. Combine the percentage of interference frequency points and the interference energy intensity value to obtain the interference energy characteristics. The interference energy characteristics reflect the overall strength and coverage of abnormal signals within the current frequency band.

[0038] In a preferred embodiment, spectral quality features and interference energy features are extracted based on a neural network evaluation model, and then spliced ​​and fused at the feature level to generate the Low Altitude Environment Signal Quality Index (LSQI).

[0039] The evaluation model employs a dual-channel neural network structure, specifically including a first processing channel and a second processing channel: The first processing channel is used to extract spectral quality features (signal strength feature vector, signal quality features, and spectrum occupancy features), estimate background noise levels, and extract interference energy features; The second processing channel is used to perform feature splicing and fusion processing on the spectral quality features and interference energy features output by the first processing channel, and output a normalized value as LSQI.

[0040] The LSQI comprehensively reflects the reliability of navigation signals (based on GNSS carrier-to-noise ratio and positioning accuracy), the integrity of surveillance signals (based on ADS-B decoding success rate and timestamp continuity), the cleanliness of the spectrum environment (based on out-of-band interference power and spectrum occupancy patterns), and the degree of interference impact (based on the proportion of interfering frequencies and energy intensity). It can quantitatively assess the ability of the current electromagnetic environment to support the safe operation of aircraft. The closer the LSQI value is to 1, the stronger the support capability of the current electromagnetic environment for the safe operation of aircraft; the closer the value is to 0, the more severe the electromagnetic environment and the higher the flight safety risk.

[0041] In practical applications, airspace controllers can intuitively assess the safety status of the current low-altitude electromagnetic environment using LSQI values. For example, an LSQI ≥ 0.8 indicates a good electromagnetic environment, which can ensure the safe operation of aircraft; 0.5 ≤ LSQI < 0.8 indicates a certain risk to the electromagnetic environment, and it is recommended to strengthen monitoring; LSQI < 0.5 indicates a poor electromagnetic environment, and it is recommended to take avoidance or control measures.

[0042] To address the mobility and suddenness of interference sources in low-altitude electromagnetic environments, this embodiment designs a rapid preliminary interference judgment mechanism based on three-dimensional power time-series characteristics. Unlike traditional long-period statistical analysis methods, this scheme employs short-time-window analysis. By extracting the power time-series characteristics of the target signal and comparing them with a preset normal fluctuation threshold, the existence of interference signals is preliminarily determined based on the comparison results. This captures transient power changes and enables rapid response to sudden interference.

[0043] The specific steps are as follows: (1) Extract the data segment to be analyzed from the power timing data of the target signal according to the preset timing analysis window length; The selection of the target signal depends on the monitoring scenario. For example, in a general monitoring mode, several frequency points with the strongest power in the low-altitude electromagnetic environment can be selected as the target signal. In an aircraft monitoring scenario, ADS-B signal frequency points or UAV remote control / image transmission frequency points can be selected as the target signal.

[0044] (2) Perform linear fitting on the data segment to be analyzed, and extract the slope of the fitted line as the slope of the power time series change; Preferably, the least squares linear fitting method is used to extract the slope of the fitted line as the power time-series change slope. The power time-series change slope reflects the overall trend and drift rate of the target signal power within the time window. If the slope is positive and the absolute value is large, it indicates that the signal power is continuously increasing, and there may be a gradually approaching moving interference source or the power of the device is gradually increasing. If the slope is negative and the absolute value is large, it indicates that the signal power is continuously decreasing, and there may be a gradually moving interference source or signal attenuation. If the slope is close to zero, it indicates that the signal power is relatively stable.

[0045] This feature is primarily used to detect persistent drift-type interference, such as slowly approaching interference sources or slowly changing equipment malfunctions.

[0046] (3) Calculate the variance of the data segment to be analyzed as the power fluctuation variance; The power fluctuation variance reflects the dispersion and fluctuation amplitude of the target signal power within the time window.

[0047] The larger the variance value, the more drastic the fluctuation of the signal power within the time window, which may indicate random noise interference or multipath effects; the smaller the variance value, the more stable the signal power and the relatively quiet environment.

[0048] This feature is primarily used to detect random fluctuation-type interference, such as power fluctuations caused by broadband noise interference or multipath fading.

[0049] (4) Calculate the power difference between adjacent sampling points of the data segment to be analyzed, and extract the maximum power difference as the power abrupt change gradient; The power abrupt change gradient reflects the instantaneous maximum rate of change of the target signal power within the time window.

[0050] A larger abrupt change gradient indicates a sudden and drastic jump in signal power, which may indicate the activation or deactivation of a sudden interference; a smaller abrupt change gradient indicates a gradual change in signal power.

[0051] This feature is primarily used to detect sudden interference, such as unauthorized devices suddenly turning on or transient pulse interference.

[0052] (5) Compare the power time-series change slope, the power fluctuation variance and the power sudden change gradient with their respective normal signal fluctuation threshold ranges. If all three are within the corresponding threshold range, they are determined to be normal signal fluctuations. If any one of them exceeds the corresponding threshold range, it is determined that there is interference signal injection.

[0053] The normal signal fluctuation threshold range is pre-calibrated as follows: Under a confirmed normal electromagnetic environment without interference, a sufficiently long time series sample is collected, and the statistical distribution of each feature value is calculated. The confidence interval of the statistical distribution is used as the normal fluctuation threshold range of each feature.

[0054] The initial judgment logic for interference signals is as follows: if the three features of power time-series change slope, power fluctuation variance, and power abrupt change gradient are all within their respective normal signal fluctuation threshold ranges, then the current window is determined to be a normal signal fluctuation, and there is no interference signal injection; if any of the above three features exceeds the corresponding normal signal fluctuation threshold range, then the current window is determined to be an interference signal injection, ensuring that different types of interference (persistent drift, random fluctuation, sudden jump, etc.) can be effectively captured and avoiding omissions.

[0055] When it is initially determined that interference signal injection exists, the spectral features of the interference signal are extracted, and the spectral features are matched with an interference type feature library to determine the interference type, including: 1. The time window in which interference signal injection is initially determined to exist is designated as the suspicious window. A normal time period without interference before or after the suspicious window is selected as the normal window (the length of the normal window is the same as that of the suspicious window). The difference spectrum between the spectrum of the suspicious window and the spectrum of the normal window is extracted as the interference signal estimate. The purpose of differential processing is to eliminate the influence of background signal and system noise and retain the pure interference signal spectrum characteristics. 2. Extract the peak position information, bandwidth information, and shape feature parameters from the differential spectrum to generate a spectrum feature vector to be matched; Spectrum peak location information: The frequency value corresponding to the point of maximum amplitude in the differential spectrum, reflecting the center frequency of the interference signal.

[0056] Spectrum bandwidth information: The frequency width corresponding to the amplitude dropping to a certain proportion of the peak value (such as 3dB or 6dB) near the peak value in the differential spectrum, reflecting the bandwidth of the interference signal.

[0057] Spectral shape characteristic parameters include spectral symmetry (whether the power distribution on the left and right sides of the peak is symmetrical), roll-off characteristics (the rate of drop at the edge of the spectrum), and side lobe distribution (whether there are side lobes on both sides of the main lobe and the number of side lobes), which comprehensively reflect the modulation characteristics and signal structure of the interference signal.

[0058] By combining the above spectral feature parameters, a spectral feature vector to be matched is generated, which represents the frequency domain characteristics of the interference signal in numerical form.

[0059] 3. Calculate the Euclidean distance between the spectrum feature vector to be matched and the reference spectrum feature vectors of various known interference signals in the interference type feature library. The smaller the Euclidean distance, the more similar the spectrum features of the interference signal to be matched are to the reference features of that type. The interference type corresponding to the reference spectrum feature vector with the smallest Euclidean distance is taken as the matching result and output as the type of the interference signal. The types of interference signals include, but are not limited to: broadband noise interference, narrowband targeting interference, frequency sweeping interference, repeater-type deception interference, and pulse interference.

[0060] 4. When the minimum Euclidean distance exceeds the preset matching threshold, it indicates that the spectral characteristics of the interference signal to be matched are not similar enough to all known types in the feature library, and the interference signal is determined to be of unknown type.

[0061] For interference signals identified as of unknown type, warning information will still be generated. In practical applications, such events can trigger manual review, or the spectral characteristics of the unknown type can be recorded as new samples for subsequent feature library updates and upgrades, enabling the interference type feature library to have self-learning and expansion capabilities.

[0062] In the complex and ever-changing low-altitude electromagnetic environment, using a fixed threshold for interference detection has fundamental drawbacks: setting the threshold too high can lead to missed detections (real interference goes undetected, threatening flight safety), while setting it too low can lead to false alarms (frequent false alarms reduce system reliability and increase control burden). To resolve this contradiction, this application's embodiments design an adaptive dynamic adjustment mechanism for the interference detection threshold, specifically implemented as follows: First layer: Adjust the basic threshold for interference detection based on historical interference detection results. Specifically, the interference detection results records within the historical time period are statistically analyzed, including the number of interference detections, the number of false alarms (the number of times the system determines that there is interference but there is actually no interference, i.e., false alarms) and the number of missed alarms (the number of times the system determines that there is no interference but there is actually interference). The current false alarm rate and the current missed alarm rate are calculated. The current false alarm rate is compared with the target value of the false alarm rate, and the current missed alarm rate is compared with the target value of the missed alarm rate. The basic threshold for interference detection is adjusted according to the comparison results and the preset step size.

[0063] Because navigation and surveillance signals are of paramount safety importance, the target value for the false alarm rate is usually set lower than the target value for the false alarm rate.

[0064] The step of adjusting the basic threshold for interference detection based on the comparison results and a preset step size specifically includes: When the current false alarm rate is greater than the target value of the false alarm rate, it indicates that the false alarm rate is too high and the basic threshold for interference detection is too low, resulting in too many false alarms. The basic threshold for interference detection is increased according to the first preset step size. When the current false alarm rate is greater than the target value of the false alarm rate and the current false alarm rate is not greater than the target value of the false alarm rate, it indicates that the false alarm rate is too high but the false alarm rate is acceptable. The basic threshold for interference detection is too high, causing real interference to be missed. The basic threshold for interference detection is reduced according to the second preset step size. When the current false alarm rate is greater than the false alarm rate target value and the current false alarm rate is greater than the false alarm rate target value, it indicates that both the false alarm rate and the false alarm rate exceed the standard. In order to ensure flight safety, the false alarm rate is reduced first, and the interference detection basic threshold is reduced according to the second preset step size. When the current false alarm rate is not greater than the target value of the false alarm rate and the current false alarm rate is not greater than the target value of the false alarm rate, it indicates that the system detection performance is good and the basic threshold for interference detection remains unchanged.

[0065] The first preset step size is larger than the second preset step size. The setting logic is as follows: raising the threshold (suppressing false alarms) uses a large step size to quickly reduce the false alarm rate and reduce the waste of airspace control resources; lowering the threshold (suppressing false alarms) uses a small step size for careful adjustment to avoid a rebound in the false alarm rate due to excessive threshold reduction.

[0066] Second layer: Corrects the basic threshold for interference detection based on current environmental characteristic parameters. The low-altitude electromagnetic environment exhibits time-varying characteristics, with significant differences in signal density and spectrum congestion across different times and regions. For example, urban centers have a high density of drones and communication equipment, resulting in high signal density and spectrum congestion; while suburban areas or nighttime hours have low signal density and relatively empty spectrum. The same basic threshold performs drastically differently in different environments, thus requiring dynamic adjustment of the basic threshold output of the first layer based on environmental characteristics.

[0067] Specifically, the current environmental characteristic parameters are read, including at least the signal density level and the degree of spectral congestion, and an environmental correction coefficient is determined based on the current environmental characteristic parameters; the interference detection base threshold is multiplied by the environmental correction coefficient to obtain the environmental correction threshold.

[0068] Preferably, the environmental correction coefficient mapping table stores correction coefficient values ​​corresponding to different combinations of signal density levels and spectrum congestion. An environmental correction coefficient > 1 indicates high signal density and spectrum congestion. In congested environments, signals overlap, increasing the noise floor. Therefore, the threshold needs to be increased to avoid background signals being misjudged as interference. An environmental correction coefficient < 1 indicates low signal density and spectrum sparseness. In sparse environments, signals are scarce, making interference easier to identify. Therefore, the threshold can be lowered to improve sensitivity. An environmental correction coefficient = 1 indicates stable electromagnetic signals.

[0069] Third layer: Adjusts the environmental correction threshold based on the signal strength fluctuation data of the current monitoring environment. Random fluctuations in the spectral environment over short timescales can also affect the stability of the detection threshold. To address this issue, a smoothing correction mechanism based on signal strength fluctuations is introduced. The environmental correction threshold is adjusted according to the signal strength fluctuation data of the current monitoring environment to obtain an adaptive interference detection threshold, specifically including: Obtain the signal strength statistics of each monitoring node within the current monitoring period, and calculate the average of the signal strength statistics of all monitoring nodes as the environmental baseline value. This environmental baseline value reflects the average level of the overall signal strength in the current airspace. Based on the environmental correction thresholds of the most recent monitoring periods, a moving average is calculated as a threshold reference value. This threshold reference value eliminates threshold abrupt changes caused by occasional noise or instantaneous fluctuations within a single period. Determine whether the change in the environmental baseline value relative to the previous monitoring period exceeds a preset environmental change sensitivity threshold (e.g., 5% or 10%, which can be flexibly set according to the actual scenario). If the change exceeds the preset environmental change sensitivity threshold, it indicates that the current airspace signal strength has changed significantly (such as a large number of aircraft entering the monitoring area at the same time, or the main interference source suddenly disappearing). The original environmental correction threshold is no longer suitable for the current environment. Then, the threshold adjustment step size is calculated according to the change, and the threshold reference value is corrected according to the adjustment step size to obtain the adaptive interference detection threshold. If the change does not exceed the preset environmental change sensitivity threshold, it indicates that the current airspace environment is relatively stable, and the environmental correction threshold is directly used as the adaptive interference detection threshold.

[0070] In this embodiment, the first layer of adjustment ensures that the system performance indicators (false alarm rate, missed alarm rate) remain stable within the preset target range. The second layer of adjustment ensures that the detection threshold adapts to significant changes in the environmental background (signal density, spectral congestion). The third layer of adjustment ensures that the detection threshold does not drastically change due to short-term signal fluctuations, maintaining the smoothness and stability of the output. The three-layer mechanism works together to enable the final adaptive interference detection threshold to possess accuracy (low false alarms), sensitivity (low missed alarms), smoothness (stability), and adaptability (tracking environmental changes) in complex and dynamic electromagnetic environments.

[0071] Furthermore, a final determination is made based on the adaptive interference detection threshold to confirm whether the interference signal exists. The preliminary judgment results of the interference signal are verified based on the adaptive interference detection threshold. If the verification result is consistent with the preliminary judgment result, for example, if the preliminary judgment indicates interference and the adaptive threshold verification also indicates interference, then the preliminary judgment result is confirmed to be accurate. If the verification result is inconsistent with the preliminary judgment result, for example, the preliminary judgment indicates interference but the adaptive threshold verification indicates no interference, the verification result shall prevail and the preliminary judgment result shall be corrected.

[0072] When the presence of the interference signal is confirmed based on the adaptive interference detection threshold, an electromagnetic interference warning message is generated. The electromagnetic interference warning message includes, but is not limited to, the following: ① Interference type The type of interference refers to the type of interference signal determined by spectral feature matching, such as broadband noise interference, narrowband aiming interference, frequency sweeping interference, repeater-type spoofing interference, pulse interference, or unknown type.

[0073] ② Interference level Based on the type of interference signal, query the preset interference type and risk level mapping table to obtain the interference level classification result.

[0074] For example: Relay-based deception jamming and narrowband targeting jamming (directly affecting navigation and positioning accuracy) → High risk level; Broadband noise interference (affects communication quality but is unlikely to directly cause navigation failure) → Medium risk level; Pulse-type interference (occasional, short-term impact) → Low risk level.

[0075] ③ Direction of interference source Specifically, based on the power intensity and time difference of arrival of the interference signal received by each distributed monitoring node, the possible direction angle of the interference signal source is calculated. Since the arrival time of the same interference signal varies between different monitoring nodes, by measuring the time difference of arrival between each node and combining it with the precise geographical location (longitude, latitude, and altitude) of each node, the location or direction angle of the interference source can be calculated using hyperbolic positioning or least squares positioning algorithms.

[0076] ④ Recommendations for handling Based on the interference level and direction of origin, corresponding suggested response measures are matched from a pre-defined response measures knowledge base. This knowledge base is an expert rule library that stores recommended response strategies for different scenarios. Examples include: "It is recommended to implement temporary airspace control within ±30° of the XX direction, and notify all aircraft within 20km of that direction to make emergency landings or change course," "It is recommended to switch to a backup navigation source (such as an inertial navigation system) and cross-validate the ADS-B data source," and "It is recommended to dispatch a handheld directional spectrum analyzer to conduct ground-based investigation of the interference source in the XX direction."

[0077] ⑤ List of aircraft identification marks within the interfered airspace Based on the remote ID signal data of the UAVs collected in step S1, a list of all flying aircraft currently in the interfered airspace is determined. This list is generated as follows: unique identifiers (such as serial numbers or session IDs) are parsed from the remote ID data, and cross-validation and deduplication are performed by combining them with the ICAO 24-bit address code in the ADS-B data, ultimately outputting a list of aircraft IDs within the interfered airspace. This list provides specific target information for targeted early warning and emergency dispatch.

[0078] In addition, the early warning information is also accompanied by a generation timestamp (recording the precise moment when the early warning was generated), a monitoring area identifier (indicating the geographical coverage area corresponding to the early warning), and a credibility score (quantified by factors such as Euclidean distance of comprehensive spectrum feature matching, consistency of multi-node detection results, and historical false alarm rate, and expressed in the form of a percentage or a 0-1 range), forming a complete standardized early warning information data packet.

[0079] This application also provides a signal quality assessment and interference early warning system for low-altitude electromagnetic environments, including: a data acquisition module, a data processing module, a preliminary judgment module, and a confirmation and early warning module.

[0080] The data acquisition module is used to acquire multi-source monitoring data, including at least spectrum monitoring data, GNSS signal quality monitoring data, ADS-B parsing data of Automatic Dependent Surveillance-Broadcast (ADS-B) signals, and UAV remote identification signal data.

[0081] The data processing module is used to perform spatiotemporal alignment preprocessing on the multi-source monitoring data, extract spectral quality features, estimate the background noise level and extract interference energy features from it, and fuse the spectral quality features and the interference energy features to generate the Low Altitude Environment Signal Quality Index (LSQI). The LSQI is used to evaluate the ability of the current electromagnetic environment to support the safe flight of the aircraft.

[0082] The preliminary judgment module is used to extract the power timing features of the target signal and compare them with a preset normal fluctuation threshold. Based on the comparison results, it is initially determined whether there is an interference signal. If so, the spectral features of the interference signal are extracted and matched with a known interference type feature library to determine the interference type.

[0083] The confirmation and early warning module is used to obtain an adaptive interference detection threshold generated based on historical interference detection results and current environmental parameters. When the existence of the interference signal is confirmed based on the adaptive interference detection threshold, electromagnetic interference early warning information is generated.

[0084] Regarding the system in the above embodiments, the specific manner in which each unit module performs operations has been described in detail in the embodiments related to the method, and will not be elaborated here.

[0085] Based on the same inventive concept, this application also provides an electronic device, including: a memory and a processor, wherein the processor is used to read and execute a computer program stored in the memory to realize the aforementioned signal quality assessment and interference early warning method for low-altitude electromagnetic environments.

[0086] Based on the same inventive concept, this application also provides a computer storage medium storing computer-executable instructions, which, when executed, implement the aforementioned signal quality assessment and interference early warning method for low-altitude electromagnetic environments.

[0087] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0088] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0089] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not described in detail in a particular embodiment can be found in the relevant descriptions of other embodiments. Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for signal quality assessment and interference early warning in low-altitude electromagnetic environments, characterized in that, The method includes: Acquire multi-source monitoring data, including at least spectrum monitoring data, GNSS signal quality monitoring data, ADS-B parsing data of Automatic Dependent Surveillance-Broadcast signals, and UAV remote identification signal data; The multi-source monitoring data is preprocessed with spatiotemporal alignment to extract spectral quality features, estimate the background noise level and extract interference energy features from it, and fuse the spectral quality features and the interference energy features to generate the Low Altitude Environment Signal Quality Index (LSQI). The LSQI is used to assess the ability of the current electromagnetic environment to support the safe flight of the aircraft. Extract the power time-series features of the target signal and compare them with a preset normal fluctuation threshold. Based on the comparison results, make a preliminary judgment on whether there is an interference signal. If so, extract the spectral features of the interference signal and match the spectral features with a known interference type feature library to determine the interference type. Based on historical interference detection results and current environmental parameters, an adaptive interference detection threshold is generated. When the existence of the interference signal is confirmed based on the adaptive interference detection threshold, an electromagnetic interference warning message is generated.

2. The method according to claim 1, characterized in that, The spatiotemporal alignment preprocessing includes time synchronization correction and spatial coordinate alignment; The time synchronization correction includes: Read the timestamp information of the built-in clock of each distributed monitoring node and calculate the clock deviation between each monitoring node and the unified time server; The original timestamps of the collected data are corrected using the clock deviation to obtain standard timestamps; Based on the standard timestamp, multi-source monitoring data within the same preset time window are associated and grouped, and sorted in ascending order by timestamp; The spatial coordinate alignment includes associating and binding the spectrum data of each monitoring node with its geographical location information.

3. The method according to claim 1, characterized in that, Based on the preprocessed spectrum monitoring data, spectrum quality features are extracted and the background noise level is estimated. The spectrum quality features include at least signal strength features, signal quality features, and spectrum occupancy features. The power peak value of each frequency range is extracted as the original signal strength value. After normalizing the original signal strength value, the average value and root mean square value are calculated to obtain the signal strength characteristics. Signal quality characteristics are generated based on the carrier-to-noise ratio of GNSS signals and the decoding success rate of ADS-B signals; Spectrum occupancy characteristics are generated based on the occupancy status of each frequency interval within the monitoring frequency band and the distribution of idle frequency points.

4. The method according to claim 3, characterized in that, Extracting interference energy features from background noise level estimates includes: The sum of the estimated background noise level and the preset interference margin is used as the interference detection benchmark threshold; Frequency points in the preprocessed spectrum monitoring data that exceed the interference detection reference threshold are marked as suspected interference frequency points; The ratio of the number of suspected interference frequency points to the total number of frequency points is used as the proportion of interference frequency points. The sum of the magnitudes by which the power values ​​of each suspected interference frequency point exceed the interference detection reference threshold is calculated as the interference energy intensity value; The interference energy characteristics are obtained by combining the proportion of the interference frequency points and the interference energy intensity value.

5. The method according to claim 1, characterized in that, The extraction of the power time-series characteristics of the target signal is compared with a preset normal fluctuation threshold, and the presence of interference signals is preliminarily determined based on the comparison results, including: According to the preset timing analysis window length, extract the data segment to be analyzed from the power timing data of the target signal; Linear fitting is performed on the data segment to be analyzed, and the slope of the fitted line is extracted as the slope of the power time series change. The variance of the data segment to be analyzed is calculated as the power fluctuation variance; Calculate the power difference between adjacent sampling points of the data segment to be analyzed, and extract the maximum power difference as the power abrupt change gradient; The power time-series change slope, the power fluctuation variance, and the power abrupt change gradient are compared with their respective normal signal fluctuation threshold ranges. If all three are within the corresponding threshold range, it is determined to be a normal signal fluctuation. If any one of them exceeds the corresponding threshold range, it is determined to be an interference signal injection.

6. The method according to claim 1, characterized in that, The step of extracting the spectral features of the interference signal and matching the spectral features with an interference type feature library to determine the interference type includes: The time window in which interference signal injection is initially determined to exist is designated as the suspicious window, and the difference spectrum between the spectrum of the suspicious window and the spectrum of the normal window is extracted as the interference signal estimate. Extract spectral peak location information, spectral bandwidth information, and spectral shape feature parameters from the differential spectrum to generate a spectral feature vector to be matched; Calculate the Euclidean distance between the spectral feature vector to be matched and the reference spectral feature vectors of various known interference signals in the interference type feature library, and take the interference type corresponding to the reference spectral feature vector with the smallest Euclidean distance as the matching result, and output the type of the interference signal; When the minimum Euclidean distance exceeds the preset matching threshold, the interference signal is determined to be of unknown type.

7. The method according to claim 1, characterized in that, The step of generating an adaptive interference detection threshold based on historical interference detection results and current environmental parameters includes: The system records interference detection results within historical time periods, including the number of interference detections, false alarms, and missed alarms, and calculates the current false alarm rate and the current missed alarm rate. Compare the current false alarm rate with the target false alarm rate, and the current missed alarm rate with the target missed alarm rate; adjust the basic threshold for interference detection based on the comparison results and the preset step size. Read the current environmental characteristic parameters, including at least the signal density level and the degree of spectral congestion, and determine the environmental correction coefficient based on the current environmental characteristic parameters; Multiply the interference detection baseline threshold by the environmental correction coefficient to obtain the environmental correction threshold; The environmental correction threshold is adjusted based on the signal strength fluctuation data of the current monitoring environment to obtain the adaptive interference detection threshold.

8. The method according to claim 7, characterized in that, The step of adjusting the basic threshold for interference detection based on the comparison results and a preset step size includes: When the current false alarm rate is greater than the target value of the false alarm rate, the basic threshold for interference detection is increased by a first preset step size. When the current false alarm rate is greater than the false alarm rate target value and the current false alarm rate is not greater than the false alarm rate target value, the interference detection base threshold is reduced by the second preset step size. When the current false alarm rate is greater than the false alarm rate target value and the current missed alarm rate is greater than the missed alarm rate target value, the interference detection base threshold is reduced according to the second preset step size; When the current false alarm rate is not greater than the target value of the false alarm rate and the current false alarm rate is not greater than the target value of the false alarm rate, the basic threshold for interference detection remains unchanged. Wherein, the first preset step size is greater than the second preset step size.

9. The method according to claim 7 or 8, characterized in that, The step of correcting the environmental correction threshold based on the signal strength fluctuation data of the current monitoring environment to obtain the adaptive interference detection threshold includes: Obtain the signal strength statistics of each monitoring node within the current monitoring period, and calculate the average of the signal strength statistics of all monitoring nodes as the environmental baseline value; Based on the environmental correction thresholds of the most recent monitoring periods, a moving average value is calculated as a threshold reference value; Determine whether the change in the environmental baseline value relative to the previous monitoring period exceeds a preset environmental change sensitivity threshold. If so, calculate the threshold adjustment step size based on the change amplitude, and correct the threshold reference value according to the adjustment step size to obtain the adaptive interference detection threshold. Otherwise, the environmental correction threshold is used as the adaptive interference detection threshold.

10. The method according to claim 1, characterized in that, Determining the presence of the interference signal based on the adaptive interference detection threshold includes: The preliminary judgment results of the interference signal are verified based on the adaptive interference detection threshold. If the verification result is consistent with the preliminary judgment result, then the preliminary judgment result is confirmed to be accurate; If the verification result is inconsistent with the preliminary judgment result, the verification result shall prevail and the preliminary judgment result shall be corrected. When the existence of the interference signal is confirmed based on the adaptive interference detection threshold, an electromagnetic interference warning message is generated. The electromagnetic interference warning message includes the interference type, interference level, direction of interference source, handling suggestions, and a list of UAV identification tags in the affected airspace.

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