Unmanned aerial vehicle communication interference detection method and system

Abstract

The invention provides an unmanned aerial vehicle communication interference detection method and system, and belongs to the technical field of signal detection, and the method comprises the steps: monitoring the current communication state of an unmanned aerial vehicle in real time, determining real-time communication data, and obtaining the historical communication parameters of the unmanned aerial vehicle in a normal flight state; analyzing the real-time communication data and the historical communication parameters, and determining an abnormal frequency band signal, an abnormal strength signal and an abnormal power signal; determining a sub-autocorrelation matrix of each abnormal sub-signal and the actual interference time and the actual interference position of the interference source; and carrying out interference classification on the actual interference time and the actual interference position of the interference source of all the abnormal sub-information, and determining an interference suppression strategy of each interference classification. The communication abnormity of the unmanned aerial vehicle can be found in time, communication interruption, data loss or flight deviation caused by interference are reduced, the task success rate and flight safety of the unmanned aerial vehicle are improved, the communication stability and reliability of the unmanned aerial vehicle are improved, and the stability of a communication link is ensured.

Description

Technical Field

[0001] The present invention relates to the field of signal detection technology, and in particular to a method and system for detecting interference in unmanned aerial vehicle communications. Background Art

[0002] Drones play a key role in many fields due to their flexibility, relatively low cost, and ability to perform high-risk tasks. For example, drones are widely used in the military for reconnaissance, intelligence gathering, target tracking, electronic warfare, and attack tasks; drones are used in logistics and transportation, agricultural monitoring, and building and infrastructure monitoring in civil and commercial applications; drones can be used for police operations, disaster relief, and firefighting, search and rescue, or resource delivery; drones provide bird's-eye view shooting angles in film and television production, news reporting, and other industries, capturing perspectives that are difficult for humans to reach. However, drones are vulnerable to external interference threats such as communication interference, navigation deception, or signal blocking, and interference detection becomes critical.

[0003] Therefore, the present invention provides a method and system for detecting unmanned aerial vehicle communication interference. Summary of the invention

[0004] The present invention provides a method and system for detecting interference in UAV communication, which are used to determine abnormal frequency band signals, abnormal intensity signals and abnormal power signals by analyzing the real-time communication data and historical communication parameters of the UAV, and determine the sub-autocorrelation matrix of each abnormal sub-signal, as well as the actual interference time and actual interference position of each abnormal sub-signal, classify all abnormal sub-information and determine the interference suppression strategy, so as to timely discover UAV communication anomalies, reduce communication interruptions, data loss or flight deviations caused by interference, improve the mission success rate and flight safety of the UAV, enhance the stability and reliability of UAV communication, and ensure the stability of the communication link.

[0005] In one aspect, the present invention provides a method for detecting communication interference of an unmanned aerial vehicle, comprising: 101: monitor the current communication status of the UAV in real time to determine the real-time communication data, and obtain the historical communication parameters of the UAV under the normal flight state; 102: Analyze real-time communication data and historical communication parameters to determine abnormal frequency band signals, abnormal strength signals, and abnormal power signals; 103: Analyze the abnormal frequency band signal, the abnormal intensity signal, and the abnormal power signal to determine a sub-autocorrelation matrix of each abnormal sub-signal; 104: Determine the actual interference time and actual interference position of the corresponding interference source based on the autocorrelation matrix of each abnormal sub-signal; 105: Classify the actual interference time and actual interference position of the interference sources of all abnormal sub-information, and determine the interference suppression strategy for each interference classification.

[0006] According to a method for detecting communication interference of a drone provided by the present invention, real-time communication data includes a real-time signal of the drone within a specified time window, a real-time communication channel corresponding to the real-time signal, real-time signal strength, real-time signal power, receiving positions of multiple receivers, and signal arrival time of the real-time signal received by multiple receivers, wherein the real-time signal includes all real-time sub-signals within the specified time window; The historical communication parameters include the historical communication frequency band and historical signal strength of the UAV within the historical specified time window.

[0007] According to a UAV communication interference detection method provided by the present invention, real-time communication data and historical communication parameters are analyzed to determine abnormal frequency band signals, including: Analyze all historical communication frequency bands within the specified time window to determine the historical normal communication frequency band of the UAV; Determine whether all real-time communication frequency bands within the specified time window are consistent with historical normal communication frequency bands, extract all real-time communication sub-frequency bands that are inconsistent with historical normal communication frequency bands, determine abnormal communication frequency bands, and extract all real-time sub-signals within the abnormal communication frequency band to determine as abnormal frequency band signals; All real-time communication sub-frequency bands that are consistent with historical normal communication frequency bands are extracted to determine the normal communication frequency band, and all real-time sub-signals within the normal communication frequency band are extracted to determine as normal frequency band signals.

[0008] According to a UAV communication interference detection method provided by the present invention, real-time communication data and historical communication parameters are analyzed to determine abnormal strength signals, including: Extract the historical signal strength of the drone within the normal frequency band signal and all historical specified time windows; Determine a signal strength range and the number of sub-signals of each signal strength within the signal strength range based on historical signal strengths, and draw a strength histogram; Determine the intensity kurtosis and the intensity skewness of the intensity histogram, determine the fitting distribution type based on the intensity kurtosis and the intensity skewness, fit the historical signal intensity based on the fitting distribution type, and determine the intensity signal range based on the fitting result; Determine whether all real-time sub-signal strengths within the specified time window are within the strength signal range, extract all real-time sub-signal strengths that are not within the strength signal range, and determine that the real-time sub-signals corresponding to all real-time sub-signal strengths are abnormal strength signals.

[0009] According to a UAV communication interference detection method provided by the present invention, real-time communication data and historical communication parameters are analyzed to determine abnormal power signals, including: Perform spectrum analysis on the real-time signal power of the UAV within a specified time window to determine the real-time spectrum graph; An abnormal peak is determined based on the real-time spectrum diagram, all real-time sub-signal powers within the abnormal peak are extracted, and the real-time sub-signals corresponding to all the extracted real-time sub-signal powers are determined to be abnormal power signals.

[0010] According to a UAV communication interference detection method provided by the present invention, an abnormal frequency band signal, an abnormal intensity signal, and an abnormal power signal are analyzed to determine the autocorrelation matrix of each abnormal sub-signal, including: Calculating the autocorrelation value of the abnormal sub-signal based on the abnormal frequency band signal, the abnormal intensity signal and the abnormal power signal respectively; ; ; in, Indicates abnormal sub-signal The autocorrelation value at spatial offset d and time offset m, , k=1 indicates abnormal frequency band signal, k=2 indicates abnormal intensity signal, k=3 indicates abnormal power signal, represents the abnormal sub-signal with spatial position i and time point j in the abnormal signal k, represents the weight of the signal at spatial position i, time point j, spatial offset d, and time offset m, Indicates abnormal sub-signal The signal value after spatial offset d and time offset m is, Indicates abnormal sub-signal The sub-offset mean within the sub-space offset window and the sub-time offset window, Indicates abnormal sub-signal At the signal value of the spatial offset a and the time offset b, a represents the spatial offset value of the abnormal sub-signal, and b represents the time offset value of the abnormal sub-signal. represents the subspace offset window of the abnormal k signal, represents the sub-time offset window of the abnormal k signal, and ; Based on all autocorrelation values ​​of each abnormal sub-signal at different spatial offsets and different time offsets, a sub-autocorrelation matrix corresponding to the abnormal sub-signal is constructed, and all sub-autocorrelation matrices are standardized, wherein the rows of the sub-autocorrelation matrix include the autocorrelation values ​​of all different time offsets under the same spatial offset, and the columns of the sub-autocorrelation matrix include the autocorrelation values ​​of all different spatial offsets under the same time offset.

[0011] According to a UAV communication interference detection method provided by the present invention, the actual interference time and actual interference position of the corresponding interference source are determined based on the autocorrelation matrix of each abnormal sub-signal, including: Determine a row autocorrelation threshold based on all autocorrelation values ​​of each row in each standardized sub-autocorrelation matrix, compare the row autocorrelation threshold with all autocorrelation values ​​in the corresponding row, and determine a row autocorrelation value set of each autocorrelation matrix based on all autocorrelation values ​​greater than the corresponding row autocorrelation threshold; Determine the upper quartile and the lower quartile of the row autocorrelation value set of each autocorrelation matrix, determine whether there is a row pole in the row autocorrelation value set based on the upper quartile and the lower quartile of the autocorrelation value set, if there is a row pole, determine the first interference time of the corresponding abnormal sub-signal based on the row pole, and determine the critical time of the corresponding abnormal sub-signal based on all autocorrelation values ​​in the row autocorrelation value set of the autocorrelation matrix except the row pole; if there is no row pole, determine the critical time of the abnormal sub-signal based on all autocorrelation values ​​in the row autocorrelation value set of the autocorrelation matrix; Determine a column autocorrelation threshold based on all autocorrelation values ​​of each column in each standardized sub-autocorrelation matrix, compare the column autocorrelation threshold with all autocorrelation values ​​in the corresponding column, and determine a column autocorrelation value set of each autocorrelation matrix based on all autocorrelation values ​​greater than the corresponding column autocorrelation threshold; Determine the upper quartile and the lower quartile of the column autocorrelation value set of each autocorrelation matrix, determine whether there is a column pole in the column autocorrelation value set based on the upper quartile and the lower quartile of the column autocorrelation value set, if there is a column pole, based on the column pole as the first interference position of the corresponding abnormal sub-signal, and based on all autocorrelation values ​​in the column autocorrelation value set of the autocorrelation matrix except the column pole, determine the key position of the corresponding abnormal sub-signal; if there is no column pole, determine the key position of the abnormal sub-signal based on all autocorrelation values ​​in the column autocorrelation value set of the autocorrelation matrix; Determine the arrival time difference based on the signal arrival time of each abnormal sub-signal at the multiple receivers, and determine the arrival time difference based on the real-time signal power of each abnormal sub-signal and the positions of the multiple receivers; Determine a second interference time and a second interference position of an interference source of the abnormal sub-signal based on the arrival time difference and the arrival phase difference of the multiple receivers of each abnormal sub-signal; If the first interference time of the abnormal sub-signal is equal to the second interference time, determine the first interference time as the actual interference time of the abnormal sub-signal; if the first interference time of the abnormal sub-signal is not equal to the second interference time, determine the actual interference time based on the second interference time of the abnormal sub-signal and the critical time; If the first interference position of the abnormal sub-signal is equal to the second interference position, the first interference position is determined as the actual interference position of the abnormal sub-signal; if the first interference position of the abnormal sub-signal is not equal to the second interference position, the actual interference position is determined based on the second interference position of the abnormal sub-signal and the key position.

[0012] On the other hand, the present invention also provides a UAV communication interference detection system, comprising: Detection module: monitors the current communication status of the drone in real time to determine the real-time communication data and obtain the historical communication parameters of the drone under normal flight conditions; Abnormal module: analyzes real-time communication data and historical communication parameters to determine abnormal frequency band signals, abnormal strength signals, and abnormal power signals; Analysis module: Analyze the abnormal frequency band signal, abnormal intensity signal and abnormal power signal to determine the sub-autocorrelation matrix of each abnormal sub-signal; Determination module: Determine the actual interference time and actual interference position of the corresponding interference source based on the autocorrelation matrix of each abnormal sub-signal; Classification module: classifies the actual interference time and actual interference location of the interference sources of all abnormal sub-information, and determines the interference suppression strategy for each interference classification.

[0013] Compared with the prior art, the present invention has the following beneficial effects: By analyzing the real-time communication data and historical communication parameters of the drone, abnormal frequency band signals, abnormal strength signals and abnormal power signals are determined, and the sub-autocorrelation matrix of each abnormal sub-signal, as well as the actual interference time and actual interference position of each abnormal sub-signal, all abnormal sub-information is classified and the interference suppression strategy is determined. This can timely detect drone communication anomalies, reduce communication interruptions, data loss or flight deviations caused by interference, improve the drone's mission success rate and flight safety, enhance the stability and reliability of drone communications, and ensure the stability of the communication link. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0015] Figure 1 It is a flow chart of a method for detecting interference in UAV communication provided by an embodiment of the present invention.

[0016] Figure 2 It is a structural schematic diagram of a UAV communication interference detection system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Embodiment 1: The embodiment of the present invention provides a method for detecting communication interference of a drone. Figure 1 As shown, including: 101: monitor the current communication status of the UAV in real time to determine the real-time communication data, and obtain the historical communication parameters of the UAV under the normal flight state; 102: Analyze real-time communication data and historical communication parameters to determine abnormal frequency band signals, abnormal strength signals, and abnormal power signals; 103: Analyze the abnormal frequency band signal, the abnormal intensity signal, and the abnormal power signal to determine a sub-autocorrelation matrix of each abnormal sub-signal; 104: Determine the actual interference time and actual interference position of the corresponding interference source based on the autocorrelation matrix of each abnormal sub-signal; 105: Classify the actual interference time and actual interference position of the interference sources of all abnormal sub-information, and determine the interference suppression strategy for each interference classification.

[0019] In this embodiment, the abnormal frequency band signal represents all real-time sub-signals in a frequency band where the frequency used for real-time communication is inconsistent with the historical normal communication frequency band.

[0020] In this embodiment, the abnormal intensity signal refers to all real-time sub-signals corresponding to the real-time sub-signal intensities that are not within the intensity signal range.

[0021] In this embodiment, the abnormal power signal represents all real-time sub-signals corresponding to the powers of all real-time sub-signals within the abnormal peak of the real-time spectrum graph.

[0022] In this embodiment, the sub-autocorrelation matrix includes autocorrelation values ​​of the corresponding abnormal sub-signal at all different time offsets under the same spatial offset, and autocorrelation values ​​of all different spatial offsets under the same time offset.

[0023] The beneficial effects of the above technical solution are as follows: by analyzing the real-time communication data and historical communication parameters of the UAV, determining the abnormal frequency band signals, abnormal strength signals and abnormal power signals, and determining the sub-autocorrelation matrix of each abnormal sub-signal, as well as the actual interference time and actual interference position of each abnormal sub-signal, classifying all abnormal sub-information and determining the interference suppression strategy, the UAV communication anomalies can be discovered in time, communication interruptions, data loss or flight deviations caused by interference can be reduced, the mission success rate and flight safety of the UAV can be improved, the stability and reliability of the UAV communication can be improved, and the stability of the communication link can be ensured.

[0024] Embodiment 2: The embodiment of the present invention provides a method for detecting communication interference of a drone, wherein the real-time communication data includes a real-time signal of the drone within a specified time window, a real-time communication channel corresponding to the real-time signal, real-time signal strength, real-time signal power, receiving positions of multiple receivers, and signal arrival time of the real-time signal received by multiple receivers, wherein the real-time signal includes all real-time sub-signals within the specified time window; The historical communication parameters include the historical communication frequency band and historical signal strength of the UAV within the historical specified time window.

[0025] In this embodiment, the real-time communication data includes real-time signals of all communication conditions of the drone within a specified time window, and the communication conditions include different distances (near, medium, and far), angles, obstacles (such as walls and trees), weather conditions (sunny, rainy, and windy), etc.

[0026] In this embodiment, the real-time communication channel refers to the frequency channel or bandwidth used by the drone for communication within a specified time window, which is generally used to distinguish different communication links to avoid interference and manage spectrum resources.

[0027] In this embodiment, the real-time signal strength refers to the strength of the drone signal received by the receiver within a specified time window. It indicates the power of the signal at the receiving point and can reflect the quality of the communication signal.

[0028] In this embodiment, the real-time signal power refers to the signal power sent by the drone within a specified time window, which represents the signal energy transmitted by the drone.

[0029] In this embodiment, the historical communication frequency band refers to the communication frequency range used by the drone within a historically specified time window. The frequency band usually includes a certain frequency range rather than a single frequency, indicating the spectrum resources used by the communication equipment over a period of time.

[0030] In this embodiment, the historical signal strength refers to the signal strength when the receiver receives the historical communication signal of the drone within the historical specified time window.

[0031] The beneficial effects of the above technical solution are: by determining the real-time communication data and historical communication parameters of the drone, a data basis is provided for determining abnormal frequency band signals, abnormal intensity signals and abnormal power signals.

[0032] Embodiment 3: The embodiment of the present invention provides a method for detecting communication interference of a drone, which analyzes real-time communication data and historical communication parameters to determine abnormal frequency band signals, including: Analyze all historical communication frequency bands within the specified time window to determine the historical normal communication frequency band of the UAV; Determine whether all real-time communication frequency bands within the specified time window are consistent with historical normal communication frequency bands, extract all real-time communication sub-frequency bands that are inconsistent with historical normal communication frequency bands, determine abnormal communication frequency bands, and extract all real-time sub-signals within the abnormal communication frequency bands to determine as abnormal frequency band signals; All real-time communication sub-frequency bands that are consistent with historical normal communication frequency bands are extracted to determine the normal communication frequency band, and all real-time sub-signals within the normal communication frequency band are extracted to determine as normal frequency band signals.

[0033] In this embodiment, the communication frequency bands used by the drone in the past period of time are analyzed to determine the frequency bands it usually uses, which are usually those frequency ranges that are stable and reliable in long-term use.

[0034] In this embodiment, by comparing the historical normal communication frequency band and the real-time communication frequency band, it is determined whether the current communication frequency is consistent with the past frequency range. In real-time communication, those frequency sub-bands that are inconsistent with the historical normal communication frequency band are found. These inconsistent frequency bands may represent abnormal signals because drones usually do not communicate on these frequency bands.

[0035] In this embodiment, based on the determined abnormal communication frequency bands, all real-time sub-signals within these abnormal frequency bands are further extracted. These sub-signals are transmitted within the abnormal frequency bands and are therefore marked as “abnormal frequency band signals”.

[0036] In this embodiment, frequency band parts consistent with historical normal communication frequency bands are extracted from real-time communication, which are called normal communication frequency bands. They conform to historical communication patterns and are therefore considered to be normal and reliable communication frequency bands.

[0037] The beneficial effects of the above technical solution are as follows: by analyzing real-time communication data and historical communication parameters, abnormal frequency band signals can be determined, which can provide data basis for determining abnormal strength signals and timely discover abnormal communication frequency bands of drones.

[0038] Embodiment 4: The embodiment of the present invention provides a method for detecting communication interference of a drone, which analyzes real-time communication data and historical communication parameters to determine abnormal strength signals, including: Extract the historical signal strength of the drone within the normal frequency band signal and all historical specified time windows; Determine a signal strength range and the number of sub-signals of each signal strength within the signal strength range based on historical signal strengths, and draw a strength histogram; Determine the intensity kurtosis and the intensity skewness of the intensity histogram, determine the fitting distribution type based on the intensity kurtosis and the intensity skewness, fit the historical signal intensity based on the fitting distribution type, and determine the intensity signal range based on the fitting result; Determine whether all real-time sub-signal strengths within the specified time window are within the strength signal range, extract all real-time sub-signal strengths that are not within the strength signal range, and determine that the real-time sub-signals corresponding to all real-time sub-signal strengths are abnormal strength signals.

[0039] In this embodiment, the historical signal strength represents the signal strength between the ground station and the normal flight under all communication conditions in all historical specified time windows.

[0040] In this embodiment, the horizontal axis of the intensity histogram represents the range of signal intensity, and the vertical axis represents the number of signals within the range.

[0041] In this embodiment, the skewness measures the symmetry of the historical signal strength distribution. If the strength histogram skewness is close to 0, the data distribution is symmetrical; if the strength histogram skewness is greater than 0, the data is right-skewed; if the strength histogram skewness is less than 0, the data is left-skewed.

[0042] In this embodiment, the kurtosis measures the kurtosis characteristics of the historical signal intensity distribution. The kurtosis of the intensity histogram is close to 3, indicating a normal distribution. The kurtosis value of the intensity histogram is greater than 3, indicating that the data is concentrated near the mean (with a sharp peak) and has a long tail (high peak, thick tail). The kurtosis value of the intensity histogram is less than 3, indicating that the data distribution is relatively flat and the tail is short (low peak, thin tail).

[0043] In this embodiment, the fitting distribution type is determined based on the intensity kurtosis and the intensity skewness. For example, when the skewness of the intensity histogram is close to 0 and the kurtosis is close to 3, the normal distribution can be used; when the skewness of the intensity histogram is greater than 0 (right-skewed), the historical signal intensity distribution has a long tail on the right, the lognormal distribution can be used; when the intensity histogram is right-skewed and the kurtosis value is greater than 3, the gamma distribution can be used; when the skewness of the intensity histogram is close to 0 but the kurtosis is greater than 3, the Student's T distribution is used, etc.

[0044] In this embodiment, the intensity signal range is determined based on the fitting results, for example: if the intensity histogram is approximately normally distributed, the normal range is set to be within two standard deviations of the mean, covering about 95% of the data; if the data distribution is irregular or multi-peaked, the XX quantile method is used to set the range based on the selected fitting distribution type, for example, quantiles from 5% to 95% are selected as the normal signal intensity range.

[0045] The beneficial effects of the above technical solution are as follows: by analyzing real-time communication data and historical communication parameters and determining abnormal strength signals, abnormal communication strength of the drone can be discovered in a timely manner, reducing communication interruptions, data loss or flight deviations caused by interference.

[0046] Embodiment 5: The embodiment of the present invention provides a method for detecting communication interference of a drone, which analyzes real-time communication data and historical communication parameters to determine abnormal power signals, including: Perform spectrum analysis on the real-time signal power of the UAV within a specified time window to determine the real-time spectrum graph; An abnormal peak is determined based on the real-time spectrum diagram, all real-time sub-signal powers within the abnormal peak are extracted, and the real-time sub-signals corresponding to all the extracted real-time sub-signal powers are determined to be abnormal power signals.

[0047] In this embodiment, spectrum analysis converts the time domain representation of the real-time signal within the specified time window (i.e., the power that changes with time) into a frequency domain representation (i.e., the intensity of different frequency components in the signal). Through the spectrum graph, the frequency distribution of all signals (regardless of strength) will be displayed.

[0048] In this embodiment, abnormal peaks are found by analyzing the real-time spectrum graph. An abnormal peak refers to a situation where the signal power is significantly higher than the normal level at a specific frequency. This abnormal peak may represent signal interference, abnormal transmission power or external attack signals.

[0049] In this embodiment, all sub-signal powers are extracted from the frequency range where the abnormal peak is located. The sub-signal power refers to the power value of the signal transmitted within the frequency band of the abnormal peak.

[0050] The beneficial effects of the above technical solution are as follows: by analyzing real-time communication data and historical communication parameters and determining abnormal power signals, abnormal communication power of the UAV can be discovered in a timely manner, further reducing communication interruptions, data loss or flight deviations caused by interference.

[0051] Embodiment 6: The embodiment of the present invention provides a method for detecting interference in UAV communication, which analyzes abnormal frequency band signals, abnormal intensity signals, and abnormal power signals to determine the autocorrelation matrix of each abnormal sub-signal, including: Calculating the autocorrelation value of the abnormal sub-signal based on the abnormal frequency band signal, the abnormal intensity signal and the abnormal power signal respectively; ; ; in, Indicates abnormal sub-signal The autocorrelation value at spatial offset d and time offset m, , k=1 indicates abnormal frequency band signal, k=2 indicates abnormal intensity signal, k=3 indicates abnormal power signal, represents the abnormal sub-signal with spatial position i and time point j in the abnormal signal k, represents the weight of the signal at spatial position i, time point j, spatial offset d, and time offset m, Indicates abnormal sub-signal The signal value after spatial offset d and time offset m is, Indicates abnormal sub-signal The sub-offset mean within the sub-space offset window and the sub-time offset window, Indicates abnormal sub-signal At the signal value of the spatial offset a and the time offset b, a represents the spatial offset value of the abnormal sub-signal, and b represents the time offset value of the abnormal sub-signal. represents the subspace offset window of the abnormal k signal, represents the sub-time offset window of the abnormal k signal, and ; Based on all autocorrelation values ​​of each abnormal sub-signal at different spatial offsets and different time offsets, a sub-autocorrelation matrix corresponding to the abnormal sub-signal is constructed, and all sub-autocorrelation matrices are standardized, wherein the rows of the sub-autocorrelation matrix include the autocorrelation values ​​of all different time offsets under the same spatial offset, and the columns of the sub-autocorrelation matrix include the autocorrelation values ​​of all different spatial offsets under the same time offset.

[0052] In this embodiment, the sub-autocorrelation matrix is ​​a matrix of time offset number×spatial offset number.

[0053] In this embodiment, the time offset indicates an offset within a specified time window.

[0054] In this embodiment, the sub-offset mean value represents the average value of all abnormal sub-signals within the sub-space offset window and the sub-time offset window.

[0055] In this embodiment, a represents the offset along the spatial axis, which is Change to , which means that within a local range around the spatial position i (i.e., the width is window).

[0056] In this embodiment, b represents the offset along the time axis, which is Change to , which means that within a local range around time point j (i.e., the width is window).

[0057] The beneficial effects of the above technical solution are as follows: by analyzing the abnormal frequency band signal, abnormal intensity signal and abnormal power signal to determine the autocorrelation matrix of each abnormal sub-signal, the autocorrelation of all abnormal sub-signals at different spatial offsets and different time offsets can be determined, providing data support for determining the actual interference time and actual interference position of the abnormal sub-signal interference source, so as to improve the mission success rate and flight safety of the UAV.

[0058] Embodiment 7: The embodiment of the present invention provides a method for detecting interference in UAV communication, which determines the actual interference time and actual interference position of the corresponding interference source based on the autocorrelation matrix of each abnormal sub-signal, including: Determine a row autocorrelation threshold based on all autocorrelation values ​​of each row in each standardized sub-autocorrelation matrix, compare the row autocorrelation threshold with all autocorrelation values ​​in the corresponding row, and determine a row autocorrelation value set of each autocorrelation matrix based on all autocorrelation values ​​greater than the corresponding row autocorrelation threshold; Determine the upper quartile and the lower quartile of the row autocorrelation value set of each autocorrelation matrix, determine whether there is a row pole in the row autocorrelation value set based on the upper quartile and the lower quartile of the autocorrelation value set, if there is a row pole, determine the first interference time of the corresponding abnormal sub-signal based on the row pole, and determine the critical time of the corresponding abnormal sub-signal based on all autocorrelation values ​​in the row autocorrelation value set of the autocorrelation matrix except the row pole; if there is no row pole, determine the critical time of the abnormal sub-signal based on all autocorrelation values ​​in the row autocorrelation value set of the autocorrelation matrix; Determine a column autocorrelation threshold based on all autocorrelation values ​​of each column in each standardized sub-autocorrelation matrix, compare the column autocorrelation threshold with all autocorrelation values ​​in the corresponding column, and determine a column autocorrelation value set of each autocorrelation matrix based on all autocorrelation values ​​greater than the corresponding column autocorrelation threshold; Determine the upper quartile and the lower quartile of the column autocorrelation value set of each autocorrelation matrix, determine whether there is a column pole in the column autocorrelation value set based on the upper quartile and the lower quartile of the column autocorrelation value set, if there is a column pole, based on the column pole as the first interference position of the corresponding abnormal sub-signal, and based on all autocorrelation values ​​in the column autocorrelation value set of the autocorrelation matrix except the column pole, determine the key position of the corresponding abnormal sub-signal; if there is no column pole, determine the key position of the abnormal sub-signal based on all autocorrelation values ​​in the column autocorrelation value set of the autocorrelation matrix; Determine the arrival time difference based on the signal arrival time of each abnormal sub-signal at the multiple receivers, and determine the arrival time difference based on the real-time signal power of each abnormal sub-signal and the positions of the multiple receivers; Determine a second interference time and a second interference position of an interference source of the abnormal sub-signal based on the arrival time difference and the arrival phase difference of the multiple receivers of each abnormal sub-signal; If the first interference time of the abnormal sub-signal is equal to the second interference time, determine the first interference time as the actual interference time of the abnormal sub-signal; if the first interference time of the abnormal sub-signal is not equal to the second interference time, determine the actual interference time based on the second interference time of the abnormal sub-signal and the critical time; If the first interference position of the abnormal sub-signal is equal to the second interference position, the first interference position is determined as the actual interference position of the abnormal sub-signal; if the first interference position of the abnormal sub-signal is not equal to the second interference position, the actual interference position is determined based on the second interference position of the abnormal sub-signal and the key position.

[0059] In this embodiment, all autocorrelation values ​​in the row autocorrelation value set of each autocorrelation matrix are sorted from small to large, and the upper quartile and the lower quartile of the row autocorrelation value set after sorting are determined based on the interquartile range method.

[0060] In this embodiment, all autocorrelation values ​​in the column autocorrelation value set of each autocorrelation matrix are sorted from small to large, and the upper quartile and the lower quartile of the column autocorrelation value set are determined based on the interquartile range method for the sorted column subcorrelation set.

[0061] In this embodiment, whether there is an extreme point in the autocorrelation value set is determined based on the upper quartile and the lower quartile of the autocorrelation value set. This can be done by judging whether there is an autocorrelation value in the autocorrelation value set that is greater than the upper quartile plus the adjustment parameter multiplied by the difference between the upper quartile and the lower quartile. If so, the autocorrelation value is determined to be an extreme point, wherein the extreme point can be a row extreme point or a column extreme point.

[0062] In this embodiment, the arrival time difference indicates the time difference of an abnormal sub-signal arriving at different receiving points from a transmission source.

[0063] In this embodiment, the arrival phase difference indicates the phase difference of an abnormal sub-signal at different receiving points.

[0064] In this embodiment, the second interference time is constructed by constructing an equation group through the arrival time difference between each receiver and the interference source, and the second interference time of the interference source is solved by the least square method or other optimization algorithms.

[0065] In this embodiment, the second interference position infers the distance difference between the interference source and each receiver according to the arrival time difference, thereby determining the position of the interference source.

[0066] In this embodiment, the actual interference time is determined based on the second interference time of the abnormal sub-signal and the critical time. The weighted average method or the least square method can be used to combine the second interference time and the critical time to determine the actual interference time of the abnormal sub-signal. In this embodiment, the actual interference position is determined based on the second interference position of the abnormal sub-signal and the key position. The weighted average method or the least squares method can be used to combine the second interference position and the key position set to determine the actual interference position of the abnormal sub-signal.

[0067] The beneficial effects of the above technical solution are as follows: by determining the actual interference time and actual interference position of the interference sources corresponding to all abnormal sub-signals, it is possible to ensure that the UAV flies in a controlled environment, provide a clear goal for adopting interference suppression strategies, and improve the mission success rate and flight safety of the UAV.

[0068] Embodiment 8: The embodiment of the present invention also provides a UAV communication interference detection system, such as Figure 2 As shown, including: Detection module: monitors the current communication status of the drone in real time to determine the real-time communication data and obtain the historical communication parameters of the drone under normal flight conditions; Abnormal module: analyzes real-time communication data and historical communication parameters to determine abnormal frequency band signals, abnormal strength signals, and abnormal power signals; Analysis module: Analyze the abnormal frequency band signal, abnormal intensity signal and abnormal power signal to determine the sub-autocorrelation matrix of each abnormal sub-signal; Determination module: Determine the actual interference time and actual interference position of the corresponding interference source based on the autocorrelation matrix of each abnormal sub-signal; Classification module: classifies the actual interference time and actual interference location of the interference sources of all abnormal sub-information, and determines the interference suppression strategy for each interference classification.

[0069] The beneficial effects of the above technical solution are as follows: by analyzing the real-time communication data and historical communication parameters of the UAV, determining the abnormal frequency band signals, abnormal strength signals and abnormal power signals, and determining the sub-autocorrelation matrix of each abnormal sub-signal, as well as the actual interference time and actual interference position of each abnormal sub-signal, classifying all abnormal sub-information and determining the interference suppression strategy, the UAV communication anomalies can be discovered in time, communication interruptions, data loss or flight deviations caused by interference can be reduced, the mission success rate and flight safety of the UAV can be improved, the stability and reliability of the UAV communication can be improved, and the stability of the communication link can be ensured.

[0070] The embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art may understand and implement it without creative effort.

[0071] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting interference in UAV communication, characterized in that: include: 101: monitor the current communication status of the UAV in real time to determine the real-time communication data, and obtain the historical communication parameters of the UAV under the normal flight state; 102: Analyze real-time communication data and historical communication parameters to determine abnormal frequency band signals, abnormal strength signals, and abnormal power signals; 103: Analyze the abnormal frequency band signal, the abnormal intensity signal, and the abnormal power signal to determine a sub-autocorrelation matrix of each abnormal sub-signal; 104: Determine the actual interference time and actual interference position of the corresponding interference source based on the autocorrelation matrix of each abnormal sub-signal; 105: Classify the actual interference time and actual interference position of the interference sources of all abnormal sub-information, and determine the interference suppression strategy for each interference classification.

2. According to the method for detecting communication interference of unmanned aerial vehicles (UAVs) according to claim 1, the real-time communication data includes the real-time signal of the UAV within a specified time window, the real-time communication channel corresponding to the real-time signal, the real-time signal strength, the real-time signal power, the receiving positions of multiple receivers, and the signal arrival time when the multiple receivers receive the real-time signal, wherein: The real-time signal includes all real-time sub-signals within the specified time window; The historical communication parameters include the historical communication frequency band and historical signal strength of the UAV within the historical specified time window.

3. A method for detecting interference in UAV communication according to claim 2, characterized in that: Analyze real-time communication data and historical communication parameters to identify abnormal frequency band signals, including: Analyze all historical communication frequency bands within the specified time window to determine the historical normal communication frequency band of the UAV; Determine whether all real-time communication frequency bands within the specified time window are consistent with historical normal communication frequency bands, extract all real-time communication sub-frequency bands that are inconsistent with historical normal communication frequency bands, determine abnormal communication frequency bands, and extract all real-time sub-signals within the abnormal communication frequency bands to determine as abnormal frequency band signals; All real-time communication sub-frequency bands that are consistent with historical normal communication frequency bands are extracted to determine the normal communication frequency band, and all real-time sub-signals within the normal communication frequency band are extracted to determine as normal frequency band signals.

4. A method for detecting interference in UAV communication according to claim 3, characterized in that: Analyze real-time communication data as well as historical communication parameters to identify abnormal strength signals, including: Extract the historical signal strength of the drone within the normal frequency band signal and all historical specified time windows; Determine a signal strength range and the number of sub-signals of each signal strength within the signal strength range based on historical signal strengths, and draw a strength histogram; Determine the intensity kurtosis and the intensity skewness of the intensity histogram, determine the fitting distribution type based on the intensity kurtosis and the intensity skewness, fit the historical signal intensity based on the fitting distribution type, and determine the intensity signal range based on the fitting result; Determine whether all real-time sub-signal strengths within the specified time window are within the strength signal range, extract all real-time sub-signal strengths that are not within the strength signal range, and determine that the real-time sub-signals corresponding to all real-time sub-signal strengths are abnormal strength signals.

5. The method for detecting interference in UAV communication according to claim 2, characterized in that: Analyze real-time communication data and historical communication parameters to identify abnormal power signals, including: Perform spectrum analysis on the real-time signal power of the UAV within a specified time window to determine the real-time spectrum graph; An abnormal peak is determined based on the real-time spectrum diagram, all real-time sub-signal powers within the abnormal peak are extracted, and the real-time sub-signals corresponding to all the extracted real-time sub-signal powers are determined to be abnormal power signals.

6. The method for detecting interference in UAV communication according to claim 1, characterized in that: Analyze the abnormal frequency band signal, abnormal intensity signal and abnormal power signal to determine the autocorrelation matrix of each abnormal sub-signal, including: Calculating the autocorrelation values ​​of the abnormal sub-signals based on the abnormal frequency band signal, the abnormal intensity signal and the abnormal power signal respectively; ;in, Indicates abnormal sub-signal The autocorrelation value at spatial offset d and time offset m, , k=1 indicates abnormal frequency band signal, k=2 indicates abnormal intensity signal, k=3 indicates abnormal power signal, represents the abnormal sub-signal with spatial position i and time point j in the abnormal signal k, represents the weight of the signal at spatial position i, time point j, spatial offset d, and time offset m, Indicates abnormal sub-signal The signal value after spatial offset d and time offset m is, Indicates abnormal sub-signal The sub-offset mean within the sub-space offset window and the sub-time offset window, Indicates abnormal sub-signal At the signal value of the spatial offset a and the time offset b, a represents the spatial offset value of the abnormal sub-signal, and b represents the time offset value of the abnormal sub-signal. represents the subspace offset window of the abnormal k signal, represents the sub-time offset window of the abnormal k signal, and ; Based on all autocorrelation values ​​of each abnormal sub-signal at different spatial offsets and different time offsets, a sub-autocorrelation matrix corresponding to the abnormal sub-signal is constructed, and all sub-autocorrelation matrices are standardized, wherein the rows of the sub-autocorrelation matrix include the autocorrelation values ​​of all different time offsets under the same spatial offset, and the columns of the sub-autocorrelation matrix include the autocorrelation values ​​of all different spatial offsets under the same time offset.

7. A method for detecting interference in UAV communication according to claim 6, characterized in that: Determine the actual interference time and actual interference position of the corresponding interference source based on the autocorrelation matrix of each abnormal sub-signal, including: Determine a row autocorrelation threshold based on all autocorrelation values ​​of each row in each standardized sub-autocorrelation matrix, compare the row autocorrelation threshold with all autocorrelation values ​​in the corresponding row, and determine a row autocorrelation value set of each autocorrelation matrix based on all autocorrelation values ​​greater than the corresponding row autocorrelation threshold; Determine the upper quartile and the lower quartile of the row autocorrelation value set of each autocorrelation matrix, determine whether there is a row pole in the row autocorrelation value set based on the upper quartile and the lower quartile of the autocorrelation value set, if there is a row pole, determine the first interference time of the corresponding abnormal sub-signal based on the row pole, and determine the critical time of the corresponding abnormal sub-signal based on all autocorrelation values ​​in the row autocorrelation value set of the autocorrelation matrix except the row pole; if there is no row pole, determine the critical time of the abnormal sub-signal based on all autocorrelation values ​​in the row autocorrelation value set of the autocorrelation matrix; Determine a column autocorrelation threshold based on all autocorrelation values ​​of each column in each standardized sub-autocorrelation matrix, compare the column autocorrelation threshold with all autocorrelation values ​​in the corresponding column, and determine a column autocorrelation value set of each autocorrelation matrix based on all autocorrelation values ​​greater than the corresponding column autocorrelation threshold; Determine the upper quartile and the lower quartile of the column autocorrelation value set of each autocorrelation matrix, determine whether there is a column pole in the column autocorrelation value set based on the upper quartile and the lower quartile of the column autocorrelation value set, if there is a column pole, based on the column pole as the first interference position of the corresponding abnormal sub-signal, and based on all autocorrelation values ​​in the column autocorrelation value set of the autocorrelation matrix except the column pole, determine the key position of the corresponding abnormal sub-signal; if there is no column pole, determine the key position of the abnormal sub-signal based on all autocorrelation values ​​in the column autocorrelation value set of the autocorrelation matrix; Determine the arrival time difference based on the signal arrival time of the multiple receivers of each abnormal sub-signal respectively, and at the same time, determine the arrival time difference based on the real-time signal power of each abnormal sub-signal and the positions of the multiple receivers respectively; Determine a second interference time and a second interference position of an interference source of the abnormal sub-signal based on the arrival time difference and the arrival phase difference of the multiple receivers of each abnormal sub-signal; If the first interference time of the abnormal sub-signal is equal to the second interference time, determine the first interference time as the actual interference time of the abnormal sub-signal; if the first interference time of the abnormal sub-signal is not equal to the second interference time, determine the actual interference time based on the second interference time of the abnormal sub-signal and the critical time; If the first interference position of the abnormal sub-signal is equal to the second interference position, the first interference position is determined as the actual interference position of the abnormal sub-signal; if the first interference position of the abnormal sub-signal is not equal to the second interference position, the actual interference position is determined based on the second interference position of the abnormal sub-signal and the key position.

8. A UAV communication interference detection system, characterized in that: include: Detection module: monitors the current communication status of the drone in real time to determine the real-time communication data and obtain the historical communication parameters of the drone under normal flight conditions; Abnormal module: analyzes real-time communication data and historical communication parameters to determine abnormal frequency band signals, abnormal strength signals, and abnormal power signals; Analysis module: Analyze the abnormal frequency band signal, abnormal intensity signal and abnormal power signal to determine the sub-autocorrelation matrix of each abnormal sub-signal; Determination module: Determine the actual interference time and actual interference position of the corresponding interference source based on the autocorrelation matrix of each abnormal sub-signal; Classification module: classifies the actual interference time and actual interference location of the interference sources of all abnormal sub-information, and determines the interference suppression strategy for each interference classification.

Images