Unmanned aerial vehicle remote signal analysis method, device, equipment and storage medium

Abstract

This invention relates to the field of unmanned aerial vehicle (UAV) monitoring technology, and discloses a method, device, electronic device, and storage medium for remote signal analysis of UAVs. The method includes: parsing a remote identification signal broadcast by a UAV acquired by a preset receiving device to obtain UAV identification and flight status data; calculating wireless communication data in the spatial environment where the receiving device is located based on wireless environmental parameters and transmission performance parameters; performing communication analysis on network signal quality parameters to obtain communication analysis data; performing positioning analysis on satellite positioning signal parameters to obtain positioning analysis data; and performing cross-analysis on the flight status data, wireless communication data, communication analysis data, and positioning analysis data corresponding to the UAV identification to obtain signal analysis results. Through the signal parsing, environmental parameter calculation, communication analysis, positioning analysis, and data cross-analysis implemented in this invention, the efficiency of remote signal analysis of UAVs can be improved.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) monitoring technology, and in particular to a method, apparatus, device, and storage medium for remote signal analysis of UAVs. Background Technology

[0002] With the rapid development of the drone industry, the number of drones in airspace has surged, and behaviors such as "unauthorized flights" and "air traffic disruption" seriously threaten the security of sensitive areas. Drones equipped with Remote ID (Remote Identity) recognition capabilities can effectively identify and track them by broadcasting their identity and status information in real time. The Remote ID receiver, as a core infrastructure, is responsible for receiving and decoding information broadcast by drones via Bluetooth, 2.4GHz / 5GHz Wi-Fi, and other methods.

[0003] However, existing Remote ID receiving devices have significant limitations, lacking adaptability to complex environments and affecting the reliability of monitoring. The public frequency bands on which these devices rely are susceptible to interference from civilian Wi-Fi and other sources, leading to decreased reception sensitivity, unstable coverage, and difficulty in achieving continuous monitoring. Furthermore, these devices only focus on receiving broadcast-style remote identification signals, neglecting to monitor cellular communication quality and satellite navigation signal integrity. Consequently, they cannot accurately determine whether signal anomalies are caused by the drone itself or environmental interference, resulting in delayed regulatory decisions, low response efficiency, and consequently, low efficiency in remote drone signal analysis. Summary of the Invention

[0004] This invention provides a method, apparatus, device, and storage medium for remote signal analysis of unmanned aerial vehicles (UAVs), the main purpose of which is to solve the problem of low efficiency in remote signal analysis of UAVs.

[0005] To achieve the above objectives, the present invention provides a remote signal analysis method for unmanned aerial vehicles (UAVs), comprising: The remote identification signal broadcast by the drone, obtained by the preset receiving device, is analyzed to obtain the drone identification and flight status data; Obtain the wireless environment parameters and transmission performance parameters of the receiving device, and calculate the wireless communication data of the spatial environment in which the receiving device is located based on the wireless environment parameters and the transmission performance parameters; Obtain network signal quality parameters within the space environment, perform communication analysis on the network signal quality parameters, and obtain communication analysis data; The satellite positioning signal parameters of the receiving device are obtained, and the satellite positioning signal parameters are analyzed to obtain positioning analysis data. The flight status data corresponding to the UAV identifier, the wireless communication data, the communication analysis data, and the positioning analysis data are cross-analyzed to obtain signal analysis results.

[0006] The present invention also provides a remote signal analysis device for unmanned aerial vehicles (UAVs), the device comprising: The signal analysis module is used to analyze the remote identification signal broadcast by the drone acquired by the preset receiving device to obtain the drone identification and flight status data; The parameter calculation module is used to obtain the wireless environment parameters and transmission performance parameters of the receiving device, and calculate the wireless communication data of the spatial environment in which the receiving device is located based on the wireless environment parameters and the transmission performance parameters. The communication analysis module is used to acquire network signal quality parameters in the space environment, perform communication analysis on the network signal quality parameters, and obtain communication analysis data. The positioning analysis module is used to acquire satellite positioning signal parameters of the receiving device, perform positioning analysis on the satellite positioning signal parameters, and obtain positioning analysis data. The signal analysis module is used to perform cross-analysis on the flight status data corresponding to the UAV identifier, the wireless communication data, the communication analysis data, and the positioning analysis data to obtain the signal analysis results.

[0007] The present invention also provides an electronic device, the electronic device comprising: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the aforementioned UAV remote signal analysis method.

[0008] The present invention also provides a computer-readable storage medium storing at least one computer program, which is executed by a processor in an electronic device to implement the above-described UAV remote signal analysis method.

[0009] This invention, through parsing the remote identification signal broadcast by a UAV acquired by a preset receiving device, obtains UAV identification and flight status data. Simultaneously, it collects wireless environment parameters, transmission performance parameters, network signal quality parameters, and satellite positioning signal parameters of the space environment where the receiving device is located, generating wireless communication data, communication analysis data, and positioning analysis data respectively. Furthermore, it cross-analyzes the flight status data corresponding to the UAV identification with the aforementioned three types of environmental signal quality data, and through mutual verification between the data, effectively identifies risks such as location differences, communication anomalies, misjudgments of coverage blind spots, and unreliable navigation signals, forming signal analysis results, thereby improving the efficiency of remote UAV signal analysis. Therefore, the UAV remote signal analysis method, device, equipment, and storage medium proposed in this invention can solve the problem of low efficiency in remote UAV signal analysis. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating a remote signal analysis method for unmanned aerial vehicles (UAVs) provided in an embodiment of the present invention. Figure 2 This is a functional block diagram of a remote signal analysis device for unmanned aerial vehicles provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device for implementing the remote signal analysis method for unmanned aerial vehicles (UAVs) according to an embodiment of the present invention.

[0011] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0012] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0013] To address the problems of weak correlation between UAV identity and trajectory, inaccurate assessment of monitoring range, missed detection of network transmission failures, and insufficient positioning reliability caused by the lack of environmental signal quality perception in existing UAV remote signal analysis methods, an embodiment of the present invention provides a UAV remote signal analysis method. This method analyzes remote identification signals, collects wireless environmental parameters, transmission performance parameters, network signal quality parameters, and satellite positioning signal parameters, and then obtains signal analysis results through environmental parameter calculation, communication analysis, positioning analysis, and data cross-referencing, thereby improving the efficiency of UAV remote signal analysis.

[0014] Reference Figure 1 The diagram shown is a flowchart illustrating a remote signal analysis method for unmanned aerial vehicles (UAVs) according to an embodiment of this application. In this embodiment, the remote signal analysis method for UAVs includes: S1. Analyze the remote identification signal broadcast by the drone obtained by the preset receiving device to obtain the drone identification and flight status data.

[0015] In this embodiment of the invention, the preset receiving device is a remote identification signal receiving device equipped with a programmable wireless module, a cellular network communication module, a GNSS module, and an embedded calculator, used to receive and parse the remote identification signal broadcast by the UAV; the remote identification signal is a signal frame received by the receiving device from the UAV periodically broadcast via Bluetooth or Wi-Fi (2.4GHz / 5GHz) frequency band, conforming to the ASTM F3411 or GB 46750-2025 standard format; the UAV identifier is the unique identification of the UAV parsed from the remote identification signal, and the flight status data is the status information parsed from the remote identification signal, including the UAV's location information (such as real-time latitude and longitude coordinates and altitude), velocity vector, and timestamp.

[0016] In this embodiment of the invention, parsing the remote identification signal broadcast by a drone acquired by a preset receiving device to obtain drone identification and flight status data includes: extracting a protocol type identifier and channel index parameters from the remote identification signal broadcast by the drone acquired by the preset receiving device; selecting signal demodulation parameters corresponding to the protocol type identifier from a preset configuration parameter library; correcting the signal demodulation parameters according to the channel index parameters to obtain target demodulation parameters; and demodulating the remote identification signal based on the target demodulation parameters to obtain the drone identification and flight status data corresponding to the remote identification signal.

[0017] In detail, the protocol type identifier is an enumerated identifier used to distinguish the wireless communication protocol used by the remote identification signal; the channel index parameters are a set of quantitative indicators of the quality characteristics of the remote identification signal in the physical layer transmission channel, including received signal strength indication, signal-to-noise ratio and channel state information.

[0018] In this embodiment of the invention, the protocol type identifier and channel index parameters are extracted from the remote identification signal broadcast by the UAV obtained from the preset receiving device, including: Acquire candidate frames corresponding to remote identification signals; extract phase trajectories from the preamble region within the candidate frames to obtain phase change feature sequences; match the phase change feature sequences with a preset modulation type template library to identify the modulation type of the candidate frames; determine the protocol type identifier of the remote identification signal based on the modulation type and the carrier frequency of the candidate frames; extract channel index parameters from the candidate frames in the physical layer channel of the corresponding protocol type.

[0019] In detail, the programmable wireless module continuously performs broadband spectrum scanning in the 2.4GHz and 5GHz frequency bands, downconverts the received radio frequency signals to baseband, generates a digital baseband signal stream after analog-to-digital conversion, extracts the frame length value of each data frame in the digital baseband signal stream, and compares it with the preset remote identification signal frame length range (e.g., Bluetooth broadcast frames are usually between 30 and 50 bytes, and Wi-Fi beacon frames are usually around 100 bytes). At the same time, it records the interval between the arrival times of two consecutive data frames and determines whether the interval is stable within the range of 1 second ± 20 milliseconds (this deviation value corresponds to the clock drift allowed by the drone broadcast cycle). Data frames that meet the requirements of having a frame length within the range of remote identification signal frame length and a stable interval are marked as candidate frames for remote identification signals.

[0020] Specifically, based on the start time and preamble length of the candidate frame (e.g., Bluetooth: 72μs, Wi-Fi: 16μs), a complex sampling sequence of the preamble region is extracted from the candidate frame. Point-by-point instantaneous phase calculation is performed on the complex sampling sequence, and an initial phase sequence is obtained through arctangent operation. A differential operation is performed on the initial phase sequence (i.e., the phase value of the previous sampling point is subtracted from the phase value of the next sampling point to eliminate the linear phase accumulation caused by the carrier frequency offset) to obtain a differential phase sequence. The differential phase sequence is then low-pass filtered to obtain a phase change feature sequence.

[0021] In this embodiment of the invention, the phase change feature sequence is nonlinearly aligned with the template sequences in a preset modulation type template library one by one, the cumulative Euclidean distance between them is calculated, and the modulation mode corresponding to the template with the smallest cumulative distance is selected as the identification result, and the modulation type is output. The preset modulation type template library stores standard phase change feature templates for Bluetooth GFSK modulation and Wi-Fi OFDM modulation. For example, the Bluetooth GFSK template is a continuous and slow phase change curve (constant phase change rate, maximum frequency deviation ±160kHz), and the Wi-Fi OFDM template is a rapidly jumping pseudo-random phase distribution (phase jump caused by subcarrier modulation).

[0022] Further, the center carrier frequency value of the candidate frame is read. If the modulation type is identified as GFSK and the carrier frequency falls within the range of 2.402GHz to 2.480GHz (Bluetooth classic band), the protocol type is determined to be Bluetooth (BLE). If the modulation type is identified as OFDM, the frequency band is further distinguished: if the carrier frequency falls within the range of 2.412GHz to 2.472GHz, the protocol type is determined to be 2.4GHz Wi-Fi; if the carrier frequency falls within the range of 5.150GHz to 5.850GHz, the protocol type is determined to be 5GHz Wi-Fi. The above identification results are encoded into enumerated identifiers (such as PROTOCOL_BLE, PROTOCOL_WIFI_24G, PROTOCOL_WIFI_5G) and output as the protocol type identifier.

[0023] In this embodiment of the invention, the configuration parameter library is a structured data table pre-stored in the non-volatile memory of the embedded calculator. The data table uses the protocol type identifier as the index key to map and store the demodulation parameters corresponding to each protocol, including the GFSK modulation index and bandwidth parameters for Bluetooth, and the OFDM subcarrier spacing, guard interval length, and default demodulation threshold for Wi-Fi 2.4G / 5G. The data table is queried according to the extracted protocol type identifier, and the corresponding demodulation parameter set is read after matching the index key to obtain the signal demodulation parameters.

[0024] Furthermore, when the signal-to-noise ratio (SNR) in the channel performance parameters is lower than the default demodulation threshold in the signal demodulation parameters, the decision threshold of the demodulator is lowered and a soft decision mode is enabled to output log-likelihood ratio soft information. When the SNR is higher than the default demodulation threshold, a hard decision mode is adopted to reduce processing latency, thus obtaining demodulation mode parameters. For Wi-Fi signals, based on the SNR ranking results of each subcarrier in the channel state information, high SNR subcarriers are marked as priority demodulation regions, and the frequency domain equalization coefficient is dynamically adjusted to obtain frequency domain equalization parameters. The demodulation mode parameters, frequency domain equalization parameters, and signal demodulation parameters are then combined to form the target demodulation parameters.

[0025] In this embodiment of the invention, the baseband processing unit of the programmable wireless module is configured according to the protocol type identifier, and the modulation and demodulation configuration in the target demodulation parameters is loaded; demodulation and channel decoding are performed on the physical layer payload in the candidate frame corresponding to the remote identification signal; the Bluetooth signal uses the Viterbi algorithm to perform error correction decoding on the LLR soft information; the Wi-Fi signal uses the LDPC iterative decoding algorithm to decode the high signal-to-noise ratio subcarrier data in the priority demodulation area to obtain the decoded bitstream; semantic parsing is performed on the bitstream; frames with the message type of location information are extracted from the parsed data structure; and the UAV unique identifier, real-time latitude and longitude coordinates, altitude, velocity vector and timestamp are extracted from the corresponding fields of the frame; geofence validity is verified on the latitude and longitude coordinates; and the data that passes the verification is output as the final UAV identifier and flight status data.

[0026] S2. Obtain the wireless environment parameters and transmission performance parameters of the receiving device, and calculate the wireless communication data of the spatial environment in which the receiving device is located based on the wireless environment parameters and transmission performance parameters.

[0027] In this embodiment of the invention, wireless environment parameters are characteristic quantities of the open frequency band electromagnetic spectrum state within the spatial environment in which the receiving device is located, such as the power spectral density of each channel, channel occupancy rate, interference signal strength, and number of interference sources; transmission performance parameters are inherent signal receiving capability indicators in the receiving device, including the receiving sensitivity threshold, demodulation signal-to-noise ratio threshold, and maximum input level; wireless communication data refers to a structured dataset generated based on the joint calculation of wireless environment parameters and transmission performance parameters, used to quantify the signal coverage capability and communication reliability of the receiving device in the current spatial environment.

[0028] In detail, the programmable wireless module performs a spectrum situation scan on the 2.4GHz and 5GHz frequency bands, collects the power spectral density, channel occupancy rate, interference signal strength and number of interference sources for each channel, and obtains wireless environment parameters; at the same time, it reads the receiver sensitivity threshold, demodulation signal-to-noise ratio threshold and maximum input level of the receiving device as transmission performance parameters.

[0029] In this embodiment of the invention, the calculation of wireless communication data in the spatial environment of the receiving device based on wireless environment parameters and transmission performance parameters includes: calculating channel interference distribution parameters in the spatial environment of the receiving device based on wireless environment parameters; calculating link margin in the spatial environment of the receiving device based on transmission performance parameters and channel interference distribution parameters; inputting the link margin into a preset path loss model to fit and generate a coverage distance boundary; and performing spatial rasterization mapping on the coverage distance boundary and the channel interference distribution parameters to obtain wireless communication data.

[0030] In detail, the power spectral density of each channel in the wireless environment parameters is normalized to generate the normalized interference intensity coefficient of each channel; the channel occupancy rate of each channel is smoothed by time window filtering to generate the steady-state channel occupancy; the normalized interference intensity coefficient and the steady-state channel occupancy are weighted and fused to generate the comprehensive interference factor of each channel; the comprehensive interference factor is spatially interpolated to construct the channel interference distribution parameter matrix centered on the receiving device.

[0031] Specifically, the receiver sensitivity threshold is extracted from the transmission performance parameters, and the current channel integrated interference power is extracted from the channel interference distribution parameters; the difference between the receiver sensitivity threshold and the integrated interference power is calculated to obtain the basic link margin; the basic link margin is corrected and compensated according to the demodulation signal-to-noise ratio threshold in the transmission performance parameters to generate the final link margin.

[0032] Furthermore, the link margin is used as the maximum allowable loss value in the path loss model. Based on the inverse function form of the preset path loss model, the maximum allowable loss value is mapped to the corresponding propagation distance. Boundary point sampling is performed on the propagation distance in different directions, and curve fitting is performed on the sampling points to generate a closed coverage distance boundary.

[0033] In this embodiment of the invention, a two-dimensional planar grid coordinate system is established with the location of the receiving device as the origin. Each grid cell is assigned a corresponding channel interference distribution parameter value and a coverage status identifier. The coverage status identifier of the grid cell is assigned a value according to the coverage distance boundary. The channel interference distribution parameters of the grid cell are rendered in layers with color to generate superimposed visual data of coverage range and interference distribution, which together with the coverage status identifier form wireless communication data.

[0034] S3. Obtain network signal quality parameters within the space environment, perform communication analysis on the network signal quality parameters, and obtain communication analysis data.

[0035] In this embodiment of the invention, the network signal quality parameters are a set of quantitative indicators of network transmission reliability, specifically including reference signal received power (RSRP), signal-to-interference-plus-noise ratio (SINR), user plane round-trip time (RTT), and packet loss rate.

[0036] In detail, the reference signal received power and signal-to-interference-plus-noise ratio of the target area and neighboring cells are periodically read through the diagnostic interface of the cellular communication module. The user plane round-trip time is obtained through active detection, and the uplink packet loss rate within a preset period is calculated. The above parameters are then aligned by timestamp and packaged to generate a network signal quality parameter set.

[0037] In this embodiment of the invention, the communication analysis data is a structured dataset generated after multi-dimensional quantitative evaluation of network signal quality, used to characterize the reliability of the uplink transmission path of network-based remote identification signals; the wireless communication data evaluates the coverage and reception capability of receiving devices for wireless signals broadcast by UAVs via Bluetooth or Wi-Fi; and the communication analysis data evaluates the support capability of the network infrastructure at the location of the receiving device for remote identification signals transmitted by UAVs via cellular networks.

[0038] In this embodiment of the invention, communication analysis is performed on network signal quality parameters to obtain communication analysis data, including: extracting time-series fluctuation features of network signal quality parameters to obtain signal quality trend features; adaptively correcting a preset network quality judgment threshold based on the signal quality trend features to obtain a judgment boundary; calculating a network transmission reliability factor of network signal quality parameters based on the judgment boundary; and associating and encapsulating the signal quality trend features and the network transmission reliability factor to obtain communication analysis data of network signal quality parameters.

[0039] In detail, a sliding window statistical method is used to calculate the local mean and variance of the continuously acquired network signal quality parameter sequence, extract the fluctuation amplitude and rate of change of the network signal quality parameters on the time axis, and use a time series decomposition algorithm to split the parameter sequence corresponding to the network signal quality parameters into long-term trend components and random fluctuation components, identify the overall change direction and periodicity of the network signal quality parameters, and perform density-weighted sampling on key change nodes based on a non-uniform sampling strategy to generate a time-series fluctuation feature set (i.e., signal quality trend features) that characterizes the dynamic evolution of signal quality.

[0040] Specifically, based on the fluctuation amplitude and rate of change in the signal quality trend characteristics, a dynamic threshold adjustment function is constructed: the larger the fluctuation amplitude, the more lenient the judgment threshold is; the faster the rate of change, the higher the response sensitivity of the judgment threshold; an environmental interference sensing mechanism is introduced to dynamically adjust the confidence interval width of the judgment threshold according to the intensity of the random fluctuation component in the signal quality trend characteristics (e.g., the confidence interval width is increased by 10% for every 3dB increase in the intensity of the random fluctuation component), and the corrected judgment threshold boundary is output as the judgment boundary.

[0041] Furthermore, the network signal quality parameters are mapped to the evaluation coordinate system formed by the decision boundary, and the relative positional relationship between the network signal quality parameter values ​​and the decision boundary is calculated (the deeper the network signal quality parameter value is inside the decision boundary, the better the transmission quality in that dimension). The normalized score (0~1 interval) of the network signal quality parameters (RSRP, SINR, RTT, packet loss rate) under the decision boundary is calculated. The score calculation formula is (parameter value - lower boundary) / (upper boundary - lower boundary). The parameters are then weighted and fused according to preset weights (RSRP weight 0.3, SINR weight 0.3, RTT weight 0.2, packet loss rate weight 0.2) to generate a factor characterizing the reliability of network transmission.

[0042] In this embodiment of the invention, the signal quality trend features are correlated with the network transmission reliability factor using timestamps as indexes. Key turning points in the trends of the two are identified and matched and labeled. A causal mapping relationship between signal quality changes and transmission reliability fluctuations is established. The correlated trend features, reliability factors and causal mapping relationship are sequentially encapsulated to obtain communication analysis data.

[0043] S4. Obtain the satellite positioning signal parameters of the receiving device, perform positioning analysis on the satellite positioning signal parameters, and obtain positioning analysis data.

[0044] In this embodiment of the invention, the satellite positioning signal parameters are multi-dimensional quantitative indicators of the GNSS receiving module's acquisition status of satellite signals and positioning solution quality. The satellite positioning signal parameters include: (visible) satellite number, orbital parameters, signal-to-noise ratio of each satellite, number of satellites participating in the positioning solution, horizontal accuracy factor, vertical accuracy factor, position accuracy factor, receiver's own latitude and longitude coordinates, and altitude.

[0045] In detail, the system continuously captures satellite signals from GPS, BeiDou, and GLONASS through a multi-system compatible GNSS receiver module, parses satellite numbers and orbital parameters from satellite navigation messages, extracts the measured carrier-to-noise ratio values ​​of each satellite signal from the receiving channel, and reads the list of satellites currently participating in positioning, three-dimensional accuracy factors, and the receiver's own position information from the positioning calculation engine. The above parameters are then packaged according to timestamps to generate a satellite positioning signal parameter set.

[0046] In this embodiment of the invention, the positioning analysis data includes positioning accuracy factor, positioning reliability level, and a set of selected satellites; the positioning accuracy factor refers to the accuracy factor of the receiving device's own positioning output by the GNSS module (including horizontal accuracy factor HDOP, vertical accuracy factor VDOP, or position accuracy factor PDOP).

[0047] In this embodiment of the invention, positioning analysis is performed on satellite positioning signal parameters to obtain positioning analysis data, including: performing stability analysis on the signal-to-noise ratio in the satellite positioning signal parameters to obtain the signal stability coefficient; calculating the geometric distribution characteristics based on the satellite number and orbital parameters in the satellite positioning signal parameters; filtering the satellite numbers in the satellite positioning signal parameters based on the signal stability coefficient and geometric distribution characteristics to obtain filtered satellite numbers; and performing positioning calculations on the satellites corresponding to the filtered satellite numbers to obtain positioning analysis data.

[0048] In detail, the continuously acquired carrier-to-noise ratio (CNR) is divided into several observation segments according to a preset duration. The average CNR within each observation segment is calculated as the segment reference value. The deviation of each sampling point from the segment reference value is calculated as the fluctuation amplitude. The rate of change of the reference values ​​of adjacent observation segments is calculated as the mean drift trend. The number of abnormal sampling points with deviations exceeding a preset threshold is counted as the abnormal jump point distribution. The fluctuation amplitude, mean drift trend, and abnormal jump point distribution are weighted and fused to obtain the signal stability coefficient, which characterizes the quality stability of the satellite signal within the observation period. The smaller the fluctuation amplitude, the smoother the mean drift trend, and the fewer the abnormal jump points, the higher the signal stability coefficient.

[0049] Specifically, the instantaneous position coordinates of each satellite in the spatiotemporal reference system are calculated based on the satellite number and orbital parameters. The azimuth and elevation angles of each satellite relative to the receiving equipment are constructed based on the spatial position of the receiving equipment. Then, the distribution density and angular coverage of each satellite in space are calculated. Finally, the geometric distribution characteristics of each satellite are generated through spatial vector analysis.

[0050] Furthermore, satellites with a stability coefficient lower than 0.6 are screened out, and then satellites with high redundancy in geometric distribution characteristics are removed according to the azimuth uniformity requirement (for example, among multiple satellites with similar elevation angles in the same azimuth interval, only the one with the highest signal-to-noise ratio is retained), thus obtaining the selected satellite numbers.

[0051] In this embodiment of the invention, a weighted least squares estimation algorithm is used to solve the three-dimensional position coordinates and clock error parameters of the receiving device. At the same time, the difference between the observed value and the estimated value is calculated as the residual. The root mean square of the residual is obtained by statistical analysis. Based on the root mean square of the residual, the number of satellites corresponding to the selected satellite numbers, the geometric distribution characteristics, and the mean signal stability coefficient of the satellites involved in the solution, the error confidence interval of the current positioning result is comprehensively evaluated. The positioning result is divided into three levels: high confidence (≥8 effective satellites, PDOP≤2, solution residual ≤3 meters), medium confidence (5~7 effective satellites, PDOP≤4, solution residual ≤8 meters), and low confidence (≤4 effective satellites, PDOP>4, solution residual>8 meters). Finally, positioning analysis data including positioning coordinates, positioning accuracy factor, and positioning confidence level are obtained.

[0052] S5. Perform cross-analysis on the flight status data, wireless communication data, communication analysis data, and positioning analysis data corresponding to the UAV identifier to obtain signal analysis results.

[0053] In this embodiment of the invention, the signal analysis result refers to a comprehensive data set generated by cross-validating and fusing flight status data, wireless communication data, communication analysis data, and positioning analysis data under a unified spatiotemporal reference, using the UAV identifier as an index.

[0054] In this embodiment of the invention, the flight status data, wireless communication data, communication analysis data, and positioning analysis data corresponding to the UAV identifier are cross-analyzed to obtain signal analysis results. This includes: acquiring the location information and timestamp from the flight status data corresponding to the UAV identifier, and acquiring the positioning accuracy factor and positioning confidence from the positioning analysis data; performing position confidence analysis on the receiving device based on the location information, timestamp, and positioning accuracy factor to obtain position deviation confidence; acquiring the coverage distance boundary and channel interference distribution parameters from the wireless communication data, and performing communication confidence analysis on the receiving device based on the position deviation confidence, coverage distance boundary, and communication analysis data to obtain communication confidence results; and performing multi-level data fusion of the position deviation confidence, positioning confidence, communication confidence results, and channel interference distribution parameters to obtain signal analysis results.

[0055] In detail, the UAV position information in the flight status data and the receiving device's own position information in the positioning analysis data are converted to the same spatial reference frame. The Euclidean distance between the two in the horizontal direction is calculated as the original position deviation. The standard deviation of the receiving device's positioning error is calculated based on the positioning accuracy factor. The original position deviation is divided by the standard deviation for normalization, and the position deviation confidence level is obtained as a multiple of the standard deviation. When the multiple is less than 1, it is judged as high confidence level, 1 to 3 times is medium confidence level, and more than 3 times is low confidence level.

[0056] Specifically, the position deviation confidence level is mapped to a weight coefficient, with high confidence level assigned a weight of 0.6, medium confidence level 0.3, and low confidence level 0.1. The coverage distance boundary is compared with the UAV's position information to determine whether the UAV is within the coverage area and generate a coverage status identifier. The network transmission reliability factor is extracted from the communication analysis data and weighted by the weight coefficient and the coverage status identifier. When the UAV is within the coverage area and the network transmission reliability factor is greater than 0.7, a high communication confidence level is output; when the UAV is within the coverage area but the reliability factor is between 0.3 and 0.7, a medium communication confidence level is output; and when the UAV is outside the coverage area or the reliability factor is less than 0.3, a low communication confidence level is output, thus obtaining the communication confidence result.

[0057] Furthermore, the first level matches and verifies the position deviation confidence level with the positioning reliability level. When both are at a high level, a positioning reliability indicator is output; otherwise, a positioning risk indicator is output. The second level correlates the communication confidence level result with the current channel integrated interference factor in the channel interference distribution parameters. When the communication confidence level is high and the interference factor is below the threshold, a communication reliability indicator is output; otherwise, a communication risk indicator is output. The third level combines and maps the positioning reliability indicator / risk indicator with the communication reliability indicator / risk indicator to generate a four-level signal integrity level (normal / positioning anomaly / communication anomaly / dual anomaly), and associates it with UAV identification, flight status data, and risk type description to form a structured signal analysis result.

[0058] like Figure 2 The diagram shown is a functional block diagram of a remote signal analysis device for unmanned aerial vehicles provided in an embodiment of the present invention.

[0059] The UAV remote signal analysis device 200 of this invention can be installed in an electronic device. Depending on the functions implemented, the UAV remote signal analysis device 200 may include a signal parsing module 201, a parameter calculation module 202, a communication analysis module 203, a positioning analysis module 204, and a signal analysis module 205. The module described in this invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and which are stored in the memory of the electronic device.

[0060] In this embodiment, the functions of each module / unit are as follows: Signal parsing module 201 is used to parse the remote identification signal broadcast by the UAV acquired by the preset receiving device to obtain UAV identification and flight status data; The parameter calculation module 202 is used to obtain the wireless environment parameters and transmission performance parameters of the receiving device, and calculate the wireless communication data of the spatial environment in which the receiving device is located based on the wireless environment parameters and the transmission performance parameters. The communication analysis module 203 is used to acquire network signal quality parameters in the space environment, perform communication analysis on the network signal quality parameters, and obtain communication analysis data. The positioning analysis module 204 is used to acquire the satellite positioning signal parameters of the receiving device, perform positioning analysis on the satellite positioning signal parameters, and obtain positioning analysis data. The signal analysis module 205 is used to perform cross-analysis on the flight status data corresponding to the UAV identifier, the wireless communication data, the communication analysis data, and the positioning analysis data to obtain the signal analysis results.

[0061] In detail, each module in the UAV remote signal analysis device 200 described in this embodiment of the invention uses the same technical means as the UAV remote signal analysis method described in the accompanying drawings, and can produce the same technical effect, which will not be repeated here.

[0062] like Figure 3 The diagram shown is a structural schematic of an electronic device for implementing a remote signal analysis method for unmanned aerial vehicles (UAVs) according to an embodiment of the present invention.

[0063] The electronic device 1 may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and capable of running on the processor 10, such as a UAV remote signal analysis program.

[0064] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., executing a remote signal analysis program for a drone) and calls data stored in the memory 11 to perform various functions of the electronic device and process data.

[0065] The memory 11 includes at least one type of readable storage medium, including flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of an electronic device, such as a portable hard drive. In other embodiments, the memory 11 can be an external storage device of the electronic device, such as a plug-in portable hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc. Furthermore, the memory 11 can include both internal and external storage units of the electronic device. The memory 11 can be used not only to store application software and various types of data installed on the electronic device, such as the code of a drone remote signal analysis program, but also to temporarily store data that has been output or will be output.

[0066] The communication bus 12 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0067] The communication interface 13 is used for communication between the aforementioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, Bluetooth interface, etc.), typically used to establish communication connections between the electronic device and other electronic devices. The user interface may be a display, an input unit (such as a keyboard), or optionally, a standard wired or wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device and to display a visual user interface.

[0068] Figure 3 Only electronic devices with components are shown; it will be understood by those skilled in the art that... Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0069] For example, although not shown, the electronic device may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0070] It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

[0071] Specifically, the specific implementation method of the processor 10 for the above instructions can be referred to the description of the relevant steps in the corresponding embodiments of the accompanying drawings, and will not be repeated here.

[0072] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0073] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement the UAV remote signal analysis method of any of the above embodiments. It should be noted that the computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0074] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, 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 other division methods may be used in actual implementation.

[0075] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0076] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0077] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0078] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within the invention. No appended diagram markings in the claims should be construed as limiting the scope of the claims.

[0079] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a system claim may also be implemented by a single unit or device through software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any specific order.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for remote signal analysis of unmanned aerial vehicles (UAVs), characterized in that, The method includes: The remote identification signal broadcast by the drone, obtained by the preset receiving device, is analyzed to obtain the drone identification and flight status data; Obtain the wireless environment parameters and transmission performance parameters of the receiving device, and calculate the wireless communication data of the spatial environment in which the receiving device is located based on the wireless environment parameters and the transmission performance parameters; Obtain network signal quality parameters within the space environment, perform communication analysis on the network signal quality parameters, and obtain communication analysis data; The satellite positioning signal parameters of the receiving device are obtained, and the satellite positioning signal parameters are analyzed to obtain positioning analysis data. The flight status data corresponding to the UAV identifier, the wireless communication data, the communication analysis data, and the positioning analysis data are cross-analyzed to obtain signal analysis results.

2. The UAV remote signal analysis method as described in claim 1, characterized in that, The method of parsing the remote identification signal broadcast by the drone acquired by the preset receiving device yields drone identification and flight status data, including: Extract the protocol type identifier and channel index parameters from the remote identification signal broadcast by the drone obtained from the preset receiving device; Select the signal demodulation parameters corresponding to the protocol type identifier from the preset configuration parameter library; The signal demodulation parameters are corrected based on the channel index parameters to obtain the target demodulation parameters. The remote identification signal is demodulated based on the target demodulation parameters to obtain the UAV identifier and flight status data corresponding to the remote identification signal.

3. The UAV remote signal analysis method as described in claim 2, characterized in that, The extraction of protocol type identifier and channel index parameters from the remote identification signal broadcast by the UAV obtained from the preset receiving device includes: Obtain the candidate frame corresponding to the remote identification signal; Phase trajectory extraction is performed on the preamble region within the candidate frame to obtain a phase change feature sequence; The phase change feature sequence is matched with a preset modulation type template library to identify the modulation type of the candidate frame; The protocol type identifier of the remote identification signal is determined based on the modulation type and the carrier frequency of the candidate frame; Channel index parameters are extracted from the candidate frames in the physical layer channel of the corresponding protocol type.

4. The UAV remote signal analysis method as described in claim 1, characterized in that, The step of calculating the wireless communication data of the receiving device in its spatial environment based on the wireless environment parameters and the transmission performance parameters includes: Based on the wireless environment parameters, calculate the channel interference distribution parameters of the spatial environment in which the receiving device is located; The link margin of the receiving device in the spatial environment is calculated based on the transmission performance parameters and the channel interference distribution parameters. The link margin is input into a preset path loss model to fit and generate the coverage distance boundary; The coverage distance boundary and the channel interference distribution parameters are spatially rasterized and mapped to obtain wireless communication data.

5. The UAV remote signal analysis method as described in claim 1, characterized in that, The communication analysis of the network signal quality parameters to obtain communication analysis data includes: Temporal fluctuation features are extracted from the network signal quality parameters to obtain signal quality trend features; Based on the signal quality trend characteristics, the preset network quality judgment threshold is adaptively corrected to obtain the judgment boundary; The network transmission reliability factor is calculated based on the determination boundary to determine the network signal quality parameters. The signal quality trend characteristics are correlated and encapsulated with the network transmission reliability factor to obtain communication analysis data of the network signal quality parameters.

6. The UAV remote signal analysis method as described in claim 1, characterized in that, The step of performing positioning analysis on the satellite positioning signal parameters to obtain positioning analysis data includes: The signal carrier-to-noise ratio in the satellite positioning signal parameters is analyzed for stability to obtain the signal stability coefficient. Calculate the geometric distribution characteristics based on the satellite number and orbit parameters in the satellite positioning signal parameters; The satellite numbers in the satellite positioning signal parameters are filtered based on the signal stability coefficient and the geometric distribution characteristics to obtain the filtered satellite numbers; Positioning calculations are performed on the satellites corresponding to the selected satellite numbers to obtain positioning analysis data.

7. The UAV remote signal analysis method as described in claim 1, characterized in that, The step of performing cross-analysis on the flight status data corresponding to the UAV identifier, the wireless communication data, the communication analysis data, and the positioning analysis data to obtain signal analysis results includes: Obtain the location information and timestamp from the flight status data corresponding to the UAV identifier, and obtain the positioning accuracy factor and positioning reliability from the positioning analysis data; Based on the location information, the timestamp, and the positioning accuracy factor, the receiving device is subjected to a location confidence analysis to obtain a location deviation confidence level. The coverage distance boundary and channel interference distribution parameters in the wireless communication data are obtained, and the communication confidence analysis is performed on the receiving device based on the location deviation confidence, the coverage distance boundary and the communication analysis data to obtain the communication confidence result; The position deviation confidence, positioning confidence, communication confidence, and channel interference distribution parameters are fused at multiple levels to obtain signal analysis results.

8. A remote signal analysis device for unmanned aerial vehicles (UAVs), characterized in that, The device includes: The signal analysis module is used to analyze the remote identification signal broadcast by the drone acquired by the preset receiving device to obtain the drone identification and flight status data; The parameter calculation module is used to obtain the wireless environment parameters and transmission performance parameters of the receiving device, and calculate the wireless communication data of the spatial environment in which the receiving device is located based on the wireless environment parameters and the transmission performance parameters. The communication analysis module is used to acquire network signal quality parameters in the space environment, perform communication analysis on the network signal quality parameters, and obtain communication analysis data. The positioning analysis module is used to acquire satellite positioning signal parameters of the receiving device, perform positioning analysis on the satellite positioning signal parameters, and obtain positioning analysis data. The signal analysis module is used to perform cross-analysis on the flight status data corresponding to the UAV identifier, the wireless communication data, the communication analysis data, and the positioning analysis data to obtain the signal analysis results.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the UAV remote signal analysis method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the UAV remote signal analysis method as described in any one of claims 1 to 7.

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