A method and system for rapid positioning of unmanned aerial vehicles based on telemetry data

Abstract

The application provides a kind of unmanned plane fast positioning method and system based on telemetry data, it is related to unmanned plane technical field, the application is by obtaining the wideband digital signal of bearing telemetry data and the preset feature parameter set;Time-frequency distribution data is generated based on wideband digital signal, to determine the target carrier frequency, target baud rate and target phase information of the downlink signal of target unmanned plane;According to target carrier frequency and target phase information, generate baseband symbol sequence;According to the preset constraint length and generating polynomial, the state sequence corresponding to state transition path is backtracked and mapped into telemetry data stream;Protocol frame structure analysis and unpacking are carried out on telemetry data stream, to determine the geographic coordinate position of target unmanned plane, realizes the blind demodulation of weak unmanned plane telemetry signal and the fast direct acquisition of high-precision geographic coordinate in complex electromagnetic environment.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to a method and system for rapid UAV positioning based on telemetry data. Background Technology

[0002] With the rapid development of the low-altitude economy, radio monitoring and positioning technology for non-cooperative drones is playing an increasingly important role in urban security and the protection of sensitive areas. In particular, intercepting drone downlink telemetry signals and analyzing the geographic coordinate information within them has become a mainstream technical means in current general security scenarios.

[0003] Existing solutions typically employ energy detection or simple spectrum scanning methods to detect targets. This involves determining the presence of a drone signal by detecting whether the signal amplitude within a specific frequency band exceeds a preset noise threshold. After narrowing down the approximate signal range, standard demodulation algorithms are used to process the intercepted signal, attempting to extract link characteristic parameters to achieve target identification and tracking.

[0004] However, in complex electromagnetic environments such as urban centers, high background noise and significant co-channel interference make weak UAV telemetry signals easily drowned out by the noise floor, causing energy threshold-based detection methods to fail to identify targets or generate numerous false alarms. Simultaneously, traditional demodulation methods lack deep error correction capabilities to combat transmission errors in low signal-to-noise ratio environments. Once the signal is interfered with and errors occur, it becomes impossible to reconstruct valid telemetry data frames. Therefore, existing technologies suffer from the technical problem of failing to effectively lock onto and resolve weak telemetry signals in complex electromagnetic environments, leading to positioning failures. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for rapid UAV positioning based on telemetry data, in order to solve the technical problem in the prior art that it is impossible to effectively lock onto and analyze weak telemetry signals in complex electromagnetic environments, thus leading to positioning failure.

[0006] Firstly, this application provides a method for rapid UAV positioning based on telemetry data, including:

[0007] Acquire broadband digital signals carrying telemetry data and a preset set of characteristic parameters;

[0008] Based on broadband digital signal generation, time-frequency distribution data is generated. Cyclic spectrum estimation is used to map the time-frequency distribution data to the cyclic frequency domain to obtain the spectrum correlation function. The spectrum correlation function is then matched with a preset set of feature parameters to determine the target carrier frequency, target baud rate, and target phase information of the downlink signal of the target UAV.

[0009] Based on the target carrier frequency and target phase information, the broadband digital signal is digitally down-converted and filtered to obtain the filtered signal. Then, the filtered signal is symbol timing recovery is performed on the target baud rate as a reference to generate a baseband symbol sequence.

[0010] Based on the preset constraint length and generator polynomial, a mesh diagram corresponding to the baseband symbol sequence is constructed. Based on the mesh diagram, the state transition path is determined by the maximum likelihood path search, and the state sequence corresponding to the state transition path is back-mapped into the telemetry data stream.

[0011] The protocol frame structure of the telemetry data stream is analyzed and de-encapsulated. The geographic location field of the payload is extracted from the data frames of the telemetry data stream to determine the geographic coordinates of the target UAV.

[0012] Optionally, time-frequency distribution data is generated based on broadband digital signals. Cyclic spectrum estimation is used to map the time-frequency distribution data to the cyclic frequency domain to obtain a spectral correlation function. This spectral correlation function is then matched with a preset set of feature parameters to determine the target carrier frequency, target baud rate, and target phase information of the target UAV's downlink signal, including:

[0013] Based on the preset time window length, the broadband digital signal is windowed and segmented to obtain multiple segmented signals;

[0014] Perform a short-time Fourier transform on each segmented signal to obtain time-frequency distribution data, which is a time-frequency matrix representing the distribution characteristics of the broadband digital signal in the time and frequency dimensions.

[0015] By performing a Fourier transform on the time-frequency matrix in the time dimension through cyclic spectrum estimation, a spectral correlation function in the cyclic frequency domain is obtained. The spectral correlation function has both a cyclic frequency dimension and a frequency dimension.

[0016] Based on the spectral correlation function, determine the discrete spectral peaks corresponding to the cyclic frequency values ​​in the preset feature parameter set;

[0017] The coordinates of the discrete spectral peak in the frequency dimension are used as the target carrier frequency of the downlink signal of the target UAV, and the coordinates of the discrete spectral peak in the cyclic frequency dimension are used as the target baud rate. The complex phase angle of the discrete spectral peak is calculated as the target phase information.

[0018] Optionally, based on the target carrier frequency and target phase information, the broadband digital signal is digitally down-converted and filtered to obtain a filtered signal, including:

[0019] Construct a discrete-time sequence with the same number of sampling points as the broadband digital signal, and calculate the complex exponential function value at each time point in the discrete-time sequence based on the target carrier frequency and target phase information. Construct a complex exponential signal based on all the complex exponential function values.

[0020] The mixed signal is obtained by performing point-by-point complex multiplication between the broadband digital signal and the complex exponential signal.

[0021] The cutoff frequency is determined based on the target baud rate, and the mixing signal is low-pass filtered using the cutoff frequency to obtain the filtered signal.

[0022] Optionally, symbol timing recovery is performed on the filtered signal based on the target baud rate to generate a baseband symbol sequence, including:

[0023] Based on the target baud rate, calculate the symbol period of the filtered signal and use the symbol period as the sampling interval;

[0024] The filtered signal is interpolated and resampled using the sampling interval to obtain the interpolated signal;

[0025] Calculate the timing error of each symbol in the interpolated signal, and use the timing error to adjust the control parameters for the next interpolation resampling until the timing error converges, thus determining the optimal sampling time for each symbol.

[0026] Extract the target symbol corresponding to each optimal sampling time from the interpolated signal to form a baseband symbol sequence.

[0027] Optionally, based on a preset constraint length and generator polynomial, a mesh diagram corresponding to the baseband symbol sequence is constructed, including:

[0028] The number of binary bits is determined according to the preset constraint length, and each full permutation combination of the number of binary bits is defined as a state node;

[0029] The system uses a pre-defined generator polynomial to determine whether there is a transition relationship between any two state nodes. When a transition relationship exists, a state transition branch is established, and the theoretical output value corresponding to each state transition branch is calculated.

[0030] Determine the total number of symbols in the baseband symbol sequence and generate a time step sequence that is equal in number to the total number of symbols and arranged in chronological order.

[0031] Configure all state nodes and all state transition branches for each time step in the time step sequence to construct a mesh graph.

[0032] Secondly, this application provides a rapid positioning system for unmanned aerial vehicles (UAVs) based on telemetry data, comprising:

[0033] The acquisition module is used to acquire the broadband digital signal carrying telemetry data and the preset set of characteristic parameters;

[0034] The matching module is used to generate time-frequency distribution data based on broadband digital signals, map the time-frequency distribution data to the cyclic frequency domain using cyclic spectrum estimation, obtain the spectrum correlation function, and perform feature matching between the spectrum correlation function and a preset set of feature parameters to determine the target carrier frequency, target baud rate, and target phase information of the downlink signal of the target UAV.

[0035] The filtering module is used to perform digital down-conversion and filtering on the broadband digital signal according to the target carrier frequency and target phase information to obtain the filtered signal, and to perform symbol timing recovery on the filtered signal based on the target baud rate to generate a baseband symbol sequence.

[0036] The construction module is used to construct a mesh diagram corresponding to the baseband symbol sequence based on the preset constraint length and generator polynomial. Based on the mesh diagram, the state transition path is determined by the maximum likelihood path search, and the state sequence corresponding to the state transition path is back-mapped into the telemetry data stream.

[0037] The analysis module is used to perform protocol frame structure analysis and de-encapsulation on the telemetry data stream, and extract the geographic location field of the payload from the data frames of the telemetry data stream to determine the geographic coordinates of the target UAV.

[0038] Thirdly, this application provides an electronic device, comprising:

[0039] Memory, used to store computer programs;

[0040] A processor is used to execute computer programs to implement the steps of a rapid UAV localization method based on telemetry data as described in the first aspect above.

[0041] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the rapid UAV positioning method based on telemetry data described in the first aspect above.

[0042] This application provides a rapid UAV positioning method based on telemetry data. By acquiring wideband raw signals and loading preset nominal parameters, it ensures the integrity of data acquisition by the monitoring system in complex electromagnetic environments. It overcomes the shortcomings of traditional energy detection methods in identifying noise-saturated signals in low signal-to-noise ratio environments, achieving blind detection and high-precision parameter estimation of weak target signals. It ensures the accuracy and integrity of demodulated data and enables high-precision, real-time geographic coordinate positioning of non-cooperative UAV targets.

[0043] Furthermore, by windowing and segmenting the broadband digital signal and performing a short-time Fourier transform to generate a time-frequency matrix, and then performing a Fourier transform in the time dimension to map the data to a cyclic frequency domain consisting of a cyclic frequency dimension and a frequency dimension, a spectral correlation function is obtained. Subsequently, a discrete spectral peak matching a preset set of feature parameters is searched in the spectral correlation function. Using the coordinate values ​​of the discrete spectral peak in different dimensions and the complex phase angle, the target carrier frequency, target baud rate, and target phase information of the downlink signal of the target UAV are determined respectively. This solves the problem of missed detection or false alarm caused by weak signal strength in traditional methods, and improves the sensitivity and robustness of non-cooperative target detection. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 A flowchart illustrating a rapid UAV positioning method based on telemetry data, provided for an embodiment of this application;

[0046] Figure 2 A schematic diagram of a process for obtaining a telemetry data stream is provided in an embodiment of this application;

[0047] Figure 3 A flowchart illustrating the architecture of a rapid UAV positioning system based on telemetry data, provided in this application embodiment;

[0048] Figure 4 A schematic diagram of a rapid positioning system for unmanned aerial vehicles based on telemetry data is provided in an embodiment of this application;

[0049] Figure 5 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0050] To address the challenges of existing solutions failing to identify weak UAV telemetry signals obscured by noise in complex electromagnetic environments such as urban centers due to high background noise and strong co-channel interference, and the inability of traditional demodulation methods to reconstruct valid data under low signal-to-noise ratio conditions due to a lack of deep error correction capabilities, thus leading to positioning failures, this application utilizes the cyclostationary characteristics of signals to focus and feature-match weak signal energy in the cyclic frequency domain. This allows for precise identification of the carrier frequency, baud rate, and phase information of the target signal in low signal-to-noise ratio environments, overcoming the sensitivity bottleneck of energy detection. Subsequently, a Viterbi decoding algorithm is used to construct a grid map for maximum likelihood path search, and a global optimal decision mechanism is employed to correct transmission errors, ensuring complete data reconstruction. Finally, geographic coordinates are directly obtained through protocol parsing, achieving highly reliable and rapid positioning of non-cooperative UAVs in complex environments.

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] The core of this application is to provide a method for rapid UAV positioning based on telemetry data, and a flowchart of one specific implementation is shown below. Figure 1 As shown, the method includes:

[0053] Step 101: Acquire the broadband digital signal carrying the telemetry data and the preset set of characteristic parameters.

[0054] In this step, telemetry data refers to the data stream transmitted in the UAV's downlink, including its own flight status and location information. This specifically includes, but is not limited to, parameters such as longitude, latitude, altitude, speed, battery voltage, and flight attitude. This data is typically encapsulated in specific communication protocol frames, such as MAVLink protocol frames, for transmission. Wideband digital signals refer to digital sequences obtained by high-speed sampling of the monitoring frequency band using a wideband radio frequency receiver and subsequent analog-to-digital conversion. These signals are superimposed with the target UAV signal, background noise, and other co-channel interference signals.

[0055] The preset feature parameter set refers to a set of parameters pre-stored in the storage unit to describe the inherent attributes of the communication signals of the target UAV to be monitored. This set serves as a benchmark template for subsequent signal search and matching. Specifically, this feature parameter set includes, but is not limited to, the nominal carrier frequency. Nominal baud rate and the cyclic frequency value generated by the coupling of the two. Among them, the nominal carrier frequency represents the theoretical center frequency of the target signal when no frequency offset occurs, the nominal baud rate represents the theoretical rate of signal symbol transmission, and the cycle frequency value represents the frequency points where the statistical characteristics of the signal exhibit periodic changes.

[0056] In this embodiment, firstly, a high-sensitivity broadband radio frequency receiver deployed around the monitoring area continuously acquires analog signals from the target monitoring frequency band. Then, the acquired analog signals are converted into discrete digital sequences, thus obtaining a broadband digital signal carrying telemetry data. This signal is stored in two in-phase and quadrature paths in complex form, preserving the amplitude and phase information. Simultaneously, a preset set of feature parameters is loaded from a local database. For example, based on a certain model of UAV from brand A, its nominal carrier frequency is preset to 2405MHz, its nominal baud rate to 10Mbps, and the corresponding cyclic frequency value to be 10MHz.

[0057] Step 102: Generate time-frequency distribution data based on broadband digital signals, map the time-frequency distribution data to the cyclic frequency domain using cyclic spectrum estimation to obtain the spectral correlation function, and perform feature matching between the spectral correlation function and the preset feature parameter set to determine the target carrier frequency, target baud rate and target phase information of the downlink signal of the target UAV.

[0058] In this step, time-frequency distribution data refers to a two-dimensional matrix describing the energy distribution characteristics of a broadband digital signal in the joint time and frequency domains, typically calculated using a short-time Fourier transform. Cyclic spectrum estimation is a technique that utilizes the cyclostationary properties of signals in statistics to extract hidden periodic features from a signal using a specific algorithm. The cyclofrequency domain is a two-dimensional transform domain composed of the frequency dimension and the cyclic frequency dimension. In this domain, random noise tends to be smoothed, while periodic signal energy focuses to form peaks.

[0059] The spectral correlation function is a complex function defined in the cyclic frequency domain, and its amplitude reflects the spectral correlation strength of the signal at a specific frequency and the cyclic frequency. The target UAV refers to the non-cooperative UAV to be monitored. The downlink signal refers to the radio signal, including telemetry data, transmitted by the UAV to the ground station. The target carrier frequency, target baud rate, and target phase information refer to the signal physical parameters, including environmental biases and initial states, actually measured from the noisy signal using algorithms.

[0060] Step 201: According to the preset time window length, the broadband digital signal is windowed and segmented to obtain multiple segmented signals.

[0061] In this step, the preset time window length refers to the time span parameter set based on signal characteristics and algorithm resolution requirements for truncating continuous signals.

[0062] In this embodiment of the application, firstly, according to the preset time window length, such as With 1024 sampling points and an overlap rate of 50%, the acquired broadband digital signal is truncated in the time domain. For each truncated data segment, it is multiplied point-by-point with a window function of the same length. Through this process, the non-stationary broadband signal can be approximated as a series of short-time stationary segmented signals.

[0063] The choice of window function is crucial for suppressing spectral leakage. Besides the Hamming and Hanning windows, Blackman or Kaiser windows can be selected based on the actual signal-to-noise ratio environment. For example, when there is strong adjacent channel interference in the monitoring environment, a Blackman window with lower sidelobe levels is chosen; when higher frequency resolution is required, a rectangular window with a narrower main lobe is selected. The overlap rate can also be dynamically adjusted between 25% and 75% to balance computational load and time resolution.

[0064] Step 202: Perform a short-time Fourier transform on each segmented signal to obtain time-frequency distribution data. The time-frequency distribution data is a time-frequency matrix that represents the distribution characteristics of the broadband digital signal in the time and frequency dimensions.

[0065] In this step, the time-frequency matrix refers to a two-dimensional complex matrix composed of the results of short-time Fourier transform. Its rows represent frequency indices, its columns represent time segment indices, and the values ​​of the matrix elements represent the complex amplitude of the signal at a specific time and frequency.

[0066] In this embodiment, a Fast Fourier Transform is performed on each windowed segmented signal. The transform results of all segmented signals are arranged in chronological order to construct a two-dimensional time-frequency matrix. For example, the time-frequency matrix has the following form:

[0067]

[0068] in Represents the number of frequency points. Represents the number of time periods.

[0069] Step 203: Perform a Fourier transform on the time-frequency matrix in the time dimension through cyclic spectrum estimation to obtain the spectral correlation function in the cyclic frequency domain. The spectral correlation function has a cyclic frequency dimension and a frequency dimension.

[0070] In this step, the cycle frequency value refers to the frequency at which the statistical characteristics of the signal repeat. For communication signals, it is usually associated with the carrier frequency and baud rate.

[0071] In this embodiment, a Fast Fourier Transform is performed again along the time dimension, i.e., the column direction of the matrix, for each frequency channel (i.e., each row) in the time-frequency matrix. After the transform, the data is transformed from the time-frequency domain (…). ) is mapped to the cyclic frequency domain ( ), generated spectral correlation function As shown in formula (1):

[0072] (1)

[0073] in, For the time-frequency matrix in time and frequency Complex values ​​at that location, For the cycle frequency variable. This function is defined in the cycle frequency dimension. and frequency dimension Complex functions on the surface can enhance the energy of signals with cyclostationary characteristics while suppressing stable background noise.

[0074] Step 204: Based on the spectral correlation function, determine the discrete spectral peaks corresponding to the cyclic frequency values ​​in the preset feature parameter set.

[0075] In this step, the cycle frequency value refers to the theoretically expected cycle frequency position of the target UAV signal, which is stored in advance, for example, corresponding to the nominal baud rate. The cyclic frequency. Discrete spectral peaks refer to sharp points in the cyclic frequency domain whose amplitudes are higher than the surrounding background noise; they correspond to strong periodic components present in the signal.

[0076] In this embodiment of the application, the cyclic frequency dimension of the spectral correlation function is determined by the cyclic frequency values ​​in a preset feature parameter set, such as... A search window is set with 10MHz as the center. Within this window, the amplitude of the spectral correlation function is scanned to find the point with the largest amplitude. To improve the robustness of the detection, this embodiment uses a dynamic threshold criterion: if the amplitude of the maximum point is... satisfy If the value is such that the point is determined to be a discrete spectral peak, then... and These represent the mean and standard deviation of the background noise power within the search window, respectively. This is a preset detection factor used to balance the false alarm rate and the missed detection rate. If this maximum value exceeds the preset detection threshold, the point is determined to be a discrete spectral peak, and its precise coordinate position and complex value in the cyclic frequency domain are recorded.

[0077] Step 205: Use the coordinates of the discrete spectral peak in the frequency dimension as the target carrier frequency of the downlink signal of the target UAV, and use the coordinates of the discrete spectral peak in the cyclic frequency dimension as the target baud rate. Calculate the complex phase angle of the discrete spectral peak as the target phase information.

[0078] In this step, the complex phase angle refers to the angle between the complex number and the positive direction of the real axis in the complex plane. It is calculated using the arctangent function and represents the initial phase state of the signal.

[0079] In this embodiment, the coordinate information of the discrete spectral peaks is read. This information is then mapped to the frequency dimension. The coordinate values ​​on the surface are directly extracted and used as the target carrier frequency. For example, the measured coordinate value is ; in the dimension of cycle frequency The coordinate values ​​on the surface are extracted and used as the target baud rate. For example, the measured coordinate value is At the same time, it should be clarified that at the cycle frequency... And frequency The complex value of the discrete spectral peak at a given location directly reflects the initial phase deviation of the carrier wave. The complex value at this discrete spectral peak is extracted. Using the formula Calculate its phase angle; for example, the target phase information is calculated as follows: The target carrier frequency of the target UAV was ultimately determined to be... The target baud rate is The target phase information is .

[0080] Step 103: Based on the target carrier frequency and target phase information, perform digital down-conversion and filtering on the broadband digital signal to obtain the filtered signal, and perform symbol timing recovery on the filtered signal with the target baud rate as a reference to generate a baseband symbol sequence.

[0081] In this step, the filtered signal refers to the zero-IF baseband analog signal obtained after digital down-conversion to eliminate carrier frequency offset and phase rotation, and low-pass filtering to remove high-frequency noise. The baseband symbol sequence refers to the discrete numerical sequence representing digital symbol information extracted from the continuous filtered signal at the optimal sampling time using symbol timing recovery technology.

[0082] Step 301: Construct a discrete-time sequence with the same number of sampling points as the broadband digital signal, and calculate the complex exponential function value at each time point in the discrete-time sequence based on the target carrier frequency and target phase information, and construct a complex exponential signal based on all the complex exponential function values.

[0083] In this step, the discrete-time series refers to a set of consecutive integer indices used to represent the time axis of the discrete signal, the length of which is strictly consistent with the number of sampling points of the original wideband digital signal. The complex exponential function value refers to a complex value constructed based on Euler's formula, used to generate the local oscillator signal in the digital domain. The complex exponential signal is the digital local oscillator waveform composed of this series of complex exponential function values ​​arranged in time sequence.

[0084] In this embodiment, the phase consistency at zero cycle frequency is analyzed using cyclic spectrum analysis to determine the phase deviation obtained by blind detection. That is, the target phase information is mapped into the local oscillator signal. This is achieved through a complex exponential signal. This phase compensation term is introduced in This allows the mixed baseband signal to achieve phase alignment at the start moment, thus avoiding the complex carrier phase acquisition process in traditional coherent demodulation. First, the total number of sampling points of the broadband digital signal is obtained. Construct a discrete time series Then, using the target carrier frequency... and target phase information The complex exponential function value at each moment is calculated point by point according to formula (2):

[0085] (2)

[0086] in This is the sampling rate. Ultimately, this will be... The calculated combination of complex values ​​forms a complex exponential signal with the same length as the original signal. For example, in a scenario monitoring a Brand A drone, the target carrier frequency... Target phase The constructed complex exponential signal is a complex sine wave sequence that rotates with that frequency and phase.

[0087] Step 302: Perform point-by-point complex multiplication on the broadband digital signal and the complex exponential signal to obtain the mixed signal.

[0088] In this step, the mixed signal refers to the signal after the spectrum shifting operation, in which the center of the spectrum has been moved from the radio frequency carrier frequency to near the zero frequency, but the high-frequency image components have not yet been filtered out.

[0089] In this embodiment of the application, a broadband digital signal is read. With complex exponential signals Perform point-by-point complex multiplication on these two sequences, i.e., execute the formula. This operation is equivalent to spectrum shifting in the frequency domain, moving the spectral center of the target signal from... The frequency was shifted to 0Hz, and the initial phase deviation was corrected to obtain the mixing signal. For example, the acquired broadband digital signal is multiplied point by point with the above complex sine wave sequence to shift the center of the target signal to 0Hz.

[0090] Step 303: Determine the cutoff frequency based on the target baud rate, and use the cutoff frequency to perform low-pass filtering on the mixing signal to obtain the filtered signal.

[0091] In this step, the cutoff frequency refers to the highest frequency boundary that the low-pass filter allows the signal to pass through, and it is usually set to be slightly greater than half of the signal's baseband bandwidth. The filtered signal refers to the pure zero-IF baseband signal after high-frequency noise has been filtered out.

[0092] In this embodiment of the application, firstly, based on the target baud rate Determine the cutoff frequency of the filter. For example, if , usually set To balance signal integrity and noise suppression, a digital low-pass filter, such as a finite impulse response filter, is then designed using this cutoff frequency, and the mixed signal is... The input signal is subjected to convolution operation. This process filters out high-frequency harmonics and out-of-band background noise generated during mixing, resulting in a smooth, filtered signal as the output.

[0093] Step 311: Calculate the symbol period of the filtered signal based on the target baud rate, and use the symbol period as the sampling interval.

[0094] In this step, the symbol period refers to the duration of transmitting a digital symbol, and its value is equal to the reciprocal of the baud rate. The sampling interval refers to the theoretical time difference between two adjacent sampling moments in symbol timing recovery to obtain the optimal sampling point for each symbol.

[0095] In this embodiment of the application, the target baud rate is directly utilized. Through formula The symbol period is calculated. For example, according to... Given the baud rate, the theoretical symbol period is calculated to be approximately The calculated time value Set as the baseline sampling interval for subsequent resampling processes.

[0096] Step 312: Use the sampling interval to interpolate and resample the filtered signal to obtain the interpolated signal.

[0097] In this step, the interpolated signal refers to the digital signal that, after resampling, is synchronized with the data rate as an integer multiple of the symbol rate.

[0098] In the embodiments of this application, the sampling interval is used. Using a step size, the signal slides along the time axis of the filtered signal. Since the optimal sampling time may lie between two original sampling points, cubic spline interpolation or polynomial interpolation algorithms are used to calculate the signal amplitude values ​​corresponding to these non-integer times, generating the interpolated signal. For example, using this... Interpolate the waveform at intervals.

[0099] Step 313: Calculate the timing error of each symbol in the interpolated signal, and use the timing error to adjust the control parameters for the next interpolation resampling until the timing error converges, and determine the optimal sampling time corresponding to each symbol.

[0100] In this step, timing error refers to the time deviation between the current sampling time and the ideal optimal sampling time, such as the point where the eye diagram is at its maximum opening, and is usually calculated using the Gardner algorithm. Control parameters refer to the fractional intervals used in the interpolation filter to fine-tune the sampling position. The optimal sampling time is the sampling position where the timing error converges to near zero.

[0101] In this embodiment, the timing error is calculated using a Gardner error detector for each symbol in the interpolated signal. The error is smoothed by the input loop filter, and the output is fed back to the numerically controlled oscillator to adjust the control parameters for the next interpolation. As the feedback loop continues, the timing error gradually converges to near zero. At this point, the position locked by the CNC oscillator is the optimal sampling time for each symbol. For example, using the Gardner algorithm to detect the sampling deviation of each symbol, it is found that the deviation decreases from the initial... Gradually decrease and stabilize at Within this range, the contraction occurs.

[0102] Step 314: Extract the target symbol corresponding to each optimal sampling time from the interpolated signal to form a baseband symbol sequence.

[0103] In this step, the target symbol refers to the discrete value extracted from the continuous waveform at the optimal sampling time, representing the signal state at that moment. The baseband symbol sequence refers to the data stream composed of these sequentially extracted target symbols, after removing the carrier and timing offset.

[0104] In this embodiment, at each optimal sampling time, the amplitude value of the interpolated signal at that time is directly read as the target symbol. All extracted target symbols are arranged in chronological order to form a discrete numerical sequence, thus obtaining the baseband symbol sequence. For example, at these optimal time points with minimal error, the voltage values ​​of the waveform are read one by one, such as... This string of values ​​is the generated baseband symbol sequence.

[0105] Step 104: Based on the preset constraint length and generator polynomial, construct a mesh diagram corresponding to the baseband symbol sequence. Based on the mesh diagram, determine the state transition path through maximum likelihood path search, and backtrack the state sequence corresponding to the state transition path to the telemetry data stream.

[0106] In this step, the preset constraint length refers to the number of stages in the shift register of the convolutional encoder plus one, which determines the encoder's memory depth and the total number of states. The preset generator polynomial is a mathematical expression used to define the logical relationship between the input bits and output bits of the convolutional encoder. The mesh diagram is a topological structure formed by unfolding the state transition diagram of the convolutional encoder on the time axis, where the horizontal axis represents the time step and the vertical axis represents all possible state nodes.

[0107] Maximum likelihood path search is an algorithmic process that finds the path with the minimum cumulative metric value (i.e., the path most likely corresponding to the actual transmitted sequence) in a trellis graph, typically implemented using the Viterbi algorithm. A state transition path is the optimal connection determined in the trellis graph that spans all time steps. A state sequence is the set of encoder internal states represented by each node on this path. Telemetry data stream is the original binary data bit stream, after error correction and restoration, parsed by backtracking the state transition path.

[0108] Step 401: Determine the number of binary bits according to the preset constraint length, and define each full permutation of the binary bits as a state node.

[0109] In this step, the number of binary bits refers to the number of state bits in the internal shift register of the convolutional encoder used to store historical information, and its value is equal to the constraint length minus one. A state node is a node in the mesh diagram that represents the state of the encoder's internal registers at a specific moment, and each node corresponds to a possible combination of binary bits.

[0110] In this embodiment, the preset constraint length is first read. Calculate the number of binary bits Then exhaustively list All possible combinations of binary numbers, i.e., permutations from all 0s to all 1s. Each combination is defined as an independent state node, generating a total of [number missing]. A state node. For example, if the preset constraint length... Therefore, the binary number of bits is determined to be 6, and the set of state nodes is defined as follows.

[0111] Step 402: Use a preset generator polynomial to determine whether there is a transition relationship between any two state nodes. If there is a transition relationship, establish a state transition branch and calculate the theoretical output value corresponding to each state transition branch.

[0112] In this step, the transition relationship refers to the logical probability that the encoder can jump from the current state to the next state when a new bit is input. A state transition branch is a directed connection between two state nodes with a transition relationship, representing one encoding operation. The theoretical output value refers to the ideal symbol value that the encoder should output when a specific state transition occurs, according to the generator polynomial logic.

[0113] In this embodiment, for any two state nodes in the trellis diagram, such as state A and state B, according to the working principle of convolutional coding, it is determined whether state A will transition to state B when the input bit is 0 or 1. If such a possibility exists, a state transition branch is established between A and B. Then, using a preset generator polynomial, an XOR operation is performed on the input bit and the current state bit to calculate the encoded output bit corresponding to this transition, and it is mapped to the modulation symbol value, i.e., the theoretical output value. For example, a generator polynomial commonly used in communication standards, i.e., octal representation, is used. Specifically, the octal number 133 converted to binary is The octal number 171 converted to binary is The bitwise operation logic for each corresponding encoded output is as follows:

[0114]

[0115]

[0116] in, For the current input bits, Indicates the process Historical bit state after stage register delay This represents the XOR operation. When the input bit is 1 and the current state results in an output code of 11, the theoretical output value is... .

[0117] Step 403: Determine the total number of symbols in the baseband symbol sequence and generate a time step sequence arranged in chronological order that is equal to the total number of symbols.

[0118] In this step, the total number of symbols refers to the total number of discrete values ​​included in the baseband symbol sequence. The time step sequence refers to the set of discrete moments used to construct the time axis of the trellis plot, and its length is consistent with the length of the received signal.

[0119] In this embodiment of the application, the length of the statistical baseband symbol sequence is... Generate a from arrive The integer sequence is used as the time step sequence to provide a time dimension reference for the unfolding of the mesh diagram. For example, if the total number of symbols is 100, the generated time step sequence is... .

[0120] Step 404: Configure all state nodes and all state transition branches for each time step in the time step sequence to construct a mesh graph.

[0121] In this embodiment of the application, for each moment in the time step sequence All of them are deployed in this column position. Each state node is represented by a state node, and the inherent transition branch connections between these nodes are preserved. A complete mesh diagram is constructed by repeating the same state transition structure along the time axis. For example, in the above... At each point in time in the sequence, the aforementioned 64 state nodes and corresponding 128 state transition branches are arranged.

[0122] like Figure 2 As shown, Figure 2 This is a schematic diagram of a process for obtaining a telemetry data stream, provided as an embodiment of this application.

[0123] Step 411: Extract the actual received symbol corresponding to the current time step from the baseband symbol sequence, and calculate the branch metric between the theoretical output value and the actual received symbol for each state transition branch in the mesh diagram within the current time step.

[0124] In this step, the actual received symbol refers to the specific value read from the received baseband symbol sequence at the current processing time. The branch metric is a numerical value that measures the difference between the received symbol and the theoretical symbol, usually expressed using Euclidean distance or Hamming distance; the smaller the difference, the smaller the metric.

[0125] In this embodiment, each state node in the mesh graph at the current time step is taken as the target node. For each state transition branch connected to the target node, its theoretical output value is read. Simultaneously, read the actual received symbols at the current moment. The square of the difference between the two is the Euclidean distance, calculated using the formula: This yields the branch metric for that branch. For example, if the currently received symbol... The theoretical output value of a certain branch Then the branch metric value of that branch is calculated. .

[0126] Step 412: Based on the branch metric and the cumulative path metric of the previous time step, determine the surviving path and the updated cumulative path metric for each state node in the current time step.

[0127] In this step, the cumulative path metric refers to the total difference accumulated along a path from the start of the grid graph to the current time. The candidate path metric refers to the total metric of potential paths leading to the current target node. The surviving path is the optimal path that has the smallest cumulative metric for a specific state node at the current time.

[0128] In this embodiment, for each target node at the current time step, all state transition branches connected to that node are first obtained. For each branch, the starting node it connects to is located at the previous time step, and the cumulative path metric value stored at that starting node is read. This cumulative value is added to the branch metric value of the current branch to obtain the candidate path metric value of that branch. Next, all candidate path metric values ​​connected to the target node are compared, and the one with the smallest value is selected. The branch corresponding to the smallest candidate value is retained, and it is connected to the surviving path determined by the starting node at the previous time step to form the updated surviving path to the current target node. At the same time, the smallest candidate value is updated and stored as the cumulative path metric value of the current target node.

[0129] For example, there are two branches to reach state 00: branch A comes from state 00, with a theoretical output of +1 and a branch metric of 0.01; branch B comes from state 01, with a theoretical output of -1 and a branch metric of 3.61. Assume that the cumulative value of state 00 in the previous time step was 5, and the cumulative value of state 01 was 2. Then the total cost of taking branch A is... The total cost of taking branch B is Therefore, for the current state 00, branch A is retained as part of the surviving path, and the cumulative value is updated to 5.01.

[0130] Step 413: After traversing all time steps, select the surviving path corresponding to the smallest cumulative path metric value in the last time step as the state transition path.

[0131] In this step, the state transition path refers to the path that minimizes the global cumulative error throughout the entire mesh graph, representing the most likely transmission sequence.

[0132] In this embodiment of the application, after processing the first... After a certain time step, examine the cumulative path metric of all state nodes at that moment. Find the node with the smallest value and confirm its entire surviving path as the final state transition path. For example, in At time 10, the cumulative metric value of state 10 is found to be the smallest, so the full path leading to state 10 is selected as the state transition path.

[0133] Step 414: Combine the input bits corresponding to each state transition branch on the state transition path according to the reverse time order to obtain the telemetry data stream.

[0134] In this step, the input bit refers to the original information bit that causes a specific state transition. For example, input 0 causes the state to change from 00 to 00, and input 1 causes the state to change from 00 to 10.

[0135] In this embodiment of the application, along a defined state transition path, from time... Back to For each branch on the path, its corresponding input bit (0 or 1) is deduced according to the encoding rules. All extracted bits are rearranged in chronological order, reversed, and combined to form the original binary data stream, thus obtaining the telemetry data stream. For example, the branches on the backtracking path correspond to input bits (1, 0, 1, 1, 0...), and the resulting telemetry data stream after reversal is a binary sequence. .

[0136] To ensure the accuracy of backtracking, a sufficient backtracking depth needs to be set. Backtracking depth refers to the number of nodes traced backward in the mesh graph from the current moment. Typically, the backtracking depth is set to 5 to 9 times the constraint length KK. For example, regarding the constraint length... The convolutional encoding, with a backtracking depth that can be set to... Up to 63 symbol cycles. If the backtracking depth is too short, decoding performance may degrade; if it is too long, storage overhead and processing latency will increase. In this application, a backtracking depth of 60 is preferably set, that is, the backtracking depth is calculated up to... Output only begins at a specific time. The decoded bits at each time step are used to ensure that the maximum likelihood path converges to the globally optimal path.

[0137] Step 105: Perform protocol frame structure analysis and de-encapsulation on the telemetry data stream, and extract the geographic location field of the payload from the data frames of the telemetry data stream to determine the geographic coordinates of the target UAV.

[0138] In this step, the payload refers to the portion of the data frame that actually carries the application data after removing protocol control overhead such as frame headers, frame trailers, and check bits. The geolocation field refers to a specific byte segment in the payload dedicated to storing longitude, latitude, and altitude values. The geographic coordinate location refers to the actual location information of the UAV in the standard geographic coordinate system obtained after numerical transformation.

[0139] Step 501: Using the frame header identifier in the telemetry data stream that matches the preset communication protocol as the starting position, determine the length information of the data frame in the telemetry data stream, and extract the data segment to be verified and the check bit from the data frame.

[0140] In this step, the frame header identifier refers to a specific binary or hexadecimal numerical sequence specified by the communication protocol to indicate the start of a data frame. A data frame is a data packet containing complete communication information, typically composed of a frame header, length bytes, payload, and checksum. Length information refers to a numerical value indicating the total length of the data frame or the length of the payload, usually stored in a specific position after the frame header. The data segment to be checked refers to the data segment within the data frame that needs to be checked.

[0141] The integrity check section typically covers all bytes from the frame header to the end of the payload. The check bit is a redundant code value located at the end of the data frame used to verify the correctness of data transmission.

[0142] In this embodiment, the frame header identifier defined by the target UAV communication protocol, such as the Micro Aircraft Link Protocol, is first loaded. For example, for Micro Aircraft Link Protocol version v1, the frame header identifier is 0xFE. An example of the frame structure of the preset communication protocol is shown in Table 1.

[0143]

[0144] Next, a bit-by-bit sliding search is performed on the telemetry data stream. Since the Viterbi decoder output is a continuous bit stream, byte boundaries need to be determined by detecting the frame header identifier. For example, assuming the telemetry data stream output by the Viterbi decoder is a continuous string of bits, 8 bits of binary data are extracted each time a bit is slid and converted to hexadecimal for comparison with 0xFE. Once a match is found, the start position of the data frame is locked. Subsequently, the byte immediately following the frame header is read as length information; for example, reading the next byte 0x1C (28 bytes) determines the payload length, thus defining the entire data frame. Finally, the portion of the data frame excluding the last two bytes is extracted as the data segment to be checked, and the last two bytes are extracted as check bits.

[0145] Step 502: Perform cyclic redundancy check on the data segment to be checked. After the check passes, remove the frame header identifier and check bit to obtain the payload.

[0146] In this embodiment, a cyclic redundancy check generator polynomial, such as the CRC-16-CCITT standard, is used to calculate a 16-bit checksum for the data segment to be checked. This calculated value is then compared with the extracted check bits. For example, the cyclic redundancy check is calculated on the frame excluding the last two bytes, yielding a checksum of 0xABCD, which matches the 0xABCD at the end of the frame, indicating a successful check. If the two are equal, the data frame is deemed complete and error-free. Subsequently, the frame header identifier, length byte, and check bits are discarded, retaining only the middle data portion, thus obtaining the payload.

[0147] Step 503: Extract the geographic location field from the payload according to the preset field offset.

[0148] In this step, the field offset refers to the byte distance within the payload, such as longitude or latitude, relative to the payload's starting position. The geolocation field is a continuous sequence of bytes storing longitude, latitude, and altitude values, typically stored as a 32-bit signed integer.

[0149] In this embodiment, the message definition document of the communication protocol is consulted to determine the message ID and corresponding field structure of the geographic location information. For example, for a global location message with message ID 33, according to the standard micro-aircraft link protocol v1 definition, the latitude field offset is 4 bytes, the longitude field offset is 8 bytes, and the altitude field offset is 12 bytes. Based on these offsets, the corresponding 4 bytes of data, i.e., the geographic location field, are accurately located and read from the payload. For example, bytes 4 to 7 are extracted from the payload as the latitude field value 305000000, and bytes 8 to 11 are the longitude field value 1143000000.

[0150] Step 504: Divide the geographic location field by the preset scaling factor to obtain the geographic coordinates of the target UAV in the preset geographic coordinate system.

[0151] In this step, the preset scaling factor refers to the multiplier used to convert floating-point geographic coordinates to integers for transmission; it is typically 1. The default geographic coordinate system refers to the global standard coordinate system used to describe the location of the UAV.

[0152] In this embodiment of the application, the 32-bit integer geographic location fields, such as longitude integer values ​​and latitude integer values, are divided by a preset scaling factor, such as... This division operation converts the integer back to a floating-point number with a decimal point, thus obtaining the precise longitude, latitude, and altitude of the target UAV in the WGS84 coordinate system. For example, dividing the two values ​​by... To obtain latitude ,longitude The geographic coordinates of the target drone were determined.

[0153] This application embodiment ensures the integrity of data acquisition by the monitoring system in complex electromagnetic environments by acquiring wide-band raw signals and loading preset nominal parameters. It overcomes the shortcomings of traditional energy detection methods in identifying noise-submerged signals in low signal-to-noise ratio environments, achieving blind detection and high-precision parameter estimation of weak target signals. It ensures the accuracy and integrity of demodulated data and enables high-precision, real-time geographic coordinate positioning of non-cooperative target UAVs.

[0154] For example, such as Figure 3 As shown, Figure 3 This application provides an architectural flowchart of a rapid UAV positioning system based on telemetry data. The core objective is the target UAV, which continuously transmits downlink signals containing its own status information to its authorized ground control station during flight. The key to this application is that the rapid UAV positioning system deployed in the monitoring area can simultaneously intercept these downlink signals.

[0155] The positioning system internally executes a sophisticated and automated processing chain, the process of which is as follows: First, the intercepted broadband digital signal is analyzed. Time-frequency distribution data is generated and mapped to the cyclic frequency domain using a cyclic spectrum estimation method to obtain the spectral correlation function. This function is then matched with a preset set of feature parameters to blindly determine the key parameters of the signal, namely the target carrier frequency, target baud rate, and target phase information, in a low signal-to-noise ratio environment. Subsequently, based on these parameters, the original signal is digitally down-converted and filtered to obtain the filtered signal. Symbol timing recovery is then performed using the target baud rate as a reference to generate a baseband symbol sequence.

[0156] Next, based on the known constraint length and generator polynomial, a corresponding mesh diagram is constructed for the baseband symbol sequence. The optimal state transition path is determined through maximum likelihood path search, and this path is back-mapped into an error-free telemetry data stream. This process possesses strong error correction capabilities. Finally, protocol frame structure analysis and de-encapsulation are performed on the telemetry data stream. The geographic location field is extracted from the payload of the data frame, and after conversion, the precise geographic coordinates of the target UAV can be directly obtained.

[0157] Figure 3 The diagram clearly shows two signal paths: one is the path from the UAV to the positioning system, which is the data source for signal analysis; the other is the original communication link from the UAV to the ground control station, which is utilized by the positioning system. The entire solution enables blind demodulation of weak, non-cooperative UAV telemetry signals and rapid, direct acquisition of high-precision geographic coordinates in complex electromagnetic environments.

[0158] Figure 4 This is a schematic diagram illustrating a specific implementation of a rapid UAV positioning system based on telemetry data, as provided in this application. (Refer to...) Figure 4 The system may include:

[0159] The acquisition module 21 is used to acquire the broadband digital signal carrying telemetry data and the preset set of characteristic parameters;

[0160] Matching module 22 is used to generate time-frequency distribution data based on broadband digital signals, map the time-frequency distribution data to the cyclic frequency domain using cyclic spectrum estimation to obtain the spectrum correlation function, and perform feature matching between the spectrum correlation function and a preset set of feature parameters to determine the target carrier frequency, target baud rate and target phase information of the downlink signal of the target UAV.

[0161] The filtering module 23 is used to perform digital down-conversion and filtering on the broadband digital signal according to the target carrier frequency and target phase information to obtain the filtered signal, and to perform symbol timing recovery on the filtered signal based on the target baud rate to generate a baseband symbol sequence.

[0162] Module 24 is used to construct a mesh diagram corresponding to the baseband symbol sequence based on the preset constraint length and generator polynomial, determine the state transition path through maximum likelihood path search based on the mesh diagram, and back-map the state sequence corresponding to the state transition path to the telemetry data stream.

[0163] Analysis module 25 is used to perform protocol frame structure analysis and de-encapsulation on the telemetry data stream, and extract the geographic location field of the payload from the data frames of the telemetry data stream to determine the geographic coordinates of the target UAV.

[0164] This application provides an embodiment of a UAV rapid positioning system based on telemetry data to implement the aforementioned UAV rapid positioning method based on telemetry data. Therefore, the specific implementation of the UAV rapid positioning system based on telemetry data can be found in the embodiment section of the UAV rapid positioning method based on telemetry data described above. The specific implementation can be referred to the description of the corresponding embodiments, and will not be repeated here.

[0165] Figure 5 A schematic diagram of the hardware structure of the electronic device provided in an embodiment of this application is shown.

[0166] This application also provides an electronic device, including: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the above-described rapid positioning method for unmanned aerial vehicles based on telemetry data.

[0167] The electronic device may include a processor 510 and a memory 520 storing computer program instructions.

[0168] Specifically, the processor 510 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0169] Memory 520 may include mass storage for data or instructions. For example, and not limitingly, memory 520 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 520 may include removable or non-removable (or fixed) media. Where appropriate, memory 520 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 520 is non-volatile solid-state memory.

[0170] Memory may include read-only memory (ROM), random access memory (RAM), disk storage media devices, optical storage media devices, flash memory devices, and electrical, optical, or other physical / tangible memory storage devices. Therefore, typically, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to the first aspect of this disclosure.

[0171] The processor 510 reads and executes computer program instructions stored in the memory 520 to implement any of the above embodiments of a rapid UAV positioning method based on telemetry data.

[0172] In one example, the electronic device may also include a communication interface 530 and a bus 540. Wherein, such as Figure 5 As shown, the processor 510, memory 520, and communication interface 530 are connected through bus 540 and complete communication with each other.

[0173] The communication interface 530 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0174] Bus 540 includes hardware, software, or both, that couples components of an online data traffic metering device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 540 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0175] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the above-described methods for rapid UAV positioning based on telemetry data.

[0176] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0177] Embodiments of the present invention also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the rapid UAV positioning method based on telemetry data.

[0178] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0179] The foregoing has provided a detailed description of a rapid UAV positioning method and system based on telemetry data provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A method for rapid UAV positioning based on telemetry data, characterized in that, include: Acquire broadband digital signals carrying telemetry data and a preset set of characteristic parameters; Based on the broadband digital signal, time-frequency distribution data is generated. Cyclic spectrum estimation is used to map the time-frequency distribution data to the cyclic frequency domain to obtain the spectrum correlation function. The spectrum correlation function is then matched with a preset set of feature parameters to determine the target carrier frequency, target baud rate, and target phase information of the downlink signal of the target UAV. Based on the target carrier frequency and the target phase information, the broadband digital signal is digitally down-converted and filtered to obtain a filtered signal. Then, the filtered signal is symbol-timing recovered based on the target baud rate to generate a baseband symbol sequence. Based on the preset constraint length and generator polynomial, a mesh graph corresponding to the baseband symbol sequence is constructed. Based on the mesh graph, the state transition path is determined by maximum likelihood path search, and the state sequence corresponding to the state transition path is back-mapped into a telemetry data stream. The telemetry data stream is subjected to protocol frame structure analysis and de-encapsulation. The geographic location field of the payload is extracted from the data frames of the telemetry data stream to determine the geographic coordinates of the target UAV.

2. The method according to claim 1, characterized in that, Based on the broadband digital signal, time-frequency distribution data is generated. Cyclic spectrum estimation is used to map the time-frequency distribution data to the cyclic frequency domain to obtain a spectral correlation function. This spectral correlation function is then matched with a preset set of feature parameters to determine the target carrier frequency, target baud rate, and target phase information of the target UAV's downlink signal, including: Based on a preset time window length, the broadband digital signal is windowed and segmented to obtain multiple segmented signals; Perform a short-time Fourier transform on each segmented signal to obtain time-frequency distribution data, which is a time-frequency matrix representing the distribution characteristics of the broadband digital signal in the time and frequency dimensions; By performing a Fourier transform on the time-frequency matrix in the time dimension through cyclic spectrum estimation, a spectral correlation function in the cyclic frequency domain is obtained, wherein the spectral correlation function has a cyclic frequency dimension and the frequency dimension; Based on the spectral correlation function, determine the discrete spectral peaks corresponding to the cyclic frequency values ​​in the preset feature parameter set; The coordinates of the discrete spectral peak in the frequency dimension are used as the target carrier frequency of the downlink signal of the target UAV, and the coordinates of the discrete spectral peak in the cyclic frequency dimension are used as the target baud rate. The complex phase angle of the discrete spectral peak is calculated as the target phase information.

3. The method according to claim 1, characterized in that, Based on the target carrier frequency and the target phase information, the broadband digital signal undergoes digital down-conversion and filtering to obtain a filtered signal, including: Construct a discrete-time sequence with the same number of sampling points as the broadband digital signal, and calculate the complex exponential function value at each time point in the discrete-time sequence based on the target carrier frequency and the target phase information, and construct a complex exponential signal based on all the complex exponential function values; The broadband digital signal and the complex exponential signal are multiplied point by point to obtain the mixed signal; The cutoff frequency is determined based on the target baud rate, and the mixing signal is low-pass filtered using the cutoff frequency to obtain the filtered signal.

4. The method according to claim 1, characterized in that, Using the target baud rate as a reference, symbol timing recovery is performed on the filtered signal to generate a baseband symbol sequence, including: Based on the target baud rate, calculate the symbol period of the filtered signal and use the symbol period as the sampling interval; The filtered signal is interpolated and resampled using the sampling interval to obtain the interpolated signal; Calculate the timing error of each symbol in the interpolated signal, and use the timing error to adjust the control parameters for the next interpolation resampling until the timing error converges, thereby determining the optimal sampling time corresponding to each symbol; The target symbol corresponding to each optimal sampling time is extracted from the interpolated signal to form a baseband symbol sequence.

5. The method according to claim 1, characterized in that, Based on the preset constraint length and generator polynomial, a mesh diagram corresponding to the baseband symbol sequence is constructed, including: The number of binary bits is determined according to the preset constraint length, and each full permutation combination of the number of binary bits is defined as a state node; The system uses a pre-defined generator polynomial to determine whether there is a transition relationship between any two state nodes. When a transition relationship exists, a state transition branch is established, and the theoretical output value corresponding to each state transition branch is calculated. Determine the total number of symbols in the baseband symbol sequence, and generate a time step sequence arranged in chronological order that is equal to the total number of symbols; Configure all state nodes and all state transition branches for each time step in the time step sequence to construct a mesh graph.

6. The method according to claim 5, characterized in that, Based on the mesh diagram, state transition paths are determined through maximum likelihood path search, and the state sequences corresponding to the state transition paths are back-mapped into telemetry data streams, including: Extract the actual received symbol corresponding to the current time step from the baseband symbol sequence, and calculate the branch metric between the theoretical output value corresponding to each state transition branch in the current time step and the actual received symbol in the mesh diagram; Based on the branch metric and the cumulative path metric of the previous time step, determine the surviving path and the updated cumulative path metric for each state node in the current time step. After traversing all time steps, the surviving path corresponding to the smallest cumulative path metric value in the last time step is selected as the state transition path. Based on the reverse chronological order, the input bits corresponding to each state transition branch on the state transition path are combined to obtain the telemetry data stream.

7. The method according to claim 1, characterized in that, The telemetry data stream undergoes protocol frame structure analysis and de-encapsulation. The geographic location field of the payload is extracted from the data frames of the telemetry data stream to determine the geographic coordinates of the target UAV, including: Using the frame header identifier in the telemetry data stream that matches the preset communication protocol as the starting position, the length information of the data frame in the telemetry data stream is determined, and the data segment to be verified and the check bit are extracted from the data frame. Cyclic redundancy check is performed on the data segment to be checked. After the check passes, the frame header identifier and the check bit are removed to obtain the payload. Based on a preset field offset, extract the geographic location field from the payload; Divide the geographic location field by a preset scaling factor to obtain the geographic coordinates of the target UAV in a preset geographic coordinate system.

8. A rapid positioning system for unmanned aerial vehicles (UAVs) based on telemetry data, characterized in that, include: The acquisition module is used to acquire the broadband digital signal carrying telemetry data and the preset set of characteristic parameters; The matching module is used to generate time-frequency distribution data based on the broadband digital signal, map the time-frequency distribution data to the cyclic frequency domain using cyclic spectrum estimation to obtain the spectrum correlation function, and perform feature matching between the spectrum correlation function and a preset set of feature parameters to determine the target carrier frequency, target baud rate and target phase information of the downlink signal of the target UAV. The filtering module is used to perform digital down-conversion and filtering on the broadband digital signal according to the target carrier frequency and the target phase information to obtain the filtered signal, and to perform symbol timing recovery on the filtered signal based on the target baud rate to generate a baseband symbol sequence. The construction module is used to construct a mesh graph corresponding to the baseband symbol sequence according to a preset constraint length and generator polynomial, determine the state transition path through maximum likelihood path search based on the mesh graph, and back-map the state sequence corresponding to the state transition path to a telemetry data stream. The analysis module is used to perform protocol frame structure analysis and de-encapsulation on the telemetry data stream, and extract the geographic location field of the payload from the data frames of the telemetry data stream to determine the geographic coordinates of the target UAV.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of a rapid UAV positioning method based on telemetry data as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables a rapid UAV positioning method based on telemetry data as described in any one of claims 1 to 7.

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