An unmanned aerial vehicle high-precision positioning and tracking method and device based on an AFDM waveform

Abstract

The embodiment of the application provides a kind of unmanned plane high-precision positioning and tracking method and device based on AFDM waveform, through echo signal and local linear frequency modulation reference signal analog domain dechirp mixing is low-pass filtered and baseband sampling to obtain baseband difference frequency data write signal processing buffer area, combined with affine frequency division multiplexing modulation parameter driven discrete affine Fourier transform time delay Doppler domain two-dimensional spectrum peak search and interpolation refinement to obtain target distance estimate and Doppler frequency shift estimate encapsulation as positioning and tracking parameter group, through flight control unit according to preset trajectory constraint condition executes position solution to obtain current positioning coordinate joint tracking filter state update to obtain target tracking trajectory data output to ground monitoring terminal, effectively solve the deficiency of traditional technology in analog domain difference frequency preprocessing, time delay Doppler domain high-precision parameter estimation and flight control linkage tracking filtering output etc., provide technical support for unmanned plane high-precision positioning and accurate tracking.

Description

Technical Field

[0001] This application relates to the field of data processing, specifically to a method and apparatus for high-precision positioning and tracking of unmanned aerial vehicles based on AFDM waveforms. Background Technology

[0002] Existing UAV positioning and tracking methods have significant shortcomings. Traditional systems are weak in analog domain preprocessing of echo signals and baseband difference frequency data acquisition and buffering. They typically rely on direct sampling in the digital domain or simple mixing, lacking the ability to perform analog domain dechirping preprocessing and baseband difference frequency data normalization and buffering based on the echo signal acquired by the UAV's onboard receiver and the local linear frequency modulated reference signal to obtain the difference frequency signal, perform low-pass filtering to obtain the filtered signal, and perform baseband sampling to obtain baseband difference frequency data, which is then written into the signal processing buffer. This results in the inability to effectively separate and extract the distance and velocity coupling information in the echo signal under high-maneuverability environments, making it difficult to provide a high-quality baseband difference frequency data foundation for subsequent high-precision parameter estimation in the time-delay Doppler domain.

[0003] Furthermore, existing technologies face bottlenecks in calculating the two-dimensional spectrum in the time-delay Doppler domain driven by simulated radio frequency multiplexing modulation parameters and in estimating high-precision target parameters. Most systems lack the capability to obtain a two-dimensional spectrum in the time-delay Doppler domain by performing a discrete affine Fourier transform on the baseband difference frequency data based on simulated radio frequency multiplexing modulation parameters, to obtain coarsely estimated parameters through peak search based on the two-dimensional spectrum, and then to refine the target range estimate and Doppler frequency shift estimate through interpolation, encapsulating these into a positioning and tracking parameter set. This capability results in difficulty simultaneously meeting the engineering requirements of accurate positioning and tracking in time-varying Doppler scenarios involving high-speed, highly maneuverable UAV targets, severely restricting the system's practical application performance in complex dynamic environments.

[0004] Existing systems have technical shortcomings in flight control-linked position calculation and tracking filter state update output. They lack a closed-loop output mechanism that drives position calculation and multi-source parameter joint tracking filter state update based on preset trajectory constraints, inputs positioning and tracking parameter sets into the UAV flight control unit to calculate the current positioning coordinates, combines the current positioning coordinates with the Doppler frequency shift estimate, inputs the combined current positioning coordinates and Doppler frequency shift estimate into the tracking filter to perform state updates and obtain target tracking trajectory data, and then outputs the data to the ground monitoring terminal. This hinders the UAV's comprehensive perception capability for continuous and accurate target positioning and smooth trajectory tracking in complex mission scenarios. Solving these problems is crucial for improving the UAV's high-precision autonomous positioning and continuous target tracking capabilities in complex dynamic environments. Summary of the Invention

[0005] To address the problems in the existing technology, this application provides a method and apparatus for high-precision positioning and tracking of UAVs based on AFDM waveforms. It can effectively solve the shortcomings of traditional technologies in analog domain difference frequency preprocessing, time delay Doppler domain high-precision parameter estimation, and flight control linkage tracking filter output, providing technical support for high-precision positioning and accurate tracking of UAVs.

[0006] To solve at least one of the above problems, this application provides the following technical solution: Firstly, this application provides a high-precision positioning and tracking method for unmanned aerial vehicles (UAVs) based on AFDM waveforms, including: The difference frequency signal is obtained by performing analog domain dechirping mixing on the echo signal collected by the UAV airborne receiver and the local linear frequency modulation reference signal. The difference frequency signal is then processed by low-pass filtering to obtain a filtered signal. The filtered signal is then sampled by baseband to obtain baseband difference frequency data and written into the signal processing buffer. The baseband difference frequency data is read from the signal processing buffer. A discrete affine Fourier transform is performed on the baseband difference frequency data according to the RF division multiplexing modulation parameters to obtain a time-delay Doppler domain two-dimensional spectrum. Peak search is performed on the time-delay Doppler domain two-dimensional spectrum to obtain coarse estimation parameters. The coarse estimation parameters are refined by interpolation to obtain target distance estimation and Doppler frequency shift estimation. The target distance estimation and Doppler frequency shift estimation are encapsulated into a positioning and tracking parameter set. The positioning and tracking parameter set is input into the UAV flight control unit. The position is calculated based on the preset trajectory constraints to obtain the current positioning coordinates. The current positioning coordinates and the Doppler frequency shift estimate are jointly input into the tracking filter to perform state update to obtain the target tracking trajectory data. The target tracking trajectory data is then output to the ground monitoring terminal.

[0007] Furthermore, it also includes: acquiring echo signals from the UAV airborne receiver and extracting their carrier frequency and phase information to obtain echo characteristic parameters; generating a local linear frequency modulation reference signal based on the chirp slope and subcarrier spacing in the simulated radio frequency multiplexing modulation parameters; and aligning the local linear frequency modulation reference signal with the echo characteristic parameters in the time domain to obtain a synchronization signal pair. The synchronization signal is multiplied by the input analog mixer to obtain the mixing output signal. The high-frequency component and the low-frequency component in the mixing output signal are separated to obtain the difference frequency signal. The difference frequency signal is written to the analog signal output port for subsequent filtering processing.

[0008] Furthermore, it also includes: reading the difference frequency signal from the analog signal output port and configuring a low-pass filter according to a preset cutoff frequency parameter, inputting the difference frequency signal into the low-pass filter to perform high-frequency component suppression to obtain a filtered signal, and outputting the filtered signal to the input terminal of the sampling module; The sampling clock frequency is determined based on the simulated radio frequency multiplexing modulation parameters and the analog-to-digital converter is configured. The filtered signal is converted into a discrete sampling sequence according to the sampling clock frequency. The discrete sampling sequence is then encapsulated into baseband difference frequency data according to a preset frame format and written into the signal processing buffer.

[0009] Furthermore, it also includes: reading baseband difference frequency data from the signal processing buffer in frame sequence and extracting the chirp slope and subcarrier number from the analog radio frequency division multiplexing modulation parameters; constructing a discrete affine Fourier transform kernel matrix based on the chirp slope and the subcarrier number; and performing matrix multiplication operation on the baseband difference frequency data and the discrete affine Fourier transform kernel matrix to obtain a two-dimensional spectrum in the time-delay Doppler domain. The time-delay Doppler domain two-dimensional spectrum is divided into two-dimensional grids with time delay axis and Doppler axis. Within the two-dimensional grid, a grid-by-grid amplitude comparison is performed according to a preset amplitude threshold condition to obtain a set of candidate peak points. The grid index corresponding to the maximum amplitude is extracted from the set of candidate peak points to obtain coarse estimation parameters.

[0010] Furthermore, it also includes: extracting the peak grid index and the amplitude values ​​of its adjacent grids from the coarse estimation parameters; performing sub-grid offset calculation based on the amplitude values ​​using a parabolic interpolation algorithm to obtain the time delay axis refinement offset and the Doppler axis refinement offset; combining the peak grid index and the time delay axis refinement offset and converting them according to the distance resolution parameter to obtain the target distance estimate; and combining the peak grid index and the Doppler axis refinement offset and converting them according to the Doppler resolution parameter to obtain the Doppler frequency shift estimate. The target distance estimate and the Doppler frequency shift estimate are assembled into a positioning and tracking parameter group according to a preset parameter group format. The positioning and tracking parameter group is then appended with a timestamp identifier and written into the parameter output buffer for the flight control unit to call.

[0011] Furthermore, it also includes: reading the positioning and tracking parameter set from the parameter output buffer and extracting the target distance estimate and timestamp identifier therein; transmitting the positioning and tracking parameter set to the UAV flight control unit via the data interface; loading preset trajectory constraints in the flight control unit and parsing to obtain the trajectory boundary parameters and speed limit parameters. Based on the target distance estimate and the attitude data output by the UAV inertial measurement unit, a coordinate system transformation is performed to obtain the relative position vector in the body coordinate system. The relative position vector and the track boundary parameters are constrained and verified, and coordinate mapping is performed in combination with the geographic reference datum to obtain the current positioning coordinates.

[0012] Furthermore, it also includes: assembling the current positioning coordinates and the Doppler frequency shift estimate according to the state vector format to obtain an observation input vector; inputting the observation input vector into a tracking filter and reading the historical state vector and covariance matrix stored in the filter; performing state prediction and measurement update based on the observation input vector and the historical state vector according to the Kalman filter gain calculation rule to obtain a current state estimation vector; and extracting and encapsulating the position and velocity components in the current state estimation vector into target tracking trajectory data. The target tracking trajectory data is appended with UAV identification and timestamp information to obtain a trajectory data frame. The trajectory data frame is then encoded and modulated by the data link communication module to obtain a downlink transmission signal. The downlink transmission signal is then sent to the ground monitoring terminal via the air-to-ground communication link and written into the trajectory display buffer.

[0013] Secondly, this application provides a high-precision positioning and tracking device for unmanned aerial vehicles based on AFDM waveforms, comprising: The mixing module is used to perform analog domain dechirping mixing with the echo signal collected by the UAV airborne receiver and the local linear frequency modulation reference signal to obtain the difference frequency signal, to perform low-pass filtering on the difference frequency signal to obtain the filtered signal, and to perform baseband sampling on the filtered signal to obtain baseband difference frequency data and write it into the signal processing buffer. The calculation module is used to read the baseband difference frequency data from the signal processing buffer, perform discrete affine Fourier transform on the baseband difference frequency data according to the affine radio frequency division multiplexing modulation parameters to obtain a time-delay Doppler domain two-dimensional spectrum, perform peak search based on the time-delay Doppler domain two-dimensional spectrum to obtain coarse estimation parameters, refine the coarse estimation parameters through interpolation to obtain target distance estimation and Doppler frequency shift estimation, and encapsulate the target distance estimation and Doppler frequency shift estimation into a positioning and tracking parameter set; The positioning module is used to input the positioning and tracking parameter set into the UAV flight control unit, perform position calculation on the positioning and tracking parameter set according to preset trajectory constraints to obtain the current positioning coordinates, input the current positioning coordinates and the Doppler frequency shift estimate into the tracking filter to perform state update to obtain target tracking trajectory data, and output the target tracking trajectory data to the ground monitoring terminal.

[0014] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the AFDM waveform-based high-precision positioning and tracking method for unmanned aerial vehicles.

[0015] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the aforementioned high-precision positioning and tracking method for unmanned aerial vehicles based on AFDM waveforms.

[0016] Fifthly, this application provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the aforementioned high-precision positioning and tracking method for unmanned aerial vehicles based on AFDM waveforms.

[0017] As can be seen from the above technical solution, this application provides a high-precision positioning and tracking method and device for UAVs based on AFDM waveforms. The method involves analog domain dechirped mixing of the echo signal and the local linear frequency modulated reference signal, followed by low-pass filtering and baseband sampling to obtain baseband difference frequency data, which is then written into the signal processing buffer. Combined with discrete affine Fourier transform time-delay Doppler domain peak search and interpolation refinement driven by RF multiplexing modulation parameters, target distance estimates and Doppler frequency shift estimates are obtained and encapsulated into a positioning and tracking parameter set. The flight control unit performs position calculation based on preset trajectory constraints to obtain the current positioning coordinates, and updates the tracking filter state to obtain target tracking trajectory data, which is then output to the ground monitoring terminal. This effectively solves the shortcomings of traditional technologies in analog domain difference frequency preprocessing, time-delay Doppler domain high-precision parameter estimation, and flight control linkage tracking filter output, providing a basis for high-precision positioning and accurate tracking of UAVs. Attached Figure Description

[0018] 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.

[0019] Figure 1 This is a flowchart illustrating the high-precision positioning and tracking method for unmanned aerial vehicles based on AFDM waveforms in an embodiment of this application. Figure 2 This is a structural diagram of the high-precision positioning and tracking device for unmanned aerial vehicles based on AFDM waveforms in an embodiment of this application; Figure 3 This is a flowchart illustrating a high-precision UAV positioning and tracking method based on AFDM waveforms in a specific embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] The acquisition, storage, use, and processing of data in this application comply with relevant laws and regulations.

[0022] In view of the problems existing in the prior art, this application provides a high-precision positioning and tracking method and device for UAVs based on AFDM waveforms. The method involves chirping and mixing the echo signal with a local linear frequency modulated reference signal in the analog domain, followed by low-pass filtering and baseband sampling to obtain baseband difference frequency data, which is then written into the signal processing buffer. Combined with discrete affine Fourier transform time-delay Doppler domain peak search and interpolation refinement driven by RF division multiplexing modulation parameters, the target distance estimate and Doppler frequency shift estimate are obtained and encapsulated into a positioning and tracking parameter set. The flight control unit performs position calculation according to preset trajectory constraints to obtain the current positioning coordinates and updates the tracking filter state to obtain the target tracking trajectory data, which is then output to the ground monitoring terminal. This method effectively solves the shortcomings of traditional technologies in analog domain difference frequency preprocessing, time-delay Doppler domain high-precision parameter estimation, and flight control linkage tracking filter output, providing a basis for high-precision positioning and accurate tracking of UAVs.

[0023] To effectively address the shortcomings of traditional technologies in analog domain difference frequency preprocessing, high-precision parameter estimation in the time-delay Doppler domain, and flight control linkage tracking filter output, and to provide technical support for high-precision UAV positioning and accurate tracking, this application provides an embodiment of a high-precision UAV positioning and tracking method based on AFDM waveforms. See [link to embodiment]. Figure 1 and Figure 3 The high-precision positioning and tracking method for UAVs based on AFDM waveforms specifically includes the following: Step S101: Based on the echo signal collected by the UAV airborne receiver and the local linear frequency modulation reference signal, perform analog domain dechirping mixing to obtain the difference frequency signal, perform low-pass filtering on the difference frequency signal to obtain the filtered signal, perform baseband sampling on the filtered signal to obtain baseband difference frequency data and write it into the signal processing buffer. This embodiment uses an onboard receiver on a UAV to collect the reflected echo signal from the target. The onboard receiver is deployed in the RF compartment at the front of the UAV and includes a receiving antenna, a low-noise amplifier, and a down-conversion module. The receiving antenna adopts a broadband microstrip patch structure, and its operating frequency band covers the system design bandwidth. The gain is configured according to the link budget. After being picked up by the receiving antenna, the echo signal is processed by the RF front-end through the low-noise amplifier and the down-conversion module. If the amplitude of the echo signal is lower than the receiving sensitivity threshold, it is marked as a weak signal frame and a weak signal marker bit is registered. The receiving sensitivity threshold is determined jointly based on the system link budget and the minimum radar cross-section of the target. The weak signal marker bit is read in step S102 for adaptive adjustment of the amplitude threshold during peak search. After the echo signal is appended with a collection timestamp, it is written to the RF front-end output buffer for mixing processing.

[0024] After the echo signal is written into the RF front-end output buffer, a local linear frequency modulation (LFM) reference signal is generated based on the simulated RF multiplexing modulation parameters. The simulated RF multiplexing modulation parameters are read from the system parameter storage area and include three fields: chirp slope, subcarrier spacing, and symbol period. The chirp slope is calculated based on the ratio of system bandwidth to symbol period; the subcarrier spacing is set jointly based on the channel coherence bandwidth and Doppler spread characteristics; and the symbol period is determined based on the round-trip delay corresponding to the maximum detection distance. The local LFM reference signal is generated by a direct digital frequency synthesizer based on the chirp slope and subcarrier spacing, with the starting frequency consistent with the carrier frequency of the echo signal. If any field in the simulated RF multiplexing modulation parameters fails to be read or exceeds a preset valid range, a default parameter set is loaded and a parameter anomaly flag is registered. This parameter anomaly flag is read in step S102 for reliability evaluation of the two-dimensional spectrum in the time-delay Doppler domain. After the local LFM reference signal is generated, it is written into the reference signal buffer to await time-domain alignment processing.

[0025] Accordingly, the echo signal and the local linear frequency modulation (LFM) reference signal are time-domain aligned to obtain a synchronization signal pair. The time-domain alignment module reads the echo signal from the RF front-end output buffer and the local LFM reference signal from the reference signal buffer. Using the local LFM reference signal as a reference sequence, it performs a sliding cross-correlation operation on the echo signal and extracts the time delay offset corresponding to the cross-correlation peak value. The accuracy of the time delay offset is determined based on the system master clock frequency and the interpolation factor. The master clock is provided by an onboard crystal oscillator and frequency stabilization is achieved through a phase-locked loop. Based on the time delay offset, delay compensation is performed on the echo signal to align it with the local LFM reference signal in the time domain to form a synchronization signal pair. If the cross-correlation peak value is lower than the synchronization threshold, synchronization is considered to have failed and a re-acquisition process is triggered. The synchronization threshold is set as the mean plus a certain number of standard deviations based on the statistical distribution of cross-correlation output in a targetless scenario. The synchronization signal pair is written into the mixer input buffer to await analog domain dechirped mixing processing.

[0026] After the synchronization signal pair is written into the mixer input buffer, it is input into the analog mixer and subjected to analog domain dechirped mixing to obtain the difference frequency signal. The analog mixer adopts a double-balanced mixing structure. The local oscillator port is connected to the local linear frequency modulation reference signal, and the RF port is connected to the echo signal. The synchronization signal pair is multiplied to output a mixed output signal containing high-frequency and low-frequency components. The mixed output signal is filtered by an analog bandpass filter to extract the low-frequency component as the difference frequency signal. The upper limit of the passband is determined based on the beat frequency corresponding to the maximum detection distance. The difference frequency signal carries target distance and Doppler frequency shift information. Its frequency value is proportional to the target distance, and its phase change rate is proportional to the Doppler frequency shift. If the mixer output amplitude exceeds the upper limit of the dynamic range of the subsequent circuit, the automatic gain control module is activated to compress the amplitude. The upper limit of the dynamic range is determined based on the input voltage specification of the subsequent analog-to-digital converter. The difference frequency signal is written into the analog signal output port for low-pass filtering.

[0027] Based on the aforementioned difference frequency signal, the difference frequency signal is read from the analog signal output port and a low-pass filter is configured to suppress high-frequency components to obtain a filtered signal. The cutoff frequency of the low-pass filter is determined based on the upper limit of the difference frequency corresponding to the maximum detection distance, with a certain margin reserved. The filter order is selected based on the stopband attenuation requirements. After the difference frequency signal is processed by the low-pass filter, the high-frequency components and interference from adjacent subcarriers are suppressed, and the filtered signal is output to the input of the sampling module. If the amplitude of the filtered signal is lower than the voltage corresponding to the least significant bit of the analog-to-digital converter, zero filling is performed and an under-amplitude marker is registered. The under-amplitude marker is read in step S102 for noise background estimation in weak target scenarios. The filtered signal is temporarily stored in the pre-sampling buffer area to await baseband sampling processing.

[0028] After the filtered signal is output to the input of the sampling module, the sampling clock frequency is determined according to the simulated radio frequency division multiplexing modulation parameters, and the analog-to-digital converter (ADC) is configured to perform analog-to-digital conversion on the filtered signal to obtain a discrete sampling sequence. The sampling clock frequency is set to more than twice the cutoff frequency of the low-pass filter according to the Nyquist criterion, with a margin reserved for anti-aliasing. The ADC resolution is selected according to the system's dynamic range requirements. The filtered signal is sampled and quantized point by point according to the sampling clock frequency, and each sampling point is converted into a discrete sample value represented by a fixed-point integer. The discrete sampling sequence is encapsulated as baseband difference frequency data according to a preset frame format. The preset frame format includes four fields: frame header identifier, acquisition timestamp, number of sampling points, and sampled data. The frame header identifier is used for frame synchronization identification, the acquisition timestamp is accurate to the microsecond level for multi-frame timing alignment, the number of sampling points indicates the effective data length of the current frame, and the sampled data is arranged sequentially according to the acquisition order. If the number of sampling points in a single frame exceeds the frame length limit, frame splitting is performed, and a continuation flag is set in the frame header identifier. The baseband difference frequency data is written into the signal processing buffer as the output product of step S101, and is read by step S102 at the discrete affine Fourier transform input port for time-delay Doppler domain two-dimensional spectrum calculation.

[0029] Step S102: Read the baseband difference frequency data from the signal processing buffer, perform discrete affine Fourier transform on the baseband difference frequency data according to the RF multiplexing modulation parameters to obtain a time-delay Doppler domain two-dimensional spectrum, perform peak search based on the time-delay Doppler domain two-dimensional spectrum to obtain coarse estimation parameters, refine the coarse estimation parameters through interpolation to obtain target distance estimation and Doppler frequency shift estimation, and encapsulate the target distance estimation and Doppler frequency shift estimation into a positioning and tracking parameter group; In this embodiment, the baseband difference frequency data output in step S101 is read from the signal processing buffer in frame sequence. The signal processing buffer uses a first-in-first-out queue structure to manage multi-frame data. The reading module performs frame synchronization identification based on the frame header identifier and extracts the acquisition timestamp for multi-frame timing verification. If the difference between the acquisition timestamps of adjacent frames exceeds the allowable range of the symbol period, it is determined that the frame is lost and a null frame is inserted as a placeholder. The frame loss flag bit is used for confidence weighting processing during subsequent peak search. After frame integrity verification, the baseband difference frequency data is written into the transform operation input buffer to await discrete affine Fourier transform processing.

[0030] After the baseband difference frequency data is written into the transform operation input buffer, the anamorphic radio frequency division multiplexing modulation parameters are read from the system parameter storage area and a discrete affine Fourier transform kernel matrix is ​​constructed. The chirp slope and the number of subcarriers in the anamorphic radio frequency division multiplexing modulation parameters serve as key inputs for kernel matrix construction. The dimension of the kernel matrix is ​​determined by the number of subcarriers, and the matrix elements are calculated based on the phase rotation factor of the discrete affine Fourier transform. Matrix multiplication is performed between the baseband difference frequency data and the discrete affine Fourier transform kernel matrix. The result is a frequency domain sequence in complex form. The frequency domain sequence is rearranged along the time delay axis and the Doppler axis to obtain a two-dimensional spectrum in the time-delay Doppler domain. The two-dimensional spectrum in the time-delay Doppler domain is stored in the form of a two-dimensional complex matrix, with row indices corresponding to time delay grids and column indices corresponding to Doppler grids. The magnitude of the matrix elements represents the energy response at the corresponding time delay and Doppler combination. The two-dimensional spectrum in the time-delay Doppler domain is written into the spectrum data buffer for peak search processing.

[0031] Accordingly, the two-dimensional spectrum in the time-delay Doppler domain is divided into two-dimensional grids along the time-delay axis and the Doppler axis, and a peak search is performed to obtain coarse estimation parameters. The peak search module first performs magnitude calculation on the two-dimensional spectrum in the time-delay Doppler domain to obtain the amplitude spectrum. The amplitude threshold is determined jointly based on the noise background estimate and a preset false alarm probability. The noise background estimate is obtained by averaging the edge regions of the amplitude spectrum. Within the two-dimensional grid, a grid-by-grid amplitude comparison is performed according to the amplitude threshold condition. Grids with amplitudes exceeding the threshold are included in the candidate peak point set. The grid index corresponding to the maximum amplitude is extracted from the candidate peak point set as the coarse estimation parameter. The coarse estimation parameter includes two fields: the time-delay axis grid index and the Doppler axis grid index. If the candidate peak point set is empty, it is determined that no target has been detected, and a detection failure flag is registered. The detection failure flag is read in step S103 to trigger the track extrapolation mode of the tracking filter. The coarse estimation parameters are written into the interpolation processing input buffer for refinement processing.

[0032] After the coarse estimation parameters are written into the interpolation processing input buffer, interpolation refinement is performed based on the coarse estimation parameters to obtain the target distance estimate and the Doppler frequency shift estimate. The interpolation refinement module extracts the amplitude values ​​of the peak grid and its adjacent grids from the two-dimensional spectrum in the time-delay Doppler domain, and performs parabolic interpolation operations along the time delay axis and the Doppler axis respectively. The parabolic interpolation fits a quadratic curve based on the three-point amplitude values ​​and calculates the extreme point offset. Both the time delay axis refinement offset and the Doppler axis refinement offset are expressed in grid units and their values ​​are limited to positive and negative half grids. The target distance estimate is obtained by adding the time delay axis grid index to the time delay axis refinement offset and multiplying it by the distance resolution parameter, which is determined by the ratio of the speed of light to the system bandwidth. The Doppler frequency shift estimate is obtained by adding the Doppler axis grid index to the Doppler axis refinement offset and multiplying it by the Doppler resolution parameter, which is determined by the reciprocal of the symbol period. The target distance estimate and Doppler frequency shift estimate are written to the input of the parameter encapsulation module and await encapsulation processing.

[0033] Based on the aforementioned target distance estimate and Doppler frequency shift estimate, a positioning and tracking parameter set is obtained by assembling fields according to a preset parameter set format. The positioning and tracking parameter set includes four fields: target distance estimate, Doppler frequency shift estimate, estimation timestamp, and confidence level flag. The target distance estimate is stored in meters, the Doppler frequency shift estimate is stored in Hertz, the estimation timestamp is inherited from the acquisition timestamp of the baseband difference frequency data, and the confidence level flag is determined jointly based on the peak signal-to-noise ratio and interpolation residual. If the target distance estimate exceeds the system's maximum detection range or the Doppler frequency shift estimate exceeds the maximum unambiguous Doppler range, it is marked as out of bounds and an out-of-bounds flag is registered. This out-of-bounds flag is read in step S103 for constraint correction during position calculation. The positioning and tracking parameter set is appended with a frame sequence number as a primary key and written to the parameter output buffer for reading at the flight control unit data interface in step S103 for position calculation and tracking filtering processing.

[0034] Step S103: Input the positioning and tracking parameter set into the UAV flight control unit, perform position calculation on the positioning and tracking parameter set according to the preset trajectory constraints to obtain the current positioning coordinates, input the current positioning coordinates and the Doppler frequency shift estimate into the tracking filter to perform state update to obtain target tracking trajectory data, and output the target tracking trajectory data to the ground monitoring terminal.

[0035] In this embodiment, the positioning and tracking parameter set output in step S102 is read from the parameter output buffer and transmitted to the UAV flight control unit. The flight control unit is connected to the signal processing module via an onboard data bus, and the data interface uses a serial communication protocol to transmit parameters. After the positioning and tracking parameter set is parsed by the interface, three fields are extracted: target distance estimate, Doppler frequency shift estimate, and estimated timestamp. If the out-of-bounds flag or confidence flag in the parameter set indicates an anomaly, the parameter correction process is initiated. The correction strategy extrapolates and compensates based on historical valid parameters. After the parameter parsing is completed, the parameters are written to the position calculation module input buffer to await coordinate calculation.

[0036] After the positioning and tracking parameter set is written into the input buffer of the position calculation module, preset trajectory constraints are loaded and position calculation is performed to obtain the current positioning coordinates. The preset trajectory constraints are read from the flight mission configuration file and include track boundary parameters and speed limit parameters. The track boundary parameters define the spatial range in which the UAV is allowed to fly, and the speed limit parameters constrain the maximum maneuvering speed of the UAV. The position calculation module obtains the current attitude data from the inertial measurement unit and performs a coordinate system transformation on the target distance estimate and attitude data to obtain the relative position vector in the body coordinate system. The relative position vector is combined with a geographic reference datum to perform coordinate mapping. The geographic reference datum is determined by the geodetic coordinates of the takeoff point and the heading reference angle. If the mapping result exceeds the track boundary parameters, boundary constraint correction is performed and a constraint trigger flag is registered. The current positioning coordinates are represented in the form of a longitude, latitude, and altitude triplet and written into the tracking filter input buffer.

[0037] Accordingly, the current positioning coordinates and the Doppler frequency shift estimate are jointly input into the tracking filter to perform state updates and obtain target tracking trajectory data. The tracking filter adopts an extended Kalman filter structure, and the state vector contains six elements, including three-dimensional position components and three-dimensional velocity components. The current positioning coordinates and the Doppler frequency shift estimate are assembled according to the observation vector format, and the Doppler frequency shift estimate is converted to a radial velocity observation. The filter reads the historical state vector and covariance matrix from the internal storage area and performs a one-step prediction based on the system state transition equation to obtain a priori state estimation. The difference between the observation vector and the priori state estimation is weighted by Kalman gain and then updated to the state vector, with the covariance matrix updated synchronously. If the detection failure flag indicates that no target is detected, the measurement update is skipped and only state prediction is performed. The position and velocity components are extracted from the updated state vector and encapsulated into target tracking trajectory data.

[0038] Based on the aforementioned target tracking trajectory data, a trajectory data frame is formed by adding UAV identification and timestamp information and output to the ground monitoring terminal. The trajectory data frame contains seven fields: UAV identification, estimated timestamp, three-dimensional position coordinates, three-dimensional velocity vector, and tracking status flag. The tracking status flag indicates whether the current tracking is stable, the trajectory is extrapolated, or a new target has been acquired. The trajectory data frame undergoes channel coding and modulation processing via the data link communication module to form a downlink transmission signal. The modulation method and coding rate are adaptively selected based on the current channel quality. The downlink transmission signal is sent to the ground monitoring terminal via the air-to-ground communication link. After receiving the signal, the ground monitoring terminal performs demodulation and decoding and writes the trajectory data to the display buffer for real-time situation presentation. The target tracking trajectory data, as the output of step S103, is simultaneously written to the onboard storage module for offline analysis.

[0039] As described above, the high-precision UAV positioning and tracking method based on AFDM waveform provided in this application can obtain baseband difference frequency data by chirping and mixing the echo signal and the local linear frequency modulation reference signal in the analog domain, followed by low-pass filtering and baseband sampling, and writing it into the signal processing buffer. Combined with discrete affine Fourier transform time delay Doppler domain two-dimensional spectrum peak search and interpolation refinement driven by RF multiplexing modulation parameters, the target distance estimate and Doppler frequency shift estimate are obtained and encapsulated into a positioning and tracking parameter set. The flight control unit performs position calculation according to preset trajectory constraints to obtain the current positioning coordinates and updates the tracking filter state to obtain the target tracking trajectory data, which is then output to the ground monitoring terminal. This effectively solves the shortcomings of traditional technologies in analog domain difference frequency preprocessing, time delay Doppler domain high-precision parameter estimation, and flight control linkage tracking filter output, providing high-precision positioning and accurate tracking for UAVs.

[0040] In one embodiment of the high-precision positioning and tracking method for UAVs based on AFDM waveforms in this application, the method may further include the following: Step S201: Obtain the echo signal from the UAV airborne receiver and extract its carrier frequency and phase information to obtain echo characteristic parameters. Generate a local linear frequency modulation reference signal based on the chirp slope and subcarrier spacing in the simulated radio frequency multiplexing modulation parameters. Time-domain align the local linear frequency modulation reference signal with the echo characteristic parameters to obtain a synchronization signal pair. Step S202: Perform a multiplication operation on the input analog mixer with the synchronization signal to obtain a mixing output signal. Perform spectrum separation on the high-frequency and low-frequency components in the mixing output signal to obtain a difference frequency signal. Write the difference frequency signal into the analog signal output port for subsequent filtering processing.

[0041] This embodiment acquires echo signals from an onboard receiver of a UAV and extracts their carrier frequency and phase information to obtain echo characteristic parameters. The RF front-end module of the UAV onboard receiver performs down-conversion processing on the echo signal and outputs an intermediate frequency (IF) signal. The carrier frequency extraction module performs a short-time Fourier transform on the IF signal and detects the spectral peak position to obtain a carrier frequency estimate. Phase information is obtained by coherently demodulating the IF signal with a local reference carrier. The in-phase and quadrature components of the demodulated output are used to calculate the instantaneous phase value. If the carrier frequency estimate deviates from the nominal carrier frequency by more than the allowable range, it is determined to be frequency drift and a frequency offset anomaly flag is registered. The frequency offset anomaly flag is read in step S301 for frequency compensation of the mixed output signal. The echo characteristic parameters include two fields: the carrier frequency estimate and the instantaneous phase sequence. After adding an acquisition timestamp, these are written to the characteristic parameter buffer to await time-domain alignment processing.

[0042] After the echo characteristic parameters are written to the characteristic parameter buffer, a local linear frequency modulation (LFM) reference signal is generated based on the chirp slope and subcarrier spacing in the simulated radio frequency division multiplexing (RFD) modulation parameters. The simulated RFD modulation parameters are read from the system parameter storage area. The chirp slope defines the frequency change rate of the LFM signal, and the subcarrier spacing defines the frequency domain distance between adjacent subcarriers. The local LFM reference signal is generated by a direct digital frequency synthesizer (DFD). The starting frequency is set according to the carrier frequency estimate in the echo characteristic parameters to achieve frequency alignment, and the modulation slope is configured according to the chirp slope. If the DFD output amplitude is abnormal, it switches to a backup synthesis channel and registers the channel switching flag. After the local LFM reference signal is generated, it is written to the reference signal buffer to await time-domain alignment.

[0043] Accordingly, the local linear frequency modulation (LFM) reference signal and the echo characteristic parameters are time-domain aligned to obtain a synchronization signal pair. The time-domain alignment module reads the instantaneous phase sequence from the echo characteristic parameters from the characteristic parameter buffer and the phase sequence of the local LFM reference signal from the reference signal buffer. Using the local LFM reference signal as a reference, a phase difference operation is performed on the echo signal to extract the time delay offset. Based on the time delay offset, sampling-point-level delay compensation is performed on the echo signal to align it with the local LFM reference signal in the time domain to form a synchronization signal pair. If the time delay offset exceeds the maximum unambiguous time delay range, ambiguity calculation is performed and the ambiguity calculation flag is registered. The synchronization signal pair is written into the mixer input buffer for use in step S202.

[0044] In this embodiment, the synchronization signal output in step S201 is multiplied by the input analog mixer to obtain the mixed output signal. The analog mixer reads the synchronization signal pair from the mixer input buffer, connects the local linear frequency modulation reference signal to the local oscillator port, and connects the aligned echo signal to the RF port. A point-by-point multiplication operation is performed on the two input signals. The spectrum of the output signal includes the high-frequency component corresponding to the sum of the frequencies of the two input signals and the low-frequency component corresponding to the frequency difference. If the mixer output is saturated, input attenuation adjustment is initiated and a saturation flag is registered. This saturation flag is used for gain compensation calculation during the filtering process in step S301. The mixed output signal is written to the input of the spectrum separation module for high and low frequency component separation processing.

[0045] Based on the aforementioned mixing output signal, a spectrum separation process is performed to extract the low-frequency component, resulting in a difference frequency signal, which is then written to the analog signal output port. The spectrum separation module employs an analog low-pass filter, with the cutoff frequency set to ensure sufficient suppression of high-frequency components. After processing by the analog low-pass filter, the high-frequency components of the mixing output signal are attenuated, leaving only the low-frequency component, the difference frequency signal, in the output. The difference frequency signal carries target distance and Doppler frequency shift information, and its instantaneous frequency is linearly related to the target radial distance. If the amplitude of the difference frequency signal is lower than the noise floor of the subsequent circuit, a weak signal marker is registered. The difference frequency signal, as the output product of step S202, is written to the analog signal output port for reading at the filter input port in step S301 for high-frequency residual component suppression and signal conditioning before baseband sampling.

[0046] In one embodiment of the high-precision positioning and tracking method for UAVs based on AFDM waveforms in this application, the method may further include the following: Step S301: Read the difference frequency signal from the analog signal output port and configure a low-pass filter according to the preset cutoff frequency parameter. Input the difference frequency signal into the low-pass filter to perform high-frequency component suppression to obtain the filtered signal. Output the filtered signal to the input terminal of the sampling module. Step S302: Determine the sampling clock frequency based on the simulated radio frequency multiplexing modulation parameters and configure the analog-to-digital converter. Perform analog-to-digital conversion on the filtered signal according to the sampling clock frequency to obtain a discrete sampling sequence. Encapsulate the discrete sampling sequence into baseband difference frequency data according to a preset frame format and write it into the signal processing buffer.

[0047] In this embodiment, the difference frequency signal output in step S202 is read from the analog signal output port, and a low-pass filter is configured according to the preset cutoff frequency parameters. The preset cutoff frequency parameters are read from the system parameter storage area, and their values ​​are determined jointly based on the maximum effective bandwidth of the difference frequency signal and the sampling clock frequency to meet the requirements of the Nyquist sampling theorem. The low-pass filter is implemented using an active analog filter circuit. The filter order and passband ripple parameters are preset according to the signal conditioning accuracy requirements, and the cutoff frequency is dynamically set through adjustment using a programmable resistor array. If the frequency offset anomaly flag indicates carrier frequency drift, adaptive correction is performed on the cutoff frequency parameters to compensate for the spectral broadening introduced by the frequency offset. After the low-pass filter is configured, the system waits for the difference frequency signal input.

[0048] After the low-pass filter is configured, the difference frequency signal is input to the low-pass filter to perform high-frequency component suppression, resulting in a filtered signal. The difference frequency signal is input to the low-pass filter from the analog signal output port via an impedance matching network. The filter attenuates the high-frequency residual components in the difference frequency signal that exceed the cutoff frequency. The high-frequency residual components mainly originate from the sum frequency component leakage generated by the mixing operation in step S202 and the harmonic components introduced by the mixer's nonlinearity. The spectrum of the filtered signal retains only the effective difference frequency components below the cutoff frequency, and the out-of-band suppression is determined according to the filter order. If the saturation flag indicates that the mixer output is saturated, gain compensation is performed on the filtered signal to restore the signal's dynamic range. The filtered signal is written to the input of the sampling module for analog-to-digital conversion processing.

[0049] Accordingly, the sampling clock frequency is determined and the analog-to-digital converter (ADC) is configured based on the simulated radio frequency multiplexing (RFD) modulation parameters. The number of subcarriers and symbol period in the simulated RFD modulation parameters are used to calculate the effective signal bandwidth. The sampling clock frequency is set to at least twice the effective signal bandwidth to meet the sampling theorem requirements and to reserve anti-aliasing margin. The quantization bit depth of the ADC is configured according to the system's dynamic range requirements, and the reference voltage is set based on the peak amplitude of the filtered signal to optimize the quantization signal-to-noise ratio. The sampling clock is generated by multiplying the local crystal oscillator via a phase-locked loop (PLL), and the clock phase is synchronized with the local linear frequency modulation (LFM) reference signal to ensure sampling coherence. After the ADC configuration is complete, it enters the sampling-ready state.

[0050] After the analog-to-digital converter (ADC) enters the sampling-ready state, the filtered signal is converted to digital signal according to the sampling clock frequency to obtain a discrete sampling sequence. The filtered signal is input to the analog input channel of the ADC from the sampling module input terminal. The ADC performs quantization processing on the filtered signal at each rising edge of the sampling clock and outputs digital codewords. The discrete sampling sequence consists of consecutive digital codewords arranged in time sequence, and each sampling point contains two fields: quantization amplitude value and sampling index. If the weak signal flag indicates that the difference frequency signal amplitude is low, digital gain compensation is activated to improve the utilization rate of effective quantization bits. The discrete sampling sequence is written to the frame encapsulation module input terminal to await data formatting processing.

[0051] Based on the aforementioned discrete sampling sequence, baseband difference frequency data is encapsulated according to a preset frame format and written to the signal processing buffer. The preset frame format defines five field areas: frame header identifier, frame length, acquisition timestamp, sampling parameters, and payload. The frame header identifier is used for frame synchronization identification, and the acquisition timestamp is read from the system real-time clock. The discrete sampling sequence is used as the payload to fill the corresponding area of ​​the frame format, and a cyclic redundancy check (CRC) code is appended to the frame tail for transmission integrity verification. The baseband difference frequency data is written to the signal processing buffer as the output product of step S302, and is read in the signal processing buffer in step S401 for discrete affine Fourier transform processing and time-delay Doppler domain two-dimensional spectrum generation.

[0052] In one embodiment of the high-precision positioning and tracking method for UAVs based on AFDM waveforms in this application, the method may further include the following: Step S401: Read baseband difference frequency data from the signal processing buffer in frame sequence and extract the chirp slope and subcarrier number from the analog radio frequency division multiplexing modulation parameters. Construct a discrete affine Fourier transform kernel matrix based on the chirp slope and the subcarrier number. Perform matrix multiplication operation on the baseband difference frequency data and the discrete affine Fourier transform kernel matrix to obtain the time delay Doppler domain two-dimensional spectrum. Step S402: Divide the time-delay Doppler domain two-dimensional spectrum into two-dimensional grids with time delay axis and Doppler axis. Perform grid-by-grid amplitude comparison within the two-dimensional grids according to preset amplitude threshold conditions to obtain a set of candidate peak points. Extract the grid index corresponding to the maximum amplitude from the set of candidate peak points to obtain coarse estimation parameters.

[0053] In this embodiment, the baseband difference frequency data output in step S302 is read sequentially from the signal processing buffer and the chirp slope and subcarrier number are extracted from the simulated radio frequency division multiplexing modulation parameters. The signal processing buffer uses a first-in-first-out queue structure to manage multiple frames of data. The reading module performs frame synchronization identification and verifies the cyclic redundancy check code based on the frame header identifier to ensure data integrity. The simulated radio frequency division multiplexing modulation parameters are synchronously read from the system parameter storage area. The chirp slope parameter defines the frequency change rate of the linear frequency modulation signal, and the subcarrier number parameter determines the dimension of the transform matrix. If the frame integrity check fails, the current frame is discarded and a frame loss flag is registered. The frame loss flag is used for confidence weighting during peak search. The baseband difference frequency data and modulation parameters are written into the transform operation input buffer to await kernel matrix construction.

[0054] After the baseband difference frequency data and modulation parameters are written into the transform operation input buffer, a discrete affine Fourier transform kernel matrix is ​​constructed based on the chirp slope and the number of subcarriers. The kernel matrix dimension is determined by the number of subcarriers, with row indices corresponding to time-delay domain sampling points and column indices corresponding to Doppler domain frequency points. Matrix elements are calculated based on the phase rotation factor of the discrete affine Fourier transform, which is determined by the product of the chirp slope and the time-frequency index. A pre-computation strategy is used in the kernel matrix construction process to reduce real-time computational load; if the chirp slope parameter changes, the kernel matrix reconstruction process is triggered. After the kernel matrix is ​​constructed, it is written into the matrix operation module to await multiplication with the baseband difference frequency data.

[0055] Accordingly, the baseband difference frequency data and the discrete affine Fourier transform kernel matrix are multiplied to obtain a two-dimensional spectrum in the time-delay Doppler domain. The baseband difference frequency data is rearranged into a column vector according to the intra-frame sampling point order as the right operand of the matrix multiplication, and the kernel matrix is ​​used as the left operand. The matrix multiplication operation adopts a block parallel strategy to improve the throughput, and the result is a frequency domain sequence in complex form. The frequency domain sequence is rearranged along the time delay axis and the Doppler axis to obtain a two-dimensional spectrum in the time-delay Doppler domain. The two-dimensional spectrum is stored in the form of a complex matrix, and the magnitude of the matrix elements represents the energy response at the corresponding time delay and Doppler combination. The two-dimensional spectrum in the time-delay Doppler domain is written to the spectrum data buffer for reading in step S402 to perform peak search processing.

[0056] In this embodiment, the time-delay Doppler domain two-dimensional spectrum output in step S401 is divided into two-dimensional grids along the time delay axis and the Doppler axis. The grid spacing along the time delay axis is determined by the reciprocal of the system bandwidth to correspond to the distance resolution, and the grid spacing along the Doppler axis is determined by the reciprocal of the symbol period to correspond to the velocity resolution. The total number of grids in the two-dimensional grid is equal to the product of the number of grids along the time delay axis and the number of grids along the Doppler axis. Each grid corresponds to an element position in the time-delay Doppler domain two-dimensional spectrum matrix. After the grid division is completed, the magnitude of the time-delay Doppler domain two-dimensional spectrum is calculated to obtain the amplitude spectrum. The amplitude spectrum is stored in the form of a real number matrix for subsequent threshold comparison processing.

[0057] In one embodiment of the high-precision positioning and tracking method for UAVs based on AFDM waveforms in this application, the method may further include the following: Step S501: Extract the peak grid index and the amplitude values ​​of its adjacent grids from the coarse estimation parameters. Based on the amplitude values, perform sub-grid offset calculation using a parabolic interpolation algorithm to obtain the time delay axis refinement offset and the Doppler axis refinement offset. Combine the peak grid index with the time delay axis refinement offset and convert it according to the distance resolution parameter to obtain the target distance estimate. Combine the peak grid index with the Doppler axis refinement offset and convert it according to the Doppler resolution parameter to obtain the Doppler frequency shift estimate. Step S502: Assemble the target distance estimate and the Doppler frequency shift estimate according to the preset parameter group format to obtain the positioning and tracking parameter group, attach a timestamp identifier to the positioning and tracking parameter group and write it into the parameter output buffer for the flight control unit to call.

[0058] This embodiment extracts the peak raster index and the amplitude values ​​of its adjacent rasters from the coarse estimation parameters output in step S402. The coarse estimation parameters are read from the interpolation processing input buffer. The peak raster index contains two fields: the time delay axis raster index and the Doppler axis raster index. The peak raster and the amplitude values ​​of its one adjacent raster along the time delay axis and the Doppler axis are extracted from the time-delay Doppler domain two-dimensional spectrum in the spectral data buffer, for a total of five amplitude values ​​for subsequent interpolation calculations. If the peak raster is located at the boundary of the two-dimensional spectrum, a boundary extrapolation strategy is used to estimate the missing amplitude values ​​of adjacent rasters and register the boundary marker bits. The extracted amplitude values ​​are written to the input of the interpolation calculation module to await sub-raster offset calculation.

[0059] After the amplitude value is written to the input of the interpolation module, the time delay axis refinement offset and Doppler axis refinement offset are calculated based on the amplitude value using a parabolic interpolation algorithm. The parabolic interpolation algorithm is executed independently along the time delay axis and the Doppler axis, fitting a quadratic curve with the amplitude values ​​of three points: the peak grid and its two adjacent grids. The position of the extreme point of the quadratic curve is obtained by differentiating the fitting coefficients and setting the derivative to zero; the offset of the extreme point relative to the peak grid is the sub-grid offset. Both the time delay axis refinement offset and the Doppler axis refinement offset are expressed in grid units, with the value range limited to within positive and negative half a grid to ensure interpolation validity. If the boundary marker indicates that the peak is located on the boundary, constraint correction is performed on the offset. The refinement offset is written to the input of the parameter conversion module to wait for the distance and Doppler frequency shift estimates to be calculated.

[0060] Accordingly, the peak grid index is combined with the time delay axis refinement offset and converted according to the distance resolution parameter to obtain the target distance estimate. The time delay axis grid index and the time delay axis refinement offset are added to obtain the refined time delay axis position, which is represented in grid units. The distance resolution parameter is calculated from the ratio of the speed of light to the system bandwidth and read from the system parameter storage area. The refined time delay axis position is multiplied by the distance resolution parameter to obtain the target distance estimate, which is represented in meters. If the target distance estimate exceeds the system's maximum detection range, an out-of-range flag is registered. The target distance estimate is written to the parameter encapsulation module input terminal to await joint encapsulation with the Doppler frequency shift estimate.

[0061] After the target distance estimate is written to the parameter encapsulation module input, the peak grid index is combined with the Doppler axis refinement offset and converted according to the Doppler resolution parameter to obtain the Doppler frequency shift estimate. The Doppler axis grid index and the Doppler axis refinement offset are added to obtain the refined Doppler axis position. The Doppler resolution parameter is determined by the reciprocal of the symbol period and is read from the system parameter storage area. The refined Doppler axis position is multiplied by the Doppler resolution parameter to obtain the Doppler frequency shift estimate, which is expressed in Hertz. If the Doppler frequency shift estimate exceeds the maximum unambiguous Doppler range, an out-of-bounds flag is registered. The Doppler frequency shift estimate is written to the parameter encapsulation module input and merged with the target distance estimate, awaiting field assembly.

[0062] In this embodiment, the target distance estimate and Doppler shift estimate output in step S501 are assembled into a positioning and tracking parameter set according to a preset parameter set format. The preset parameter set format defines four areas: target distance estimate field, Doppler shift estimate field, confidence flag field, and status flag field. The target distance estimate and Doppler shift estimate are filled into their respective field areas. The confidence flag is determined jointly based on the peak signal-to-noise ratio and interpolation residual. The status flag summarizes the status information of the detection failure flag and the out-of-bounds flag. After field assembly, the positioning and tracking parameter set is written to the timestamp appending module input, awaiting flag addition.

[0063] Based on the aforementioned positioning and tracking parameter set, a timestamp is appended and written to the parameter output buffer for use by the flight control unit. The timestamp is read from the system's real-time clock, expressed in Coordinated Universal Time (UTC) format, and accurate to the microsecond level to meet the requirements of multi-frame timing verification. The timestamp is appended to the frame header of the positioning and tracking parameter set to form a complete parameter data frame, and the frame sequence number is synchronously appended as the primary key for data traceability. The positioning and tracking parameter set, as the output of step S502, is written to the parameter output buffer for use in step S601 at the flight control unit data interface for position calculation and tracking filtering.

[0064] In one embodiment of the high-precision positioning and tracking method for UAVs based on AFDM waveforms in this application, the method may further include the following: Step S601: Read the positioning and tracking parameter set from the parameter output buffer and extract the target distance estimate and timestamp identifier. Transmit the positioning and tracking parameter set to the UAV flight control unit via the data interface. Load the preset trajectory constraints in the flight control unit and parse the trajectory boundary parameters and speed limit parameters. Step S602: Based on the target distance estimate and the attitude data output by the UAV inertial measurement unit, perform coordinate system transformation to obtain the relative position vector in the body coordinate system. Perform constraint verification on the relative position vector and the track boundary parameters, and combine with the geographic reference datum to perform coordinate mapping to obtain the current positioning coordinates.

[0065] This embodiment reads the positioning and tracking parameter set output in step S501 from the parameter output buffer and extracts the target distance estimate and timestamp identifier. The parameter output buffer adopts a double-buffer structure to achieve read-write separation. The reading module extracts the positioning and tracking parameter set in chronological order according to the frame sequence number primary key. The target distance estimate is stored in meters, and the timestamp identifier is inherited from the acquisition timestamp of the baseband difference frequency data for time alignment of multi-source data. If the confidence flag in the positioning and tracking parameter set indicates low confidence, a parameter quality degradation flag is registered. The parameter quality degradation flag is used for positioning accuracy evaluation during coordinate mapping in step S602. After the positioning and tracking parameter set is extracted, it is written to the data interface sending buffer for transmission processing.

[0066] After the positioning and tracking parameter set is written to the data interface transmission buffer, it is transmitted to the UAV flight control unit via the data interface. The data interface uses an airborne serial bus protocol for parameter transmission, and the transmission frame format includes a source address, a target address, a payload, and a checksum field. The data receiving module of the flight control unit performs verification on the transmission frame and extracts the payload. If the verification fails, it requests retransmission and registers a communication error flag. After being parsed by the interface, the positioning and tracking parameter set is written to the parameter processing buffer of the flight control unit, awaiting the loading of trajectory constraints.

[0067] Accordingly, preset trajectory constraints are loaded into the flight control unit, and the trajectory boundary parameters and speed limit parameters are parsed to obtain them. The preset trajectory constraints are read from the flight mission configuration file, which is stored in the non-volatile memory of the flight control unit. The trajectory boundary parameters define the spatial range within which the UAV is allowed to fly, described in the form of a three-dimensional envelope and including three sub-parameters: maximum flight altitude, minimum flight altitude, and horizontal movement radius. The speed limit parameters constrain the UAV's maximum maneuver speed, including two sub-parameters: maximum horizontal speed and maximum vertical speed. If the boundary marker indicates that a parameter has exceeded its limit, a boundary correction strategy is pre-loaded for use during constraint verification in step S602. After the trajectory constraints are loaded, they are written to the input of the coordinate transformation module, awaiting position calculation.

[0068] This embodiment performs coordinate system transformation based on the target distance estimate and the attitude data output by the UAV's inertial measurement unit to obtain the relative position vector in the body coordinate system. The attitude data output by the inertial measurement unit includes three Euler angle components: roll, pitch, and yaw. The acquisition timestamp of the attitude data is compared with the timestamp of the positioning and tracking parameter group to achieve time alignment. The coordinate system transformation module constructs a rotation matrix based on the Euler angles and projects the radial velocity obtained by converting the target distance estimate and the Doppler shift estimate onto the three axes of the body coordinate system. The relative position vector, with the origin of the body coordinate system as a reference, includes three elements: forward, right, and downward components. The relative position vector is written to the input of the constraint verification module for boundary constraint verification.

[0069] After the relative position vector is written to the input of the constraint verification module, the relative position vector and the track boundary parameters are subjected to constraint verification, and coordinate mapping is performed in conjunction with the geographic reference datum to obtain the current positioning coordinates. The constraint verification module compares each component of the relative position vector with the track boundary parameters. If any component exceeds the corresponding boundary range, the boundary constraint correction process is triggered and the constraint trigger flag is registered. The geographic reference datum is determined by the geodetic coordinates of the takeoff point and the heading reference angle. The geodetic coordinates of the takeoff point are stored in the form of longitude, latitude, and altitude triplets. The coordinate mapping module maps the constraint-verified relative position vector to the geodetic coordinate system through rotation and translation transformation to obtain the current positioning coordinates. The current positioning coordinates are represented in the form of longitude, latitude, and altitude triplets and are written into the tracking filter input buffer as the output product of step S602 for step S701 to read and execute the status update and target tracking trajectory data generation.

[0070] In one embodiment of the high-precision positioning and tracking method for UAVs based on AFDM waveforms in this application, the method may further include the following: Step S701: Assemble the current positioning coordinates and the Doppler frequency shift estimate according to the state vector format to obtain the observation input vector. Input the observation input vector into the tracking filter and read the historical state vector and covariance matrix stored in the filter. Based on the observation input vector and the historical state vector, perform state prediction and measurement update according to the Kalman filter gain calculation rule to obtain the current state estimation vector. Extract the position component and velocity component in the current state estimation vector and encapsulate them into target tracking trajectory data. Step S702: Add UAV identifier and timestamp information to the target tracking trajectory data to obtain trajectory data frame. Encode and modulate the trajectory data frame through the data link communication module to obtain downlink transmission signal. Send the downlink transmission signal to the ground monitoring terminal through the air-to-ground communication link and write it into the trajectory display buffer.

[0071] In this embodiment, the current positioning coordinates and the Doppler frequency shift estimate are assembled according to the state vector format to obtain the observation input vector. The current positioning coordinates are read from the output buffer of the position calculation module and include three components: longitude, latitude, and altitude. The Doppler frequency shift estimate is read from the output buffer of the interpolation refinement module. The Doppler frequency shift estimate is converted into a radial velocity observation using a velocity conversion formula, which is determined based on the linear relationship between the carrier wavelength and the Doppler frequency shift. The observation input vector is assembled according to the observation vector format preset by the tracking filter and includes four fields: three-dimensional position observation and radial velocity observation. If the out-of-bounds flag indicates that the parameter is out of bounds, constraint pruning is performed on the observation input vector. The observation input vector is written to the input of the tracking filter and awaits state update processing.

[0072] After the observed input vector is written to the input of the tracking filter, the historical state vector and covariance matrix stored within the filter are read. The tracking filter employs an extended Kalman filter structure, and the state vector contains six elements: three-dimensional position components and three-dimensional velocity components. The historical state vector records the optimal state estimate output in the previous filtering cycle, and the covariance matrix represents the uncertainty of the state estimate. If the detection failure flag indicates no target detection, the measurement update is skipped, and only state prediction is performed; the filter enters track extrapolation mode. The historical state vector and covariance matrix are read from the filter's internal storage area and written to the state update calculation module to await Kalman filtering calculation.

[0073] Accordingly, based on the observed input vector and the historical state vector, state prediction and measurement update are performed according to the Kalman filter gain calculation rules to obtain the current state estimation vector. In the state prediction stage, the historical state vector is extrapolated to the current time step based on the system state transition equation to obtain the prior state estimate. The state transition equation is constructed using a uniform motion model. In the measurement update stage, the difference between the observed input vector and the observed predicted value of the prior state estimate is calculated to obtain the innovation vector. The Kalman gain is calculated jointly based on the prior covariance matrix and the observation noise covariance matrix. The innovation vector is weighted by the Kalman gain and then superimposed onto the prior state estimate to obtain the current state estimation vector. The covariance matrix is ​​synchronously updated and written back to the filter's internal storage area. The current state estimation vector is written to the input of the state extraction module to await trajectory data encapsulation.

[0074] After the current state estimation vector is written to the input of the state extraction module, the position and velocity components are extracted and encapsulated into target tracking trajectory data. The position component contains the optimal estimate of the three-dimensional position coordinates, and the velocity component contains the optimal estimate of the three-dimensional velocity vector. The target tracking trajectory data is encapsulated according to a preset trajectory data format, including three field areas: position coordinates, velocity vector, and tracking status flag. The tracking status flag is set to stable tracking, track extrapolation, or new target acquisition status according to the current working mode of the filter. The target tracking trajectory data is written to the trajectory output buffer for step S702 to read, execute data frame encapsulation, and downlink transmission processing.

[0075] In this embodiment, the target tracking trajectory data output in step S701 is appended with a UAV identifier and timestamp information to obtain a trajectory data frame. The UAV identifier is read from the onboard configuration storage area to distinguish different platforms in multi-UAV collaborative scenarios, and the timestamp is read from the system real-time clock and is accurate to the millisecond level. The trajectory data frame contains seven fields: UAV identifier, timestamp, position coordinates, velocity vector, and tracking status flag. A cyclic redundancy check (CRC) code is appended to the end of the frame for transmission integrity verification. After the trajectory data frame is encapsulated, it is written to the input of the data link communication module for encoding and modulation processing.

[0076] Based on the aforementioned trajectory data frames, the data link communication module performs encoding and modulation processing to obtain downlink transmission signals, which are then sent to the ground monitoring terminal. The data link communication module performs channel coding on the trajectory data frames to enhance anti-interference capabilities; the coding method and coding rate are adaptively selected based on the current channel quality. The encoded data is digitally modulated and mapped into a baseband symbol sequence, with modulation methods supporting multi-level phase shift keying and quadrature amplitude modulation. The baseband symbol sequence undergoes up-conversion processing to generate a radio frequency downlink transmission signal, which is then amplified and transmitted by the airborne antenna. After receiving the downlink transmission signal, the ground monitoring terminal performs demodulation and decoding to recover the trajectory data frames. Once the trajectory data frames pass verification, they are written to the trajectory display buffer for real-time situation presentation and historical trajectory playback.

[0077] To effectively address the shortcomings of traditional technologies in analog domain difference frequency preprocessing, high-precision parameter estimation in the time-delay Doppler domain, and flight control linkage tracking filter output, and to provide technical support for high-precision UAV positioning and tracking, this application provides an embodiment of an AFDM waveform-based UAV high-precision positioning and tracking device for implementing all or part of the aforementioned AFDM waveform-based UAV high-precision positioning and tracking method. See [link to embodiment]. Figure 2 The high-precision positioning and tracking device for unmanned aerial vehicles based on AFDM waveforms specifically includes the following components: The mixing module 10 is used to perform analog domain dechirping mixing based on the echo signal collected by the UAV airborne receiver and the local linear frequency modulation reference signal to obtain the difference frequency signal, to perform low-pass filtering on the difference frequency signal to obtain the filtered signal, and to perform baseband sampling on the filtered signal to obtain baseband difference frequency data and write it into the signal processing buffer. The calculation module 20 is used to read the baseband difference frequency data from the signal processing buffer, perform discrete affine Fourier transform on the baseband difference frequency data according to the affine radio frequency division multiplexing modulation parameters to obtain a time-delay Doppler domain two-dimensional spectrum, perform peak search based on the time-delay Doppler domain two-dimensional spectrum to obtain coarse estimation parameters, refine the coarse estimation parameters through interpolation to obtain target distance estimation and Doppler frequency shift estimation, and encapsulate the target distance estimation and Doppler frequency shift estimation into a positioning and tracking parameter group; The positioning module 30 is used to input the positioning and tracking parameter set into the UAV flight control unit, perform position calculation on the positioning and tracking parameter set according to the preset trajectory constraints to obtain the current positioning coordinates, input the current positioning coordinates and the Doppler frequency shift estimate into the tracking filter to perform state update to obtain target tracking trajectory data, and output the target tracking trajectory data to the ground monitoring terminal.

[0078] As described above, the high-precision UAV positioning and tracking device based on AFDM waveform provided in this application can obtain baseband difference frequency data by chirping and mixing the echo signal and the local linear frequency modulation reference signal in the analog domain, followed by low-pass filtering and baseband sampling, and writing it into the signal processing buffer. Combined with discrete affine Fourier transform time delay Doppler domain two-dimensional spectrum peak search and interpolation refinement driven by RF multiplexing modulation parameters, the target distance estimate and Doppler frequency shift estimate are obtained and encapsulated into a positioning and tracking parameter set. The flight control unit performs position calculation according to preset trajectory constraints to obtain the current positioning coordinates and updates the tracking filter state to obtain the target tracking trajectory data, which is then output to the ground monitoring terminal. This effectively solves the shortcomings of traditional technologies in analog domain difference frequency preprocessing, time delay Doppler domain high-precision parameter estimation, and flight control linkage tracking filter output, providing high-precision positioning and accurate tracking for UAVs.

[0079] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the AFDM waveform-based high-precision positioning and tracking method for unmanned aerial vehicles.

[0080] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described high-precision UAV positioning and tracking method based on AFDM waveforms.

[0081] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described high-precision UAV positioning and tracking method based on AFDM waveforms.

[0082] In this embodiment of the invention, the baseband difference frequency data is obtained by analog domain dechirped mixing of the echo signal and the local linear frequency modulation reference signal, followed by low-pass filtering and baseband sampling. This data is written into the signal processing buffer. Combined with discrete affine Fourier transform time-delay Doppler domain peak search and interpolation refinement driven by RF multiplexing modulation parameters, the target distance estimate and Doppler frequency shift estimate are obtained and encapsulated into a positioning and tracking parameter set. The flight control unit performs position calculation according to preset trajectory constraints to obtain the current positioning coordinates. The target tracking trajectory data is then obtained by updating the tracking filter state and output to the ground monitoring terminal. This effectively solves the shortcomings of traditional technologies in analog domain difference frequency preprocessing, time-delay Doppler domain high-precision parameter estimation, and flight control linkage tracking filter output, providing high-precision positioning and accurate tracking for UAVs.

[0083] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0084] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0085] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0086] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0087] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A high-precision positioning and tracking method for unmanned aerial vehicles based on AFDM waveforms, characterized in that, The method includes: The difference frequency signal is obtained by performing analog domain dechirping mixing on the echo signal collected by the UAV airborne receiver and the local linear frequency modulation reference signal. The difference frequency signal is then processed by low-pass filtering to obtain a filtered signal. The filtered signal is then sampled by baseband to obtain baseband difference frequency data and written into the signal processing buffer. The baseband difference frequency data is read from the signal processing buffer. A discrete affine Fourier transform is performed on the baseband difference frequency data according to the RF division multiplexing modulation parameters to obtain a time-delay Doppler domain two-dimensional spectrum. Peak search is performed on the time-delay Doppler domain two-dimensional spectrum to obtain coarse estimation parameters. The coarse estimation parameters are refined by interpolation to obtain target distance estimation and Doppler frequency shift estimation. The target distance estimation and Doppler frequency shift estimation are encapsulated into a positioning and tracking parameter set. The positioning and tracking parameter set is input into the UAV flight control unit. The position is calculated based on the preset trajectory constraints to obtain the current positioning coordinates. The current positioning coordinates and the Doppler frequency shift estimate are jointly input into the tracking filter to perform state update to obtain the target tracking trajectory data. The target tracking trajectory data is then output to the ground monitoring terminal.

2. The high-precision positioning and tracking method for UAVs based on AFDM waveforms according to claim 1, characterized in that, The step of performing analog domain dechirped mixing between the echo signal acquired by the UAV's onboard receiver and the local linear frequency modulated reference signal to obtain the difference frequency signal includes: The echo signal is obtained from the UAV airborne receiver and its carrier frequency and phase information are extracted to obtain echo characteristic parameters. A local linear frequency modulation reference signal is generated based on the chirp slope and subcarrier spacing in the simulated radio frequency multiplexing modulation parameters. The local linear frequency modulation reference signal and the echo characteristic parameters are time-domain aligned to obtain a synchronization signal pair. The synchronization signal is multiplied by the input analog mixer to obtain the mixing output signal. The high-frequency component and the low-frequency component in the mixing output signal are separated to obtain the difference frequency signal. The difference frequency signal is written to the analog signal output port for subsequent filtering processing.

3. The high-precision positioning and tracking method for UAVs based on AFDM waveforms according to claim 1, characterized in that, The step of low-pass filtering the difference frequency signal to obtain a filtered signal, performing baseband sampling on the filtered signal to obtain baseband difference frequency data, and writing it into the signal processing buffer includes: The difference frequency signal is read from the analog signal output port and a low-pass filter is configured according to the preset cutoff frequency parameter. The difference frequency signal is input into the low-pass filter to perform high-frequency component suppression to obtain the filtered signal. The filtered signal is then output to the input terminal of the sampling module. The sampling clock frequency is determined based on the simulated radio frequency multiplexing modulation parameters and the analog-to-digital converter is configured. The filtered signal is converted into a discrete sampling sequence according to the sampling clock frequency. The discrete sampling sequence is then encapsulated into baseband difference frequency data according to a preset frame format and written into the signal processing buffer.

4. The high-precision positioning and tracking method for UAVs based on AFDM waveforms according to claim 1, characterized in that, The process involves reading the baseband difference frequency data from the signal processing buffer, performing a discrete affine Fourier transform on the baseband difference frequency data according to the RF division multiplexing modulation parameters to obtain a two-dimensional spectrum in the time-delay Doppler domain, and performing a peak search based on the two-dimensional spectrum in the time-delay Doppler domain to obtain coarse estimation parameters, including: The baseband difference frequency data is read from the signal processing buffer in frame sequence and the chirp slope and subcarrier number are extracted from the RF multiplexing modulation parameters. A discrete affine Fourier transform kernel matrix is ​​constructed based on the chirp slope and the subcarrier number. The baseband difference frequency data and the discrete affine Fourier transform kernel matrix are multiplied to obtain the time delay Doppler domain two-dimensional spectrum. The time-delay Doppler domain two-dimensional spectrum is divided into two-dimensional grids with time delay axis and Doppler axis. Within the two-dimensional grid, a grid-by-grid amplitude comparison is performed according to a preset amplitude threshold condition to obtain a set of candidate peak points. The grid index corresponding to the maximum amplitude is extracted from the set of candidate peak points to obtain coarse estimation parameters.

5. The high-precision positioning and tracking method for UAVs based on AFDM waveforms according to claim 1, characterized in that, The process of refining the coarse estimation parameters through interpolation to obtain target distance estimates and Doppler frequency shift estimates, and then encapsulating the target distance estimates and Doppler frequency shift estimates into a positioning and tracking parameter set, including: The peak grid index and the amplitude values ​​of its adjacent grids are extracted from the coarse estimation parameters. Based on the amplitude values, the sub-grid offset is calculated using a parabolic interpolation algorithm to obtain the time delay axis refinement offset and the Doppler axis refinement offset. The peak grid index and the time delay axis refinement offset are combined and converted according to the distance resolution parameter to obtain the target distance estimate. The peak grid index and the Doppler axis refinement offset are combined and converted according to the Doppler resolution parameter to obtain the Doppler frequency shift estimate. The target distance estimate and the Doppler frequency shift estimate are assembled into a positioning and tracking parameter group according to a preset parameter group format. The positioning and tracking parameter group is then appended with a timestamp identifier and written into the parameter output buffer for the flight control unit to call.

6. The high-precision positioning and tracking method for UAVs based on AFDM waveforms according to claim 1, characterized in that, The step of inputting the positioning and tracking parameter set into the UAV flight control unit and performing position calculation on the positioning and tracking parameter set according to preset trajectory constraints to obtain the current positioning coordinates includes: The positioning and tracking parameter set is read from the parameter output buffer and the target distance estimate and timestamp are extracted. The positioning and tracking parameter set is transmitted to the UAV flight control unit via the data interface. The flight control unit loads the preset trajectory constraints and parses the trajectory boundary parameters and speed limit parameters. Based on the target distance estimate and the attitude data output by the UAV inertial measurement unit, a coordinate system transformation is performed to obtain the relative position vector in the body coordinate system. The relative position vector and the track boundary parameters are constrained and verified, and coordinate mapping is performed in combination with the geographic reference datum to obtain the current positioning coordinates.

7. The high-precision positioning and tracking method for UAVs based on AFDM waveforms according to claim 1, characterized in that, The step of jointly inputting the current positioning coordinates and the Doppler frequency shift estimate into the tracking filter to perform state updates and obtain target tracking trajectory data, and then outputting the target tracking trajectory data to the ground monitoring terminal, includes: The current positioning coordinates and the Doppler frequency shift estimate are assembled according to the state vector format to obtain the observation input vector. The observation input vector is input into the tracking filter and the historical state vector and covariance matrix stored in the filter are read. Based on the observation input vector and the historical state vector, state prediction and measurement update are performed according to the Kalman filter gain calculation rule to obtain the current state estimation vector. The position component and velocity component in the current state estimation vector are extracted and encapsulated into target tracking trajectory data. The target tracking trajectory data is appended with UAV identification and timestamp information to obtain a trajectory data frame. The trajectory data frame is then encoded and modulated by the data link communication module to obtain a downlink transmission signal. The downlink transmission signal is then sent to the ground monitoring terminal via the air-to-ground communication link and written into the trajectory display buffer.

8. A high-precision positioning and tracking device for unmanned aerial vehicles based on AFDM waveforms, characterized in that, The device includes: The mixing module is used to perform analog domain dechirping mixing with the echo signal collected by the UAV airborne receiver and the local linear frequency modulation reference signal to obtain the difference frequency signal, to perform low-pass filtering on the difference frequency signal to obtain the filtered signal, and to perform baseband sampling on the filtered signal to obtain baseband difference frequency data and write it into the signal processing buffer. The calculation module is used to read the baseband difference frequency data from the signal processing buffer, perform discrete affine Fourier transform on the baseband difference frequency data according to the affine radio frequency division multiplexing modulation parameters to obtain a time-delay Doppler domain two-dimensional spectrum, perform peak search based on the time-delay Doppler domain two-dimensional spectrum to obtain coarse estimation parameters, refine the coarse estimation parameters through interpolation to obtain target distance estimation and Doppler frequency shift estimation, and encapsulate the target distance estimation and Doppler frequency shift estimation into a positioning and tracking parameter set; The positioning module is used to input the positioning and tracking parameter set into the UAV flight control unit, perform position calculation on the positioning and tracking parameter set according to preset trajectory constraints to obtain the current positioning coordinates, input the current positioning coordinates and the Doppler frequency shift estimate into the tracking filter to perform state update to obtain target tracking trajectory data, and output the target tracking trajectory data to the ground monitoring terminal.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the high-precision positioning and tracking method for UAVs based on AFDM waveforms as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the high-precision positioning and tracking method for unmanned aerial vehicles based on AFDM waveforms as described in any one of claims 1 to 7.

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