Positioning and tracking method and system for low-altitude unmanned aerial vehicle

Abstract

The invention relates to the technical field of unmanned aerial vehicle detection and tracking, in particular to a low-altitude unmanned aerial vehicle positioning and tracking method and system. The method comprises the following steps: acquiring sound spectrum time sequence data and radar plot data of an unmanned aerial vehicle; extracting a first sound spectrum characteristic parameter and a second sound spectrum characteristic parameter; extracting a first radar characteristic parameter and a second radar characteristic parameter; determining initial position estimation of the unmanned aerial vehicle through a first judgment condition; determining optimized position estimation of the unmanned aerial vehicle through a second judgment condition; and tracking the unmanned aerial vehicle by using an improved particle filtering algorithm to obtain a final position and a tracking trajectory of the unmanned aerial vehicle. According to the invention, through heterogeneous sensor data fusion, environment adaptive feature extraction, multistage intelligent judgment optimization and improved particle filter tracking, the precision, robustness and real-time performance of unmanned aerial vehicle detection in a complex environment are effectively improved, and the regional monitoring and countering capability is significantly enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle detection and tracking, and particularly relates to a positioning and tracking method and system for low-altitude unmanned aerial vehicles. BACKGROUND

[0002] In recent years, with the rapid popularization of unmanned aerial vehicle technology, it has brought convenience, but also caused a series of public safety problems such as privacy disclosure, airspace safety and even illegal transportation. Therefore, developing efficient and reliable unmanned aerial vehicle detection and tracking technology has become an urgent need in the field of low-altitude security. At present, the main detection means mainly relies on radar, photoelectric and acoustic sensors or simple combination of single or simple combination of sensors, aiming to realize identification and positioning by analyzing the physical characteristics of the target.

[0003] However, the existing unmanned aerial vehicle detection and tracking scheme still has significant defects. First, the radar system is challenged by multipath effect and ground clutter in complex urban environment, and the detection ability of low-altitude slow small target is limited; the acoustic system is easily polluted by environmental noise, and the sound wave propagation is easily affected by temperature, humidity, air pressure and other atmospheric conditions, resulting in decreased positioning accuracy. Secondly, the existing multi-sensor fusion method is mostly simple data layer or decision layer fusion, lacking intelligent evaluation of data quality and consistency. When the data of different sensors conflict or the signal-to-noise ratio difference is large, the reliability of the fusion result is poor, and even the performance may be worse than that of a single sensor. Thirdly, the existing tracking algorithm, such as standard Kalman filter or particle filter, has a prominent model mismatch problem when facing high-maneuvering unmanned aerial vehicles, and it is difficult to ensure the accuracy and smoothness of tracking at the same time.

[0004] In view of the above technical defects, the present application fuses acoustic and radar heterogeneous sensor information in depth, and introduces a series of adaptive processing and intelligent judgment mechanisms, aiming to significantly improve the robustness, positioning accuracy and tracking prediction ability of unmanned aerial vehicle detection in complex environment. SUMMARY

[0005] In view of the defects in the prior art, the present application provides a positioning and tracking method and system for low-altitude unmanned aerial vehicles.

[0006] In a first aspect, the present application provides a positioning and tracking method for low-altitude unmanned aerial vehicles, comprising the following steps: The acoustic spectrum time series data and the radar plot data of the unmanned aerial vehicle are acquired; first acoustic spectrum feature parameters and second acoustic spectrum feature parameters are extracted by using the acoustic spectrum time series data; first radar feature parameters and second radar feature parameters are extracted by using the radar plot data; a preliminary position estimation of the unmanned aerial vehicle is determined by a first judgment condition based on the first acoustic spectrum feature parameters and the first radar feature parameters; an optimized position estimation of the unmanned aerial vehicle is determined by a second judgment condition based on the preliminary position estimation; and a final position and a tracking trajectory of the unmanned aerial vehicle are obtained by using an improved particle filtering algorithm for unmanned aerial vehicle tracking according to the optimized position estimation.

[0007] Optionally, acquiring the acoustic spectrum time series data and the radar plot data of the unmanned aerial vehicle comprises: acquiring acoustic wave signals of the unmanned aerial vehicle; performing noise reduction processing on the acoustic wave signals by using an improved joint noise reduction algorithm to obtain a noise reduction processing result; performing signal enhancement processing based on the noise reduction processing result to obtain a signal enhancement processing result; and performing windowed short-time Fourier transform processing according to the signal enhancement processing result to obtain the acoustic spectrum time series data; acquiring radar echo signals of the unmanned aerial vehicle; and performing preprocessing on the radar echo signals to obtain the radar plot data, wherein the preprocessing comprises pulse compression, Doppler processing and plot extraction.

[0008] Optionally, extracting the first acoustic spectrum feature parameters and the second acoustic spectrum feature parameters by using the acoustic spectrum time series data comprises: calculating an accumulated energy distribution of a time-frequency spectrum by using the acoustic spectrum time series data; constructing a modified sound speed model based on the accumulated energy distribution; obtaining a modified sound speed by the modified sound speed model; constructing a propagation delay compensation model according to the modified sound speed; obtaining the first acoustic spectrum feature parameters, including a spectral roll-off point, by the propagation delay compensation model; constructing an improved spectral flux calculation model by using the acoustic spectrum time series data; and obtaining the second acoustic spectrum feature parameters, including a spectral flux, by the improved spectral flux calculation model.

[0009] Optionally, the modified sound speed model satisfies the following expression: , wherein, is the modified sound speed, is the ambient temperature, is the ambient air pressure, is the reference air pressure, is the air pressure nonlinear coefficient, is the ambient humidity, , is the first-order and second-order humidity influence coefficient, is the sound speed gradient compensation term, is the turbulent disturbance compensation term, and the propagation delay compensation model satisfies the following expression: , wherein, is the compensated spectral roll-off point frequency, is the original spectral roll-off point frequency, is the propagation time delay estimation value, is the dominant wavelength, is the dispersion effect correction factor, is the corrected sound speed, the improved spectral flux calculation model satisfies the following expression: , wherein, is the enhanced spectral flux, is the total number of frequency bands, is the weight coefficient of the th frequency band, is the th frequency band at the moment of t, is the spectral amplitude of the th frequency band at the moment of t, is the spectral amplitude of the th frequency band at the moment of t, is the nonlinear amplification exponent, is the frame energy normalization factor, is the energy normalization exponent, is the transient enhancement factor, is the transient component flux, is the modulation feature weight coefficient, is the modulation spectral flux.

[0010] Optionally, the first radar feature parameter and the second radar feature parameter are extracted from the radar track data, including: extracting a micro-Doppler feature from the radar track data; constructing an instantaneous frequency variance calculation model based on the micro-Doppler feature; obtaining a first radar feature parameter through the instantaneous frequency variance calculation model, the first radar feature parameter including an instantaneous frequency variance; constructing an improved odd-even mode decomposition coefficient calculation model from the radar track data; and obtaining a second radar feature parameter through the odd-even mode decomposition coefficient calculation model, the second radar feature parameter including an odd mode coefficient and an even mode coefficient.

[0011] Optionally, determining the preliminary position estimation of the UAV based on the first acoustic spectrum feature parameter and the first radar feature parameter through a first judgment condition comprises: quantifying the uncertainty of an acoustic spectrum preliminary position and a radar preliminary position based on the first acoustic spectrum feature parameter and the first radar feature parameter to obtain an uncertainty quantification result; calculating the Euclidean distance of the acoustic spectrum preliminary position and the radar preliminary position according to the uncertainty quantification result; determining a first preset threshold value by using the signal-to-noise ratio of acoustic spectrum data and radar data; comparing the size of the Euclidean distance and the first preset threshold value; if the Euclidean distance is less than the first preset threshold value, adopting a weighted fusion to output the preliminary position estimation; if the Euclidean distance is not less than the first preset threshold value, selecting the position of the data source with a high signal-to-noise ratio as the preliminary position estimation.

[0012] Optionally, determining the optimized position estimation of the UAV based on the preliminary position estimation through a second judgment condition comprises: obtaining a position uncertainty quantification value of the preliminary position estimation; determining a second preset threshold value; comparing the size of the position uncertainty quantification value and the second preset threshold value; if the position uncertainty quantification value is not higher than the second preset threshold value, taking the preliminary position estimation as the optimized position estimation; if the position uncertainty quantification value is higher than the second preset threshold value, triggering a position optimization process based on the second judgment condition: calculating the consistency score of acoustic spectrum and radar data; determining a third preset threshold value; comparing the size of the consistency score and the third preset threshold value; if the consistency score is lower than the third preset threshold value, performing position re-estimation; if the consistency score is not lower than the third preset threshold value, taking the preliminary position estimation as the optimized position estimation.

[0013] Optionally, tracking the UAV by using an improved particle filter algorithm according to the optimized position estimation to obtain the final position and tracking trajectory of the UAV comprises: obtaining an initialization result of a particle filter state according to the optimized position estimation; designing an improved particle filter algorithm framework based on the initialization result, wherein the framework comprises a joint likelihood function, an adaptive weight adjustment mechanism, noise optimization, and a long short-term memory network; designing a fusion strategy model according to the particle filter algorithm framework; obtaining the final position and tracking trajectory of the UAV through the fusion strategy model.

[0014] Optionally, the joint likelihood function introduces feature matching degree and timing consistency constraints, and satisfies the following expression: , wherein, is the joint likelihood value of a particle, is an adaptive weighting factor, , , are the reference likelihood functions based on acoustic spectrum and radar respectively, 、 are the acoustic spectrum feature vector and radar feature vector measured at current time respectively, 、 are the particles corresponding acoustic spectrum and radar feature prediction vectors, is the acoustic spectrum feature error variance, denotes vector inner product, and the fusion strategy model satisfies the following expression: , wherein, is the final tracking trajectory state; is the state estimation output by particle filtering; is the state predicted by the LSTM network; is the fusion weight; is the Kalman gain matrix used for fusing the latest observation ; is the observation matrix.

[0015] In a second aspect, the application provides a low-altitude unmanned aerial vehicle positioning and tracking system, which uses the low-altitude unmanned aerial vehicle positioning and tracking method, and comprises: an acquisition module configured to acquire acoustic spectrum time series data and radar plot data of the unmanned aerial vehicle; an analysis module configured to extract a first acoustic spectrum feature parameter and a second acoustic spectrum feature parameter by using the acoustic spectrum time series data, extract a first radar feature parameter and a second radar feature parameter by using the radar plot data, determine a preliminary position estimation of the unmanned aerial vehicle by using a first judgment condition based on the first acoustic spectrum feature parameter and the first radar feature parameter, determine an optimized position estimation of the unmanned aerial vehicle by using a second judgment condition based on the preliminary position estimation, and track the unmanned aerial vehicle by using an improved particle filtering algorithm according to the optimized position estimation; and an output module configured to output a final position and a tracking trajectory of the unmanned aerial vehicle.

[0016] Compared with the prior art, the application has the following beneficial effects: 1. The precision and robustness of unmanned aerial vehicle detection and positioning in a complex environment are improved. By fusing acoustic spectrum and radar data of two types of heterogeneous sensors, a multi-source information complementary perception system is constructed, and a dynamic adaptive threshold and data consistency optimization mechanism are designed, so that the performance decline of existing sensors in a noisy, cluttered or signal attenuation environment is effectively overcome. 2. Optimized the accuracy and environmental adaptability of feature extraction. In acoustic feature extraction, a modified sound speed model and propagation delay compensation were introduced, significantly reducing the impact of atmospheric conditions on acoustic positioning; in radar feature extraction, improved instantaneous frequency variance and polarization odd-even mode decomposition coefficients were used to enhance the characterization of UAV micro-motion characteristics and scattering mechanisms, making the features more distinguishable; 3. Realized intelligent judgment and adaptive optimization of the position estimation process. By designing multi-level judgment conditions, the fusion strategy or optimization based on graph neural network was dynamically selected according to data quality such as signal-to-noise ratio, uncertainty, and consistency, avoiding error amplification caused by blind fusion in data conflict or poor quality; 4. Enhanced the real-time performance and prediction ability of tracking. The improved particle filter algorithm combined joint likelihood function and adaptive process noise adjustment, and combined with LSTM trajectory prediction for weighted output, not only improved the tracking accuracy of UAV maneuvering motion, but also realized effective prediction of future trajectory, providing forward-looking information for countermeasures decision; 5. Improved the engineering practicability and automation level. From sensor deployment, data preprocessing, feature extraction to fusion tracking, the whole process considered environmental variability and computational efficiency in actual application, through parameter adaptive adjustment, model optimization and automatic judgment mechanism, reduced the dependence on external calibration and manual intervention, more suitable for long-term and extensive regional monitoring applications. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 A flowchart of a low-altitude UAV positioning and tracking method according to an embodiment of the present application; Figure 2 A structural schematic diagram of a low-altitude UAV positioning and tracking system according to an embodiment of the present application. DETAILED DESCRIPTION

[0018] The specific embodiments of the present application will be described in detail below. It should be noted that the embodiments described herein are only for illustration and do not limit the present application. In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present application. However, it is obvious to those skilled in the art that the present application does not have to be implemented with these specific details. In other examples, well-known circuits, software or methods are not specifically described in order not to obscure the present application.

[0019] References throughout this specification to "one embodiment", "an embodiment", "one example" or "an example" mean that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment of the application. The appearances of the phrases "in one embodiment" or "in an embodiment" or "one example" or "an example" in various places in the specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics can be combined in any suitable

[0020] Referring to Figure 1 Embodiments of the application provide a positioning and tracking method for low-altitude unmanned aerial vehicles, which comprises the following steps: S1. Acquire acoustic spectrum time series data and radar track data of the unmanned aerial vehicle.

[0021] In one embodiment, an acoustic sensor array and a radar system are first deployed in the monitoring area. The acoustic sensor array adopts a uniform linear array layout, and the sensor spacing is set according to the half-wavelength principle of the typical acoustic frequency of the unmanned aerial vehicle to optimize spatial sampling and avoid aliasing. The radar system selects a frequency-modulated continuous wave radar with an X-band (8 GHz~12 GHz) working frequency band to balance resolution and atmospheric attenuation. The monitoring area is pre-calibrated, including measuring background noise and clutter interference data, and establishing a geographic coordinate system for data fusion and position mapping.

[0022] Further, the acoustic spectrum time series data of the unmanned aerial vehicle is acquired.

[0023] Specifically, the acoustic sensor array synchronously collects original acoustic wave signals at a sampling rate of 44.1 kHz, ensuring coverage of the typical acoustic spectrum range of the unmanned aerial vehicle propeller and motor, such as 50 Hz to 15 kHz. During the collection process, environmental parameters including temperature, humidity and air pressure are monitored in real time, and time stamps and sensor location information are embedded. Then, the original acoustic wave signals are preprocessed, including noise reduction processing and signal enhancement processing. The noise reduction processing adopts an improved joint noise reduction algorithm based on wavelet transform-independent component analysis. This algorithm introduces an adaptive threshold function and an optimized independent component analysis separation process based on the standard wavelet transform-independent component analysis, solving the problem of performance limitation of existing methods in non-stationary noise environment. The specific improvements include: 1. An adaptive threshold function based on local signal-to-noise ratio is used to dynamically adjust the wavelet denoising strength.

[0024] 2. Introduce sound source characteristic constraints in the separation stage of independent component analysis to improve the separation effect of UAV sound signals.

[0025] The proposed joint noise reduction algorithm significantly improves noise reduction performance while maintaining signal integrity by jointly optimizing wavelet thresholding and independent component analysis separation processes. The algorithm's adaptive threshold function... Defined as: , in, The noise standard deviation estimated by the median is obtained by calculating the absolute deviation of the median from the first-level detail coefficients after wavelet decomposition. The signal length is directly taken from the number of sampling points in a single data frame acquired by the acoustic sensor. To determine the local signal-to-noise ratio, a sliding window of length 256 points is used. The ratio of signal power to noise power is calculated on the preprocessed acoustic signal. The noise power is obtained through statistics from silent segments. This is the signal-to-noise ratio offset, derived from statistical analysis of a large amount of experimental data. To adjust the parameters, optimization was performed on the validation set using a grid search method. The optimized objective function for independent component analysis was obtained. as follows: , in, The separation matrix is ​​obtained by iteratively updating it using the natural gradient descent algorithm. It is a nonlinear function, adjusted according to the non-Gaussian properties of the UAV acoustic signal. For the first Individual components The probability density function prior is obtained by fitting a Gaussian mixture model based on a historical database of UAV acoustic signals. The observed signal is obtained by assembling a hybrid signal matrix from multi-channel acoustic sensor data after wavelet denoising preprocessing. For the Frobenius norm, It is the identity matrix. This is the matrix transpose symbol. , The regularization parameter is obtained through cross-validation. It is through the separation matrix Transformed observation signal The Each independent component is the total number of sound sources of the UAV. The signal enhancement processing removes low-frequency wind noise and high-frequency interference by band-pass filtering (50 Hz~15 kHz), and applies gain control to compensate for propagation loss. For the pre-processed data, a windowed short-time Fourier transform (STFT) processing is performed, and the specific steps of the processing are as follows: the long-period sound wave signal is divided into multiple short-periods, wherein the frame length is 2048 sampling points; the Blackman-Harris window function is applied to each frame of signal to reduce spectral leakage; the window is slid with an overlap rate of 75% to ensure time continuity; and the fast Fourier transform is performed on each windowed signal to obtain the spectrum of the time period. Finally, the STFT processing result is organized into acoustic spectrum time series data, Further, radar track data of the UAV is acquired.

[0026] Specifically, the radar system transmits a frequency-modulated continuous wave signal at a pulse repetition frequency of 10 kHz and receives a return signal. During acquisition, radar attitude and position data are recorded synchronously. Then, the return signal is pre-processed, including pulse compression, Doppler processing, and track extraction; the pulse compression uses matched filtering processing to improve range resolution, the Doppler processing extracts velocity information through fast Fourier transform (FFT) and applies moving target indication (MTI) filtering to suppress static clutter, and the track extraction uses a constant false alarm rate (CFAR) detection algorithm to identify valid target tracks, forming track data containing range, bearing, velocity, and signal-to-noise ratio. After pre-processing, radar track data is obtained.

[0027] In this embodiment, the acoustic sensor array acquires a 5-second-long raw sound wave signal at a sampling rate of 44.1 kHz, with an ambient temperature of 25 degrees Celsius, a humidity of 60%, and an air pressure of 1013 hectopascals. The signal-to-noise ratio of the pre-processed signal improves from the initial 5 dB to 18 dB. The short-time Fourier transform results in a time-frequency spectrum matrix with a size of 536x1025, corresponding to a time resolution of 46.4 milliseconds and a frequency resolution of 21.5 Hz. The radar system operates at an X-band with a pulse repetition frequency of 10 kHz, and acquires return signals for the same period. After pulse compression, the range resolution reaches 0.15 meters, and the Doppler processing velocity resolution is 0.2 m / s. Through constant false alarm rate detection, 32 valid tracks are extracted, and the track data contains range, bearing, velocity, and signal-to-noise ratio fields, with the maximum signal-to-noise ratio being 22 dB.

[0028] S2. Extracting first acoustic spectrum feature parameters and second acoustic spectrum feature parameters using the acoustic spectrum time series data.

[0029] In one embodiment, first acoustic spectrum feature parameters are calculated based on the acoustic spectrum time series data obtained in step S1 through a multi-stage feature extraction process, which includes spectral roll-off points and takes into account the physical characteristics of sound wave propagation and the influence of environmental factors.

[0030] Specifically, first, the acoustic spectrum time series data is subjected to fine time-frequency analysis. On the basis of windowed short-time Fourier transform in S1 step, a multi-resolution spectrum analysis framework is adopted, and an adaptive window length adjustment mechanism is introduced to solve the limitations of existing fixed window length in non-stationary signal analysis.

[0031] Further, the cumulative energy distribution of the time-frequency spectrum is calculated. The cumulative energy distribution is obtained by integrating along the time axis on the basis of the calculation of the time-frequency spectrum, and is used to quantify the time-domain cumulative characteristics of the signal energy.

[0032] Further, based on the cumulative energy distribution, a spectrum roll-off point is determined using a modified sound speed model. The spectrum roll-off point is defined as the frequency point at which the cumulative energy reaches a certain proportion of 85% of the total energy, and this proportion is obtained through a large amount of experimental data statistics. The modified sound speed model introduces a humidity gradient compensation term and a gas pressure nonlinear correction factor on the basis of the standard sound speed formula, which significantly improves the accuracy of sound speed calculation under complex atmospheric conditions. The expression of the modified sound speed model is as follows: , wherein, is the modified sound speed, is the ambient temperature, which is directly measured with an accuracy of 0.1 by a digital temperature sensor deployed in the acoustic sensor array; is the ambient pressure, which is collected by a high-precision pressure sensor with a sampling rate of 1 Hz and is smoothed by Kalman filtering; is the reference pressure, which is used as a standardized benchmark value; is the nonlinear coefficient of gas pressure, which is calibrated by sound speed measurement experiments under different altitudes; is the ambient humidity, which is continuously monitored by a capacitive humidity sensor; 、 is the first and second order humidity influence coefficients, which are obtained by polynomial fitting based on humidity gradient experimental data; is the sound speed gradient compensation term, which is calculated from the three-dimensional temperature field gradient measured by the distributed temperature sensor array arranged in the monitoring area; is the turbulence disturbance compensation term, which is calculated from the wind speed fluctuation data on the sound wave propagation path.

[0033] Further, based on the modified sound speed obtained from the modified sound speed model, the propagation delay compensation of the spectrum roll-off point frequency value is performed. The compensation process adopts a method combining time delay estimation and frequency domain correction, and is realized by the following expression of the propagation delay compensation model: , wherein, The compensated spectral roll-off frequency is output as the first spectral characteristic parameter. The original spectrum roll-off point frequency was accurately extracted from the cumulative energy distribution curve using cubic spline interpolation. The propagation delay estimate is calculated by combining the generalized cross-correlation function with the geometric configuration of the sensor array; The dominant wavelength is calculated by combining the wavelength corresponding to the peak frequency of the acoustic spectrum with the dispersion relation. This is the dispersion effect correction factor, calculated using a dispersion characteristic model of sound waves propagating in the atmosphere; To correct the speed of sound, the above corrected speed of sound model is used.

[0034] Furthermore, the first acoustic spectrum feature parameters are output through the propagation delay compensation model.

[0035] Furthermore, a second spectral characteristic parameter is extracted, which includes spectral flux. Its calculation process incorporates a multi-band weighting mechanism and a transient component enhancement strategy based on traditional spectral flux calculation to better characterize the dynamic characteristics of the UAV's acoustic signal. The expression for the improved spectral flux calculation model is as follows: , in, The enhanced spectral flux is output as the second spectral characteristic parameter. The total number of frequency bands is set to 256 based on the spectral resolution. For the first The weighting coefficients for each frequency band are allocated using the information entropy weighting method based on the energy distribution characteristics of UAV acoustic signals in different frequency bands. for Time of the first Spectral amplitude of each frequency band for Time of the first The spectral amplitude of each frequency band is obtained from the STFT results after amplitude calibration. The nonlinear amplification index was determined through signal dynamic range optimization experiments. The frame energy normalization factor is obtained by normalizing the total energy of the current frame by combining the energy statistics of historical frames. The energy normalization index is obtained through statistical analysis of a large amount of acoustic signal data. The transient enhancement factor is dynamically adjusted through the output of a transient detection algorithm based on wavelet transform. The transient component flux is obtained by extracting high-frequency transient components through wavelet packet decomposition and combining them with Hilbert transform. These are the modulation feature weighting coefficients, obtained through modulation spectrum analysis and optimization. To modulate the spectral flux, the periodic modulation component in the acoustic spectrum is extracted to calculate.

[0036] Further, a second acoustic spectrum feature parameter is output by the improved spectral flux calculation model.

[0037] In this embodiment, the cumulative energy distribution is calculated based on the acoustic spectrum time series data, and the sound speed value is 346.2 m / s by using the modified sound speed model. The original spectrum roll-off point frequency is 8.2 kHz, which is modified to 8.35 kHz after propagation delay compensation, as the first acoustic spectrum feature parameter. At the same time, the enhanced spectral flux is calculated, and the 256 frequency band weight coefficients are distributed by information entropy weighting, the frame energy normalization factor is 0.85, and the transient enhancement factor is 0.32. The final value of the second acoustic spectrum feature parameter is 0.67.

[0038] S3. Using the radar plot data, a first radar feature parameter and a second radar feature parameter are extracted.

[0039] In one embodiment, first, based on the radar plot data obtained in the S2 step, the first radar feature parameter is calculated by a multi-level feature extraction process, which includes the instantaneous frequency variance of the micro-Doppler feature, and the extraction process considers the micro-motion characteristics of the unmanned aerial vehicle rotor and the time-frequency analysis of the radar signal.

[0040] Specifically, first, the radar plot data is subjected to fine time-frequency analysis. On the basis of pulse compression and Doppler processing in the S1 step, an adaptive kernel time-frequency distribution method is used, and by introducing a kernel function parameter optimization mechanism, the limitations of existing time-frequency analysis methods in cross-term suppression and resolution balance are solved.

[0041] Further, based on the results of the fine time-frequency analysis, the micro-Doppler feature is extracted, which is represented by the frequency modulation component caused by the rotation of the unmanned aerial vehicle rotor and the vibration of the body in the radar echo signal. A micro-Doppler separation algorithm based on variational mode decomposition is used to decompose the composite radar signal into multiple intrinsic mode functions, and the mode components related to the rotor micro-motion are selected.

[0042] Further, based on the micro-Doppler feature, the instantaneous frequency variance of the micro-Doppler feature is calculated as the first radar feature parameter. The time-varying weight and frequency domain confidence evaluation are introduced in the standard variance formula to improve the feature's ability to distinguish between different types and motion states of unmanned aerial vehicles. The expression of the improved instantaneous frequency variance calculation model is as follows: , wherein, is the enhanced instantaneous frequency variance, which is output as the first radar feature parameter, is the number of sampling points of the instantaneous frequency sequence, which is determined by the number of time-frequency analysis points in the radar signal processing chain; is the time-varying weight of the th sampling point, which is dynamically calculated based on the local signal-to-noise ratio of the time-frequency spectrum at the moment and the gradient change rate of the instantaneous frequency; is the instantaneous frequency of the th sampling point, which is accurately extracted from the micro-Doppler modal component through the Hilbert transform combined with the Teager-Kaiser energy operator; is the weighted average frequency, which is calculated by the weighted average of and ; is the variance weight coefficient of the time-frequency distribution, which is optimized by the singular value decomposition result of the time-frequency distribution matrix; is the time-frequency distribution variance, which is calculated by the variance of the Wigner-Ville distribution matrix; is the confidence adjustment factor, which is determined based on the envelope smoothness of the micro-Doppler modal component; is the frequency domain confidence evaluation item, which is comprehensively calculated by the spectral flatness and autocorrelation function peak sidelobe ratio of the instantaneous frequency sequence.

[0043] Further, the first radar feature parameter is output through the improved instantaneous frequency variance calculation model.

[0044] Further, the second radar feature parameter is extracted, which includes the odd-even mode decomposition coefficient of the polarization scattering matrix. The calculation process introduces adaptive scattering mechanism discrimination and azimuth angle compensation on the basis of existing polarization decomposition, so as to more accurately represent the scattering characteristics of the unmanned aerial vehicle target. The expression of the improved odd-even mode decomposition coefficient calculation model is as follows: ， , wherein, is the odd mode coefficient, is the even mode coefficient, which are jointly output as the second radar feature parameter; , are the polarization scattering matrix elements of horizontal transmission and horizontal reception and vertical transmission and vertical reception, respectively, which are extracted from the full polarization scattering matrix measured by the radar system; is the Frobenius norm, which is used to calculate the overall difference of the matrix elements; is the asymmetric scattering enhancement factor, which is calculated based on the ratio of the amplitude of the cross-polarization component to the co-polarization component; is the incident angle compensation coefficient, which is calculated by combining the relative geometric relationship between the radar and the target with the electromagnetic scattering model; is the incident angle a compensation function of the azimuth angle, fitted by a physical-optics model based angular response function; is a symmetric scattering enhancement factor, calculated based on the co-polarization correlation coefficient and scattering entropy; is an azimuth angle compensation coefficient, calculated by the azimuth angle of the target relative to the radar line-of-sight combined with the array antenna pattern compensation; is an azimuth angle a compensation function of the azimuth angle, fitted by a polynomial fitting of the measured antenna pattern data.

[0045] It should be noted that the data of the above parameters can be extracted and calculated from the full-polarization scattering matrix point data obtained by the radar.

[0046] Further, the second radar feature parameter is output by the improved odd-even mode decomposition coefficient calculation model.

[0047] In this embodiment, the radar point data is subjected to adaptive kernel time-frequency analysis, and four intrinsic mode functions are obtained by using variational mode decomposition. The micro-Doppler feature is extracted therefrom, the instantaneous frequency sequence length is 128 points, and the time-varying weight is calculated based on the local signal-to-noise ratio. The enhanced instantaneous frequency variance is calculated as 45.6 Hz2, serving as the first radar feature parameter. Full-polarization scattering matrix analysis shows that the component is 0.35, the component is 0.28, and the odd-even mode decomposition coefficients are 0.72 and 0.65, respectively, serving as the second radar feature parameter.

[0048] S4. Based on the first acoustic spectrum feature parameter and the first radar feature parameter, a preliminary position estimate of the unmanned aerial vehicle is determined by a first judgment condition.

[0049] In one embodiment, first, based on the first acoustic spectrum feature parameter extracted in the S2 step and the first radar feature parameter extracted in the S3 step, the acoustic spectrum preliminary position and the radar preliminary position are respectively calculated by a multi-source data registration and space-time alignment process. The acoustic spectrum preliminary position is calculated by using a direction of arrival positioning algorithm based on generalized cross-correlation time delay estimation, and the time difference of arrival of the sound wave signal received by the acoustic sensor array is used to inverse the spatial coordinates of the unmanned aerial vehicle. The radar preliminary position is calculated by using a point track correlation positioning algorithm based on polar coordinate-Cartesian coordinate conversion, and the distance, azimuth and elevation angle measured by the radar are used to directly solve the spatial coordinates of the unmanned aerial vehicle.

[0050] Further, the uncertainty quantification of the acoustic preliminary position and the radar preliminary position is implemented by constructing an acoustic positioning error ellipsoid and a radar positioning error ellipsoid, respectively, wherein the major axis direction of the acoustic positioning error ellipsoid is determined by the geometric configuration of the acoustic sensor array, and the major axis length is calculated by the Cramer-Rao lower bound of the time delay estimation; the major axis direction of the radar positioning error ellipsoid is determined by the radar beam pointing direction, and the major axis length is calculated by the range resolution, the angle resolution and the eigenvalue decomposition result of the covariance matrix.

[0051] Further, based on the quantified acoustic preliminary position and the radar preliminary position, the preliminary position estimation of the unmanned aerial vehicle is determined by a first judgment condition.

[0052] Specifically, first, the Euclidean distance of the acoustic preliminary position and the radar preliminary position is calculated, that is, the two-norm of the coordinate difference of the two positions is taken.

[0053] Further, a first preset threshold is determined, which is no longer a fixed value, but is dynamically adjusted according to environmental conditions and data quality, and the expression of its determination model is as follows: , wherein, is the first preset threshold, is a basic distance threshold, which is determined by the statistical quantile of the acoustic and radar positioning error in the historical data; is a signal-to-noise ratio adjustment coefficient, which is obtained based on positioning accuracy experiments under different signal-to-noise ratio levels; and are the signal-to-noise ratios of the acoustic data and the radar data, respectively, which are obtained by the signal-to-noise ratio calculation model in steps S1 and S3; is a signal-to-noise ratio normalization factor, which is used to balance the influence of the signal-to-noise ratio; is an uncertainty adjustment coefficient, which is obtained by Monte Carlo simulation of the uncertainty of the position estimation; and are the variances of the acoustic and radar position estimations, respectively, which are calculated by the covariance matrix propagation of the positioning algorithm; is the maximum allowed variance, which is set according to the system performance requirements.

[0054] Further, the size of the Euclidean distance and the first preset threshold is compared, and the result is as follows: If the Euclidean distance is less than the first preset threshold, the weighted fusion is adopted to output the preliminary position estimation: , wherein, is the output preliminary position estimation, is the acoustic preliminary position, is the preliminary position of the radar, , are the weights of the acoustic spectrum and the radar respectively, which are dynamically calculated based on their respective position estimation variance and signal-to-noise ratio; If the Euclidean distance is greater than or equal to the first preset threshold, the position of the data source with high signal-to-noise ratio is selected as the preliminary position estimation, that is: If , = 1 ; If , = 0 .

[0055] Further, the preliminary position estimation of the unmanned aerial vehicle is output , which is the initial value of the unmanned aerial vehicle position obtained after fusing the acoustic spectrum and the radar initial positioning information, and is to be further optimized.

[0056] It should be noted that the first judgment condition is based on whether the Euclidean distance between the acoustic spectrum and the radar preliminary position is less than the dynamic first preset threshold, to determine whether to adopt weighted fusion or to prefer the position of a single data source as the preliminary position estimation.

[0057] In this embodiment, the acoustic spectrum preliminary position is calculated to have coordinates of 125.3 meters, 87.6 meters, and 15.2 meters, and the radar preliminary position has coordinates of 123.1 meters, 85.9 meters, and 14.8 meters. The Euclidean distance between the two is 3.2 meters. In the dynamic first preset threshold calculation, the basic distance threshold is 5 meters, the acoustic spectrum signal-to-noise ratio is 18 dB, the radar signal-to-noise ratio is 22 dB, the position estimation variances are 0.8 and 0.5 respectively, and the final threshold is 4.1 meters. Since 3.2 meters is less than 4.1 meters, weighted fusion is adopted to obtain the preliminary position estimation, the acoustic spectrum weight is 0.45, the radar weight is 0.55, and the fused coordinates are 124.1 meters, 86.7 meters, and 15.0 meters.

[0058] S5. Based on the preliminary position estimation, an optimized position estimation of the unmanned aerial vehicle is determined through a second judgment condition.

[0059] In one embodiment, first, based on the preliminary position estimation obtained in the S4 step, it is judged whether position optimization is needed through a multi-dimensional uncertainty evaluation and data consistency verification process. The uncertainty evaluation is realized by calculating the eigenvalues of the covariance matrix of the preliminary position estimation, and specifically, a confidence interval estimation method based on Fisher information matrix is adopted to quantify the dispersion degree of the position estimation in the three-dimensional space. Through the uncertainty evaluation, the position uncertainty of the preliminary position estimation, that is, the dispersion degree, is obtained.

[0060] It should be noted that the position uncertainty obtained above is a quantitative value.

[0061] Further, a second preset threshold is determined, which is adaptively adjusted according to the system positioning accuracy requirement and the environmental dynamic characteristics, and the calculation model thereof is as follows: , Wherein, is the second preset threshold; is a basic uncertainty threshold, which is obtained offline according to the size of the monitoring area and the typical motion speed of the unmanned aerial vehicle; 、 is an adaptive adjustment coefficient, which respectively reflects the influence degree of position variance and time update interval on the threshold, and is obtained through historical data regression analysis; is the variance of the preliminary position estimate, which is calculated by the trace of the covariance matrix of the preliminary position estimate; is a variance normalization factor, which is used to balance the magnitude of variance; is a data update time interval, which is determined by the timing characteristics of the acoustic spectrum and radar data acquisition and processing chain; is a time decay constant, which is set according to the system response speed requirement.

[0062] Further, the size of the position uncertainty quantitative value of the preliminary position estimate and the second preset threshold is compared; if the position uncertainty quantitative value is higher than the second preset threshold, the position optimization process based on the second judgment condition is triggered; if the position uncertainty quantitative value is not higher than the second preset threshold, the whole position optimization process is skipped, and the preliminary position estimate obtained in step S4 is directly used as the final optimized position estimate.

[0063] For the position optimization process of the second judgment condition, specifically, the consistency score of acoustic spectrum and radar data is first calculated, which is constructed by fusing multi-dimensional information such as position difference, feature similarity and time sequence continuity, and the calculation model thereof is as follows: , Wherein, is the consistency score; 、 are the acoustic spectrum preliminary position vector and the radar preliminary position vector respectively, which are obtained from the output of step S4; is the Euclidean norm; is the position error variance, which is calculated by the joint covariance matrix of acoustic spectrum and radar positioning error; 、 、 is the weight coefficient, which respectively represents the importance of position, feature and time sequence component, and is allocated by the analytic hierarchy process combined with expert knowledge; , are the acoustic spectrum feature vector and the radar feature vector respectively, which are concatenated from the first and second feature parameters extracted in S2 and S3 steps; is the time series correlation of acoustic spectrum and radar data, which is obtained by calculating the Pearson correlation coefficient of acoustic spectrum time series data and radar point trail data in a sliding window.

[0064] Further, a third preset threshold is determined, which is set based on the minimum requirement of data consistency of the system, and dynamically adjusted considering sensor performance and environmental interference, and its expression is: , wherein, is the third preset threshold; is the basic consistency score threshold, which is obtained by statistical distribution of scores of correct and incorrect association cases in a large amount of experimental data; is the signal-to-noise ratio influence factor, which represents the influence degree of sensor data quality on consistency requirement, and is obtained by experimental calibration; are the signal-to-noise ratios of acoustic spectrum and radar data respectively; is the maximum signal-to-noise ratio reference value of the system.

[0065] Further, the consistency score is compared with the third preset threshold, and the results are as follows: If the consistency score is lower than the third preset threshold, a multi-source data fusion model based on graph neural network is started to perform position re-estimation. The graph neural network model takes acoustic spectrum feature nodes and radar feature nodes as input, and constructs a space-time heterogeneous graph, wherein the node attributes include feature parameters, position initial value and time stamp, and the edge attributes include sensor type association weight and space-time proximity. The node feature update rule of the graph neural network adopts the existing multi-head graph attention mechanism.

[0066] After the graph neural network propagates through multiple layers, it aggregates global information through the readout layer and outputs the re-estimated position, which is used as the optimized position estimate.

[0067] If the consistency score is not lower than the third preset threshold, the preliminary position estimate is directly used as the optimized position estimate.

[0068] It should be noted that the second judgment condition is based on whether the consistency score of acoustic spectrum and radar data is lower than the third preset threshold to determine whether to start the graph neural network for position optimization.

[0069] ​In this embodiment, the eigenvalues ​​of the covariance matrix of the initial location estimation are 1.8, 1.2, and 0.9, respectively, and the calculated location uncertainty is 1.2. The second preset threshold has a base value of 1.0, which is adjusted to 1.15 after considering the variance factor and time update interval. Since 1.2 is higher than 1.15, the location optimization process is triggered. In the consistency score calculation, the location difference term is 0.75, the feature similarity is 0.82, and the temporal correlation is 0.68, with a weighted total score of 0.76. The third preset threshold is 0.78. Since 0.76 is lower than 0.78, graph neural network optimization is initiated. After three layers of graph attention propagation, the re-estimated coordinates of 127.2 meters, 89.1 meters, and 15.8 meters are output as the optimized location estimate.

[0070] S6. Based on the optimized position estimation, the improved particle filter algorithm is used to track the UAV to obtain the final position and tracking trajectory of the UAV.

[0071] In one embodiment, the particle filter state is first initialized based on the optimized position estimate obtained in step S5. This initialization process employs an adaptive particle distribution strategy, generating an initial particle set by constructing a Gaussian mixture model centered on the optimized position estimate and with position uncertainty as the covariance. The number of particles is dynamically adjusted according to the state space dimension and computational resources. It is important to note that these particles are used to approximate random samples representing the posterior probability distribution of the UAV's motion state.

[0072] Furthermore, an improved particle filter algorithm framework is designed. This framework introduces a joint likelihood function, an adaptive weight adjustment mechanism, and residual-driven process noise optimization on the basis of standard particle filtering. It also integrates Long Short-Term Memory (LSTM) trajectory prediction to achieve high-precision estimation and prediction of UAV motion state.

[0073] Specifically, for each particle, a joint likelihood function based on the acoustic spectrum and radar waves is calculated. This joint likelihood function introduces feature matching degree and temporal consistency constraints on the basis of the traditional Gaussian likelihood model, and its expression is as follows: , in, For particles The joint likelihood value; The adaptive weighting factor is dynamically calculated based on the ratio of the current acoustic spectrum data signal-to-noise ratio to the radar data signal-to-noise ratio. , These are the baseline likelihood functions based on acoustic spectrum and radar, respectively, calculated using the Gaussian kernel function of the measured values ​​and particle prediction values; , These are the acoustic spectrum feature vector and radar feature vector measured at the current moment, respectively, which are composed of the feature parameters extracted in steps S2 and S3. , Particles The corresponding acoustic spectrum and radar feature prediction vectors are obtained through the feature-state mapping function; The variance of the acoustic spectrum feature error is obtained by statistical analysis of feature matching errors in historical data. This represents the inner product of vectors, used to measure feature similarity.

[0074] Furthermore, the particle weights are normalized, and a system resampling strategy is adopted to avoid particle degradation. The system resampling introduces weight smoothing and particle diffusion operations on the basis of standard polynomial resampling to maintain particle diversity.

[0075] Furthermore, statistical analysis is performed on the residuals of the particle filter, and the process noise covariance matrix is ​​dynamically adjusted. The adjustment model is as follows: , in, for The process noise covariance matrix at time step; This is a smoothing factor used to control the decay rate of historical noise information, and is set according to the dynamic characteristics of the system. This is the residual vector, which is the difference between the observed value and the predicted mean of the particle set; The residual sample covariance matrix; The residual weighting coefficient is dynamically adjusted based on the whitening test results of the residual sequence. The state-observation Jacobian matrix is ​​approximated by particle set statistical linearization; The prior state covariance matrix is ​​calculated from the particle set.

[0076] Furthermore, a time series prediction model is introduced, using a Long Short-Term Memory (LSTM) network to predict the UAV trajectory. The LSTM network takes a historical state sequence as input and outputs state predictions for multiple future time steps. These predictions are then weighted and fused with the particle filter output to obtain the final tracking trajectory. The fusion strategy is as follows: , in, This is the final tracking trajectory status; State estimation for particle filter output; The state predicted by the LSTM network; For fusion weights, the estimated covariance based on particle filtering and the prediction error of LSTM are adaptively calculated. This is the Kalman gain matrix, used to fuse the latest observations. ; This is the observation matrix.

[0077] Further, the final position and tracking trajectory of the UAV are outputted, and countermeasures including early warning, interference signal emission or automatic tracking locking are triggered based on the trajectory prediction result.

[0078] It should be noted that the final position is obtained by weighted fusion of the particle filter output state and the LSTM predicted trajectory, and the tracking trajectory is composed of a sequence of final positions at consecutive time steps.

[0079] In the embodiment, 500 particles are generated by initializing the particle filter, and Gaussian mixture models are constructed based on the optimized position estimates of 127.2 meters, 89.1 meters and 15.8 meters. In the joint likelihood function calculation, the adaptive weighting factor is 0.6, the acoustic spectrum feature matching degree is 0.88, and the radar feature matching degree is 0.91. The process noise covariance matrix is adjusted by residual driving, and the smoothing factor is 0.8. The LSTM network predicts the future 5-step trajectory based on the past 20 state sequences, and the prediction error is 0.25 meters. The final fused UAV position coordinates are 128.5 meters, 90.3 meters and 16.1 meters, the tracking trajectory shows that the UAV moves at a speed of 8 m / s to the northeast, triggers a level 3 warning and starts automatic tracking locking.

[0080] See Figure 2 , Figure 2 is a structural schematic diagram of a low-altitude UAV positioning and tracking system in an embodiment of the present application. The system includes an input device, a processor, an output device and a memory, which are connected to each other, wherein the memory is used to store a computer program, the computer program includes program instructions, the processor is configured to call the program instructions, the system uses the low-altitude UAV positioning and tracking method, the output device includes an acquisition module, the processor includes an analysis module, and the output device includes an output module. The acquisition module is used to acquire acoustic spectrum time series data and radar plot data of the UAV. The analysis module is used to extract first acoustic spectrum feature parameters and second acoustic spectrum feature parameters using the acoustic spectrum time series data, extract first radar feature parameters and second radar feature parameters using the radar plot data, determine a preliminary position estimate of the UAV based on the first acoustic spectrum feature parameters and the first radar feature parameters through a first judgment condition, determine an optimized position estimate of the UAV based on the preliminary position estimate through a second judgment condition, and track the UAV using an improved particle filter algorithm according to the optimized position estimate. The output module is used to output a final position and a tracking trajectory of the UAV.

[0081] In summary, the application combines the heterogeneous data fusion of acoustic spectrum and radar, combines the environment-adaptive feature extraction model, the multi-level intelligent optimization judgment mechanism based on dynamic threshold and graph neural network, and the improved particle filtering algorithm fusing LSTM prediction, and constructs a high-precision, strong-robustness, intelligent-adaptive unmanned aerial vehicle detection and tracking system, effectively solves the technical problems of limited sensor performance, low data fusion reliability and insufficient tracking prediction accuracy in complex environment.

[0082] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should be covered in the scope of the claims and description of the present application.

Claims

1. A positioning and tracking method for a low-altitude unmanned aerial vehicle (UAV), characterized in that, The method includes the following steps: Acquire acoustic spectrum time-series data and radar spot data of the UAV; Using the aforementioned acoustic spectrum time-series data, extract the first acoustic spectrum feature parameters and the second acoustic spectrum feature parameters; Using the radar spot data, extract the first radar feature parameters and the second radar feature parameters; Based on the first acoustic spectrum feature parameters and the first radar feature parameters, a preliminary position estimate of the UAV is determined through the first judgment condition. Based on the preliminary position estimate, the optimized position estimate of the UAV is determined by the second judgment condition; Based on the optimized position estimation, the improved particle filter algorithm is used to track the UAV, and the final position and tracking trajectory of the UAV are obtained.

2. The positioning and tracking method for a low-altitude unmanned aerial vehicle according to claim 1, characterized in that, Acquiring the acoustic spectrum time-series data and radar spot data of the UAV includes: Acquire the acoustic signal of the UAV; use an improved joint noise reduction algorithm to perform noise reduction processing on the acoustic signal to obtain the noise reduction processing result; perform signal enhancement processing based on the noise reduction processing result to obtain the signal enhancement processing result; and perform windowed short-time Fourier transform processing based on the signal enhancement processing result to obtain the acoustic spectrum time series data. The radar echo signal of the UAV is acquired; the radar echo signal is preprocessed to obtain radar spot data, the preprocessing including pulse compression, Doppler processing and spot extraction.

3. The positioning and tracking method for a low-altitude unmanned aerial vehicle according to claim 1, characterized in that, Using the aforementioned acoustic spectrum time-series data, the extraction of the first acoustic spectrum feature parameters and the second acoustic spectrum feature parameters includes: Using the time-series acoustic spectrum data, the cumulative energy distribution of the time spectrum is calculated; based on the cumulative energy distribution, a modified sound velocity model is constructed; the modified sound velocity is obtained through the modified sound velocity model; a propagation delay compensation model is constructed based on the modified sound velocity; and a first acoustic spectrum feature parameter is obtained through the propagation delay compensation model, wherein the first acoustic spectrum feature parameter includes the spectral roll-off point. Using the aforementioned acoustic spectrum time-series data, an improved spectral flux calculation model is constructed; through the improved spectral flux calculation model, a second acoustic spectrum feature parameter is obtained, the second acoustic spectrum feature parameter including spectral flux.

4. The positioning and tracking method for a low-altitude unmanned aerial vehicle according to claim 3, characterized in that, The modified sound speed model satisfies the following expression: ， in, To correct for the speed of sound, For ambient temperature, For ambient air pressure, For reference air pressure, This is the nonlinear coefficient of air pressure. For ambient humidity, , These are the first-order and second-order influence coefficients of humidity. This is the sound speed gradient compensation term. As the turbulence disturbance compensation term, the propagation delay compensation model satisfies the following expression: ， in, The frequency of the spectral roll-off point after compensation. The original spectrum roll-off frequency, This is an estimate of the propagation delay. For the dominant wavelength, This is a correction factor for dispersion effects. To correct for the speed of sound, the improved spectral flux calculation model satisfies the following expression: ， in, For enhanced spectral flux, This represents the total number of frequency bands. For the first Weighting coefficients for each frequency band for Time of the first Spectral amplitude of each frequency band for Time of the first Spectral amplitude of each frequency band It is a non-linear amplification index. The frame energy normalization factor. The energy normalization index, As a transient enhancement factor, For transient component flux, These are the modulation feature weighting coefficients. This is the modulation spectral flux.

5. The positioning and tracking method for a low-altitude unmanned aerial vehicle according to claim 1, characterized in that, Using the radar spot data, extracting the first radar feature parameters and the second radar feature parameters includes: Using the radar spot data, micro-Doppler features are extracted; based on the micro-Doppler features, an instantaneous frequency variance calculation model is constructed; through the instantaneous frequency variance calculation model, a first radar feature parameter is obtained, the first radar feature parameter including the instantaneous frequency variance; Using the radar spot data, an improved odd-even mode decomposition coefficient calculation model is constructed; through the odd-even mode decomposition coefficient calculation model, a second radar characteristic parameter is obtained, which includes odd mode coefficient and even mode coefficient.

6. The positioning and tracking method for a low-altitude unmanned aerial vehicle according to claim 1, characterized in that, Based on the first acoustic spectrum feature parameters and the first radar feature parameters, and through the first judgment condition, the preliminary position estimate of the UAV is determined as follows: Based on the first acoustic spectrum feature parameters and the first radar feature parameters, uncertainty quantization is performed on the preliminary acoustic spectrum position and the preliminary radar position to obtain the uncertainty quantization result; Based on the uncertainty quantification results, the Euclidean distance between the preliminary acoustic spectrum position and the preliminary radar position is calculated; The first preset threshold is determined by using the signal-to-noise ratio of acoustic spectrum data and radar data; The Euclidean distance is compared with the first preset threshold. If the Euclidean distance is less than the first preset threshold, a weighted fusion is used to output a preliminary location estimate. If the Euclidean distance is not less than the first preset threshold, the location of the data source with a high signal-to-noise ratio is selected as the preliminary location estimate.

7. The positioning and tracking method for a low-altitude unmanned aerial vehicle according to claim 1, characterized in that, Based on the preliminary position estimate, the optimized position estimate of the UAV is determined by the second judgment condition, including: Obtain the quantized value of the position uncertainty of the preliminary position estimate; Determine the second preset threshold; The position uncertainty quantification value is compared with the second preset threshold; if the position uncertainty quantification value is not higher than the second preset threshold, the preliminary position estimate is used as the optimized position estimate; if the position uncertainty quantification value is higher than the second preset threshold, a position optimization process based on a second judgment condition is triggered. Calculate the consistency score between the acoustic spectrum and radar data; Determine the third preset threshold; The consistency score is compared with the third preset threshold; if the consistency score is lower than the third preset threshold, a position re-estimation is performed; if the consistency score is not lower than the third preset threshold, the preliminary position estimate is used as the optimized position estimate.

8. The positioning and tracking method for a low-altitude unmanned aerial vehicle according to claim 1, characterized in that, Based on the optimized position estimation, the improved particle filter algorithm is used for UAV tracking to obtain the UAV's final position and tracking trajectory, including: Based on the optimized position estimation, the initialization result of the particle filter state is obtained; Based on the initialization results, an improved particle filter algorithm framework is designed, which includes a joint likelihood function, an adaptive weight adjustment mechanism, noise optimization, and a long short-term memory network. Based on the particle filter algorithm framework, a fusion strategy model is designed; The final position and tracking trajectory of the UAV are obtained through the fusion strategy model.

9. A positioning and tracking method for a low-altitude unmanned aerial vehicle according to claim 8, characterized in that, The joint likelihood function incorporates feature matching degree and temporal consistency constraints, satisfying the following expression: ， in, For particles The joint likelihood value, As an adaptive weighting factor, , These are the baseline likelihood functions based on acoustic spectrum and radar, respectively. , These are the acoustic spectrum feature vector and radar feature vector measured at the current moment, respectively. , Particles The corresponding acoustic spectrum and radar feature prediction vector, The variance of the acoustic spectrum feature error. The fusion strategy model, representing the vector inner product, satisfies the following expression: ， in, This is the final tracking trajectory status; State estimation for particle filter output; The state predicted by the LSTM network; For weighting; This is the Kalman gain matrix, used to fuse the latest observations. ; This is the observation matrix.

10. A positioning and tracking system for a low-altitude unmanned aerial vehicle (UAV), the system using a positioning and tracking method for a low-altitude UAV as described in any one of claims 1 to 9, characterized in that, The system includes: The acquisition module is used to acquire the acoustic spectrum time-series data and radar spot data of the UAV; The analysis module is used to extract a first acoustic spectrum feature parameter and a second acoustic spectrum feature parameter using the acoustic spectrum time-series data; extract a first radar feature parameter and a second radar feature parameter using the radar spot data; determine a preliminary position estimate of the UAV based on the first acoustic spectrum feature parameter and the first radar feature parameter through a first judgment condition; determine an optimized position estimate of the UAV based on the preliminary position estimate through a second judgment condition; and perform UAV tracking using an improved particle filter algorithm based on the optimized position estimate. The output module is used to output the final position and tracking trajectory of the drone.

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