A low-altitude unmanned aerial vehicle detection method and device, computer equipment and storage medium

Abstract

The application belongs to the field of low-altitude unmanned aerial vehicle radar detection, and relates to a low-altitude unmanned aerial vehicle detection method, device, computer equipment and storage medium.The method comprises the following steps: initializing a radar system and receiving echo signals; performing full desquaring processing on the received echo signals to realize signal compression; performing improved Keystone transformation on the echo signals after full desquaring processing, wherein the improved Keystone transformation dynamically adjusts transformation parameters to adaptively compensate for the distance walking phenomenon caused by non-uniform target motion; performing distance-Doppler two-dimensional Fourier transformation on the echo signals after improved Keystone transformation; identifying real targets from the generated two-dimensional distance-Doppler spectrum and estimating the position and speed parameters of the real targets; and displaying the estimated position and speed parameters.The signal processing precision is improved, the target detection capability is enhanced, parameter estimation is realized, and the display intuitiveness is improved.

Description

Technical Field

[0001] This invention relates to the field of low-altitude unmanned aerial vehicle (UAV) radar detection technology, and in particular to a low-altitude UAV detection method, apparatus, computer equipment, and storage medium. Background Technology

[0002] As the scale of aircraft increases, traditional ground-based surveillance faces numerous challenges. For example, linear frequency modulated radar, with its excellent range, high resolution, and anti-multipath interference capabilities, is widely used for target identification and tracking of aircraft in this field. In the future, with the use of aircraft swarm strategies, the radar identification sampling rate will be forced to increase, thereby significantly increasing the echo bandwidth of the radar signal. This leads to an increase in server computational load, ultimately affecting the radar's real-time ranging and velocity measurement. At the same time, as the flight speed of UAVs further increases, their small radar cross-section and high maneuverability will be amplified. When radar performs long-term coherent accumulation on flight swarms, it is prone to echo energy dispersion, resulting in severe range drift and reduced detection and parameter estimation accuracy.

[0003] Traditional UAV detection methods, based on direct radar sampling, are difficult to use for long-term, large-scale, continuous detection of high-speed moving UAVs, and their applications have the following limitations:

[0004] The high sampling rate and high bandwidth usage greatly increase the information throughput and computational load of subsequent processing.

[0005] Distance travel problem: As the speed of UAVs increases, their small radar cross-section and high maneuverability will be amplified. When radar performs long-term coherent accumulation on flight swarms, it is easy to cause the echo energy to disperse, resulting in distance travel. Traditional correction methods use the classic Keystone transform based on the assumption of uniform target motion to correct distance travel. However, in reality, UAVs have acceleration in their motion state, which will lead to a decrease in the parameter estimation accuracy of the classic transform.

[0006] Imaging information issues: In order to achieve real-time performance and reduce computational load, traditional radar ranging and velocity measurement often calculate one-dimensional range images and one-dimensional Doppler images separately, which makes it difficult for ground analysts to refer to the data. Summary of the Invention

[0007] To address the aforementioned technical problems, this invention provides a low-altitude unmanned aerial vehicle (UAV) detection method, employing the following technical solution, including the following steps:

[0008] Initialize the radar system and receive echo signals;

[0009] The received echo signal is subjected to full deskewing to achieve signal compression;

[0010] An improved Keystone transform is applied to the echo signal after full deskewing processing. The improved Keystone transform adaptively compensates for distance movement caused by non-uniform target motion by dynamically adjusting the transform parameters.

[0011] The echo signal after the improved Keystone transform is subjected to a range-Doppler two-dimensional Fourier transform, which transforms it into a two-dimensional domain that can intuitively display the target distance and velocity, thus completing the final estimation and detection of the target parameters;

[0012] From the generated two-dimensional range-Doppler spectrum, the real target is identified and its position and velocity parameters are estimated;

[0013] The estimated position and velocity parameters are displayed.

[0014] Preferably, the steps of initializing the radar system and receiving echo signals specifically include:

[0015] Set the radar operating mode and parameters, generate and transmit detection signals;

[0016] It receives the echo signal reflected from the target and converts it into an intermediate frequency or baseband signal;

[0017] Converting continuous analog signals into discrete digital signals.

[0018] Preferably, the step of performing full deskewing on the received echo signal to achieve signal compression specifically includes:

[0019] A local copy, or local reference signal, synchronized with the transmitted signal is generated for mixing with the echo signal;

[0020] The echo signal is mixed with the local reference signal to linearly convert the target time delay information into frequency information.

[0021] High-frequency components and noise generated by mixing are filtered out, while useful difference frequency signals are retained, thus achieving bandwidth compression.

[0022] Preferably, the step of performing an improved Keystone transform on the echo signal after full deskewing, wherein the improved Keystone transform adaptively compensates for distance movement caused by non-uniform target motion by dynamically adjusting the transform parameters, specifically includes:

[0023] By scaling, the distance traveled is linearly compensated on the slow time axis;

[0024] Introduce a complex adaptive coefficient and set the initial parameters for the gradient descent iteration;

[0025] An iterative algorithm is used to automatically find adaptive coefficients that enable the target energy to achieve optimal focusing in the range-Doppler domain.

[0026] Preferably, the step of converting the echo signal after the improved Keystone transform into a two-dimensional domain that can intuitively display the target distance and velocity, and completing the final estimation and detection of the target parameters, specifically includes:

[0027] Analyze the frequency components of the signal within each pulse, transform the signal to the range domain, and form a one-dimensional range image;

[0028] Analyze the phase change of the signal on the slow time axis at each range unit, transform the signal to the Doppler frequency domain, and extract the target velocity information;

[0029] The distance and Doppler information are integrated into a two-dimensional matrix, and the coordinate mapping from frequency to the actual physical quantities of distance and velocity is completed.

[0030] Preferably, the step of identifying the real target from the generated two-dimensional range-Doppler spectrum and estimating its position and velocity parameters specifically includes:

[0031] In the presence of background noise and clutter, the true target point is detected with a constant false alarm probability;

[0032] For each target point detected by CFAR, extract its distance and velocity values;

[0033] The detected target information is organized and packaged into a standardized data format.

[0034] Preferably, the step of displaying the estimated position and velocity parameters specifically includes:

[0035] The processing results are presented in an intuitive graphical format;

[0036] Transmit the test results to the superior system or other related systems;

[0037] Record key data and events throughout the entire system operation process. The key data includes: raw data, intermediate processing results, final target information, system status, and abnormal events.

[0038] To address the aforementioned technical problems, the present invention also provides a low-altitude unmanned aerial vehicle (UAV) detection device, which employs the following technical solution, including:

[0039] The receiving module is used to initialize the radar system and receive echo signals.

[0040] The processing module is used to perform full deskewing on the received echo signal to achieve signal compression;

[0041] The correction module is used to perform an improved Keystone transform on the echo signal after full deskewing processing. The improved Keystone transform adaptively compensates for the distance movement caused by non-uniform target motion by dynamically adjusting the transformation parameters.

[0042] The conversion module is used to perform a range-Doppler two-dimensional Fourier transform on the echo signal after the improved Keystone transform, and convert it into a two-dimensional domain that can intuitively display the target distance and velocity, so as to complete the final estimation and detection of the target parameters.

[0043] The estimation module is used to identify the real target from the generated two-dimensional range-Doppler spectrum and estimate its position and velocity parameters;

[0044] The display module is used to display the estimated position and velocity parameters.

[0045] To address the aforementioned technical problems, the present invention also provides a computer device that employs the technical solution described below, comprising a memory and a processor. The memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the aforementioned low-altitude unmanned aerial vehicle (UAV) detection method.

[0046] To address the aforementioned technical problems, the present invention also provides a computer-readable storage medium, which employs the technical solution described below. The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the aforementioned low-altitude unmanned aerial vehicle (UAV) detection method.

[0047] Compared with the prior art, the present invention has the following main advantages:

[0048] (1) Improved signal processing accuracy: Full deskewing and signal compression of the echo signal effectively reduced noise and interference components in the signal, improving signal quality. The improved Keystone transform, by introducing adaptive coefficients and iterative optimization, effectively compensated for the phase error caused by the non-uniform motion of the target, thereby improving the peak-to-sidelobe ratio of the target in the range-Doppler spectrum, accurately correcting the range drift problem caused by the target motion, ensuring the accuracy of the signal in subsequent processing, and providing a reliable basis for subsequent target parameter estimation;

[0049] (2) Enhanced target detection capability: The echo signal is converted to the two-dimensional domain to generate a two-dimensional range-Doppler spectrum. This intuitive display method can clearly present the target's distance and velocity information, making it easier to identify real targets from complex backgrounds. This greatly improves the detection sensitivity of low-altitude UAVs and reduces the false alarm rate.

[0050] (3) Accurate parameter estimation is achieved: After identifying the real target from the two-dimensional spectrum, its position and velocity parameters can be accurately estimated. This accurate parameter estimation is crucial for tracking and identifying low-altitude UAVs, and helps to grasp the flight status and trajectory of UAVs in a timely manner, providing an accurate basis for subsequent countermeasures;

[0051] (4) Improved intuitiveness of display: By displaying the estimated position and velocity parameters, operators can intuitively understand the target situation without complicated analysis and interpretation, and can make decisions quickly, which improves the practicality and operational efficiency of the entire detection system. Attached Figure Description

[0052] To more clearly illustrate the solutions in this invention, the accompanying drawings used in the description of the embodiments of this invention will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0053] Figure 1 This is a flowchart of an embodiment of the low-altitude unmanned aerial vehicle (UAV) detection method of the present invention;

[0054] Figure 2 This is a schematic diagram of the structure of one embodiment of the low-altitude unmanned aerial vehicle (UAV) detection device of the present invention;

[0055] Figure 3 yes Figure 2 A schematic diagram of the specific implementation structure;

[0056] Figure 4 yes Figure 3 Implementation flowchart of low-altitude unmanned aerial vehicle (UAV) detection device;

[0057] Figure 5 This is a schematic diagram of the structure of an embodiment of the computer device of the present invention. Detailed Implementation

[0058] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; the terminology used herein in the specification is for the purpose of describing particular embodiments only and is not intended to limit the invention; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings are used to distinguish different objects and not to describe a particular order.

[0059] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0060] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0061] It should be noted that the low-altitude UAV detection method provided in the embodiments of the present invention is generally executed by a server / terminal device, and correspondingly, the low-altitude UAV detection device is generally installed in the server / terminal device.

[0062] It should be understood that the number of terminal devices, networks, and servers is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be used.

[0063] Example 1

[0064] Please refer to Figure 1 The diagram illustrates a flowchart of an embodiment of the low-altitude unmanned aerial vehicle (UAV) detection method of the present invention. The low-altitude UAV detection method includes the following steps:

[0065] Step S1: Initialize the radar system and receive echo signals.

[0066] In this embodiment, the electronic equipment (e.g., server / terminal device) on which the low-altitude UAV detection method runs can receive low-altitude UAV detection requests via wired or wireless connections. It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAXX connections, Zigbee connections, UWB (ultra-wideband) connections, and other currently known or future-developed wireless connection methods.

[0067] In this embodiment, step S1, initializing the radar system and receiving echo signals, may specifically include the following steps:

[0068] S11 sets the radar operating mode and parameters, and generates and transmits detection signals.

[0069] The waveform parameters of the linear frequency modulated continuous wave (LFMCW) radar are configured, and the LFMCW signal is generated and transmitted by the radio frequency front end. Operating parameters include: carrier frequency. signal bandwidth Frequency sweep cycle Pulse repetition period .

[0070] Proper parameter configuration is essential for ensuring system performance. and This determines the radar's range resolution. and This affects the speed measurement range and the maximum unambiguous distance.

[0071] S12 receives the echo signal reflected from the target and converts it into an intermediate frequency or baseband signal.

[0072] Signals are received by a radar antenna, amplified by a low-noise amplifier, and then down-converted using a mixer.

[0073] This process converts high-frequency radio frequency signals into lower-frequency intermediate frequency signals, reducing the difficulty and hardware cost of subsequent AD sampling.

[0074] S13 converts a continuous analog signal into a discrete digital signal.

[0075] The down-converted signal is sampled using an analog-to-digital converter, and a digitized complex baseband signal is output. : ,in, : No. The complex baseband echo signal received in one pulse cycle; The amplitude of the echo signal; A rectangular window function characterizes the signal within a pulse width. It exists in memory; Radar carrier frequency; : Frequency modulation slope; : Fast time, changing within one pulse cycle; Slow time represents different pulse periods. ; The time delay of the echo signal relative to the transmitted signal.

[0076] This formula describes the model of the digital baseband echo signal received by the radar. This formula is the starting point for all subsequent signal processing, accurately modeling the echo signal, which contains the target's range and velocity information (implied in the time delay). middle).

[0077] The purpose of step S1 is to complete the parameter configuration of the radar system, signal transmission, and the acquisition and digitization of the raw echo signal, so as to provide a high-quality data foundation for subsequent signal processing.

[0078] Step S2: Perform full deskewing on the received echo signal to achieve signal compression.

[0079] In this embodiment, step S2, which involves performing full deskewing on the received echo signal to achieve signal compression, may specifically include the following steps:

[0080] S21 generates a local copy, or local reference signal, synchronized with the transmitted signal, which is used for mixing with the echo signal.

[0081] In a digital signal processor, a local reference signal is digitally generated based on the known transmitted signal parameters: ,in, : Local reference signal, the other parameters are defined in the echo signal formula.

[0082] This formula defines a local reference signal used for de-chirping. This reference signal serves as the basis for de-chirping, and its frequency variation follows the same pattern as the transmitted signal.

[0083] S22 mixes the echo signal with the local reference signal, linearly converting the target time delay information into frequency information.

[0084] echo signal Complex conjugate with local reference signal Multiplication:

[0085] ,in, Beat signal, The amplitude of the signal after mixing. Target echo delay, for moving targets ,in The initial distance, This represents the radial velocity.

[0086] This formula presents the mathematical expression for the beat signal (intermediate frequency signal) obtained after mixing.

[0087] After mixing, the signal's phase term includes linear terms and constant terms related to the target distance and velocity. Most importantly, the time delay... Converted to a frequency of A single-frequency signal (for a stationary target).

[0088] The function of step S22 is to mix the echo signal with the local reference signal and linearly convert the target time delay information into frequency information.

[0089] S23 filters out high-frequency components and noise generated by mixing, retaining the useful difference frequency signal, thus achieving bandwidth compression.

[0090] Intermittent beat signal Perform digital low-pass filtering.

[0091] After filtering, the bandwidth of the output signal is greatly compressed, changing from a wideband signal of hundreds of MHz to a narrowband signal (typically a few MHz to tens of MHz) that corresponds only to the target distance range. This significantly reduces the requirements for the AD sampling rate and the amount of data to be processed in subsequent data processing, which is key to achieving low system complexity.

[0092] The function of step S23 is to filter out the high-frequency components and noise generated by mixing, retain the useful difference frequency signal, and achieve bandwidth compression.

[0093] Step S2 is the core of preprocessing in this embodiment. Its purpose is to convert the broadband LFMCW signal into a narrowband signal through mixing, thereby significantly reducing the data bandwidth and sampling rate requirements, while completing the initial extraction of distance information.

[0094] Step S3: Perform an improved Keystone transform on the echo signal after full deskewing. The improved Keystone transform dynamically adjusts the transform parameters to adaptively compensate for the distance movement caused by the non-uniform motion of the target.

[0095] During radar operation, range travel occurs when there is relative motion between the target and the radar. This range travel causes the echo signal to shift within the range cell, preventing different pulse echo signals from aligning correctly within the same range cell, thus severely impacting the radar's accuracy in target detection and parameter estimation. The core function of the Keystone transform is to effectively correct this range travel problem caused by the relative motion of the target.

[0096] Specifically, the Keystone transform rearranges and aligns signals across different range cells by scaling the signals in the range-Doppler domain. It cleverly alters the signal distribution in the range-Doppler domain, allowing signal energy that was previously dispersed due to range travel to refocus in the correct location. After Keystone transform processing, the echo signals received by the radar are accurately aligned across the range cells, providing a solid foundation for subsequent signal processing and analysis.

[0097] In this embodiment, step S3, which involves performing improved Keystone transform and distance travel correction on the echo signal after full deskewing and signal compression, may specifically include the following steps:

[0098] S31 uses scale transformation to linearly compensate for distance travel on the slow time axis.

[0099] In the Keystone transformation, a virtual slow-time variable is introduced. Rescale the slow time axis: ,in, Virtual slow time Carrier frequency, : Frequency modulation slope, Quickly.

[0100] This formula defines the Keystone transformation relationship and establishes the true slow time. With virtual slow time The mapping between them.

[0101] The core idea of ​​this transformation is to use a scaling factor. This process decouples the coupling terms between range and velocity in the echo phase. For a uniformly moving target, after this transformation, its echo energy will be corrected to the same range cell between different pulses, thus eliminating range drift.

[0102] The purpose of step S31 is to perform linear compensation on the slow time axis by scaling.

[0103] S32 introduces a complex adaptive coefficient and sets the initial parameters for the gradient descent iteration.

[0104] Introducing a complex adaptive coefficient into the classical transformation formula And set the initial parameters for the gradient descent iteration: ,in, The adaptive coefficient is a complex number with a magnitude close to 1, primarily used for phase fine-tuning. : Index variable.

[0105] Iteration parameter initialization includes: initial coefficients: (Degenerates into a classical transformation); Learning rate (step size): Convergence threshold: Maximum number of iterations: .

[0106] This formula defines the improved Keystone transformation relation, where Used for dynamically optimizing the transformation process.

[0107] Adaptive coefficients The introduction of this feature makes the transformation process no longer fixed, but can be dynamically adjusted according to the actual motion state of the target, which greatly improves the algorithm's adaptability and focusing performance for highly maneuverable and non-uniform moving targets.

[0108] The purpose of step S32 is to introduce an optimizable complex coefficient for dynamic compensation to address the performance degradation of the classic Keystone transform caused by non-uniform motion of the target (such as acceleration).

[0109] S33 uses an iterative algorithm to automatically find adaptive coefficients that enable the target energy to achieve optimal focusing in the range-Doppler domain.

[0110] An optimization method based on gradient descent is adopted, using image sharpness of the distance-Doppler spectrum as the objective function. The specific implementation process is as follows:

[0111] (1) Loop start: For the first loop... The next iteration.

[0112] (2) Transformation and Imaging: Using the current coefficients For signal Perform Keystone transform and two-dimensional FFT to obtain the distance-Doppler spectrum. .

[0113] (3) Calculate sharpness :calculate The sharpness of the main target region in the spectrum (such as image entropy, peak-to-sidelobe ratio, etc.) indicates that the higher the sharpness, the better the focus.

[0114] (4) Calculate the gradient Using the finite difference method, for The real and imaginary parts are perturbed separately, and the sharpness function is calculated. The gradient.

[0115] (5) Update coefficient: .

[0116] (6) Convergence judgment: If or If the iteration stops, output the result. Otherwise, continue iterating.

[0117] This iterative optimization process is what distinguishes this method from traditional fixed-parameter algorithms. It enables the system to have self-learning capabilities, allowing it to automatically and accurately compensate for phase errors introduced by higher-order motions in complex real-world flight scenarios, thereby achieving super-resolution focusing on each target point in the UAV swarm.

[0118] The purpose of step S33 is to automatically find adaptive coefficients that enable the target energy to achieve optimal focusing in the range-Doppler domain through an iterative algorithm. .

[0119] Step S4 involves performing a range-Doppler two-dimensional Fourier transform on the echo signal after the improved Keystone transform, converting it into a two-dimensional domain that can intuitively display the target's distance and velocity, thus completing the final estimation and detection of the target parameters.

[0120] In this embodiment, step S4, which involves performing a range-Doppler two-dimensional Fourier transform on the echo signal after the improved Keystone transform, converting it into a two-dimensional domain that can intuitively display the target's distance and velocity, and completing the final estimation and detection of the target parameters, may specifically include the following steps:

[0121] S41, analyze the frequency components of the signal within each pulse, transform the signal to the range domain, and form a one-dimensional range image.

[0122] For the corrected signal matrix, along the fast time... Perform a Fast Fourier Transform on the dimension:

[0123] ,in, Distance-frequency-slow time domain signal Fourier transform operator along the fast time dimension : Distance frequency, distance to target Proportional .

[0124] The purpose of this formula is to convert the signal from the fast time domain to the distance frequency domain.

[0125] After this transformation, the target forms a spike on its true range cell, achieving focusing in the range dimension. (Range resolution) In this embodiment, .

[0126] The function of step S41 is to analyze the frequency components of the signal within each pulse, transform the signal to the range domain, and form a one-dimensional range image.

[0127] S42 analyzes the phase change of the signal on the slow time axis in each range cell, transforms the signal to the Doppler frequency domain, and extracts the target velocity information.

[0128] The signal matrix after FFT in the distance dimension Along virtual slow time Perform a Fast Fourier Transform on the dimension: ,in, Two-dimensional distance-Doppler spectrum Fourier transform operator along the slow time dimension Doppler frequency, relative to the target radial velocity Proportional ,in λ is the wavelength.

[0129] The purpose of this formula is to convert the signal from the slow time domain to the Doppler frequency domain and generate the final two-dimensional distance-Doppler spectrum.

[0130] After this transformation, the moving target forms a spike on its corresponding Doppler frequency unit, achieving focusing in the velocity dimension. Velocity resolution. ,in To accumulate pulse count.

[0131] The purpose of step S42 is to analyze the phase change of the signal on the slow time axis in each range cell, transform the signal to the Doppler frequency domain, and extract the target velocity information.

[0132] S43 integrates distance and Doppler information into a two-dimensional matrix and completes the coordinate mapping from frequency to the actual physical quantities distance and velocity.

[0133] The results of the two FFT steps above are combined into a two-dimensional matrix. And convert its x and y coordinates to velocity respectively. and distance .

[0134] The generated range-Doppler spectrum serves as the direct basis for target detection and recognition. In this spectrum, an ideal point target is represented by a sharp peak, with its vertical axis precisely corresponding to the target's distance and its horizontal axis precisely corresponding to the target's radial velocity. This allows operators or automated detection algorithms to intuitively and accurately obtain the positional velocity information of each UAV target.

[0135] The purpose of step S43 is to integrate the distance and Doppler information into a two-dimensional matrix and complete the coordinate mapping from frequency to actual physical quantities (distance, velocity).

[0136] Step S5: Identify the real target from the generated two-dimensional range-Doppler spectrum and estimate its position and velocity parameters.

[0137] In this embodiment, step S5, identifying the real target from the generated two-dimensional range-Doppler spectrum and estimating its position and velocity parameters, may specifically include the following steps:

[0138] S51 detects real target points with a constant false alarm probability amidst background noise and clutter.

[0139] In two-dimensional distance-Doppler spectrum The CFAR detection algorithm (such as cell average CFAR) is applied.

[0140] CFAR detection can adaptively estimate the local background noise level and set a dynamic detection threshold, thereby maintaining stable detection performance in complex and changing noise environments and effectively suppressing false alarms.

[0141] The purpose of step S51 is to detect the real target point with a constant false alarm probability amidst background noise and clutter.

[0142] S52 extracts the distance and velocity values ​​for each target point detected by CFAR.

[0143] For the detected peak points Interpolation algorithms (such as parabolic interpolation) can be used to overcome the quantization error of discrete units and obtain more accurate estimates. and .

[0144] Accurate parameter estimation is fundamental to achieving stable target tracking and subsequent situational analysis. Interpolation algorithms can significantly improve the accuracy of parameter measurement, making it superior to the system's theoretical resolution.

[0145] The purpose of step S52 is to accurately extract the distance and velocity values ​​for each target point detected by CFAR.

[0146] S53 organizes and encapsulates the detected target information to form a standardized data format.

[0147] Create a data structure for each detected target, which can contain: target ID, distance estimate. Speed ​​estimates Signal-to-noise ratio, timestamp, etc.

[0148] Structured target information facilitates transmission, display, and storage within the system, and provides standardized input for higher-level processing (such as track association, target identification, and threat assessment).

[0149] The purpose of step S53 is to organize and encapsulate the detected target information to form a standardized data format for easy use later.

[0150] Step S6: Display the estimated position and velocity parameters.

[0151] In this embodiment, step S6, displaying the estimated position and velocity parameters, may specifically include the following steps:

[0152] S61 presents the processing results in an intuitive graphical format.

[0153] The system displays the range-Doppler spectrum in real time on a graphical user interface, marks the detected targets, and lists the detailed parameters of each target.

[0154] Visualization is key to human-computer interaction, enabling operators to quickly grasp the situation on-site and make decisions.

[0155] The purpose of step S61 is to present the processing results in an intuitive graphical way, such as to users like radar operators.

[0156] S62 transmits the detection results to the superior system or other related systems.

[0157] Structured target information data packets are sent to the monitoring center, tracking processor, or alarm system via standard communication interfaces such as Ethernet and serial ports.

[0158] This detection system has been integrated with a larger-scale security system, supporting collaborative monitoring and automated response.

[0159] The purpose of step S62 is to transmit the detection results to the superior system or other related systems.

[0160] S63 records key data and events throughout the entire system operation process. Key data includes: raw data, intermediate processing results, final target information, system status, and abnormal events.

[0161] The system log library continuously records raw data and intermediate processing results (such as adaptive coefficients during iteration). and sharpness ( ), final target information, system status, and abnormal events.

[0162] A robust logging system provides indispensable data support for evaluating algorithm performance, reproducing problem scenarios, and optimizing system parameters, greatly enhancing the system's maintainability and continuous evolution capabilities.

[0163] The purpose of step S63 is to record key data and events throughout the entire system operation process for post-event analysis, fault diagnosis, and algorithm optimization.

[0164] The beneficial effects of implementing this embodiment are:

[0165] (1) Improved signal processing accuracy: Full deskewing and signal compression of the echo signal effectively reduced noise and interference components in the signal, improving signal quality. The improved Keystone transform, by introducing adaptive coefficients and iterative optimization, effectively compensated for the phase error caused by the non-uniform motion of the target, thereby improving the peak-to-sidelobe ratio of the target in the range-Doppler spectrum, accurately correcting the range drift problem caused by the target motion, ensuring the accuracy of the signal in subsequent processing, and providing a reliable basis for subsequent target parameter estimation;

[0166] (2) Enhanced target detection capability: The echo signal is converted to the two-dimensional domain to generate a two-dimensional range-Doppler spectrum. This intuitive display method can clearly present the target's distance and velocity information, making it easier to identify real targets from complex backgrounds. This greatly improves the detection sensitivity of low-altitude UAVs and reduces the false alarm rate.

[0167] (3) Accurate parameter estimation is achieved: After identifying the real target from the two-dimensional spectrum, its position and velocity parameters can be accurately estimated. This accurate parameter estimation is crucial for tracking and identifying low-altitude UAVs, and helps to grasp the flight status and trajectory of UAVs in a timely manner, providing an accurate basis for subsequent countermeasures;

[0168] (4) Improved intuitiveness of display: By displaying the estimated position and velocity parameters, operators can intuitively understand the target situation without complicated analysis and interpretation, and can make decisions quickly, which improves the practicality and operational efficiency of the entire detection system.

[0169] This invention can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This invention can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0170] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware through computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0171] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0172] Example 2

[0173] Further reference Figure 2 As a response to the above Figure 1 The present invention provides an embodiment of a low-altitude unmanned aerial vehicle (UAV) detection device, which is similar to the method shown. Figure 1 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0174] like Figure 2 As shown, the low-altitude UAV detection device 70 in this embodiment includes: a receiving module 71, a processing module 72, a correction module 73, a conversion module 74, an estimation module 75, and a display module 76. Wherein:

[0175] The receiving module 71 is used to initialize the radar system and receive echo signals.

[0176] Processing module 72 is used to perform full deskewing on the received echo signal to achieve signal compression;

[0177] The correction module 73 is used to perform an improved Keystone transform on the echo signal after full deskewing processing. The improved Keystone transform adaptively compensates for the distance movement caused by non-uniform target motion by dynamically adjusting the transformation parameters.

[0178] The conversion module 74 is used to perform a range-Doppler two-dimensional Fourier transform on the echo signal after the improved Keystone transform, and convert it into a two-dimensional domain that can intuitively display the target distance and velocity, so as to complete the final estimation and detection of the target parameters.

[0179] The estimation module 75 is used to identify the real target from the generated two-dimensional range-Doppler spectrum and estimate its position and velocity parameters;

[0180] Display module 76 is used to display the estimated position and velocity parameters.

[0181] In some optional implementations of this embodiment, Figure 3 yes Figure 2 A schematic diagram of the specific implementation structure. For example... Figure 3 As shown, a low-altitude unmanned aerial vehicle (UAV) detection device includes: a preprocessing system, a range travel correction system, and a function support system. The preprocessing system further includes: a data receiving module and a full deskewing processing module. The range travel correction system further includes: an adaptive coefficient iteration module, a Keystone transformation module, a range-Doppler spectrum generation module, and a spectrum output module. The function support system further includes: a log library and a power supply module.

[0182] The data receiving module, as the system's front end, is based on a general radar signal processing platform. Its main functions include generating specific linear frequency modulated continuous wave (LFMCW) signals and transmitting them via antenna; simultaneously, it receives echo signals from the target, converting electromagnetic waves into digital signals. This module possesses high-sensitivity signal acquisition capabilities, effectively extracting weak echoes to ensure signal quality for subsequent processing. Through digital sampling and preprocessing, it provides standardized data input for subsequent full deslant processing.

[0183] The full deskewing module is the core of the preprocessing function in this embodiment. It aims to convert broadband signals into narrowband signals through mixing operations, significantly reducing data rate and computational complexity. Specifically, it multiplies each received echo signal by its complex conjugate with the local reference signal, performs beat processing, and converts the target time delay information into frequency domain information, thereby outputting a single-frequency signal whose frequency is proportional to the target distance. This processing significantly reduces the signal bandwidth, alleviating sampling pressure and providing efficient input data for subsequent Keystone transform and FFT operations, thus reducing overall data throughput and computational load.

[0184] The distance-walking correction system employs an improved Keystone transform method to accurately compensate for distance-walking phenomena caused by the high-speed movement of UAVs. By introducing a virtual slow-time variable and embedding adaptive coefficients ξ(kt,n) into the transform formula, dynamic phase correction is achieved for non-uniformly moving targets. The module incorporates an iterative optimization mechanism that continuously adjusts the adaptive coefficients using gradient descent to achieve optimal focusing of target energy in the distance-slow-time two-dimensional plane. This process effectively solves the energy dispersion problem in multi-target, highly maneuverable scenarios, improving detection accuracy and system robustness.

[0185] The adaptive coefficient iteration module is primarily responsible for dynamically adjusting key parameters during the detection process to adapt to the characteristics of low-altitude UAVs in different environments and the real-time status of the detection system. The low-altitude environment is complex and ever-changing; the flight speed, direction, and signal reflection characteristics of UAVs can change at any time. This module can automatically optimize the coefficients in the detection algorithm based on these changes, ensuring the accuracy of distance travel correction. For example, when a UAV rapidly changes its flight direction, traditional fixed coefficients may not accurately correct distance travel, while the adaptive coefficient iteration module can adjust the coefficients in real time, ensuring that the correction results always maintain high accuracy, thereby improving the overall detection system's ability to track and identify low-altitude UAVs.

[0186] Achieving adaptive coefficient iteration primarily relies on intelligent algorithms and real-time feedback mechanisms. On one hand, advanced machine learning algorithms, such as neural network algorithms, are employed. These networks are trained using a large amount of historical detection data, enabling them to learn the relationship between UAV characteristics and optimal coefficients under different environments. During actual detection, the neural network outputs appropriate coefficients in real time based on the current detection data. On the other hand, a real-time feedback loop is established. The detection system compares the results of each correction with the theoretical expectation, calculating the error value. This error value serves as a feedback signal input to the adaptive coefficient iteration module, which adjusts the coefficients according to the magnitude and direction of the error, following preset iteration rules. For example, gradient descent can be used, continuously calculating the gradient of the error function with respect to the coefficients, and gradually adjusting the coefficients along the opposite direction of the gradient until the error reaches its minimum, thus achieving adaptive optimization of the coefficients.

[0187] The main function of the Keystone transform module is to correct for range drift caused by the relative motion between the target and the detection system. In low-altitude UAV detection, the relative motion between the UAV and the detection device causes the echo signal to shift in the range direction, making it impossible to correctly align the echo signals of different pulses. This affects subsequent range Doppler processing and target parameter estimation. The Keystone transform performs specific phase compensation on the echo signal, stretching or compressing the signal at different range units in the slow time dimension. This ensures that the echo signals of different pulses are correctly aligned in the range direction, thereby eliminating the influence of range drift and providing an accurate data foundation for subsequent precise processing.

[0188] The Keystone transform is primarily implemented based on the Fast Fourier Transform (FFT) and phase multiplication. First, a range-oriented FFT is performed on the received echo signal, converting the signal from the time domain to the frequency domain, obtaining frequency components at different range cells. Then, according to the principles of the Keystone transform, a specific phase factor is multiplied by each frequency component at each range cell. This phase factor is related to parameters such as the target's velocity and the radar's pulse repetition frequency; by accurately calculating this phase factor, phase compensation of the signal is achieved. Finally, an inverse FFT is performed on the phase-compensated signal, converting the signal from the frequency domain back to the time domain, completing the Keystone transform. In practical applications, to improve computational efficiency, efficient FFT algorithms and parallel computing techniques are typically employed to achieve real-time Keystone transform processing.

[0189] The range-Doppler spectrum generation module is responsible for converting the range-walk-corrected signal into a two-dimensional spectrum suitable for target identification. First, a range-dimensional Fast Fourier Transform (FFT) is performed on each slow-time pulse to obtain a one-dimensional range image. Then, a Doppler-dimensional FFT is performed along the slow-time axis on each range cell to extract the target's radial velocity information. Finally, the two are combined into a two-dimensional range-Doppler (RD) spectrum, where each peak corresponds to a target's range and velocity parameters. This module optimizes computational efficiency through a parallel processing architecture, ensuring fast and clear detection results even under high load conditions.

[0190] As the system's terminal output component, the spectrum output module visualizes and renders the generated two-dimensional Doppler-range spectrum, supporting various output formats (such as images, data matrices, alarm information, etc.) for intuitive analysis by ground operators or for automated decision-making. The spectrum output module also features result caching and reporting capabilities, enabling integration with other monitoring systems to achieve real-time tracking and situational awareness of UAV targets, meeting the efficient operation and maintenance needs of millimeter-wave radar detection in low-altitude security scenarios.

[0191] The log library, serving as the core storage in this embodiment, is responsible for structured recording of events and data throughout the entire signal processing process. It collects and stores system status, algorithm parameters (such as the adaptive coefficient iteration process of the Keystone transform), target detection results, and operational anomalies in real time through a hierarchical and categorized approach. This design provides the system with powerful post-processing analysis and diagnostic capabilities, facilitating performance monitoring, fault diagnosis, and continuous optimization of algorithm parameters, thereby significantly improving the system's maintainability and reliability.

[0192] The power module is responsible for providing stable and reliable power support to the entire radar signal processing system, ensuring that all functional modules can operate normally in complex working environments. Specifically, it adopts a multi-level power management architecture and a wide voltage input to adapt to various deployment scenarios such as vehicle-mounted and fixed stations.

[0193] Figure 4 yes Figure 3 The implementation flowchart of the low-altitude unmanned aerial vehicle (UAV) detection device. Figure 4 As shown, this embodiment targets the detection of high-speed flying drone swarms, and the specific implementation steps are as follows:

[0194] Step S101: System initialization and signal transmission.

[0195] After the system is powered on, the data receiving module begins to operate. The radar RF front-end is configured to transmit linear frequency modulated continuous wave (LFMCW) signals. Its key parameters are set as follows: carrier frequency f0 = 24.125 GHz (K-band); signal bandwidth B = 500 MHz; sweep period T = 1 ms; pulse repetition period Tr = 2 ms.

[0196] The antenna radiates this LFMCW signal into the probe airspace.

[0197] Step S102: Echo signal reception and digitization.

[0198] The radar antenna receives echo signals from the UAV target. This signal is amplified by a low-noise amplifier, down-converted, and then converted into a digitized complex baseband signal. , The data is then transmitted to the full de-slant processing system via a high-speed interface (such as LVDS). Assume the initial target distance R0 = 100m and the radial velocity V = 30m / s.

[0199] Step S201: Complete deslant removal.

[0200] The full deskewing module runs in a digital signal processor and receives digitized echo signals.

[0201] Generate a local reference signal: Based on the known transmitted signal parameters of the system, generate a local reference signal in the digital domain that is completely synchronized with the transmitted signal. , .

[0202] Mixing and filtering: converting echo signals With local reference signal complex conjugate Multiply, perform digital mixing, and obtain the beat signal. , .

[0203] The beat signal is subjected to low-pass digital filtering to remove high-frequency components, resulting in a narrowband signal with significantly reduced bandwidth. The center frequency of this signal is proportional to the target distance, and its phase variation contains target velocity information.

[0204] Step S301: Distance walk correction based on improved Keystone transform.

[0205] Distance movement correction module for the de-slope signal Processing is performed to correct for distance travel caused by the high-speed movement of the drone.

[0206] Initialization: Set the adaptive coefficients ξ(0) = 1 + 0j, at which point it degenerates into the classic Keystone transform.

[0207] Iterative optimization: Set iteration parameters: learning rate μ=0.03, convergence threshold. Maximum number of iterations N max =20.

[0208] a. Loop start (taking the Nth iteration as an example):

[0209] (1) Scale transformation: Based on the current adaptive coefficients ξ(N), construct a virtual slow time. The original signal Sif(t,tm) is resampled to a new slow-time grid (t,tm) using linear interpolation. ^ m )superior.

[0210] (2) Generate the RD spectrum: Perform the two-dimensional FFT in step S105 on the data matrix after scaling transformation to obtain the current distance-Doppler spectrum RD(N)(f r ,f d ).

[0211] b. Calculate focus: The focus effect is evaluated using an image sharpness algorithm. Specifically, the sharpness metric J(ξ) of the main target peak region (such as the top 10 strongest units) in the RD spectrum is calculated. N ).

[0212] c. Update coefficients: The gradient is calculated using the finite difference method. Specifically, the real and imaginary parts of ξ are perturbed (Δ=0.001) respectively, and the sharpness change is calculated. Then, the coefficients are updated along the gradient direction: .

[0213] d. Termination judgment: If (Preset threshold) or N+1>N max If the iteration terminates, the optimal coefficient ξ is output. Best Otherwise, let N = N + 1 and continue iterating.

[0214] e. Final transformation: using the optimal coefficient ξ Best For the original descrambling signal S if (t,tm) performs the final Keystone transformation to obtain a signal with precisely aligned energy.

[0215] Step S401: Generate a two-dimensional distance-Doppler spectrum.

[0216] The distance-Doppler spectrum generation module processes the corrected signal.

[0217] Distance-dimensional FFT: for each slow time t ^ m A fast-time signal t is subjected to a Fast Fourier Transform (FFT) to transform the signal into the range frequency domain, resulting in a one-dimensional range image. The range resolution ΔR = c / (2B) ≈ 0.3 meters.

[0218] Doppler FFT: Slow time t for each distance cell ^ m Perform an FFT to analyze its Doppler frequencies. Velocity resolution ΔV = λ / (2MT) r ), where M is the number of accumulated pulses and λ is the wavelength.

[0219] Spectral synthesis: The results of the above two steps are combined to form a two-dimensional matrix, namely the range-Doppler (RD) spectrum. In this spectrum, each UAV target is represented by a sharp peak, with the vertical axis corresponding to the range and the horizontal axis corresponding to the radial velocity.

[0220] Step S501: Map output and target recognition.

[0221] The spectrum output module will perform further processing on the generated RD spectrum.

[0222] Constant False Alarm Rate (CFAR) Detection: The CFAR detection algorithm is applied to the RD spectrum to automatically identify real target points that exceed the background noise.

[0223] Visualization and Reporting: Detected target information (distance, speed, signal-to-noise ratio) and its corresponding RD spectrum image are displayed to the operator in real time through a graphical user interface. Simultaneously, target data can be uploaded to the higher-level monitoring center via Ethernet or serial port for triggering alarms or tracking flight paths.

[0224] Log recording: Throughout the processing, the log library continuously records key information, such as the adaptive coefficient ξ(N) for each iteration and the final focus sharpness J(ξ). N The target detection results and system operating status provide data support for subsequent performance analysis and fault diagnosis.

[0225] The beneficial effects of implementing this embodiment are:

[0226] (1) Improved signal processing accuracy: Full deskewing and signal compression of the echo signal effectively reduced noise and interference components in the signal, improving signal quality. The improved Keystone transform, by introducing adaptive coefficients and iterative optimization, effectively compensated for the phase error caused by the non-uniform motion of the target, thereby improving the peak-to-sidelobe ratio of the target in the range-Doppler spectrum, accurately correcting the range drift problem caused by the target motion, ensuring the accuracy of the signal in subsequent processing, and providing a reliable basis for subsequent target parameter estimation;

[0227] (2) Enhanced target detection capability: The echo signal is converted to the two-dimensional domain to generate a two-dimensional range-Doppler spectrum. This intuitive display method can clearly present the target's distance and velocity information, making it easier to identify real targets from complex backgrounds. This greatly improves the detection sensitivity of low-altitude UAVs and reduces the false alarm rate.

[0228] (3) Accurate parameter estimation is achieved: After identifying the real target from the two-dimensional spectrum, its position and velocity parameters can be accurately estimated. This accurate parameter estimation is crucial for tracking and identifying low-altitude UAVs, and helps to grasp the flight status and trajectory of UAVs in a timely manner, providing an accurate basis for subsequent countermeasures;

[0229] (4) Improved intuitiveness: By displaying the estimated position and velocity parameters, operators can intuitively understand the target situation without complex analysis and interpretation, enabling them to make quick decisions and improving the practicality and operational efficiency of the entire detection system. Example 3

[0230] To address the aforementioned technical problems, embodiments of the present invention also provide a computer device. Please refer to [link / reference needed]. Figure 5 , Figure 5 This is a basic structural block diagram of the computer device in this embodiment.

[0231] The aforementioned computer device 8 includes a memory 81, a processor 82, and a network interface 83 that are interconnected via a system bus. It should be noted that only the computer device 8 with components 81, 82, and 83 is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described herein is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0232] The aforementioned computer devices can be desktop computers, laptops, handheld computers, and cloud servers, among other computing devices. These devices can facilitate human-computer interaction with users through keyboards, mice, remote controls, touchpads, or voice-activated devices.

[0233] The aforementioned memory 81 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the aforementioned memory 81 may be an internal storage unit of the aforementioned computer device 8, such as the hard disk or memory of the computer device 8. In other embodiments, the aforementioned memory 81 may also be an external storage device of the aforementioned computer device 8, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 8. Of course, the aforementioned memory 81 may also include both the internal storage unit and its external storage device of the aforementioned computer device 8. In this embodiment, the aforementioned memory 81 is typically used to store the operating system and various application software installed on the aforementioned computer device 8, such as computer-readable instructions for low-altitude unmanned aerial vehicle detection methods. In addition, the aforementioned memory 81 can also be used to temporarily store various types of data that have been output or will be output.

[0234] In some embodiments, the processor 82 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 82 is typically used to control the overall operation of the computer device 8. In this embodiment, the processor 82 is used to execute computer-readable instructions stored in the memory 81 or to process data, for example, to execute computer-readable instructions for the low-altitude unmanned aerial vehicle (UAV) detection method described above.

[0235] The network interface 83 may include a wireless network interface or a wired network interface, which is typically used to establish a communication connection between the computer device 8 and other electronic devices.

[0236] The beneficial effects of implementing this embodiment are:

[0237] (1) Improved signal processing accuracy: Full deskewing and signal compression of the echo signal effectively reduced noise and interference components in the signal, improving signal quality. The improved Keystone transform, by introducing adaptive coefficients and iterative optimization, effectively compensated for the phase error caused by the non-uniform motion of the target, thereby improving the peak-to-sidelobe ratio of the target in the range-Doppler spectrum, accurately correcting the range drift problem caused by the target motion, ensuring the accuracy of the signal in subsequent processing, and providing a reliable basis for subsequent target parameter estimation;

[0238] (2) Enhanced target detection capability: The echo signal is converted to the two-dimensional domain to generate a two-dimensional range-Doppler spectrum. This intuitive display method can clearly present the target's distance and velocity information, making it easier to identify real targets from complex backgrounds. This greatly improves the detection sensitivity of low-altitude UAVs and reduces the false alarm rate.

[0239] (3) Accurate parameter estimation is achieved: After identifying the real target from the two-dimensional spectrum, its position and velocity parameters can be accurately estimated. This accurate parameter estimation is crucial for tracking and identifying low-altitude UAVs, and helps to grasp the flight status and trajectory of UAVs in a timely manner, providing an accurate basis for subsequent countermeasures;

[0240] (4) Improved intuitiveness of display: By displaying the estimated position and velocity parameters, operators can intuitively understand the target situation without complicated analysis and interpretation, and can make decisions quickly, which improves the practicality and operational efficiency of the entire detection system.

[0241] Example 4

[0242] The present invention also provides another embodiment, namely, providing a computer-readable storage medium storing computer-readable instructions that can be executed by at least one processor to cause the at least one processor to perform the steps of the low-altitude unmanned aerial vehicle detection method described above.

[0243] The beneficial effects of implementing this embodiment are:

[0244] (1) Improved signal processing accuracy: Full deskewing and signal compression of the echo signal effectively reduced noise and interference components in the signal, improving signal quality. The improved Keystone transform, by introducing adaptive coefficients and iterative optimization, effectively compensated for the phase error caused by the non-uniform motion of the target, thereby improving the peak-to-sidelobe ratio of the target in the range-Doppler spectrum, accurately correcting the range drift problem caused by the target motion, ensuring the accuracy of the signal in subsequent processing, and providing a reliable basis for subsequent target parameter estimation;

[0245] (2) Enhanced target detection capability: The echo signal is converted to the two-dimensional domain to generate a two-dimensional range-Doppler spectrum. This intuitive display method can clearly present the target's distance and velocity information, making it easier to identify real targets from complex backgrounds. This greatly improves the detection sensitivity of low-altitude UAVs and reduces the false alarm rate.

[0246] (3) Accurate parameter estimation is achieved: After identifying the real target from the two-dimensional spectrum, its position and velocity parameters can be accurately estimated. This accurate parameter estimation is crucial for tracking and identifying low-altitude UAVs, and helps to grasp the flight status and trajectory of UAVs in a timely manner, providing an accurate basis for subsequent countermeasures;

[0247] (4) Improved intuitiveness of display: By displaying the estimated position and velocity parameters, operators can intuitively understand the target situation without complicated analysis and interpretation, and can make decisions quickly, which improves the practicality and operational efficiency of the entire detection system.

[0248] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0249] Obviously, the embodiments described above are merely some embodiments of the present invention, not all embodiments. The accompanying drawings show preferred embodiments of the present invention, but do not limit the patent scope of the present invention. The present invention can be implemented in many different forms; rather, these embodiments are provided to provide a more thorough and complete understanding of the disclosure of the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the patent protection scope of this invention.

Claims

1. A method for detecting low-altitude unmanned aerial vehicles (UAVs), characterized in that, Includes the following steps: Initialize the radar system and receive echo signals; The received echo signal is subjected to full deskewing to achieve signal compression; An improved Keystone transform is applied to the echo signal after full deskewing processing. The improved Keystone transform adaptively compensates for distance movement caused by non-uniform target motion by dynamically adjusting the transform parameters. The echo signal after the improved Keystone transform is subjected to a range-Doppler two-dimensional Fourier transform, which transforms it into a two-dimensional domain that can intuitively display the target distance and velocity, thus completing the final estimation and detection of the target parameters; From the generated two-dimensional range-Doppler spectrum, the real target is identified and its position and velocity parameters are estimated; Display the estimated position and velocity parameters; The step of performing an improved Keystone transform on the echo signal after full deskewing, wherein the improved Keystone transform adaptively compensates for distance movement caused by non-uniform target motion by dynamically adjusting the transform parameters, specifically includes: By scaling, the distance traveled is linearly compensated on the slow time axis; Introduce a complex adaptive coefficient and set the initial parameters for the gradient descent iteration; An iterative algorithm is used to automatically find adaptive coefficients that enable the target energy to achieve optimal focusing in the range-Doppler domain.

2. The low-altitude unmanned aerial vehicle (UAV) detection method according to claim 1, characterized in that, The steps of initializing the radar system and receiving echo signals specifically include: Set the radar operating mode and parameters, generate and transmit detection signals; It receives the echo signal reflected from the target and converts it into an intermediate frequency or baseband signal; Converting continuous analog signals into discrete digital signals.

3. The low-altitude unmanned aerial vehicle (UAV) detection method according to claim 1, characterized in that, The step of performing full deskewing on the received echo signal to achieve signal compression specifically includes: A local copy, or local reference signal, synchronized with the transmitted signal is generated for mixing with the echo signal; The echo signal is mixed with the local reference signal to linearly convert the target time delay information into frequency information. High-frequency components and noise generated by mixing are filtered out, while useful difference frequency signals are retained, thus achieving bandwidth compression.

4. The low-altitude unmanned aerial vehicle (UAV) detection method according to claim 1, characterized in that, The step of converting the echo signal after the improved Keystone transform into a two-dimensional domain that can intuitively display the target distance and velocity, and completing the final estimation and detection of the target parameters, specifically includes: Analyze the frequency components of the signal within each pulse, transform the signal to the range domain, and form a one-dimensional range image; Analyze the phase change of the signal on the slow time axis at each range unit, transform the signal to the Doppler frequency domain, and extract the target velocity information; The distance and Doppler information are integrated into a two-dimensional matrix, and the coordinate mapping from frequency to the actual physical quantities of distance and velocity is completed.

5. The low-altitude unmanned aerial vehicle (UAV) detection method according to claim 1, characterized in that, The steps of identifying the real target from the generated two-dimensional range-Doppler spectrum and estimating its position and velocity parameters specifically include: In the presence of background noise and clutter, the true target point is detected with a constant false alarm probability; For each target point detected by CFAR, extract its distance and velocity values; The detected target information is organized and packaged into a standardized data format.

6. The low-altitude unmanned aerial vehicle (UAV) detection method according to any one of claims 1 to 5, characterized in that, The step of displaying the estimated position and velocity parameters specifically includes: The processing results are presented in an intuitive graphical format; Transmit the test results to the superior system or other related systems; Record key data and events throughout the entire system operation process. The key data includes: raw data, intermediate processing results, final target information, system status, and abnormal events.

7. A low-altitude unmanned aerial vehicle (UAV) detection device, characterized in that, include: The receiving module is used to initialize the radar system and receive echo signals. The processing module is used to perform full deskewing on the received echo signal to achieve signal compression; The correction module is used to perform an improved Keystone transform on the echo signal after full deskewing processing. The improved Keystone transform adaptively compensates for the distance movement caused by non-uniform target motion by dynamically adjusting the transformation parameters. The conversion module is used to perform a range-Doppler two-dimensional Fourier transform on the echo signal after the improved Keystone transform, and convert it into a two-dimensional domain that can intuitively display the target distance and velocity, so as to complete the final estimation and detection of the target parameters. The estimation module is used to identify the real target from the generated two-dimensional range-Doppler spectrum and estimate its position and velocity parameters; A display module is used to display the estimated position and velocity parameters; The conversion module is further used for: By scaling, the distance traveled is linearly compensated on the slow time axis; Introduce a complex adaptive coefficient and set the initial parameters for the gradient descent iteration; An iterative algorithm is used to automatically find adaptive coefficients that enable the target energy to achieve optimal focusing in the range-Doppler domain.

8. A computer device, characterized in that, It includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the low-altitude unmanned aerial vehicle detection method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the low-altitude unmanned aerial vehicle (UAV) detection method as described in any one of claims 1 to 6.

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