An anti-interference method and system based on laser FMCW radar

Abstract

This invention discloses an anti-jamming method and system based on laser FMCW radar. The method includes: converting the interference signal, echo signal, and local reference signal obtained by the anti-jamming system into a discrete signal with interference; constructing an overcomplete dictionary matrix based on the discrete signal with interference using fractional Fourier transform, and representing the discrete signal as a sparse linear model using the overcomplete dictionary matrix; reconstructing the sparse coefficient vector in the sparse linear model using an approximate message passing algorithm; calculating and reconstructing the interference signal based on the sparse coefficient vector; directly subtracting the reconstructed interference signal from the discrete signal with interference to obtain a pre-cleaned signal; and performing a second round of approximate message passing sparse reconstruction and elimination on the pre-cleaned signal to obtain the final cleaned signal. This application solves the problems of low accuracy in separating interference and targets, poor adaptability of interference reconstruction and suppression, and insufficient accuracy in target parameter estimation in existing technologies.

Description

Technical Field

[0001] This invention belongs to the field of radar anti-jamming, and particularly relates to an anti-jamming method and system based on laser FMCW radar. Background Technology

[0002] Frequency-modulated continuous wave (FMCW) laser radar employs a coherent optical detection mechanism, which significantly outperforms traditional incoherent solutions in suppressing external interference such as environmental noise and electromagnetic radiation. However, with the widespread adoption of FMCW laser radar in fields such as autonomous driving, security monitoring, and industrial inspection, the probability of mutual interference between radars will increase accordingly, becoming a key bottleneck restricting its application reliability. In extreme cases, when two FMCW laser radars with matched parameters operate at close range, signal misinterpretation is highly likely: the radar system may mistakenly identify the FMCW signal emitted by the other radar as the echo signal of its own target, thereby generating false target point clouds and severely affecting the accuracy of the detection results.

[0003] Existing anti-interference technologies are mainly divided into two categories: active anti-interference and passive anti-interference. Active anti-interference avoids interference through waveform design and parameter coordination, but it heavily relies on communication and coordination between devices, limiting its applicability in dense scenarios without cooperation. Passive anti-interference relies on signal processing to suppress interference. Traditional adaptive noise cancellation and time-domain / frequency-domain filtering methods heavily depend on prior characteristics such as the conjugate symmetry and instantaneous high amplitude of the interference signal. Furthermore, in complex scenarios with multiple superimposed interferences and overlapping interference and target time domains, the accuracy of interference and target separation drops sharply, resulting in poor adaptability of interference reconstruction, large target parameter estimation errors, and insufficient algorithm convergence stability, making it difficult to meet the anti-interference requirements in complex scenarios.

[0004] To address the problems of high hardware cost, limited adaptability, and poor adaptability of interference reconstruction in the above-mentioned anti-interference schemes, this application proposes a laser FMCW radar anti-interference method and system based on fractional Fourier transform and AMP sparse reconstruction. Summary of the Invention

[0005] The purpose of this invention is to solve the problems of low accuracy in separating interference from targets, poor adaptability in interference reconstruction and suppression, and insufficient accuracy in target parameter estimation in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: An anti-jamming system based on laser FMCW radar includes: an FMCW laser radar module, an FMCW laser radar jamming source module, a 3x1 fiber optic coupler, and an algorithm processing module; The FMCW lidar module is used to emit detection laser signals and receive echo signals, while generating a local reference signal. The FMCW lidar interference source module is used to generate interference signals with the same or similar carrier frequency as the FMCW lidar module, but with different sweep rates. A 3x1 fiber optic coupler is used to superimpose the echo signal, local reference signal, and interference signal to obtain a mixed optical signal; The algorithm processing module is used to convert the mixed optical signal into a beat frequency electrical signal with interference, and based on sparse representation and reconstruction algorithms, it separates and suppresses the interference components from the beat frequency electrical signal with interference, and outputs a clean signal.

[0007] Furthermore, the FMCW lidar transmitter module includes a linear frequency modulated continuous wave generation module, which includes a programmable bit sequence square wave generator. The output of the programmable bit sequence square wave generator is sequentially connected to a sawtooth wave signal generator and a frequency modulator. The output of the programmable bit sequence square wave generator is connected to an RZ pulse generator. Both the RZ pulse generator and the frequency modulator are connected to a multiplier. The output of the multiplier is connected to a 1×2 power divider. The output of the first continuous wave laser CW1 is sequentially connected to a Mach-Zehnder modulator, an optical filter, and a 1×2 fiber coupler. The 1×2 fiber coupler is connected to a first erbium-doped fiber amplifier and an optical circulator. The first erbium-doped fiber amplifier and the optical circulator are both connected to a 3×1 fiber coupler. The FMCW lidar interference source module includes a linear frequency modulated continuous wave (LFM) generation module, which comprises a programmable bit sequence square wave generator. The output of the programmable bit sequence square wave generator is sequentially connected to a sawtooth wave signal generator and a frequency modulator. The output of the programmable bit sequence square wave generator is connected to an RZ pulse generator. Both the output of the RZ pulse generator and the output of the frequency modulator are connected to a multiplier. The output of the multiplier is connected to the input of a 1×2 power divider. One output of the 1×2 power divider is connected to the second electrical input of a Mach-Zehnder modulator. The laser output of the second continuous wave laser (CW) is connected to the first optical input port of the Mach-Zehnder modulator. The laser output of the Mach-Zehnder modulator is sequentially connected to an optical filter and a second erbium-doped fiber amplifier. The output of the second erbium-doped fiber amplifier is connected to the third input of a 3x1 fiber coupler. The algorithm processing module includes a photodetector, a low-pass filter, and a digital signal processing chip; the output of the 3×1 fiber optic coupler is connected in sequence to the photodetector, the low-pass filter, and the digital signal processing chip.

[0008] An anti-jamming method based on laser FMCW radar includes the following steps: Step 1: Convert the interference signal, echo signal, and local reference signal into a discrete signal with interference. ; Step 2: Based on the discrete signal with interference An overcomplete dictionary matrix is ​​constructed through fractional Fourier transform, and the discrete signal is represented as a sparse linear model through the overcomplete dictionary matrix. Step 3: Reconstruct the sparse coefficient vector in the sparse linear model using an approximate message-passing algorithm; Step 4: Based on the sparse coefficient vector Calculate and reconstruct the interference signal The interference signal will be reconstructed. From discrete signals with interference Direct subtraction from the middle yields a pre-purified signal. ; Step 5: Transfer the pre-purified signal Repeat steps three and four to perform a second round of approximate message-passing sparse reconstruction and elimination to obtain the final purified signal. .

[0009] Furthermore, the specific method for obtaining the discrete signal with interference in step one is as follows: The expression for the local reference signal is: in The amplitude of the local reference signal. The starting carrier frequency, The sweep slope, For time, The imaginary unit; The target's echo signal and local reference signal obtained by the optical circulator are beat-frequency transmitted through a photodetector to obtain the target's intermediate frequency signal, which is the third... The expression for the intermediate frequency signal component of the target is: in , and The first The amplitude, Doppler shift, and path delay of the target echo; B represents bandwidth and W represents the sweep range. Indicates the amplitude of the local reference signal; The j-th interference signal generated by the FMCW lidar interference source module and the local reference signal are used to obtain the interference intermediate frequency signal by the beat frequency of the photodetector. The component expression of the interference intermediate frequency signal is: in For the amplitude of interference, ; This represents the accumulated frequency difference of the initial carrier after the interference signal and the local reference signal are beatd by a photodetector. Indicates the amplitude of the local reference signal. This represents the carrier frequency of the j-th interference. This represents the bandwidth of the j-th interference. This represents the duration of the interference corresponding to the j-th interference. This represents the Doppler frequency shift caused by the j-th interference; This represents the difference in frequency sweep slope between the interference signal and the local reference signal after being beat by the photodetector. This represents the sweep slope corresponding to the j-th interference; This represents the phase difference accumulated between the interference signal and the local reference signal after they have passed through the photodetector and been beat at the same frequency. The local reference signal, target intermediate frequency signal, and interfering intermediate frequency signal are input into a low-pass filter to obtain a beat frequency FMCW signal with interference. ;in This represents the sum of all target intermediate frequency signal components. This represents the sum of all interfering intermediate frequency signal components; the interfering beat frequency FMCW signal is sampled and discretized to obtain the interfering discrete signal. .

[0010] Furthermore, the method for constructing the overcomplete dictionary in step two is as follows: The corresponding Dirac impulse signal is constructed using fractional Fourier transform, and then subjected to the corresponding p-order discrete inverse fractional Fourier transform to obtain a time-domain complex linear frequency modulation sequence. After normalization, the time-domain complex linear frequency modulation sequence is used as a series of basis functions in the dictionary. By concatenating all basis functions column-wise, an overcomplete dictionary matrix of dimension N×L is obtained. ,in , The number of columns in the dictionary matrix. The number of sampling points represents the discrete signal. Let be the complex field matrix space of the dictionary matrix.

[0011] 6. The anti-interference method based on laser FMCW radar according to claim 5, characterized in that each column of the complete dictionary... Corresponding to a specific combination of parameters The potential interference component basis functions are in the form of discretized complex linear frequency-modulated sequences: in The normalization coefficient is... denoted by , where represents the Dirac function, and IDFRFT represents the inverse fractional Fourier transform.

[0012] Furthermore, in step two, an overcomplete dictionary matrix is ​​used. Discrete signals with interference In the p-order fractional Fourier domain, it is represented as a sparse linear model: ;in It is a sparse complex coefficient vector, where the positions of its non-zero elements indicate the basis function indices corresponding to the actual interfering components. For a composite term containing the target signal and system noise Furthermore, step three is detailed below: (1) Initialize the sparse coefficient vector Initialize the residual as a zero vector. Set the noise variance estimate to The maximum number of iterations is and convergence threshold ; Perform the linear estimation step: based on the sparse coefficient vector obtained in the t-th iteration. With residual Calculate intermediate variables ,in For an overcomplete dictionary matrix The conjugate transpose of; For intermediate variables Perform nonlinear denoising based on complex soft thresholding: for intermediate variables Extract its amplitude information through modulus extraction operation | |, minus the adaptive threshold And through positive part operation and extraction Phase information In complex exponential form Preserve the phase characteristics of the valid signal; recouple the processed amplitude information with the phase characteristics to output the updated sparse coefficient vector. Thus, the sparse coefficient vector is obtained. ; (2) Using the original discrete signal with interference Based on the previous iteration, the sparse coefficient vector after the current iteration is subtracted to obtain the initial signal residual. By introducing a gradient compensation term, the sum of the partial derivatives of the complex soft threshold function with respect to the intermediate variables is calculated, and then weighted by the ratio of the previous residual to the signal length to obtain the updated residual. (3) Check the convergence conditions or If satisfied, output the final reconstructed sparse coefficient vector. If not satisfied, then let Continue iterating; among them, Used to measure the convergence accuracy of iterative updates of sparse coefficient vectors. This represents the maximum number of iterations allowed for the algorithm to execute.

[0013] Furthermore, the updated sparse coefficient components are: in Indicates taking the positive part. To be related to the number of iterations and noise variance The relevant adaptive threshold; is a complex soft threshold function, and arg is the argument function of the complex number.

[0014] Furthermore, the updated residuals: in, Indicates the first The residual vector after the next iteration. A discrete signal vector with interference Corresponding signal, It is a sparse coefficient vector updated after complex soft thresholding. For the first The residual vector of the next iteration for length, To determine the number of atoms in a complete dictionary, For the first The i-th component of the intermediate variable in the next iteration. An adaptive threshold related to the number of iterations and noise variance. Represents the complex soft threshold function. Let be the partial derivative of the soft threshold function with respect to the intermediate variables. This is the gradient compensation term.

[0015] The beneficial effects of this invention are as follows: To address the problems of low accuracy in separating interference and targets, poor adaptability of interference reconstruction, inaccurate target parameter estimation, and insufficient algorithm convergence stability in existing technologies, this invention proposes a laser FMCW radar anti-jamming method and system based on fractional Fourier transform and AMP sparse reconstruction. It possesses the following significant advantages:

[0016] 1. Strong interference adaptability, no prior information required: Based on the energy focusing characteristics of linear frequency modulation interference components in the fractional Fourier domain, an overcomplete dictionary covering the entire parameter space is constructed. It can adaptively match linear frequency modulation interference components with different modulation frequencies and center frequencies without the need to know the prior parameters of the interference. It is suitable for scenarios with multiple interference superposition and no cooperation.

[0017] 2. High accuracy of interference-target separation: The approximate message passing algorithm is used for sparse reconstruction. Combined with complex soft threshold denoising and gradient compensation residual update mechanism, it can accurately separate time-frequency overlapping interference and target signals, effectively suppress noise and interference components, and significantly improve the signal-to-noise ratio of the target signal after interference filtering.

[0018] 3. Convergence is stable and efficient: Gradient compensation terms are introduced during the iteration of the approximate message passing algorithm to optimize the residual update direction and compensate for the nonlinear distortion caused by soft thresholding. Combined with the relative error convergence threshold and the maximum number of iterations constraint, the algorithm is guaranteed to converge quickly and stably, taking into account both anti-interference accuracy and real-time processing requirements.

[0019] 4. High applicability and no hardware cost: No modification to the radar hardware structure is required. Anti-interference function can be achieved through digital signal processing module only, and it is compatible with existing laser FMCW radar systems. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the anti-jamming system structure of the laser FMCW radar provided by the present invention; Figure 2 This is a flowchart of the anti-interference method for laser FMCW radar provided by the present invention; Figure 3 This is a diagram showing the 30m ranging results of the laser FMCW radar provided by this invention under laser FMCW interference. Figure 4 This is a diagram of the 30m ranging result after anti-interference processing using the algorithm provided in this application, as provided by the present invention.

[0021] Figure reference numerals: 1-First continuous wave laser (CW), 2-Mach-Zehnder modulator, 3-Optical filter, 4-1x2 fiber coupler, 5-First erbium-doped fiber amplifier, 6-Optical circulator, 7-3x1 fiber coupler, 8-Photodetector, 9-Low-pass filter, 10-Programmable bit sequence square wave generator, 11-Sawtooth wave signal generator, 12-Frequency modulator, 13-Programmable bit sequence square wave generator, 14-RZ pulse generator, 15-Multiplier, 16-1x2 power divider, 17-Second continuous wave laser (CW), 18-Mach-Zehnder modulator, 19-Optical filter, 20-Second erbium-doped fiber amplifier, 21-Programmable bit sequence square wave generator, 22-Sawtooth wave signal generator, 23-Frequency modulator, 24-Programmable bit sequence square wave generator, 25- RZ pulse generator, 26-multiplier, 27-1x2 power divider, 28-linear frequency modulated continuous wave generation module, 29-linear frequency modulated continuous wave generation module, 30-FMCW lidar module, 31-FMCW lidar interference source module, 32-digital signal processing chip, 33-algorithm processing module, 34-object to be detected. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0023] The application principle of the present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0024] Please refer to Figure 1 An anti-jamming system based on laser FMCW radar includes an FMCW laser radar module 30, an FMCW laser radar interference source module 31, an algorithm processing module 33, and a 3x1 fiber optic coupler 7.

[0025] The FMCW lidar module 30 includes a linear frequency modulated continuous wave generation module 28, a 1x2 power divider 16, a first continuous wave laser CW1, a Mach-Zehnder modulator 2, an optical filter 3, an optical fiber coupler 4, a first erbium-doped fiber amplifier 5, and an optical circulator 6.

[0026] The linear frequency modulated continuous wave generation module 28 includes a programmable bit sequence square wave generator 10, a sawtooth wave signal generator 11, a frequency modulator 12, a programmable bit sequence square wave generator 13, an RZ pulse generator 14, and a multiplier 15.

[0027] The output of the programmable bit sequence square wave generator 10 is sequentially connected to a sawtooth wave signal generator 11 and a frequency modulator 12. The sawtooth wave signal generator 11 generates a periodically stable sawtooth wave electrical signal with a duty cycle that meets the requirements. The sweep rate of this signal is the preset tuning rate of the radar. The sawtooth wave signal is output by the frequency modulator 12 as a frequency-modulated electrical signal that varies linearly with time. The output of the programmable bit sequence square wave generator 13 is connected to an RZ pulse generator 14 to generate a return-to-zero pulse signal with a narrow pulse width and a controllable duty cycle. Both the RZ pulse generator 14 and the frequency modulator 12 are connected to a multiplier 15. The frequency-modulated electrical signal and the return-to-zero pulse signal are combined in the multiplier 15 to obtain a composite electrical signal that combines frequency modulation and amplitude modulation. The output of the multiplier 15 is connected to a 1x2 power divider 16, which is connected to a Mach-Zehnder modulator 2. The composite electrical signal output by multiplier 15 is split into two paths by 1x2 distributor 16. The two signals are input to the corresponding ports of Mach-Zehnder modulator 2 for laser modulation.

[0028] The output of the first continuous wave laser CW1 is sequentially connected to a Mach-Zehnder modulator 2, an optical filter 3, and a 1x2 fiber coupler 4. The 1x2 fiber coupler 4 is connected to the first erbium-doped fiber amplifier 5 and an optical circulator 6. The first continuous-wave laser 1 continuously outputs a stable continuous carrier laser, which is input to the optical input port of the Mach-Zehnder modulator 2. The composite electrical signal transmitted from the 1x2 power divider 16 is input to the electrical input of the Mach-Zehnder modulator 2 to achieve dual modulation of the amplitude and frequency of the carrier laser, enabling the laser to carry linear frequency modulation information. The modulated laser signal is input to the optical filter 3, whose bandwidth is set to match the bandwidth of the linear frequency modulation signal, filtering out unwanted frequency components such as harmonics and spurious signals generated during the modulation process, resulting in a pure linear frequency modulation laser signal. The pure linear frequency modulation laser signal is input to the fiber coupler 4 with a splitting ratio of 9:1, where the signal power is distributed according to the 9:1 ratio. Nine of the linear frequency modulation laser signals are used for subsequent transmission and echo reception, and one of the linear frequency modulation laser signals is amplified and used as a local reference signal. The one of the linear frequency modulation signals is input to the first erbium-doped fiber amplifier 5, and the local reference signal output by the first erbium-doped fiber amplifier 5 is transmitted to the 3x1 fiber coupler 7.

[0029] The first erbium-doped fiber amplifier 5 is connected to the second input port of the 3x1 fiber coupler 7, and the optical circulator 6 is connected to the first input port of the 3x1 fiber coupler 7. The optical circulator 6 has unidirectional transmission characteristics, outputting a 9-way signal from the 1x2 fiber coupler 4, which can only be emitted towards the detection object 34 through the right port of the optical circulator 6. After the laser signal illuminates the detection object 34, it is reflected to form an echo signal, which is transmitted in reverse to the right port of the optical circulator 6. However, due to the unidirectional characteristics of the optical circulator 6, the echo signal can only be output from the lower output port of the optical circulator 6. The echo signal is transmitted from the lower output port of the optical circulator 6 to the first input port of the 3x1 fiber coupler 7. At the same time, the 1-way local reference signal output by the first erbium-doped fiber amplifier 5 is directly transmitted to the second input port of the 3x1 fiber coupler 7. The echo signal and the local reference signal are initially mixed here, forming the basis of the beat frequency signal.

[0030] The FMCW lidar interference source module 31 is used to generate an interfering FMCW signal with a carrier frequency similar to that of the interfering FMCW lidar module 30, but with a different sweep rate. It includes a linear frequency modulated continuous wave generation module 29, a 1x2 power divider 27, a second continuous wave laser CW17, a Mach-Zehnder modulator 18, an optical filter 19, and a second erbium-doped fiber amplifier 20.

[0031] The linear frequency modulated continuous wave generation module 29 includes a programmable bit sequence square wave generator 21, a sawtooth wave signal generator 22, a frequency modulator 23, a programmable bit sequence square wave generator 24, an RZ pulse generator 25, and a multiplier 26. The output of the programmable bit sequence square wave generator 21 is sequentially connected to the sawtooth wave signal generator 22 and the frequency modulator 23 to generate an interference frequency modulated signal. The sawtooth wave signal generator 22 generates a sawtooth wave signal with a specific sweep rate (this sweep rate is different from that of the sawtooth wave signal generator 11 in the FMCW lidar module 30). The output of the programmable bit sequence square wave generator 24 is connected to the RZ pulse generator 25 to generate a return-to-zero pulse signal for interference. The output of the RZ pulse generator 25 is connected to the first input port of the multiplier 26; the output of the frequency modulator 23 is connected to the second input port of the multiplier 26. The multiplier 26 performs a composite operation on the interference frequency modulated signal and the return-to-zero pulse to obtain an interference composite modulated signal.

[0032] The output of multiplier 26 is connected to the input of a 1x2 splitter 27, splitting the interference composite modulation signal into two paths, both of which are used for laser modulation. The laser output of the second continuous wave laser CW17 is connected to the input port of the Mach-Zehnder modulator 18, and its output carrier laser frequency is close to or exactly the same as the continuous carrier laser frequency of the first continuous wave laser CW1. The two signals of the 1x2 power splitter 27 are connected to the corresponding ports of the Mach-Zehnder modulator 18 to modulate the carrier laser and obtain an anti-interference signal. The laser output of the Mach-Zehnder modulator 18 is sequentially connected to an optical filter 19 and a second erbium-doped fiber amplifier 20. The optical filter 19 is used to filter out spurious frequencies during the modulation process of the interference signal. The second erbium-doped fiber amplifier 20 is used to amplify the power of the interference signal. The output of the second erbium-doped fiber amplifier 20 is connected to the third input of a 3x1 fiber coupler 7, so that the interference signal is combined with the echo signal and local signal of the lidar module. The 3x1 fiber optic coupler 7 is a multi-input port signal synthesizer. The echo signal from its first input port, the local reference signal from its second input port, and the interference signal from its third input port are superimposed inside the 3x1 fiber optic coupler 7 to obtain a mixed optical signal containing target echo information and interference information.

[0033] The algorithm processing module 33 includes a photodetector 8, a low-pass filter 9, and a digital signal processing chip 32. The output of the 3x1 fiber optic coupler 7 is sequentially connected to the photodetector 8, the low-pass filter 9, and the digital signal processing chip 32.

[0034] The mixed optical signal is input to photodetector 8, which converts the optical signal into a corresponding electrical signal. The frequency of this electrical signal is the frequency difference between the echo signal and the local reference signal (i.e., the beat frequency), and it is superimposed with the beat frequency of the interference signal and the local reference signal, thus it is a beat frequency electrical signal with interference. The beat frequency electrical signal is input to low-pass filter 9, whose cutoff frequency is set to the highest frequency of the matching beat frequency signal to filter out high-frequency noise and clutter in the signal. The beat frequency electrical signal output by low-pass filter 9 is the beat frequency FMCW signal with interference. This signal can be directly input to the subsequent digital signal processing chip 32 for sparse identification and reconstruction using an approximate message algorithm to separate the beat frequency components corresponding to the target echo and the interference. In this application, the carrier frequencies of the interference signal and the lidar transmitted signal are similar, but the sweep rates are different: the similar carrier frequencies ensure that the interference signal can be effectively mixed into the lidar received signal, simulating the actual interference scenario; the different sweep rates make the interference signal and the target echo signal exhibit different sparsity characteristics in the frequency domain, providing a prerequisite for the subsequent sparse identification and reconstruction of the AMP algorithm.

[0035] An anti-jamming method based on laser FMCW radar includes the following steps: according to Figure 2 As shown, this application converts the interfering beat frequency signal to the fractional Fourier domain. Since the interfering signal is a linear frequency modulated signal, it exhibits a sparse state with concentrated energy in the fractional Fourier domain. Based on this, a corresponding overcomplete dictionary matrix is ​​constructed, transforming the problem into an optimization problem. An approximate message-passing algorithm is then used to sparsely reconstruct the signal. The reconstructed interfering signal is then subtracted point by point from the original signal to obtain the cleaned signal. The specific implementation steps are as follows:

[0036] Step 1: The signal input to photodetector 8 simultaneously includes interference signal, local reference signal, and echo signal. The frequency of the local reference signal changes linearly with time, corresponding to the signal in the first erbium-doped fiber amplifier 5, and its expression is:

[0037] in, The amplitude of the local reference signal. The starting carrier frequency, The sweep slope, For time, It is the imaginary unit.

[0038] The signals received by the FMCW lidar module 30 include echoes from the target and signals from... The signal from the same-frequency band FMCW lidar interference source module 31; the target echo signal and local reference signal obtained by the optical circulator 6 are beat-frequency by the photodetector 8 to obtain the target intermediate frequency signal, which is the first The expression for the intermediate frequency signal component of the target is: in , and The first The amplitude, Doppler shift, and path delay of the target echo; B represents bandwidth, and W represents the sweep range. The above formula, which expresses the amplitude of the local reference signal, is in complex exponential form. In practice, the signal should be extracted by Hilbert transform to obtain the real part, which is the expression above.

[0039] Simultaneously, the j-th interference signal generated by the FMCW lidar interference source module 31 and the local reference signal are used to obtain the interference intermediate frequency signal by the photodetector 8 through beat frequency. The component expression of the interference intermediate frequency signal is: in For the amplitude of interference, This represents the accumulated frequency difference of the initial carrier after the interference signal and the local reference signal pass through photodetector 8 (beat frequency). Indicates the amplitude of the local reference signal, The carrier frequency of the j-th interference is represented by... This represents the bandwidth of the j-th interference. This represents the duration of the interference corresponding to the j-th interference. This represents the Doppler shift caused by the j-th interference. This represents the difference in frequency sweep slope between the interference signal and the local reference signal after passing through the photodetector for 8 beats. This represents the sweep slope corresponding to the j-th interference. This represents the phase difference accumulated between the interfering signal and the local reference signal after passing through the photodetector at 8 beats. Comparing the entire interfering signal with the local reference signal shows that the interfering signal is a linear frequency modulated continuous wave, which provides a theoretical basis for constructing a sparse representation of the interfering signal in the fractional Fourier domain.

[0040] The local reference signal, target intermediate frequency signal, and interfering intermediate frequency signal are input into low-pass filter 9 to obtain the beat frequency FMCW signal with interference. Interfering beat frequency FMCW signal For all interfering intermediate frequency signal components, target intermediate frequency signal components, and system noise The superposition, that is ;in This represents the sum of all target intermediate frequency signal components. This represents the sum of all interfering intermediate frequency (IF) signal components. The discrete signal with interference can be obtained by sampling and discretizing the interfering beat frequency (FMCW) signal. .

[0041] Step 2: Construct an overcomplete dictionary based on the analytical model of the interference signal, according to the discrete signal with interference from Step 1. Construct an overcomplete dictionary matrix using fractional Fourier transform. ,in , The number of columns in the dictionary matrix. The number of sampling points represents the discrete signal. Let be the complex field matrix space of the dictionary matrix.

[0042] Each column of the dictionary matrix Corresponding to a specific combination of parameters The potential interference component basis functions are in the form of discretized complex linear frequency-modulated sequences: in The normalization coefficient is... The Dirac function is represented by IDFRFT, and the discrete inverse fractional Fourier transform is represented by IDFRFT. Represents the time-frequency plane rotation order. For coordinate variables in the fractional Fourier domain, This represents the fractional energy peak corresponding to the coordinates. The process of constructing the dictionary matrix is ​​as follows:

[0043] Iterate through the time-frequency rotation angle corresponding to one rotation cycle, taking values ​​from 0 to 2 and the unit fractional domain energy peak. This represents all possible peak locations in the discrete grid. For any combination of parameters, the corresponding Dirac impulse signal is first constructed in the fractional Fourier transform, and then the corresponding p-order discrete inverse fractional Fourier transform is performed on it to obtain the time-domain complex linear frequency modulation sequence. After normalization, the time-domain complex linear frequency modulation sequence is used as a basis function of the dictionary. Finally, all basis functions are concatenated column-wise to obtain an overcomplete dictionary matrix of dimension N×L. .

[0044] From a physical point of view This dictionary characterizes a linear frequency modulated (LFM) signal with a specific modulation frequency and center frequency. Using this dictionary, the discrete signal with interference obtained in step one is transformed... In the p-order fractional Fourier domain, it is represented as a sparse linear model: ;in It is a sparse coefficient, and the positions of its non-zero elements indicate the basis function indices corresponding to the actual interfering components. This is a composite term containing the target intermediate frequency signal and system noise; in interference suppression scenarios, the target intermediate frequency signal has a non-sparseable characteristic relative to the overcomplete dictionary, therefore it is classified as... middle;

[0045] Step 3: Use an approximate message-passing algorithm to sparsely reconstruct the interference coefficient vector from the interfered signal. The specific steps are as follows: (1) The sparse linear model established in step two Viewed as a compressed sensing reconstruction problem, where the problem to be solved is... It is sparse; initialize the sparse coefficient vector. Initialize the residual as a zero vector. Set the noise variance estimate to The maximum number of iterations is and convergence threshold ; in the In this iteration, the following sub-steps are executed: First, perform the linear estimation step: based on the sparse coefficient vector obtained in the t-th iteration. With residual Calculate intermediate variables ,in For an overcomplete dictionary matrix The conjugate transpose of . Then, for the intermediate variable Perform nonlinear denoising based on complex soft thresholding: for each intermediate variable First, extract its amplitude information through a modulus extraction operation. |, subtract the adaptive threshold related to the current iteration number and noise variance And by taking the positive part and discarding components with amplitudes below the threshold to suppress noise; at the same time, extracting Phase information In complex exponential form The phase characteristics of the valid signal are preserved; finally, the processed amplitude is recoupled with the preserved phase to output the updated sparse coefficient vector. Thus, the sparse coefficient vector is obtained. The formula for calculating the sparse coefficient vector is as follows:

[0046] To be related to the number of iterations and noise variance The relevant adaptive threshold; This is a complex soft-threshold function, where arg is the complex argument function. That is, it only shrinks the amplitude, preserving the phase information of the function.

[0047] (2) During the iterative process of the approximate message passing algorithm, the residual is dynamically updated based on the result of the previous iteration to provide an accurate error reference for the next round of interference sparse reconstruction. The core is to integrate signal residual correction and soft threshold function gradient compensation to improve convergence stability. First, the original discrete signal with interference is used. Based on the baseline, subtracting the sparse coefficient vector updated in the current iteration yields the preliminary signal residual, reflecting the deviation between the current interference reconstruction result and the actual interference. By introducing a gradient compensation term, the sum of the partial derivatives of the complex soft thresholding function with respect to intermediate variables is calculated, and then weighted according to the ratio of the previous residual to the signal length to compensate for the nonlinear distortion caused by soft thresholding and avoid deviations in residual updates. Finally, the updated residual is obtained by superimposing the two terms. This residual retains information about the unreconstructed interference components and optimizes the iteration direction through gradient compensation, helping the algorithm to quickly converge to the accurate interference sparse coefficient vector solution. The updated residual is:

[0048] in, Indicates the first The residual vector after the next iteration. The original disturbed discrete signal vector corresponds to the disturbed discrete signal vector in step one. Corresponding signal, It is a sparse coefficient vector updated after complex soft thresholding. For the first The residual vector of the next iteration for length, To find the number of atoms in a complete dictionary, For the first The i-th component of the intermediate variable in the next iteration. An adaptive threshold related to the number of iterations and noise variance. Represents the complex soft threshold function. This is the partial derivative of the soft threshold function with respect to intermediate variables, used to compensate for distortions caused by nonlinear processing and to ensure the stability and accuracy of iterative convergence. This is the gradient compensation term.

[0049] (3) Finally, check the convergence conditions. or If satisfied, output the final reconstructed sparse coefficient vector. If not satisfied, then let Continue iterating; among them, Used to measure the convergence accuracy of iterative updates of sparse coefficient vectors. This represents the maximum number of iterations allowed for the algorithm to execute.

[0050] Step 4: Separate and suppress the reconstructed interference components from the original signal, using the sparse coefficient vector output in Step 3. Calculate the reconstructed interference signal The interference signal will be reconstructed. The original discrete signal with interference obtained in step one Direct subtraction from the middle yields a pre-purified signal. This subtraction operation is performed point-by-point in the discrete time domain, aiming to remove the main interference energy that has been identified and reconstructed. Step 5: Iteratively refine and extract target information from the interference-suppressed signal. Since the subtraction in Step 4 may leave trace amounts of interference or introduce boundary effects, the preliminarily purified signal... As input, repeat steps three and four to perform a second round of approximate message-passing sparse reconstruction and subtraction to obtain the final purified signal. After processing by this method, the original discrete signal with interference is... Zhongyou The resulting noise floor enhancement and false targets are eliminated, allowing weak targets to be revealed and ensuring the accuracy of distance, velocity, and angle estimation parameters.

[0051] Through the complete process of steps one through five, the laser FMCW radar interference component AMP sparse identification and reconstruction method proposed in this invention has formed a closed loop: by accurately modeling the temporal characteristics of the target and interference signals, an overcomplete dictionary adapted to the sparse characteristics of interference is constructed. The AMP algorithm (Approximate Message Passing Sparse Reconstruction) is used to achieve efficient reconstruction and separation of interference components. Signal purification and target parameter extraction are then completed through iterative refinement. Theoretically, this method can effectively solve problems such as noise floor elevation, false target generation, and weak target occlusion caused by interference in the same frequency band. For details, please refer to... Figure 3 and Figure 4 .

[0052] Figure 3 This image demonstrates the ranging output of a laser FMCW radar without anti-jamming processing, under conditions of a real target at 400MHz and interference from a source in the same frequency band. The image shows a significant increase in the noise floor. Due to the interference signal beating the local reference signal, generating numerous spurious frequency components, the background noise level across the entire range spectrum is far higher than normal. Simultaneously, the true target peak is obscured and difficult to distinguish; the sharp echo peak that should appear at 400MHz is covered by the floor noise caused by the interference, making it impossible to reliably extract the target range.

[0053] Figure 4 The results demonstrate ranging in the same scene after interference resistance via fractional Fourier transform combined with an approximate message-passing algorithm and sparse reconstruction. (Comparison) Figure 3As can be seen, the noise floor is effectively reduced, and after the interference signal is identified and subtracted, the background noise recovers to the theoretical thermal noise level. Simultaneously, the true target peak is clearly prominent, exhibiting a sharp, high signal-to-noise ratio peak at 400MHz, facilitating detection and distance estimation. This demonstrates that the algorithm proposed in this patent requires no prior information and has strong adaptability to interference. Furthermore, it can be seen that the algorithm, by combining an approximate message-passing algorithm to accurately reconstruct the sparse coefficient vector, achieves high-precision separation of interference and target, significantly improving the signal-to-noise ratio of the processed signal.

[0054] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An anti-jamming system based on laser FMCW radar, characterized in that, include: FMCW lidar module, FMCW lidar interference source module, 3x1 fiber coupler and algorithm processing module; The FMCW lidar module is used to emit detection laser signals and receive echo signals, while generating a local reference signal. The FMCW lidar interference source module is used to generate interference signals with the same or similar carrier frequency as the FMCW lidar module, but with different sweep rates. A 3x1 fiber optic coupler is used to superimpose the echo signal, local reference signal, and interference signal to obtain a mixed optical signal; The algorithm processing module is used to convert the mixed optical signal into a beat frequency electrical signal with interference, and based on sparse representation and reconstruction algorithms, it separates and suppresses the interference components from the beat frequency electrical signal with interference, and outputs a clean signal.

2. The anti-interference system based on laser FMCW radar according to claim 1, characterized in that, The FMCW lidar transmitter module includes a linear frequency modulated continuous wave (LFM) generation module, which includes a programmable bit sequence square wave generator. The output of the LFM generation module is sequentially connected to a sawtooth wave signal generator and a frequency modulator. The output of the LFM generation module is connected to an RZ pulse generator, and both the RZ pulse generator and the frequency modulator are connected to a multiplier. The output of the multiplier is connected to a 1×2 power divider. The output of the first continuous wave laser CW1 is sequentially connected to a Mach-Zehnder modulator, an optical filter, and a 1×2 fiber coupler. The 1×2 fiber coupler is connected to a first erbium-doped fiber amplifier and an optical circulator. The first erbium-doped fiber amplifier and the optical circulator are both connected to a 3×1 fiber coupler. The FMCW lidar interference source module includes a linear frequency modulated continuous wave (LFM) generation module, which comprises a programmable bit sequence square wave generator. The output of the programmable bit sequence square wave generator is sequentially connected to a sawtooth wave signal generator and a frequency modulator. The output of the programmable bit sequence square wave generator is connected to an RZ pulse generator. Both the output of the RZ pulse generator and the output of the frequency modulator are connected to a multiplier. The output of the multiplier is connected to the input of a 1×2 power divider. One output of the 1×2 power divider is connected to the second electrical input of a Mach-Zehnder modulator. The laser output of the second continuous wave laser (CW) is connected to the first optical input port of the Mach-Zehnder modulator. The laser output of the Mach-Zehnder modulator is sequentially connected to an optical filter and a second erbium-doped fiber amplifier. The output of the second erbium-doped fiber amplifier is connected to the third input of a 3x1 fiber coupler. The algorithm processing module includes a photodetector, a low-pass filter, and a digital signal processing chip; the output of the 3×1 fiber optic coupler is connected in sequence to the photodetector, the low-pass filter, and the digital signal processing chip.

3. An anti-interference method based on laser FMCW radar, characterized in that, Includes the following steps: Step 1: Convert the interference signal, echo signal, and local reference signal into a discrete signal with interference. ; Step 2: Based on the discrete signal with interference An overcomplete dictionary matrix is ​​constructed through fractional Fourier transform, and the discrete signal is represented as a sparse linear model through the overcomplete dictionary matrix. Step 3: Reconstruct the sparse coefficient vector in the sparse linear model using an approximate message-passing algorithm; Step 4: Based on the sparse coefficient vector Calculate and reconstruct the interference signal The interference signal will be reconstructed. From discrete signals with interference Direct subtraction from the middle yields a pre-purified signal. ; Step 5: Transfer the pre-purified signal Repeat steps three and four to perform a second round of approximate message-passing sparse reconstruction and elimination to obtain the final purified signal. .

4. The anti-interference method based on laser FMCW radar according to claim 3, characterized in that, The specific method for obtaining the discrete signal with interference in step one is as follows: The expression for the local reference signal is: in The amplitude of the local reference signal. The starting carrier frequency, The sweep slope, For time, The imaginary unit; The target's echo signal and local reference signal obtained by the optical circulator are beat-frequency transmitted through a photodetector to obtain the target's intermediate frequency signal, which is the third... The expression for each target intermediate frequency signal component is: in , and The first The amplitude, Doppler shift, and path delay of the target echo; B represents bandwidth, and W represents the sweep range. Indicates the amplitude of the local reference signal; The j-th interference signal generated by the FMCW lidar interference source module and the local reference signal are used to obtain the interference intermediate frequency signal by the beat frequency of the photodetector. The component expression of the interference intermediate frequency signal is: in For the amplitude of interference, ; This represents the accumulated frequency difference of the initial carrier after the interference signal and the local reference signal are beatd by a photodetector. Indicates the amplitude of the local reference signal. This represents the carrier frequency of the j-th interference. This represents the bandwidth of the j-th interference. This represents the duration of the interference corresponding to the j-th interference. This represents the Doppler shift caused by the j-th interference; This represents the difference in frequency sweep slope between the interference signal and the local reference signal after being beat by the photodetector. This represents the sweep slope corresponding to the j-th interference; This represents the phase difference accumulated between the interference signal and the local reference signal after they have passed through the photodetector and been beat at the same frequency. The local reference signal, target intermediate frequency signal, and interfering intermediate frequency signal are input into a low-pass filter to obtain a beat frequency FMCW signal with interference. ;in This represents the sum of all target intermediate frequency signal components. This represents the sum of all interfering intermediate frequency signal components; the interfering beat frequency FMCW signal is sampled and discretized to obtain the interfering discrete signal. .

5. The anti-interference method based on laser FMCW radar according to claim 4, characterized in that, The method for constructing an overcomplete dictionary in step two is as follows: The corresponding Dirac impulse signal is constructed using fractional Fourier transform, and then subjected to the corresponding p-order discrete inverse fractional Fourier transform to obtain a time-domain complex linear frequency modulation sequence. After normalization, the time-domain complex linear frequency modulation sequence is used as a series of basis functions in the dictionary. By concatenating all basis functions column-wise, an overcomplete dictionary matrix of dimension N×L is obtained. ,in , The number of columns in the dictionary matrix. The number of sampling points represents the discrete signal. Let be the complex field matrix space of the dictionary matrix.

6. The anti-interference method based on laser FMCW radar according to claim 5, characterized in that, Go through each column of a complete dictionary Corresponding to a specific combination of parameters The potential interference component basis functions are in the form of discretized complex linear frequency-modulated sequences: in The normalization coefficient is... denoted by , where represents the Dirac function and IDFRFT represents the inverse fractional Fourier transform.

7. The anti-interference method based on laser FMCW radar according to claim 4, characterized in that, In step two, an overcomplete dictionary matrix is ​​used. Discrete signals with interference In the p-order fractional Fourier domain, it is represented as a sparse linear model: ;in It is a sparse complex coefficient vector, where the positions of its non-zero elements indicate the basis function indices corresponding to the actual interfering components. It is a composite term that includes the target signal and system noise.

8. The anti-interference method based on laser FMCW radar according to claim 4, characterized in that, Step three is as follows: (1) Initialize the sparse coefficient vector Initialize the residual as a zero vector. ; Set the noise variance estimate as The maximum number of iterations is and convergence threshold ; Perform the linear estimation step: based on the sparse coefficient vector obtained in the t-th iteration. With residual Calculate intermediate variables ,in For an overcomplete dictionary matrix The conjugate transpose of; For intermediate variables Perform nonlinear denoising based on complex soft thresholding: for intermediate variables Extract its amplitude information through modulus extraction operation | |, minus the adaptive threshold And through positive part operation and extraction Phase information In complex exponential form Preserve the phase characteristics of the valid signal; The processed amplitude information is recoupled with the phase features, and the updated sparse coefficient vector is output. Thus, the sparse coefficient vector is obtained. ; (2) Using the original discrete signal with interference Based on the current iteration, subtract the sparse coefficient vector updated in the current iteration to obtain the preliminary signal residual; By introducing a gradient compensation term, the sum of the partial derivatives of the complex soft threshold function with respect to the intermediate variables is calculated, and then weighted by the ratio of the previous residual to the signal length to obtain the updated residual. (3) Check the convergence conditions or ; If satisfied, output the final reconstructed sparse coefficient vector. If not satisfied, then let Continue iterating; among them, Used to measure the convergence accuracy of iterative updates of sparse coefficient vectors. This represents the maximum number of iterations allowed for the algorithm to execute.

9. The anti-interference method based on laser FMCW radar according to claim 8, characterized in that, The updated sparse coefficient components are: in Indicates taking the positive part. To be related to the number of iterations and noise variance The relevant adaptive threshold; is a complex soft threshold function, and arg is the argument function of the complex number.

10. The anti-interference method based on laser FMCW radar according to claim 8, characterized in that, Updated residuals: in, Indicates the first The residual vector after the next iteration. A discrete signal vector with interference Corresponding signal, It is a sparse coefficient vector updated after complex soft thresholding. For the first The residual vector of the next iteration for length, To determine the number of atoms in a complete dictionary, For the first The i-th component of the intermediate variable in the next iteration. An adaptive threshold related to the number of iterations and noise variance. Represents the complex soft threshold function. Let be the partial derivative of the soft threshold function with respect to the intermediate variables. This is the gradient compensation term.

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