Chirp signal adaptive equalization system based on progressive filter

Abstract

The invention relates to the technical field of progressive filters, in particular to a progressive filter-based Chirp signal adaptive equalization system, which comprises a progressive filter-based Chirp signal adaptive equalization system operation process, and the progressive filter-based Chirp signal adaptive equalization system operation process comprises the following steps: S1, obtaining rainfall R and visibility V data, according to the acquired rainfall R and visibility V data, calculating a comprehensive attenuation coefficient a of the attenuation degree of the radar signal caused by the rainfall R and the visibility V in the propagation process of different environments, and performing amplitude pre-compensation on the received signal; according to the invention, the system calculates the comprehensive attenuation coefficient through obtaining the meteorological data, carries out the amplitude pre-compensation, reduces the signal attenuation, dynamically optimizes the learning rate and the filter order through combining with the system performance improvement index, and improves the target detection precision and reliability of the radar under the complex meteorological condition.

Description

Technical Field

[0001] The present invention relates to the technical field of progressive filters, and in particular to a Chirp signal adaptive equalization system based on progressive filters. Background Art

[0002] The Chirp signal adaptive equalization system based on progressive filter is a system used to improve the transmission quality of Chirp signal. Chirp signal is a signal whose frequency varies with time and whose spectrum has unique characteristics. The system gradually filters the signal using a progressive filter. During signal transmission, the adaptive equalization mechanism adjusts the equalization parameters in real time based on channel characteristics to compensate for channel distortion and interference, effectively improving signal transmission quality and ensuring that chirp signals can be accurately received and demodulated even in complex channel environments. This system is widely used in fields requiring precise signal processing, such as communications and radar. Chirp signal adaptive equalization system based on progressive filter is applied in radar signal processing. There is a problem that weather changes (such as rain and dense fog) will affect the propagation of radar signals and change the amplitude, phase and frequency characteristics of the signals. Therefore, a Chirp signal adaptive equalization system based on progressive filter is proposed to address the above problem. Summary of the Invention

[0003] The purpose of the present invention is to provide a Chirp signal adaptive equalization system based on a progressive filter to solve the problem that weather changes (such as rainfall and dense fog) in the application of the Chirp signal adaptive equalization system based on a progressive filter in the radar signal processing process will affect the propagation of the radar signal and change the amplitude, phase and frequency characteristics of the signal.

[0004] To achieve the above object, the present invention provides the following technical solutions: A Chirp signal adaptive equalization system based on a progressive filter includes an operation process of the Chirp signal adaptive equalization system based on a progressive filter, wherein the operation process of the Chirp signal adaptive equalization system based on a progressive filter includes the following steps: S1: Obtain rainfall R and visibility V data, and calculate the comprehensive attenuation coefficient a of the radar signal attenuation caused by rainfall R and visibility V factors during the propagation process in different environments based on the obtained rainfall R and visibility V data, and perform amplitude pre-compensation on the received signal; S2: Get the Chirp signal after pre-compensation , center frequency And signal bandwidth B data, and the obtained Chirp signal Perform Fourier transform to get , and then by getting Calculate power spectral density ; Get the pre-compensated Chirp signal The signal power in and noise power , by obtaining the signal power and noise power Calculate the signal-to-noise ratio SNR; S3: Define the desired signal power spectrum density according to specific application requirements and the power spectral density calculated in S2 Compare and obtain the expected signal power spectrum density The power spectral density calculated in S2 error ; According to the power spectral density error of the i-th sampling point , learning rate And the input signal delayed by i sampling points , get the update amount of the equalizer coefficient , and based on the old coefficients of the equalizer and update amount , calculate the new equalizer coefficients , the new equalizer coefficients The calculation formula is: , And according to the new equalizer coefficient Perform progressive filtering to obtain the output signal of the filter ; S4: Calculate the signal-to-noise ratio of the output signal and bit error rate ; Dynamically adjust system parameters according to system performance improvement index P: , Where, and is the signal-to-noise ratio and bit error rate of the input signal, where the bit error rate is obtained by comparing the output bit sequence with the reference sequence.

[0005] As a further optimized content of the present invention, in S1, the calculation formula of the comprehensive attenuation coefficient a is: , Where, and is an empirical coefficient related to radar frequency, and is a coefficient related to the droplet size distribution.

[0006] As a further optimized content of the present invention, in S1, the calculation formula for performing amplitude pre-compensation on the received signal is: , Where, is the received Chirp signal, d is the distance the signal propagates, is the pre-compensated signal.

[0007] As a further optimization of the present invention, in which: in S2, the power spectrum density The calculation formulas for the signal-to-noise ratio (SNR) are: , .

[0008] As a further optimization of the present invention, in which: in S3, the update amount and the filter output signal The calculation formulas are: , , Where, is the output signal of the equalizer, are the coefficients of the filter, is the order of the filter.

[0009] As a further optimization of the present invention, the evaluation of the system performance improvement index P in S4 is not only based on the current signal-to-noise ratio and bit error rate, but also compared and analyzed with past historical data to determine the changing trend of system performance and make forward-looking adjustments to system parameters accordingly.

[0010] As a further optimized content of the present invention, it includes: a meteorological parameter acquisition module for acquiring rainfall R and visibility V data; an attenuation coefficient calculation module, configured to calculate a comprehensive attenuation coefficient a according to the rainfall R and visibility V, and perform amplitude pre-compensation on the received signal; Signal analysis module for obtaining pre-compensated Chirp signals , center frequency and bandwidth, and Perform Fourier transform to obtain the frequency domain signal, and then calculate the input power spectrum density, estimate the signal power and noise power, and thus obtain the input signal-to-noise ratio; An expected spectrum setting module is used to set the expected power spectrum density according to specific application requirements; An error calculation module is used to calculate the error between the expected power spectrum density and the actual power spectrum density; Equalizer and coefficient update module; A performance evaluation module is used to recalculate the output signal-to-noise ratio and output bit error rate of the equalizer output signal, where the bit error rate is obtained by comparing the output bit sequence with the reference bit sequence; Dynamic parameter adjustment module, used to dynamically adjust the learning rate and / or filter order based on the comparison results of the system performance improvement index and the preset threshold: When the performance improvement index is higher than the threshold, increase the learning rate or increase the filter order; When the performance improvement index is lower than or equal to the threshold, reduce the learning rate or lower the filter order; The hardware implementation module is used to execute the functions of the above modules on DSP or FPGA and work in conjunction with the radar front-end receiver and decision maker.

[0011] As a further optimized content of the present invention, wherein: the equalizer and coefficient update module: Contains a set of FIR filters for weighted delay superposition of input signals; The coefficient update unit calculates the increment based on the power spectrum error, learning rate and delayed input signal, and accumulates it to the filter coefficient; The equalizer coefficients are initialized to zero vectors or preset values; When the convergence condition is met or the number of iterations reaches a predetermined upper limit, the adaptive update is terminated.

[0012] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention can reduce the impact of rainfall and dense fog on radar signal propagation. The system calculates the comprehensive attenuation coefficient by obtaining meteorological data and performs amplitude pre-compensation to reduce signal attenuation. At the same time, it uses Fourier transform and power spectral density analysis to improve the accuracy of signal-to-noise ratio calculation, dynamically adjusts the equalizer coefficient to achieve progressive filtering, and enhances the detectability of target echo signals. In combination with system performance improvement indicators, the learning rate and filter order are dynamically optimized to improve the radar's target detection accuracy and reliability under complex meteorological conditions, providing a guarantee for the radar's stable operation. 2. In the present invention, the system effectively reduces the attenuation effect of weather factors on the chirp signal amplitude by accurately calculating the comprehensive attenuation coefficient and amplitude pre-compensation. Combined with power spectrum density analysis and signal-to-noise ratio estimation, the accuracy of signal feature extraction is improved. The dynamic coefficient update mechanism of the progressive filter can suppress noise interference in real time, enhance the detectability of target echoes, and ensure that the radar maintains high-precision detection performance under changeable weather conditions. 3. In the present invention, the various modules of the system work together to form a complete signal processing chain from meteorological parameter acquisition to equalizer coefficient optimization. The learning rate and filter order are dynamically adjusted through performance improvement indicators to achieve adaptive optimization and reduce the negative impact of complex meteorological environments on signal processing performance. The efficient integration of hardware modules further ensures the real-time and stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 Flowchart of the operation of the Chirp signal adaptive equalization system based on the progressive filter of the present invention; Figure 2 This is a system block diagram of the Chirp signal adaptive equalization system based on the progressive filter of the present invention. DETAILED DESCRIPTION

[0014] See also Figure 1-2 The present invention provides a technical solution: a Chirp signal adaptive equalization system based on a progressive filter, including an operation process of the Chirp signal adaptive equalization system based on a progressive filter, and the operation process of the Chirp signal adaptive equalization system based on a progressive filter includes the following steps: S1: Obtain rainfall R and visibility V data, and calculate the comprehensive attenuation coefficient a of the radar signal attenuation caused by rainfall R and visibility V factors during the propagation process in different environments based on the obtained rainfall R and visibility V data, and perform amplitude pre-compensation on the received signal; S2: Get the Chirp signal after pre-compensation , center frequency And signal bandwidth B data, and the obtained Chirp signal Perform Fourier transform to get , and then by getting Calculate power spectral density ; Get the pre-compensated Chirp signal The signal power in and noise power , by obtaining the signal power and noise power Calculate the signal-to-noise ratio SNR; S3: Define the desired signal power spectrum density according to specific application requirements and the power spectral density calculated in S2 Compare and obtain the expected signal power spectrum density The power spectral density calculated in S2 error ; According to the power spectral density error of the i-th sampling point , learning rate And the input signal delayed by i sampling points , get the update amount of the equalizer coefficient , and based on the old coefficients of the equalizer and update amount , calculate the new equalizer coefficients , the new equalizer coefficients The calculation formula is: , And according to the new equalizer coefficient Perform progressive filtering to obtain the output signal of the filter ; S4: Calculate the signal-to-noise ratio of the output signal and bit error rate ; Dynamically adjust system parameters according to system performance improvement index P: , Where, and It is the signal-to-noise ratio and bit error rate of the input signal, where the bit error rate is obtained by comparing the output bit sequence with the reference sequence. Through multi-environmental parameter perception and real-time pre-compensation mechanism, the signal fidelity under complex meteorological conditions is significantly improved. The progressive iterative update strategy is adopted to avoid overshoot while ensuring the convergence speed. Dynamic performance monitoring and parameter adjustment form a closed-loop control to ensure that the system always operates at the optimal working point.

[0015] As a technical solution for further implementation of this solution, in S1, the calculation formula of the comprehensive attenuation coefficient a is: , Where, and is an empirical coefficient related to radar frequency, and It is a coefficient related to the droplet size distribution. The dual-coefficient joint modeling effectively takes into account the characteristics of different frequency bands and the droplet size distribution characteristics. The empirical coefficient is combined with the theoretical model to achieve the best balance between calculation accuracy and real-time performance. The parameterized expression facilitates on-site calibration and model optimization. As a technical solution for further implementing this solution, in S1, the calculation formula for amplitude pre-compensation of the received signal is: , Where, is the received Chirp signal, d is the distance the signal propagates, It is a pre-compensated signal that accurately matches the electromagnetic wave attenuation law. Distance-related compensation effectively eliminates the impact of propagation path loss on subsequent processing. The pre-stage compensation mechanism reduces the complexity of subsequent equalizer design. As a technical solution for further implementation of this solution, in S2, the power spectrum density The calculation formulas for the signal-to-noise ratio (SNR) are: , , Frequency domain power spectrum analysis accurately reveals channel distortion characteristics, the segmented integration method calculates SNR effectively distinguishes the signal main lobe from the noise floor, and two-dimensional feature extraction provides a reliable basis for equalizer design; As a technical solution for further implementation of this solution, in S3, the update amount and the filter output signal The calculation formulas are: , , Where, is the output signal of the equalizer, are the coefficients of the filter, The delay-related update calculation for the filter order ensures the correctness of the filter convergence direction. The sliding window convolution structure fully utilizes the hardware parallel computing resources, and the incremental update mechanism avoids the risk of loss of lock caused by coefficient matrix mutation. As a technical solution to further implement this solution, in S4, the system performance improvement index P is evaluated not only based on the current signal-to-noise ratio and bit error rate, but also combined with historical data for comparative analysis to determine the changing trend of system performance and make proactive adjustments to system parameters accordingly. Time domain trend analysis enhances the system's ability to predict sudden channel changes, the historical data weighting strategy improves the robustness of parameter adjustment decisions, and the proactive adjustment mechanism effectively prevents performance degradation. As a technical solution for further implementation of this plan, it includes a meteorological parameter acquisition module for obtaining rainfall R and visibility V data; An attenuation coefficient calculation module is used to calculate the comprehensive attenuation coefficient a based on the rainfall R and visibility V, and perform amplitude pre-compensation on the received signal; Signal analysis module for obtaining pre-compensated Chirp signals , center frequency and bandwidth, and Perform Fourier transform to obtain the frequency domain signal, and then calculate the input power spectrum density, estimate the signal power and noise power, and thus obtain the input signal-to-noise ratio; An expected spectrum setting module is used to set the expected power spectrum density according to specific application requirements; An error calculation module is used to calculate the error between the expected power spectrum density and the actual power spectrum density; Equalizer and coefficient update module; A performance evaluation module is used to recalculate the output signal-to-noise ratio and output bit error rate of the equalizer output signal, where the bit error rate is obtained by comparing the output bit sequence with the reference bit sequence; Dynamic parameter adjustment module, used to dynamically adjust the learning rate and / or filter order based on the comparison results of the system performance improvement index and the preset threshold: When the performance improvement index is higher than the threshold, increase the learning rate or increase the filter order; When the performance improvement index is lower than or equal to the threshold, reduce the learning rate or lower the filter order; The hardware implementation module is used to execute the functions of the above modules on a DSP or FPGA and work in conjunction with the radar front-end receiver and decision maker. The modular architecture supports distributed deployment and independent upgrades. The dynamic parameter adjustment unit enables on-demand allocation of computing resources. The hardware collaborative design ensures microsecond-level real-time response capabilities. As a technical solution for further implementation of this solution, the equalizer and coefficient update module: Contains a set of FIR filters for weighted delay superposition of input signals; The coefficient update unit calculates the increment based on the power spectrum error, learning rate and delayed input signal, and accumulates it to the filter coefficient; The equalizer coefficients are initialized to zero vectors or preset values; When the convergence condition is met or the number of iterations reaches a predetermined upper limit, the adaptive update is terminated. The FIR structure ensures the absolute stability of the system, and the zero vector initialization avoids the introduction of prior bias. The dual termination conditions take into account both convergence quality and computational efficiency.

[0016] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the method of the present invention and its core ideas. The above is only a preferred implementation method of the present invention. It should be pointed out that due to the limitations of textual expression, there are objectively infinite specific structures. For ordinary technicians in this technical field, without departing from the principles of the present invention, they can make several improvements, modifications or changes, and can also combine the above technical features in an appropriate manner; these improvements, modifications, changes or combinations, or the direct application of the inventive concept and technical solution to other occasions without improvement, should be regarded as the scope of protection of the present invention.

Claims

1. A Chirp signal adaptive equalization system based on a progressive filter, including an operation process of the Chirp signal adaptive equalization system based on a progressive filter, characterized in that: The operation process of the Chirp signal adaptive equalization system based on the progressive filter includes the following steps: S1: Obtain rainfall R and visibility V data, and calculate the comprehensive attenuation coefficient a of the radar signal attenuation caused by rainfall R and visibility V factors during the propagation process in different environments based on the obtained rainfall R and visibility V data, and perform amplitude pre-compensation on the received signal; S2: Get the Chirp signal after pre-compensation , center frequency And signal bandwidth B data, and the obtained Chirp signal Perform Fourier transform to get , and then by getting Calculate power spectral density ; Get the pre-compensated Chirp signal The signal power in and noise power , by obtaining the signal power and noise power Calculate the signal-to-noise ratio SNR; S3: Define the desired signal power spectrum density according to specific application requirements and the power spectral density calculated in S2 Compare and obtain the expected signal power spectrum density The power spectral density calculated in S2 error ; According to the power spectral density error of the i-th sampling point , learning rate And the input signal delayed by i sampling points , get the update amount of the equalizer coefficient , and based on the old coefficients of the equalizer and update amount , calculate the new equalizer coefficients , the new equalizer coefficients The calculation formula is: ， And according to the new equalizer coefficient Perform progressive filtering to obtain the output signal of the filter ; S4: Calculate the signal-to-noise ratio of the output signal and bit error rate ; Dynamically adjust system parameters according to system performance improvement index P: ， Where, and is the signal-to-noise ratio and bit error rate of the input signal, where the bit error rate is obtained by comparing the output bit sequence with the reference sequence.

2. The Chirp signal adaptive equalization system based on progressive filter according to claim 1, characterized in that: In S1, the calculation formula of the comprehensive attenuation coefficient a is: ， Where, and is an empirical coefficient related to radar frequency, and is a coefficient related to the droplet size distribution.

3. The Chirp signal adaptive equalization system based on progressive filter according to claim 1, characterized in that: In S1, the calculation formula for amplitude pre-compensation of the received signal is: ， Where, is the received Chirp signal, d is the distance the signal propagates, is the pre-compensated signal.

4. The Chirp signal adaptive equalization system based on progressive filter according to claim 1, characterized in that: In S2, the power spectral density The calculation formulas for the signal-to-noise ratio (SNR) are: ， 。 5. The Chirp signal adaptive equalization system based on progressive filter according to claim 1, characterized in that: In S3, the update amount and the filter output signal The calculation formulas are: ， ， Where, is the output signal of the equalizer, are the coefficients of the filter, is the order of the filter.

6. The Chirp signal adaptive equalization system based on progressive filter according to claim 1, characterized in that: The evaluation of the system performance improvement index P described in S4 is not only based on the current signal-to-noise ratio and bit error rate, but also compared with past historical data for analysis, so as to judge the changing trend of system performance and make forward-looking adjustments to system parameters accordingly.

7. The Chirp signal adaptive equalization system based on a progressive filter according to any one of claims 1 to 6, characterized in that: It includes a meteorological parameter acquisition module for obtaining rainfall R and visibility V data; an attenuation coefficient calculation module, configured to calculate a comprehensive attenuation coefficient a according to the rainfall R and visibility V, and perform amplitude pre-compensation on the received signal; Signal analysis module for obtaining pre-compensated Chirp signals , center frequency and bandwidth, and Perform Fourier transform to obtain the frequency domain signal, and then calculate the input power spectrum density, estimate the signal power and noise power, and thus obtain the input signal-to-noise ratio; An expected spectrum setting module is used to set the expected power spectrum density according to specific application requirements; An error calculation module is used to calculate the error between the expected power spectrum density and the actual power spectrum density; Equalizer and coefficient update module; A performance evaluation module is used to recalculate the output signal-to-noise ratio and output bit error rate of the equalizer output signal, where the bit error rate is obtained by comparing the output bit sequence with the reference bit sequence; Dynamic parameter adjustment module, used to dynamically adjust the learning rate and / or filter order based on the comparison results of the system performance improvement index and the preset threshold: When the performance improvement index is higher than the threshold, increase the learning rate or increase the filter order; When the performance improvement index is lower than or equal to the threshold, reduce the learning rate or lower the filter order; The hardware implementation module is used to execute the functions of the above modules on DSP or FPGA and work in conjunction with the radar front-end receiver and decision maker.

8. The Chirp signal adaptive equalization system based on progressive filter according to claim 7, characterized in that: The equalizer and coefficient update module: Contains a set of FIR filters for weighted delay superposition of input signals; The coefficient update unit calculates the increment based on the power spectrum error, learning rate and delayed input signal, and accumulates it to the filter coefficient; The equalizer coefficients are initialized to zero vectors or preset values; When the convergence condition is met or the number of iterations reaches a predetermined upper limit, the adaptive update is terminated.

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