Ultra-large dynamic high-performance receiver system

Abstract

The present invention discloses an ultra-large dynamic high-performance receiver system, which includes a signal receiving module, an adaptive gain control module, a signal processing module, a digital pre-distortion compensation module, an intelligent signal detection and separation module, a dynamic frequency and time domain equalization module, and an output module; the signal receiving module uses a high-performance radio frequency receiving chip to be responsible for receiving radio frequency signals from the outside and converting them into intermediate frequency signals; the adaptive gain control module uses an advanced AGC algorithm to dynamically adjust the gain of the front-end amplifier according to the received signal strength, automatically reducing the gain for strong signals to avoid saturation and appropriately increasing the gain for weak signals to improve the signal-to-noise ratio. Through adaptive gain control, digital pre-distortion compensation, intelligent signal processing, and dynamic equalization technologies, the system of the present invention can still maintain high sensitivity under extreme dynamic ranges, effectively suppress interference, improve signal purity, and ensure the stability and accuracy of signal reception.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communication technology, and particularly relates to an ultra-large dynamic high-performance receiver system. Background Art

[0002] In a receiver, the electromagnetic signal received by the antenna is fed into the receiver. An ideal receiver suppresses all unwanted noise, including other signals, and does not add any noise or interference to the desired signal. Regardless of the form or format of the signal, it can be transformed to suit the characteristics required by the detector circuit of the signal processor, and then sent to the intelligent user interface.

[0003] With the rapid development of communication technology, the performance requirements for receivers are getting higher and higher. Traditional receivers have limited signal dynamic range in processing, especially in complex electromagnetic environments where strong and weak signals coexist. They often encounter problems such as saturation, signal distortion, and decreased sensitivity, which limit the communication quality and system reliability. Therefore, it has become an urgent need to develop a new generation of receiver systems that can intelligently adapt to and optimize the processing of ultra-large dynamic range signals. For this reason, we propose an ultra-large dynamic high-performance receiver system. Summary of the Invention

[0004] The purpose of the present invention is to provide an ultra-large dynamic high-performance receiver system to solve the problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: An ultra-large dynamic high-performance receiver system, including a signal receiving module, an adaptive gain control module, a signal processing module, a digital pre-distortion compensation module, an intelligent signal detection and separation module, a dynamic frequency and time domain equalization module, and an output module;

[0006] The signal receiving module uses a high-performance radio frequency receiving chip, which is responsible for receiving external radio frequency signals and converting them into intermediate frequency signals;

[0007] The adaptive gain control module uses an advanced AGC algorithm to dynamically adjust the gain of the front-end amplifier according to the received signal strength, automatically reducing the gain for strong signals to avoid saturation, and appropriately increasing the gain for weak signals to improve the signal-to-noise ratio, thereby effectively expanding the dynamic range;

[0008] The signal processing module uses advanced digital signal processing technology to further process the signals after dynamic range adjustment. The further processing includes steps of filtering, demodulating, and decoding to extract the effective information in the signals;

[0009] The digital pre-distortion compensation module is used to integrate the pre-distortion compensation algorithm in the receiving link. Through the prior knowledge or real-time learning of strong signals, it estimates and compensates for the signal distortion caused by amplifier non-linearity, maintains the original integrity of the signal, and enhances the system's processing ability for strong signals;

[0010] The intelligent signal detection and separation module is used to combine machine learning and signal processing technologies to intelligently identify and separate signals in a mixed signal environment. Through feature extraction and classification algorithms, it accurately distinguishes strong and weak signals, extracts useful information and reduces interference in the case of signal overlap;

[0011] The dynamic frequency and time domain equalization module is used to introduce adaptive equalization technologies in the high frequency domain and time domain, dynamically adjust according to different frequency bands and time-varying channel characteristics, and improve the signal quality;

[0012] The output module is used to output the processed signal in a format that can be used by subsequent devices.

[0013] Preferably, the adaptive gain control module combines a fuzzy logic control algorithm with machine learning, real-time monitors the power level of the input signal, and precisely adjusts the gain amplitude of the pre-amplifier through multi-level adjustable gain control. The adaptive gain control module has a built-in memory function, optimizes the gain adjustment strategy according to historical signal characteristics, and improves the response speed and accuracy.

[0014] Preferably, the adaptive gain control module uses an adaptive gain control algorithm to adjust the gain of the receiver front end through a feedback mechanism to adapt to input signals of different intensities. The adaptive gain control algorithm includes the following formula:

[0015] G(t)=K P e(t)+K i ∫e(t)dt;

[0016] Where, G(t) is the gain value at the current moment; t is the time variable, representing the current time point; K P is the proportional gain coefficient, which determines the immediate response speed of the error signal; e(t) is the error signal, that is, the difference between the desired signal strength and the actual signal strength; K i is the integral gain coefficient, which is used to eliminate the static error and provide long-term adjustment; ∫e(t)dt is the integral of the error signal e(t), representing the error accumulation from the past to the present.

[0017] Preferably, the filtering step in the signal processing module specifically includes:

[0018] S1. Digital filter design: According to the characteristics of the signal and system requirements, select an appropriate filter type, design the filter coefficients, determine the filter order and cut-off frequency;

[0019] S2. Signal preprocessing: Before filtering, preprocess the received signal, such as amplification, normalization, etc., to improve the signal-to-noise ratio and dynamic range of the signal; eliminate or reduce the DC bias and noise components in the signal to create better conditions for subsequent filtering operations;

[0020] S3. Real-time filtering process: Apply the designed digital filter to the received signal to achieve the filtering effect through convolution operation; according to the type of filter, retain, enhance or suppress specific frequency components in the signal to remove interference and extract useful information;

[0021] S4. Filter performance optimization: Optimize the filter performance by adjusting the filter parameters and structure, reduce distortion and noise introduction during the filtering process; adopt adaptive filtering and multi-rate filtering techniques to improve the efficiency and accuracy of filtering;

[0022] S5. Filter result output: Output the filtered signal and then perform demodulation and decoding operations.

[0023] Preferably, the digital pre-distortion compensation module uses non-linear modeling techniques and complex digital signal processing algorithms to pre-estimate and correct the non-linear distortion of the power amplifier, including memory effects and intermodulation distortion, and then continuously iteratively updates the pre-distortion model parameters to ensure effective reduction of signal distortion and improvement of linearity at different operating points. Combining a feedback mechanism, it can monitor the output signal quality in real time, dynamically adjust the pre-distortion compensation parameters, and enhance the robustness of the system.

[0024] Preferably, the pre-distortion compensation algorithm in the digital pre-distortion compensation module includes the following formula:

[0025]

[0026] Where, y(t) represents the output signal; x(t) represents the input signal; N represents the maximum order of the Volterra model; represents the Volterra coefficient, which describes the non-linear characteristics; k 1 , k 2 , …, k n represents the delay time index, indicating the delay and combination method of the input signal; x(t - k i ) represents the value of the input signal x(t) at time t - k i , indicating the delay of the signal.

[0027] Preferably, the feature extraction and classification algorithms of the intelligent signal detection and separation module include the following formulas:

[0028]

[0029] where, represents the pre-activation output of the j-th neuron in the l-th layer; σ represents the activation function, such as ReLU, Sigmoid, etc., which is used to introduce non-linearity; W l represents the weight matrix of the l-th layer, which determines how the input is transformed; a l-1 represents the activation output of the (l - 1)-th layer, which serves as the input to the l-th layer; b l represents the bias vector of the l-th layer, which is used to shift the activation function.

[0030] Preferably, the adaptive equalization technology that introduces the high-frequency domain and time domain includes the least mean square error algorithm, and the least mean square error algorithm includes the following formula:

[0031] w(n + 1) = w(n) + μe(n)x * (n);

[0032] where, w(n + 1) represents the updated weight vector at time n + 1; w(n) represents the current weight vector at time n; μ represents the learning rate, which controls the speed of weight update; e(n) = d(n) - y(n) represents the error signal, that is, the difference between the desired output d(n) and the actual output y(n); x * (n) represents the complex conjugate of the input signal x(n) at the current moment, which is used for matching in frequency domain processing.

[0033] Preferably, it further includes an automatic calibration and optimization module, which is responsible for regularly calibrating and optimizing various parameters of the receiver system to ensure that the system always remains in the best working state. The automatic calibration and optimization module measures key indicators such as the gain, noise figure, and frequency response of the receiver through a built-in standard signal source and test algorithm, and automatically adjusts the system parameters according to the measurement results. The automatic calibration and optimization module is also used to optimize the dynamic range and signal processing algorithm of the receiver in real time according to changes in environmental conditions and signal characteristics, so as to improve the anti-interference ability and performance stability of the receiver.

[0034] Preferably, it further includes an interference suppression module, which uses interference identification and suppression algorithms to detect and identify the characteristics and sources of interference signals in real time, and takes corresponding measures for suppression or filtering. By reducing the intensity of the interference signal or changing its frequency characteristics, the signal-to-noise ratio and anti-interference ability of the receiver are improved, ensuring the stable and reliable communication quality.

[0035] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0036] (1) Through adaptive gain control, digital pre-distortion compensation, intelligent signal processing, and dynamic equalization techniques, the system of the present invention can maintain high sensitivity even under extreme dynamic ranges, effectively suppress interference, improve signal purity, and ensure the stability and accuracy of signal reception;

[0037] (2) In the face of complex and changing electromagnetic environments, the present invention can identify and adapt to signals of different intensities and types, and can effectively process signals from weak long-distance signals to adjacent strong interference signals, enhancing the robustness and applicable range of the system in various environments;

[0038] (3) Through precise signal separation and optimization processing, the present invention can effectively utilize spectrum resources even in areas with high signal density, reduce interference between signals, improve the capacity and data transmission efficiency of the communication system, and provide technical support for 5G and future communication standards. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is one of the structural block diagrams of the present invention;

[0040] Figure 2 is the flowchart of the filtering step in the present invention;

[0041] Figure 3 is the second structural block diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0043] Please refer to Figures 1 - 3 , the present invention provides a technical solution: an ultra-high dynamic high-performance receiver system, including a signal receiving module, an adaptive gain control module, a signal processing module, a digital pre-distortion compensation module, an intelligent signal detection and separation module, a dynamic frequency and time domain equalization module, and an output module;

[0044] The signal receiving module uses a high-performance radio frequency receiving chip, which is responsible for receiving external radio frequency signals and converting them into intermediate frequency signals;

[0045] The adaptive gain control module adopts an advanced AGC algorithm to dynamically adjust the gain of the front-end amplifier according to the received signal strength. It automatically reduces the gain for strong signals to avoid saturation, appropriately increases the gain for weak signals to improve the signal-to-noise ratio, and effectively expands the dynamic range;

[0046] The signal processing module uses advanced digital signal processing technology to further process the signal after dynamic range adjustment. The further processing includes steps such as filtering, demodulation, and decoding to extract the effective information in the signal;

[0047] The digital pre-distortion compensation module is used to integrate the pre-distortion compensation algorithm in the receiving link. Through the prior knowledge or real-time learning of strong signals, it estimates and compensates for the signal distortion caused by amplifier non-linearity, maintains the original integrity of the signal, and enhances the system's processing ability for strong signals;

[0048] The intelligent signal detection and separation module is used to combine machine learning and signal processing technology to intelligently identify and separate signals in a mixed signal environment. Through feature extraction and classification algorithms, it accurately distinguishes strong and weak signals, extracts useful information, and reduces interference in the case of signal overlap;

[0049] The dynamic frequency and time-domain equalization module is used to introduce adaptive equalization techniques in the high-frequency domain and time domain, dynamically adjust according to different frequency bands and time-varying channel characteristics, and improve the signal quality;

[0050] The output module is used to output the processed signal in a format that can be used by subsequent devices.

[0051] In this embodiment, preferably, the adaptive gain control module combines a fuzzy logic control algorithm with machine learning to real-time monitor the power level of the input signal and precisely adjust the gain amplitude of the pre-amplifier through multi-level adjustable gain control. The adaptive gain control module has a built-in memory function to optimize the gain adjustment strategy according to historical signal characteristics, improving the response speed and accuracy.

[0052] In this embodiment, preferably, the adaptive gain control module adopts an adaptive gain control algorithm to adjust the gain of the receiver front-end through a feedback mechanism to adapt to input signals of different intensities. The adaptive gain control algorithm includes the following formula:

[0053] G(t) = K P e(t) + K i ∫e(t)dt;

[0054] Where, G(t) is the gain value at the current moment; t is the time variable representing the current time point; K P$K$ is the proportional gain coefficient, which determines the immediate response speed of the error signal; $e(t)$ is the error signal, that is, the difference between the desired signal strength and the actual signal strength; i $K_i$ is the integral gain coefficient, which is used to eliminate the static error and provide long-term adjustment; $\int e(t)dt$ is the integral of the error signal $e(t)$, representing the error accumulation from the past to the present.

[0055] In this embodiment, preferably, the filtering step in the signal processing module specifically includes:

[0056] S1. Digital filter design: According to the characteristics of the signal and system requirements, select an appropriate filter type, design the filter coefficients, determine the order and cut-off frequency of the filter;

[0057] S2. Signal preprocessing: Before filtering, preprocess the received signal, such as amplification, normalization, etc., to improve the signal-to-noise ratio and dynamic range of the signal; eliminate or reduce the DC bias and noise components in the signal to create better conditions for subsequent filtering operations;

[0058] S3. Real-time filtering process: Apply the designed digital filter to the received signal, and achieve the filtering effect through convolution operation; according to the type of filter, retain, enhance or suppress specific frequency components in the signal to achieve the purpose of removing interference and extracting useful information;

[0059] S4. Filter performance optimization: Optimize the filter performance by adjusting the filter parameters and structure, reduce the distortion and noise introduced during the filtering process; adopt adaptive filtering and multi-rate filtering techniques to improve the efficiency and accuracy of filtering;

[0060] S5. Filter result output: Output the filtered signal, and then perform demodulation and decoding operations.

[0061] In this embodiment, preferably, the digital predistortion compensation module uses non-linear modeling techniques and complex digital signal processing algorithms to pre-estimate and correct the non-linear distortion of the power amplifier, including memory effects and intermodulation distortion, and then continuously iteratively updates the predistortion model parameters to ensure effective reduction of signal distortion and improvement of linearity at different operating points. Combining with a feedback mechanism, it monitors the output signal quality in real time and dynamically adjusts the predistortion compensation parameters to enhance the robustness of the system.

[0062] In this embodiment, preferably, the predistortion compensation algorithm in the digital predistortion compensation module includes the following formula:

[0063]

[0064] Among them, y(t) represents the output signal; x(t) represents the input signal; N represents the maximum order of the Volterra model; represents the Volterra coefficient, which describes the nonlinear characteristics; k 1 ,k 2 ,…,k n represents the delay time index, indicating the delay and combination method of the input signal; x(t - k i ) represents the value of the input signal x(t) at time t - k i , indicating the delay of the signal.

[0065] In this embodiment, preferably, the feature extraction and classification algorithm of the intelligent signal detection and separation module includes the following formula:

[0066]

[0067] Among them, represents the pre-activation output of the j-th neuron in the l-th layer; σ represents the activation function, such as ReLU, Sigmoid, etc., which is used to introduce nonlinearity; W l represents the weight matrix of the l-th layer, which determines how the input is transformed; a l-1 represents the activation output of the (l - 1)-th layer, which serves as the input of the l-th layer; b l represents the bias vector of the l-th layer, which is used to translate the activation function.

[0068] In this embodiment, preferably, the adaptive equalization technology introducing the high-frequency domain and time domain includes the least mean square error algorithm, and the least mean square error algorithm includes the following formula:

[0069] w(n + 1) = w(n) + μe(n)x * (n);

[0070] Among them, w(n + 1) represents the updated weight vector at time n + 1; w(n) represents the current weight vector at time n; μ represents the learning rate, which controls the speed of weight update; e(n) = d(n) - y(n) represents the error signal, that is, the difference between the desired output d(n) and the actual output y(n); x * (n) represents the complex conjugate of the input signal x(n) at the current moment, which is used for matching in frequency domain processing.

[0071] In this embodiment, preferably, it further includes an automatic calibration and optimization module. The automatic calibration and optimization module is responsible for regularly calibrating and optimizing various parameters of the receiver system to ensure that the system always maintains the best working state. The automatic calibration and optimization module measures key indicators such as the gain, noise figure, and frequency response of the receiver accurately through a built-in standard signal source and test algorithm, and automatically adjusts the system parameters according to the measurement results. The automatic calibration and optimization module is also used to optimize the dynamic range and signal processing algorithm of the receiver in real time according to changes in environmental conditions and signal characteristics, improving the anti-interference ability and performance stability of the receiver.

[0072] In this embodiment, preferably, it further includes an interference suppression module. The interference suppression module uses interference identification and suppression algorithms to detect and identify the characteristics and sources of interference signals in real time, and takes corresponding measures for suppression or filtering. By reducing the intensity of interference signals or changing their frequency characteristics, it improves the signal-to-noise ratio and anti-interference ability of the receiver, ensuring the stable and reliable communication quality.

[0073] Principle and advantages of the present invention:

[0074] Through adaptive gain control, digital pre-distortion compensation, intelligent signal processing, and dynamic equalization techniques, the system of the present invention can still maintain high sensitivity under extreme dynamic ranges, effectively suppress interference, improve signal purity, and ensure the stability and accuracy of signal reception; in the face of complex and changing electromagnetic environments, it can identify and adapt to different intensities and types of signals, from weak long-distance signals to adjacent strong interference signals, and can effectively process them, enhancing the robustness and applicable range of the system in various environments; through precise signal separation and optimization processing, it can effectively utilize spectrum resources even in areas with high signal density, reduce interference between signals, improve the capacity and data transmission efficiency of the communication system, and provide technical support for 5G and future communication standards.

[0075] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Ultra-large dynamic high-performance receiver system, characterized by: It includes a signal receiving module, an adaptive gain control module, a signal processing module, a digital pre-distortion compensation module, an intelligent signal detection and separation module, a dynamic frequency and time domain equalization module and an output module; The signal receiving module adopts a radio frequency receiving chip, which is responsible for receiving radio frequency signals from the outside and converting them into intermediate frequency signals; The adaptive gain control module adopts the AGC algorithm to dynamically adjust the gain of the front-end amplifier according to the received signal strength, automatically reduce the gain for strong signals to avoid saturation, and appropriately increase the gain for weak signals to improve the signal-to-noise ratio, thereby effectively expanding the dynamic range; The signal processing module uses digital signal processing technology to further process the signal after the dynamic range adjustment, and the further processing includes the steps of filtering, demodulation, and decoding to extract effective information from the signal; The digital pre-distortion compensation module is used to integrate a pre-distortion compensation algorithm in the receiving link, estimate and compensate for signal distortion caused by amplifier nonlinearity through prior knowledge of strong signals or real-time learning, maintain the original integrity of the signal, and enhance the system's processing capability for strong signals; The intelligent signal detection and separation module is used to combine machine learning and signal processing technology to intelligently identify and separate signals in a mixed signal environment, accurately distinguish strong and weak signals through feature extraction and classification algorithms, and extract useful information and reduce interference in the case of signal overlap; The dynamic frequency and time domain equalization module is used to introduce adaptive equalization technology in high frequency domain and time domain, and dynamically adjust the characteristics of different frequency bands and time-varying channels to improve signal quality; The output module is used to output the processed signal in a format that can be used by subsequent devices; the adaptive gain control module adopts a combination of fuzzy logic control algorithm and machine learning to monitor the power level of the input signal in real time, and accurately adjusts the gain amplitude of the preamplifier through multi-level adjustable gain control. The adaptive gain control module has a built-in memory function, optimizes the gain adjustment strategy according to historical signal characteristics, and improves the response speed and accuracy; the adaptive gain control module adopts an adaptive gain control algorithm to adjust the gain of the receiver front end through a feedback mechanism to adapt to input signals of different strengths. The adaptive gain control algorithm includes the following formula: G(t)=K P e(t)+K i ∫e(t)dt; Among them, G(t) is the gain value at the current moment; t is the time variable, indicating the current time point; K P is the proportional gain coefficient, which determines the instantaneous response speed of the error signal; e(t) is the error signal, that is, the difference between the expected signal strength and the actual signal strength; K i is the integral gain coefficient, which is used to eliminate static errors and provide long-term adjustment; ∫e(t)dt is the integral of the error signal e(t), which represents the accumulated error from the past to the present.

2. The ultra-large dynamic high-performance receiver system according to claim 1, characterized in that: The filtering step in the signal processing module specifically includes: S1. Digital filter design: According to the characteristics of the signal and system requirements, select the appropriate filter type, design the filter coefficients, and determine the filter order and cutoff frequency; S2. Signal preprocessing: Before filtering, the received signal is preprocessed to improve the signal-to-noise ratio and dynamic range of the signal; eliminate or reduce the DC bias and noise components in the signal; S3. Real-time filtering: Apply the designed digital filter to the received signal and achieve the filtering effect through convolution operation; according to the type of filter, retain, enhance or suppress the cutoff frequency component in the signal to remove interference and extract useful information; S4. Filtering performance optimization: By adjusting the parameters and structure of the filter, the filtering performance is optimized and the distortion and noise introduced in the filtering process are reduced; adaptive filtering and multi-rate filtering technology are used to improve the efficiency and accuracy of filtering; S5. Filtering result output: Output the signal after filtering, and then perform demodulation and decoding operations.

3. The ultra-large dynamic high-performance receiver system according to claim 1, characterized in that: The digital pre-distortion compensation module adopts nonlinear modeling technology and digital signal processing algorithm to pre-estimate and correct the nonlinear distortion of the power amplifier, including memory effect and intermodulation distortion, and then updates the pre-distortion model parameters through continuous iteration to ensure that signal distortion can be effectively reduced and linearity can be improved at different working points. In addition, the feedback mechanism is combined to monitor the output signal quality in real time, dynamically adjust the pre-distortion compensation parameters, and enhance the robustness of the system.

4. The ultra-large dynamic high-performance receiver system according to claim 3, characterized in that: The predistortion compensation algorithm in the digital predistortion compensation module includes the following formula: Where y(t) represents the output signal; x(t) represents the input signal; N represents the maximum order of the Volterra model; represents the Volterra coefficient, which describes the nonlinear characteristics; k1, k2,…, k n represents the delay time index, indicating the delay and combination of the input signal; x(tk i ) represents the input signal x(t) at time tk i The value represents the delay of the signal.

5. The ultra-large dynamic high-performance receiver system according to claim 1, characterized in that: The feature extraction and classification algorithm of the intelligent signal detection and separation module includes the following formula: in, represents the output of the jth neuron in the lth layer before activation; σ represents the activation function, which is used to introduce nonlinearity; W l represents the weight matrix of the lth layer, which determines how the input is transformed; a l-1 represents the activation output of the l-1th layer, which serves as the input of the lth layer; b l Represents the bias vector of the lth layer, which is used to translate the activation function.

6. The ultra-large dynamic high-performance receiver system according to claim 1, characterized in that: The adaptive equalization technology introduced into the high frequency domain and the time domain includes a minimum mean square error algorithm, and the minimum mean square error algorithm includes the following formula: w(n+1)=w(n)+μe(n)x * (n); Among them, w(n+1) represents the updated weight vector at time n+1; w(n) represents the current weight vector at time n; μ represents the learning rate, which controls the speed of weight update; e(n)=d(n)-y(n) represents the error signal, that is, the difference between the expected output d(n) and the actual output y(n); x * (n) represents the complex conjugate of the input signal x(n) at the current moment, which is used for matching in frequency domain processing.

7. The ultra-large dynamic high-performance receiver system according to claim 1, characterized in that: It also includes an automatic calibration and optimization module, which is responsible for regularly calibrating and optimizing various parameters of the receiver system. The automatic calibration and optimization module uses a built-in standard signal source and test algorithm to accurately measure the gain, noise factor, and frequency response of the receiver, and automatically adjusts the system parameters according to the measurement results. The automatic calibration and optimization module is also used to optimize the dynamic range and signal processing algorithm of the receiver in real time according to changes in environmental conditions and signal characteristics, thereby improving the receiver's anti-interference ability and performance stability.

8. The ultra-large dynamic high-performance receiver system according to claim 7, characterized in that: It also includes an interference suppression module, which uses an interference identification and suppression algorithm to detect and identify the characteristics and sources of interference signals in real time, take corresponding measures to suppress or filter them, and improve the signal-to-noise ratio and anti-interference capability of the receiver by reducing the intensity of the interference signal or changing its frequency characteristics.

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