Electromagnetic signal enhancement processing module

Abstract

The invention relates to the technical field of electronic communication and geological exploration, and discloses an electromagnetic signal enhancement processing module, which comprises a signal input unit used for receiving an electromagnetic signal from an external sensor and converting the electromagnetic signal into a digital signal suitable for subsequent processing; the signal preprocessing unit is used for carrying out preliminary conditioning on the input signal so as to adapt to subsequent processing; the wavelet transformation module is used for performing multi-scale time-frequency domain analysis on the input signal to extract different frequency band information of the signal; and the adaptive filtering and Kalman filtering module is used for carrying out noise suppression and dynamic optimization on the signal and enhancing the signal quality. According to the method, the electromagnetic signal is efficiently and accurately enhanced in a complex noise environment by combining wavelet transform, adaptive filtering and maximum posteriori estimation technologies, and the signal quality and the signal-to-noise ratio are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic communications and geological exploration, and in particular to an electromagnetic signal enhancement processing module. Background Art

[0002] In modern society, with the increasing application of electromagnetic signals in various fields, accurately detecting and analyzing them has become a critical issue. For example, in geological exploration, underground pipeline inspection, and environmental monitoring, electromagnetic signals serve as an important detection tool, providing a deep understanding of underground structures and their changes. However, these signals are often interfered with by environmental noise, resulting in a low signal-to-noise ratio, which affects the accuracy and reliability of the measurements.

[0003] Existing signal processing technologies typically use traditional methods to address signal noise. For example, analog filters are widely used to remove high-frequency noise, and their simplicity and low cost have led to their adoption in many systems. Furthermore, digital signal processing technology, by fine-tuning signal characteristics within the frequency domain, enables higher-precision signal processing and is also commonly used in high-performance signal enhancement systems. In some complex applications, multi-antenna array technology is used to enhance signal strength through spatial synthesis, theoretically effectively improving signal quality.

[0004] However, existing technologies still have some shortcomings. First, although analog filters can simply and effectively remove high-frequency noise, their noise suppression effect is limited, especially in complex and dynamically changing signal environments, and they cannot meet the needs of high-precision signal enhancement. Second, although digital signal processing technology can accurately adjust signal characteristics, it requires a lot of computing resources and consumes a lot of power, which limits its application in devices that require low power consumption and high efficiency. Finally, traditional multi-antenna array technology faces the problems of difficult antenna layout design and excessive system size in actual applications, and cannot meet the needs of modern miniaturized and portable devices. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides an electromagnetic signal enhancement processing module, which solves the problem in the existing technology that electromagnetic signals cannot be enhanced efficiently and accurately in complex noise environments.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an electromagnetic signal enhancement processing module, comprising: The signal input unit is used to receive electromagnetic signals from external sensors and convert them into digital signals suitable for subsequent processing; the signal preprocessing unit is used to perform preliminary conditioning on the input signals to adapt to subsequent processing; Wavelet transform module, used to perform multi-scale time-frequency domain analysis on the input signal to extract information of different frequency bands of the signal; Adaptive filtering and Kalman filtering modules are used to suppress noise and dynamically optimize signals to enhance signal quality; The signal recovery module recovers the signal through maximum a posteriori estimation to further improve the signal quality; An output module, used to output the processed signal to a display device or storage medium; The power management module is used to provide stable power to the system and optimize power consumption.

[0007] Preferably, the signal input unit includes a plurality of sensor interfaces, which are used to connect to different types of electromagnetic signal sensors to enhance the amplitude of the input signal.

[0008] Preferably, the signal preprocessing unit includes: Preamplifier, used to enhance the strength of input signals and reduce the influence of external noise; Bandpass filter, used to filter out unwanted frequency components and retain the target signal; Automatic gain controller, used to dynamically adjust the gain according to the strength of the input signal to ensure the appropriate signal amplitude.

[0009] Preferably, the wavelet transform module includes: The mother wavelet function is used to decompose the input signal in the time-frequency domain and decompose the signal into multiple sub-signals in different frequency bands; The wavelet coefficient threshold processing module is used to remove unnecessary noise coefficients and retain the main features of useful signals.

[0010] Preferably, the adaptive filtering and Kalman filtering module includes: Adaptive filter, which is used to dynamically adjust the filter parameters according to the real-time signal characteristics to minimize noise; A Kalman filter is used to recursively update the signal based on the previous signal state and the current observation data to provide the optimal signal estimate.

[0011] Preferably, the signal recovery module recovers the signal by maximum a posteriori estimation, and the maximum a posteriori estimation includes: The prior probability of the signal is used to represent the prior knowledge of the signal; The likelihood function of the signal is used to express the relationship between the signal and the observed data; The posterior probability maximization process is used to optimize the signal recovery process based on the observed data and prior knowledge.

[0012] Preferably, the output module includes: Standard data interface for transmitting processed signals to external display devices or storage devices; Data converter, used to convert the signal into a format suitable for input to external devices.

[0013] Preferably, the power management module includes: Voltage-stabilized power supply unit, used to provide stable power supply for each module; The power optimization unit is used to dynamically adjust power output according to system load changes and optimize power consumption.

[0014] Preferably, the module uses a hardware acceleration unit to increase processing speed, and the hardware acceleration unit includes: a GPU acceleration unit for accelerating wavelet transform and Kalman filtering to perform computationally intensive operations; FPGA acceleration unit, used to accelerate the adaptive filtering and maximum a posteriori estimation process.

[0015] Preferably, the module supports multiple sensor input interfaces, including radio wave sensors and magnetic field sensors, to meet different electromagnetic signal detection application requirements.

[0016] The present invention provides an electromagnetic signal enhancement processing module. It has the following beneficial effects: 1. This invention combines wavelet transforms with adaptive filtering techniques, achieving excellent results in denoising and signal enhancement. Compared to traditional filtering methods, it can more accurately handle complex background noise and improve the signal-to-noise ratio. This means that when processing electromagnetic signals in high-noise environments, the system can provide clearer and more reliable output.

[0017] 2. This invention improves the stability and efficiency of power management through a switching regulator and dynamic power regulation mechanism. The system can intelligently adjust power according to load changes, avoiding excessive consumption or voltage fluctuations. Compared with traditional power management solutions, this approach greatly enhances the system's energy efficiency and adaptability.

[0018] 3. The combination of maximum a posteriori estimation and minimum mean square error improves signal recovery accuracy. Unlike conventional recovery methods, this method can recover the closest approximation to the true signal using an optimized estimation method in complex signal environments, showing significant advantages in high-noise environments.

[0019] 4. This invention incorporates a real-time monitoring and automatic overload protection mechanism. Regardless of the system's operating state, the power management module quickly responds to overload or short-circuit conditions, automatically protecting the system from power supply damage. Compared to traditional protection schemes, this safety mechanism is more responsive and effectively prevents power supply equipment failure and damage. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a system structure diagram of the present invention; Figure 2 This is a module architecture diagram of the signal input unit of the present invention; Figure 3 This is a module architecture diagram of the signal preprocessing circuit of the present invention; Figure 4 This is a module architecture diagram of the core processor of the present invention; Figure 5 This is a module architecture diagram of the power management unit of the present invention; Figure 6 This is a module architecture diagram of the signal output interface of the present invention. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the specification of the present invention. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0022] Please see the attached Figure 1 -Attached Figure 6 , an embodiment of the present invention provides an electromagnetic signal enhancement processing module, comprising: The signal input unit receives electromagnetic signals from external sensors and converts them into digital signals suitable for subsequent processing. Its primary task is to receive electromagnetic signals from various sources and appropriately amplify and adjust them to ensure they meet the input requirements of subsequent processing modules. To this end, the signal input unit incorporates multiple functional modules, including multiple sensor interfaces, a preamplifier, and an automatic gain control module. These modules work together to ensure signal quality and processing effectiveness.

[0023] Typically, a signal input unit consists of two key components: a sensor interface and a signal gain adjustment module. The sensor interface receives signals from various sensors and performs preliminary signal conditioning. The signal gain adjustment module ensures that the input signal amplitude meets the processing requirements of subsequent modules and prevents signals that are too strong or too weak from affecting processing performance.

[0024] The signal input unit supports various sensor interfaces. These interfaces can connect to various electromagnetic signal sources, such as radio wave sensors, magnetic field sensors, and temperature sensors. Each sensor has unique signal characteristics, so the interface must be flexible enough to accommodate a variety of signal sources.

[0025] Specifically, the sensor interface module uses adapters and converters to ensure compatibility with different signal sources. For example, when connecting a radio wave sensor, the signal is first converted to a digital signal by the adapter and then passed to the system for subsequent processing. For magnetic field signals, the sensor interface performs appropriate amplification and conversion to ensure that the signal amplitude and frequency are compatible with subsequent modules.

[0026] The preamplifier in the signal input unit is used to amplify weak signals received, ensuring they are strong enough for subsequent processing. The preamplifier increases signal strength through gain while reducing the effects of external noise, preventing weak signals from affecting subsequent processing.

[0027] The preamplifier is designed with low noise and high stability in mind, ensuring that the signal is amplified without introducing additional noise. The gain of the signal amplification is adjustable and can be dynamically adjusted based on the input signal strength and signal source type. The preamplifier's gain factor, G, can be adjusted based on system requirements, using the following formula: y(t)=G·x(t); in: y(t) is the output signal (the amplified signal); x(t) is the input signal (the signal received by the sensor); G is the gain factor, which indicates the amplification factor. G can be adjusted according to actual application requirements.

[0028] Setting the gain factor, G, is a critical parameter. If the gain factor is set too low, the signal will not be fully amplified, affecting processing by subsequent modules. If the gain factor is set too high, it can easily cause signal distortion or excessive noise amplification. Therefore, the preamplifier must dynamically adjust the gain based on the specific conditions of the input signal.

[0029] In some embodiments, the signal input unit further includes an automatic gain control (AGC) module. The function of the AGC module is to automatically adjust the gain according to the amplitude of the input signal to ensure that the signal is always within an appropriate range.

[0030] The AGC module monitors the input signal amplitude in real time and adjusts the gain based on a set threshold. When the input signal is weak, the AGC module increases the gain; when the input signal is strong, the AGC module decreases the gain to prevent signal overload. In this way, the AGC module can automatically adjust the gain according to different signal sources and environmental conditions, maintaining stable signal quality.

[0031] The AGC gain adjustment formula is as follows: Gain Adjust(t)=f(Signal Strength(x(t))); in: Gain Adjust(t) is the gain adjustment value; Signal Strength(x(t)) is the strength of the input signal, usually obtained by measuring the signal amplitude; f(·) is the gain adjustment function, which dynamically adjusts the gain based on the signal strength.

[0032] The gain adjustment function f(·) of the AGC module can be implemented in a variety of ways, including threshold-based adjustment or adaptive algorithms. This gain adjustment mechanism enables the system to adaptively adjust the gain of the input signal under different environmental conditions.

[0033] In this embodiment, the gain control of the signal input unit and the adaptation function of the sensor interface can be dynamically adjusted by the control unit. The control unit determines the gain settings of the preamplifier and AGC module based on the characteristics of different input signal sources. System control can be achieved through external input or internal feedback mechanism.

[0034] The choice of gain factor G and gain adjustment function f(·) directly affects the amplification of the signal. The system automatically adjusts the gain parameters based on real-time feedback and signal quality to ensure that the signal always remains within the optimal operating range.

[0035] After the signal input unit completes initial amplification and conditioning, it is passed to the signal preprocessing module. This module performs bandpass filtering and other preliminary processing on the signal to ensure that the signal quality is suitable for subsequent frequency domain analysis and noise removal.

[0036] During transmission, the signal input unit ensures the appropriate signal amplitude, preventing the adverse effects of excessively high or low signal amplitudes on subsequent processing. The signal gain control module (including the preamplifier and AGC) ensures the system's adaptability in different environments, enabling it to cope with the challenges posed by varying signal strength.

[0037] The signal input unit, through a preamplifier and automatic gain control module, ensures that the received electromagnetic signal is input into the system at the appropriate strength for subsequent processing. The gain control strategy of the signal input unit not only adapts to the needs of different signal sources, but also maintains signal stability in dynamic noise environments.

[0038] Signal pre-processing unit, used to perform preliminary conditioning on the input signal to adapt to subsequent processing; The signal preprocessing unit directly impacts subsequent signal processing. After the signal input unit performs initial gain and amplitude adjustments, the preprocessing unit continues to refine the signal. Its primary task is to remove unwanted frequency bands through a bandpass filter, ensuring the target signal's spectral range. Simultaneously, the automatic gain control (AGC) module further optimizes the signal's amplitude to ensure it remains within a range suitable for subsequent processing modules.

[0039] This module ensures that the signal has good spectral characteristics and appropriate amplitude when entering the next processing stage (such as wavelet transform and signal recovery), thereby improving the performance of the overall system.

[0040] In this embodiment, the signal preprocessing unit includes the following components: The bandpass filter is a key module in the signal preprocessing unit. Its primary function is to filter the signal in the frequency domain, removing unwanted frequency components and retaining only the frequency band of the target signal. Different types of electromagnetic signals typically fall within different frequency ranges, so the design of the bandpass filter must be tailored to the specific characteristics of the signal source.

[0041] A bandpass filter restricts the input signal's spectrum to a target frequency band by setting a frequency range within which the signal is allowed to pass. Other frequency components, such as low- and high-frequency noise, are filtered out. The operation of a bandpass filter can be described by a frequency response function. The amplitude response function H(f) of a bandpass filter has a significant response only within a specific frequency band, while attenuating other frequencies.

[0042] Formula description: The output signal y(t) of the bandpass filter can be expressed as the convolution of the input signal x(t) and the filter impulse response h(t): in: x(t) is the input signal, the electromagnetic signal to be processed, transmitted from the signal input unit. y(t) is the output signal, which is the signal after bandpass filtering, retaining only the signal components in the target frequency band. h(t-τ) is the impulse response of the bandpass filter, which determines how the filter affects signals of different frequencies. The impulse response is the filter's response to a unit pulse input, performing a convolution operation on the signal. τ is the integral variable, representing the time offset of the signal. dτ is the differential component of the integral.

[0043] The bandpass filter uses this formula to perform time domain convolution on the input signal, eliminating low-frequency and high-frequency noise, retaining only the signal components in the target frequency band, and providing accurate signal input for subsequent modules.

[0044] The AGC module's primary function is to dynamically adjust the signal's gain to ensure the signal amplitude remains within an appropriate range. The AGC monitors the signal's amplitude, A(x(t)), in real time and automatically adjusts the gain based on a set threshold. If the signal amplitude is too low, the gain is increased; if the signal is too strong, the gain is decreased to avoid signal distortion or saturation.

[0045] When the input signal amplitude is too low, the AGC increases the gain to increase the signal amplitude; when the signal is too strong, the AGC reduces the gain to avoid signal distortion. This process ensures that the signal amplitude is always within the optimal operating range to meet subsequent signal processing requirements.

[0046] The gain adjustment of the AGC module can be described by the following formula: Gain Adjust(t)=α·A(x(t)); in: Gain Adjust(t) is the gain adjustment value. A(x(t)) is the signal amplitude, which can be obtained by calculating the peak value or root mean square (RMS) value of the signal x(t). α is the gain factor, which indicates the magnitude of the gain adjustment and is adjusted dynamically based on the signal strength.

[0047] The AGC module's gain adjustment function α is typically set dynamically based on system requirements. For example, when the signal is very weak, α will be larger, increasing the gain; while when the signal strength is high, α will be smaller to reduce distortion caused by over-amplification.

[0048] The signal preprocessing unit plays a connecting role in the system. The signal input unit first amplifies the signal and performs preliminary amplitude adjustment using automatic gain control (AGC). Next, the signal is passed to the signal preprocessing unit, where it undergoes frequency domain filtering through a bandpass filter and further gain adjustment to ensure that the signal spectrum falls within the target frequency band and has an appropriate amplitude. This process ensures that the signal is in optimal condition when it enters subsequent modules, preventing subsequent processing from being affected by signals in inappropriate frequency bands or signals that are too weak or too strong.

[0049] After preprocessing, the signal is passed to the wavelet transform module, where multi-scale time-frequency analysis is performed. The quality of signal preprocessing directly impacts the performance of the subsequent wavelet transform and signal recovery modules. Therefore, precise adjustment of the signal preprocessing unit provides high-quality signal input for subsequent processing.

[0050] The gain adjustment and bandpass filter design of the signal preprocessing unit require flexible configuration based on the actual application requirements. The AGC module's gain factor α and the bandpass filter's cutoff frequency are optimized based on the characteristics of the input signal and the noise environment. For example, in a noisy environment, the AGC module needs to increase gain to maintain signal strength; in the case of a strong input signal, the AGC module will reduce gain to avoid excessive noise amplification.

[0051] The center frequency and bandwidth of a bandpass filter are crucial to the signal processing effect. Generally speaking, the frequency range of the bandpass filter should match the frequency band of the signal to ensure that the signal spectrum is not excessively attenuated. For different signal sources, the frequency range of the bandpass filter will be adjusted to maximize signal preservation.

[0052] In some embodiments, the signal preprocessing unit can also automatically adjust based on external control to adapt to different application scenarios. For example, the system can adjust the bandpass filter parameters and AGC gain factor in real time based on changes in ambient noise or signal sources, ensuring that the signal remains optimal.

[0053] The signal preprocessing unit removes unwanted frequency components through a bandpass filter, ensuring that the signal contains only valid information within the target frequency band. The AGC module dynamically adjusts the gain to ensure that the signal amplitude remains moderate, avoiding distortion and saturation. Through these processes, the signal preprocessing unit provides high-quality input signals for subsequent signal processing modules, thereby improving the performance and stability of the entire system.

[0054] Wavelet transform module, used to perform multi-scale time-frequency domain analysis on the input signal to extract information of different frequency bands of the signal; The main purpose of the wavelet transform module is to decompose the input signal into sub-signals of multiple scales through wavelet transform, so as to facilitate subsequent signal enhancement and restoration. This process is particularly effective for non-stationary signals because the wavelet transform can simultaneously provide local information of the signal in the time domain and the frequency domain, which is crucial for signal extraction in complex noisy environments.

[0055] After the signal is processed by the signal input unit and the signal preprocessing unit, it undergoes gain control and frequency domain filtering. At this point, the signal's spectrum is confined to the target range, and the signal's amplitude is suitable for subsequent processing. The signal then enters the wavelet transform module, where multi-scale analysis further extracts local features and details.

[0056] The wavelet transform decomposes a signal into components of different scales by selecting an appropriate mother wavelet function. The wavelet transform provides time-frequency localization, meaning it can analyze changes in a signal over different time periods and frequency ranges. This makes the wavelet transform ideal for processing sudden changes, noise, and other time-varying characteristics in a signal.

[0057] In the wavelet transform, the signal x(t) is mapped to different scales and time positions (through translation and scaling operations). The wavelet transform formula convolves the input signal x(t) with the mother wavelet function ψ(t) to obtain the decomposition coefficients of the signal at different scales and time positions.

[0058] Wavelet transform formula: in: W(a,b) represents the wavelet transform coefficient of the signal x(t) at scale a and position b, which represents the local characteristics of the signal at this scale and position; x(t) is the input signal, which is the signal processed by the signal input unit and the signal preprocessing unit; ψ(t) is the mother wavelet function, which usually uses a waveform with good time-frequency localization characteristics, such as Daubechies wavelet, Coiflet wavelet, etc.; a is the scale factor, which controls the compression and expansion of the wavelet and affects the frequency analysis of the signal. A small scale factor compresses the wavelet, allowing it to analyze the high-frequency components of the signal; a large scale factor expands the wavelet, making it suitable for analyzing low-frequency components; b is the translation factor, which controls the movement of the wavelet function in time to ensure the analysis of the signal at different time points; It is the form of the wavelet function after scaling and translation adjustment, which is used for time-frequency analysis of the signal; dt: the differential part of the integral.

[0059] The wavelet transform decomposes the signal x(t) into different scales and locations, thereby obtaining the signal's local features at multiple frequency levels. The high-frequency components primarily reflect signal details, while the low-frequency components represent the overall trend. In this way, the wavelet transform can effectively extract subtle features from the signal and is particularly suitable for processing signals with abrupt or non-stationary characteristics.

[0060] A notable feature of the wavelet transform is its multiscale decomposition. The wavelet transform can decompose a signal into multiple sub-signals of varying scales. Each sub-signal at each scale represents the local characteristics of the signal at that scale. In signal processing, high-frequency components typically represent signal details and variations, while low-frequency components represent stable trends.

[0061] Specifically, multi-scale analysis is achieved by changing the scale factor a. A small scale factor causes the wavelet transform to focus on the high-frequency components of the signal (such as mutations, edges, etc.), while a larger scale factor focuses on the low-frequency components of the signal (such as stable parts, trends, etc.).

[0062] High-frequency components: These components typically contain noise and detail components of the signal. Through wavelet transform, these high-frequency components can be extracted separately for subsequent noise removal or signal enhancement.

[0063] Low-frequency part: usually represents the stable part of the signal and retains the main trend information of the signal. This information is particularly important for signal recovery and reconstruction.

[0064] The wavelet transform module, located after the signal preprocessing unit, receives the signal after it has passed through a bandpass filter and gain adjustment. The signal has already been optimized upon entering the wavelet transform module, enabling efficient time-frequency analysis. The wavelet transform effectively extracts local features of the signal by performing a multi-scale decomposition of the signal.

[0065] After passing through the wavelet transform module, the signal is decomposed into multiple sub-signals of varying scales. These decomposed sub-signals are further processed in subsequent modules, such as the signal recovery module. Specifically, the low-frequency portion (the main trend of the signal) is typically used for subsequent signal recovery or estimation, while the high-frequency portion is used for noise removal or signal enhancement.

[0066] In practical applications, wavelet transform parameters, such as the mother wavelet function ψ(t) and the scaling factor a, often need to be adjusted based on the characteristics of the signal. Choosing an appropriate mother wavelet function is crucial for signal analysis. For example, the Daubechies wavelet is suitable for processing signals with sudden changes, while the Coiflet wavelet is more suitable for processing smooth signals.

[0067] Specifically, choosing different wavelet mother functions and scale factors can help analyze the different frequency components of the signal. For noisy signals, a smaller scale factor can help capture high-frequency noise components; for more stable signals, a larger scale factor can provide long-term trends of the signal.

[0068] The wavelet-transformed signal is then decomposed into multiple frequency components. In subsequent signal processing stages, these components can be manipulated as needed. For example, a signal recovery module can use the low-frequency components to recover the main trend of the signal, while the high-frequency components can be used for noise removal.

[0069] Further processing methods: Noise removal: In some embodiments, the high-frequency portion (usually containing noise components) after wavelet transformation is removed through threshold processing to improve signal quality.

[0070] Signal enhancement: The low-frequency part of the signal can be used to restore the basic structure of the signal in the signal recovery stage, while the high-frequency part is used to extract and enhance the detailed features of the signal.

[0071] After the wavelet transform decomposes the signal into multiple sub-signals, the signal recovery module processes these sub-signals. Specifically, the low-frequency component is typically used for signal reconstruction, while the high-frequency component is used for signal denoising. The combination of the wavelet transform and the signal recovery module enables more refined signal processing, thereby improving the overall performance of the system.

[0072] The wavelet transform module decomposes the signal into multiple sub-signals with different frequency characteristics by selecting an appropriate wavelet mother function and scaling factor. The low-frequency portion of the signal represents the long-term trend, while the high-frequency portion represents the detailed changes. In this way, the wavelet transform of the signal not only improves the accuracy of signal processing, but also provides strong support for denoising, enhancement, and restoration.

[0073] Adaptive filtering and Kalman filtering modules are used to suppress noise and dynamically optimize signals to enhance signal quality; The adaptive filtering and Kalman filtering modules are located in the middle stage of the signal processing pipeline, responsible for further denoising and optimizing the signal to ensure that the signal quality meets the requirements of subsequent processing modules. After multi-scale analysis by the wavelet transform module, the signal is decomposed into multiple frequency bands. Low-frequency components typically represent the main trend of the signal, while high-frequency components often contain noise components. Based on this, the adaptive filtering and Kalman filtering modules can further process different parts of the signal, achieving efficient signal recovery and enhancement.

[0074] Generally, adaptive filters dynamically adjust their parameters based on changes in the input signal to minimize noise interference. Kalman filters, on the other hand, combine the currently observed signal with its predicted state to optimally estimate the signal, further improving the accuracy of signal recovery. The combination of these two methods effectively removes high-frequency noise and optimizes signal recovery, demonstrating superior performance in dynamic noise environments.

[0075] The core task of an adaptive filter is to dynamically adjust the filter coefficients based on the real-time characteristics of the signal, thereby maximizing the effective signal components and suppressing noise. Commonly used adaptive filtering algorithms include the LMS (least mean square) algorithm and the RLS (recursive least squares) algorithm. Adaptive filters adjust their parameters in real time based on the characteristics of the input signal (such as signal amplitude and noise level) to adapt to environmental changes.

[0076] Adaptive filtering minimizes the error between the filter output and the desired signal by continuously optimizing the filter coefficients. By comparing the difference between the filter output and the desired output, the filter adjusts its coefficients in real time to achieve the desired denoising effect.

[0077] LMS adaptive filtering formula: w(k+1)=w(k)+μe(k)x(k); in: w(k) is the filter coefficient vector, representing the filter parameters at time k; μ is the step size parameter, which controls the speed at which the filter coefficients are updated. Choosing a smaller step size improves stability, while a larger step size accelerates convergence. e(k) is the error, defined as the difference between the desired signal and the actual output signal; d(k) is the desired signal, which in this solution is typically provided by the output of the previous module or a reference signal; y(k) is the actual output signal of the filter; and x(k) is the input signal at the current moment. By minimizing the error e(k), the LMS algorithm continuously updates the filter coefficients w(k), bringing the filter output closer to the desired signal d(k), effectively removing noise from the signal.

[0078] The Kalman filter is an optimal signal recovery algorithm based on recursive estimation, particularly suitable for signal state estimation. The Kalman filter combines the current observed signal with the signal estimate at the previous moment to obtain the optimal estimate of the signal by minimizing the estimation error.

[0079] The Kalman filter recursively updates the signal estimate based on the system's state equation and observation equation. At each moment, the Kalman filter updates the signal estimate based on the current observation data y(k) and the system's predicted state. To calculate the new estimate And update the covariance P of the state k|k .

[0080] Kalman filter formula: in: is the signal estimate of the Kalman filter at time k; is the signal prediction value at time k-1; K k is the Kalman gain, which determines the weighted ratio of the current observation to the previous estimate; k is the observation signal at time k, that is, the actual observation data input to the filter; H k is the observation matrix, which represents the observation model of the signal; P k|k-1 is the state estimation covariance matrix at time k-1, reflecting the uncertainty of the previous estimate; R kis the observation noise covariance matrix, which represents the noise level in the observation signal.

[0081] Kalman gain K k The weight between the current observation value and the predicted value is determined. When the noise is small, the predicted value is trusted more; when the noise is large, more reliance is placed on the current observation data, thereby obtaining the optimal signal recovery result.

[0082] The adaptive filtering and Kalman filtering modules are complementary, operating at different stages of signal processing. Specifically, the adaptive filtering module first dynamically filters the signal, adjusting the filter coefficients in real time to respond to signal changes. The Kalman filter then recursively estimates the optimal state of the signal, further optimizing signal recovery.

[0083] After the signal undergoes multi-scale analysis through the wavelet transform module, the high-frequency portion of the signal typically contains noise, while the low-frequency portion contains the main signal trends. Adaptive filtering processes the high-frequency portion to remove noise, while the Kalman filter focuses on the low-frequency portion, accurately estimating the signal state and optimizing signal recovery. The two work together to ensure optimized signal processing at every stage.

[0084] In practical applications, the performance of adaptive filtering and Kalman filtering is highly dependent on parameter selection. For example, the step size μ in adaptive filtering affects the speed of filter convergence. A smaller step size results in slower convergence but a more stable system, while a larger step size can easily lead to system oscillation or instability. Therefore, the step size needs to be dynamically adjusted based on signal characteristics and noise levels.

[0085] Kalman gain K in Kalman filtering k and noise covariance R k It also needs to be set appropriately. The Kalman gain affects the degree of dependence on the current observation data when estimating the signal; and the noise covariance matrix R k By optimizing these parameters, the Kalman filter can achieve the best effect in different noise environments.

[0086] In some application scenarios, the parameters of the adaptive filtering and Kalman filtering modules need to be adjusted in real time according to changes in the external environment. For example, when the signal source changes or the noise environment fluctuates significantly, the system can automatically adjust the step size μ, Kalman gain K, and the Kalman filter gain according to the feedback. k and noise covariance R k , to adapt to new signal environments. In addition, combining with other signal processing techniques (such as nonlinear filtering) can further enhance the signal recovery effect, especially in complex environments.

[0087] Adaptive filtering removes noise from the signal in real time by dynamically adjusting filter coefficients, while Kalman filtering optimizes signal recovery through recursive estimation, further optimizing signal quality. Through precise parameter adjustment, both modules can adapt to signal changes in varying noise environments, providing more efficient and accurate signal enhancement and laying a solid foundation for subsequent signal recovery and analysis.

[0088] The signal recovery module recovers the signal through maximum a posteriori estimation to further improve the signal quality; The signal recovery module is responsible for the final signal recovery based on the previously mentioned signal processing steps (such as wavelet transform, adaptive filtering, and Kalman filtering). By restoring the true characteristics of the target signal, this module provides high-quality output for subsequent signal analysis, storage, or other applications. The goal of signal recovery is to extract the main features of the original signal from a noise-contaminated signal and eliminate or suppress any residual noise.

[0089] After the signal undergoes multi-layer processing through wavelet transform, adaptive filtering, and Kalman filtering, most noise is removed from the signal's various frequency components. Based on this, the signal recovery module optimizes signal recovery using methods such as maximum a posteriori estimation (MAP) or minimum mean square error (MMSE). By maximizing the signal's posterior probability or minimizing the recovery error, the signal recovery module accurately recovers the target signal.

[0090] Maximum a posteriori estimation (MAP) is a signal recovery method based on Bayesian theory. By combining prior information with observed data, it maximizes the posterior probability of the signal, thereby obtaining an optimal estimate of the signal. In this paper, the MAP method is used to recover the true characteristics of the signal and eliminate noise and interference components in the signal.

[0091] The MAP method recovers the signal by maximizing the posterior probability P(x|y). Specifically, the MAP method combines the signal's prior probability P(x) and the likelihood function P(y|x) of the observed signal and maximizes the product of the two to obtain the optimal estimate of the signal.

[0092] MAP formula: in: is the estimated value of the recovered signal, that is, the optimal result of signal recovery; P(x|y) is the posterior probability of signal x under the observed signal y, which represents the signal estimate after a given observed signal; P(y|x) is the likelihood function of the observed signal y under signal x, which describes the probability of the observed signal appearing under a given signal; P(x) is the prior probability of the signal, reflecting the prior knowledge or statistical characteristics of the signal; P(y) is the marginal probability of the observed signal; argmax x1 It means finding the value of x that maximizes P(x|y).

[0093] By maximizing P(x|y), the MAP method can fully utilize prior information and the observed signal to recover an estimate that best matches the actual signal characteristics. This process ensures the accuracy and effectiveness of signal recovery in complex noisy environments.

[0094] Minimum mean square error (MMSE) is another widely used signal recovery technique. It works by minimizing the mean squared error (MSE) between the recovered signal and the true signal. In MMSE, the system adjusts the signal recovery process based on the difference between the previous signal estimate and the observed data, ultimately making the recovered signal as close to the true signal as possible.

[0095] The MMSE method minimizes the error in the signal recovery process through the following formula to obtain the optimal recovered signal.

[0096] MMSE formula: in: is the estimated value of the restored signal; x: is the real signal, that is, the target signal; is the mean square error, which represents the difference between the true signal and the restored signal, usually the expected value of the square of the signal error; argmin x2 Indicates finding The x value to minimize.

[0097] MMSE optimizes the signal recovery process by minimizing the mean square error, making the recovered signal as close as possible to the true signal. Especially in the absence of complete prior knowledge, the MMSE method can make full use of existing observation data to recover the signal.

[0098] The signal recovery module is closely integrated with the aforementioned signal processing modules (such as the wavelet transform module, adaptive filtering, and Kalman filtering modules). After the wavelet transform module performs multi-scale analysis on the signal, its frequency components are decomposed into distinct sub-signals. The adaptive filtering and Kalman filtering modules further optimize the signal, remove high-frequency noise, and estimate the signal state. The signal recovery module then combines these optimized signals and uses methods such as MAP or MMSE to recover the key signal features.

[0099] The signal recovery module performs the final signal recovery based on the previous modules. Specifically, the signal optimized by adaptive filtering and Kalman filtering provides high-quality input. The signal recovery module further optimizes the signal through methods such as MAP or MMSE to make it closer to the original target signal.

[0100] In the signal recovery module, parameter settings for the MAP and MMSE methods are crucial. For example, the prior probability P(x) and observation model P(y|x) in the MAP algorithm need to be designed based on the characteristics of the signal. The prior probability P(x) reflects the statistical properties of the signal, such as Gaussian distribution or sparsity. The observation model P(x|y) describes the signal observation process and is typically dependent on the signal sampling method or measurement equipment.

[0101] Mean Squared Error in MMSE Method It is necessary to optimize by selecting an appropriate error metric. Different types of error metrics will affect the recovery effect, so parameter adjustment and optimization are required in different applications.

[0102] In certain application scenarios, the signal recovery module can be combined with other advanced signal processing techniques to further enhance the recovery effect. For example, compressed sensing can be combined with the signal recovery module to improve the efficiency and accuracy of the signal recovery process. In extremely noisy environments, combining techniques such as nonlinear filtering with MAP or MMSE methods can further improve signal recovery quality.

[0103] In some embodiments, the output of the signal recovery module may require further optimization and post-processing. For example, this can be achieved by further optimizing the details of the recovered signal to remove subtle noise components, or by using a high-pass filter to remove lower-frequency noise to further enhance signal quality. This post-processing can make the final recovered signal clearer and tailored to specific application requirements.

[0104] After the signal is processed through wavelet transform, adaptive filtering, and Kalman filtering, the signal recovery module further maximizes the posterior probability or minimizes the error to obtain a recovery result that is closest to the true signal. This process utilizes prior knowledge of the signal and observed data to improve the accuracy and reliability of signal recovery.

[0105] An output module, used to output the processed signal to a display device or storage medium; The output module is responsible for converting the signal processed by various signal processing modules (such as signal input, signal preprocessing, wavelet transform, filtering, and signal recovery) into a format suitable for external devices or storage media. Its goal is to transmit the enhanced and restored high-quality signal in a suitable form to downstream devices or systems for display, storage, further processing, or other applications.

[0106] After undergoing the layers of optimization in the aforementioned processing modules, the signals have achieved high quality. The output module's task is to ensure that these optimized signals are stably and accurately transmitted to the target device using the appropriate format and interface. During this process, the output module must not only convert the signal format but also meet requirements for real-time performance, stability, and reliability.

[0107] The core task of an output module is to convert processed signals into a suitable output format based on different application requirements and transmit them to the target device through the appropriate interface. Specifically, an output module can convert signals into digital, analog, or other formats to ensure compatibility with a variety of devices.

[0108] Signal format conversion: After receiving the processed signal from the signal recovery module, the output module first converts the signal format. For example, for digital signals, the output module will use an appropriate digital encoding format (such as binary or floating point format) and may use data compression technology to reduce bandwidth usage.

[0109] Analog signal output: In some applications, the output module may need to convert the signal into an analog signal. In this case, the output module needs to convert the processed digital signal into a continuous analog voltage or current signal through a digital-to-analog converter (DAC).

[0110] Digital signal output format: The signal is usually output through a standard digital protocol (such as I2C, SPI, USB, etc.). The output data format can be binary, hexadecimal, or floating-point data to meet the requirements of different devices.

[0111] Analog signal output format: The signal can be output as an analog voltage or current signal through the DAC module. The frequency range and accuracy of the analog output are usually selected according to the system requirements.

[0112] Output modules must not only support multiple signal formats and interfaces but also ensure real-time signal transmission, especially in real-time monitoring and data acquisition applications. This real-time requirement requires that output modules complete signal processing and conversion in the shortest possible time to avoid system performance degradation caused by transmission delays.

[0113] Low-latency transmission: The output module is designed to minimize data transmission delays. For example, the module uses efficient algorithms and protocols to achieve fast signal conversion and transmission.

[0114] Real-time data flow control: To ensure real-time performance, output modules typically employ data flow control techniques to prevent data from being blocked or lost during transmission. Common methods include flow control, packet buffering, and sequential transmission.

[0115] Real-time control mechanism: The output module supports real-time data stream transmission protocols (such as RTSP), ensuring that there will be no packet loss or delay problems during high-frequency signal acquisition or transmission.

[0116] In some applications, signal stability and reliability are crucial, especially when transmitting over long distances or in complex environments. Output modules must ensure stable signal transmission without data loss or errors due to environmental interference or device failure.

[0117] Error detection and correction: Output modules can use various error detection and correction technologies to ensure signal stability. Common error detection technologies include checksum and cyclic redundancy check (CRC).

[0118] Redundant design: In critical applications, output modules may also be equipped with redundancy mechanisms to ensure that signals can be transmitted through alternative paths or devices in the event of a failure.

[0119] Signal stability and reliability design: The output module implements error detection and automatic retransmission mechanisms to ensure effective recovery when packet loss or errors occur during data transmission.

[0120] Output modules need to support multiple interfaces to meet the needs of different devices and applications. Common interfaces include USB, HDMI, Ethernet, and wireless transmission. Depending on the application scenario, the output module can select the appropriate interface for signal transmission.

[0121] USB port: used to output signals to a computer or storage device. Signals can be transmitted via USB protocol and can be stored in a variety of file formats (such as CSV, WAV, MAT, etc.).

[0122] HDMI / VGA Interface: In display applications, the output module can transmit signals to the display device via the HDMI or VGA interface. In this case, the output module not only needs to convert the signal format but also ensure that the signal is compatible with the display device's resolution, refresh rate, and other parameters.

[0123] Ethernet interface: In remote transmission applications, the output module can transmit signals to other devices or servers in the network through the Ethernet interface, usually using TCP / IP protocol for data transmission.

[0124] Protocol and interface support: The output module supports standard interfaces and protocols such as USB, HDMI, VGA, Ethernet, etc., ensuring that the signal can be delivered to the target device or system in the appropriate format.

[0125] For certain high-precision applications, output modules must not only ensure accurate signal transmission but also guarantee the quality of the output signal. For example, they must have sufficient resolution and accuracy to meet the needs of precise measurement or analysis.

[0126] Signal accuracy control: The output module uses high-resolution DAC or other high-precision signal conversion technologies according to the different needs of the application to ensure that the accuracy of the output signal meets the system requirements.

[0127] Signal quality monitoring: In some embodiments, the output module can also monitor the signal quality to ensure that there is no interference or distortion during the signal transmission process.

[0128] Signal accuracy and quality control mechanism: The output module can automatically detect the quality of the output signal and provide feedback or adjustments when an abnormality is found to ensure that the signal quality is always within an acceptable range.

[0129] To adapt to future needs and different application scenarios, output modules are usually designed with an expandable and flexible structure to support new output interfaces, protocols or processing methods.

[0130] Interface expansion: The output module can increase the supported interfaces according to needs, such as conveniently adding new interfaces (such as wireless transmission, Bluetooth, etc.) through modular design.

[0131] Protocol support: Output modules can support new data transmission protocols through firmware or software upgrades, ensuring that the system can adapt to technology updates and changes.

[0132] Output modules transmit the processed, restored, and enhanced signals to external devices or storage media via a variety of interfaces and protocols. They support multiple signal formats (digital, analog, etc.) and offer real-time performance, stability, reliability, and high precision.

[0133] Power management module, used to provide stable power to the system and optimize power consumption; The power management module's primary function is to provide stable and efficient power support for the entire system, ensuring stable operation of all modules under various operating conditions and preventing power fluctuations or failures from impacting system performance. The module also includes power monitoring, power optimization, and overload protection, aiming to improve system energy efficiency and extend the life of the equipment.

[0134] Typically, the power management module collaborates with various signal processing modules (such as signal preprocessing, wavelet transform, filtering, and signal recovery) to ensure efficient and stable system operation. The power management module is responsible for monitoring power status, regulating power output, promptly responding to current and voltage fluctuations, and providing protection against potential overload conditions. Through these measures, the power management module provides a reliable power supply for the system, ensuring its normal operation.

[0135] The power input receives the input voltage from an external power source, converts it to different voltage levels through the voltage-stabilizing power supply module, and supplies it to each signal processing module. Based on system requirements, the power management module supports multiple regulated outputs, ensuring that each module receives the required stable voltage under different operating conditions.

[0136] Power input: The power input part receives external power input through an interface, such as an AC power adapter, USB power supply or battery.

[0137] Regulated power supply: A regulated power supply module converts the input voltage into different output voltages to meet the needs of each module. Regulated power supply modules use linear regulators (LDOs) or switching regulators (such as buck or boost converters) to dynamically adjust the output based on load demand.

[0138] Power supply voltage regulation formula: For a voltage regulator (such as a switching regulator), its efficiency η can be expressed as: in: P out is the output power, in watts (W); P in is the input power in watts (W); V out is the output voltage in volts (V); I out is the output current in amperes (A); V in is the input voltage in volts (V); I in is the input current in amperes (A).

[0139] The voltage regulator adjusts the input voltage V inand the output voltage V out , ensuring stable power supply and optimizing energy efficiency. An efficient voltage regulator can maintain high efficiency during load fluctuations, minimizing energy loss.

[0140] The power monitoring module monitors the power output of the power supply in real time, assesses the system load, and dynamically adjusts the power output to accommodate load changes. This not only helps improve system efficiency but also extends the life of the power supply equipment and prevents power supply overload.

[0141] Power monitoring: Through current sensors and voltage sensors, the power monitoring module monitors the system current and voltage in real time and calculates the current power demand.

[0142] Power regulation: The system adjusts the output voltage or current according to real-time power demand, ensuring that the system can obtain stable power under various load conditions and avoid unnecessary energy waste.

[0143] Power calculation formula: The power calculation formula of the power monitoring module is: P = V·I; in: P is power in watts (W); V is voltage in volts (V); I is current in amperes (A).

[0144] By monitoring power P in real time, the power management module can flexibly adjust power output to avoid excessive energy consumption or system performance degradation due to insufficient power supply.

[0145] The power management module includes overload protection to prevent system damage due to abnormal conditions such as power overload and short circuit. By monitoring current and voltage in real time, the overload protection module quickly disconnects the power supply or limits the current if an overload or short circuit is detected, ensuring system safety.

[0146] Current monitoring: The current sensor monitors the power supply current. When the current exceeds the set safety value, the overload protection module will automatically activate the protection mechanism.

[0147] Protection response: Once the current is overloaded, the overload protection module protects the equipment by disconnecting the power supply or limiting the current output.

[0148] Overload protection formula: The overload protection mechanism compares the actual current I actual With the set maximum current I max , to determine whether to start protection: I actual >I max Trigger protection; in: I actual is the actual current, indicating the current currently flowing through the power supply; Imax The maximum current is set, and protection is triggered when this value is exceeded.

[0149] This overload protection mechanism can cut off the power supply in time when the system encounters an abnormality, avoiding damage to the power module and other components.

[0150] To optimize the overall energy efficiency of the system, the power management module also includes energy optimization and energy-saving control functions. This function ensures that the system operates with minimum power consumption under different load conditions, avoiding unnecessary energy waste.

[0151] Dynamic power adjustment: The power management module dynamically adjusts the power supply output according to changes in system load. When the load is low, the system will reduce power output to reduce power consumption; when the load is high, the power supply will provide more power support.

[0152] Energy-saving mode: When the system is idle or in standby mode, the power management module can switch to energy-saving mode to reduce power consumption.

[0153] Energy-saving control formula: In energy-saving mode, the power module reduces energy consumption by reducing output power. Assume that the system load is L and the output power is P out , the power regulation in energy-saving mode can be expressed as: P out =f(L)·P max ; in: P out is the output power; L is the load ratio, which indicates the current load of the system; f(L) is the power adjustment function, which dynamically adjusts the power according to the load L; P max The maximum power indicates the maximum power output of the system at full load.

[0154] Through this power regulation, the system can automatically adjust energy consumption according to load conditions and improve energy efficiency.

[0155] The power management module ensures stable system operation under various load conditions through functions such as voltage regulation, power monitoring, overload protection, and energy optimization. It provides multiple stable voltage outputs and dynamically adjusts power distribution to meet the needs of different modules. Overload protection also provides system security, ensuring that the power module will not be damaged in abnormal situations. The power management module's design provides efficient, stable, and safe power support for the entire electromagnetic signal enhancement and processing system.

[0156] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An electromagnetic signal enhancement processing module, characterized in that: include: a signal input unit for receiving electromagnetic signals from external sensors and converting them into digital signals suitable for subsequent processing; Signal pre-processing unit, used to perform preliminary conditioning on the input signal to adapt to subsequent processing; Wavelet transform module, used to perform multi-scale time-frequency domain analysis on the input signal to extract information of different frequency bands of the signal; Adaptive filtering and Kalman filtering modules are used to suppress noise and dynamically optimize signals to enhance signal quality; The signal recovery module recovers the signal through maximum a posteriori estimation to further improve the signal quality; An output module, used to output the processed signal to a display device or storage medium; The power management module is used to provide stable power to the system and optimize power consumption.

2. The electromagnetic signal enhancement processing module according to claim 1, characterized in that: The signal input unit includes a plurality of sensor interfaces, which are used to connect to different types of electromagnetic signal sensors to enhance the amplitude of the input signal.

3. The electromagnetic signal enhancement processing module according to claim 1, characterized in that: The signal preprocessing unit includes: Preamplifier, used to enhance the strength of input signals and reduce the influence of external noise; Bandpass filter, used to filter out unwanted frequency components and retain the target signal; Automatic gain controller, used to dynamically adjust the gain according to the strength of the input signal to ensure the appropriate signal amplitude.

4. The electromagnetic signal enhancement processing module according to claim 1, characterized in that: The wavelet transform module includes: The mother wavelet function is used to decompose the input signal in the time-frequency domain and decompose the signal into multiple sub-signals in different frequency bands; The wavelet coefficient threshold processing module is used to remove unnecessary noise coefficients and retain the main features of useful signals.

5. The electromagnetic signal enhancement processing module according to claim 1, characterized in that: The adaptive filtering and Kalman filtering module includes: Adaptive filter, which is used to dynamically adjust the filter parameters according to the real-time signal characteristics to minimize noise; A Kalman filter is used to recursively update the signal based on the previous signal state and the current observation data to provide the optimal signal estimate.

6. The electromagnetic signal enhancement processing module according to claim 1, characterized in that: The signal recovery module recovers the signal by maximum a posteriori estimation, and the maximum a posteriori estimation includes: The prior probability of the signal is used to represent the prior knowledge of the signal; The likelihood function of the signal is used to express the relationship between the signal and the observed data; The posterior probability maximization process is used to optimize the signal recovery process based on the observed data and prior knowledge.

7. The electromagnetic signal enhancement processing module according to claim 1, characterized in that: The output module includes: Standard data interface for transmitting processed signals to external display devices or storage devices; Data converter, used to convert the signal into a format suitable for input to external devices.

8. The electromagnetic signal enhancement processing module according to claim 1, characterized in that: The power management module includes: Voltage-stabilized power supply unit, used to provide stable power supply for each module; The power optimization unit is used to dynamically adjust power output according to system load changes and optimize power consumption.

9. The electromagnetic signal enhancement processing module according to claim 1, characterized in that: The module uses a hardware acceleration unit to increase processing speed, and the hardware acceleration unit includes: GPU acceleration unit, used to accelerate wavelet transform and Kalman filtering for computationally intensive operations; FPGA acceleration unit, used to accelerate the adaptive filtering and maximum a posteriori estimation process.

10. The electromagnetic signal enhancement processing module according to claim 1, characterized in that: The module supports multiple sensor input interfaces, including radio wave sensors and magnetic field sensors, to meet the needs of different electromagnetic signal detection applications.

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