An ultra-low power narrowband filter implementation method based on a digital phased array system

Abstract

The application relates to the technical field of narrow-band filters, and discloses an ultra-low-power narrow-band filter implementation method based on a digital phased array system, which comprises the following steps: S1, collecting input signal data and performing adaptive preprocessing to generate preprocessed signal data; S2, performing digital beam forming processing based on the preprocessed signal data to generate digital beam forming data; and S3, performing narrow-band filter parameter matching processing according to the digital beam forming data to generate narrow-band filter parameter data; in the narrow-band filter processing process, through adaptive noise suppression and a multi-dimensional signal preprocessing mechanism, environmental electromagnetic interference on a target signal is identified and eliminated in real time, the system can guarantee the pointing accuracy of digital beam forming, the pointing deviation problem in the spatial signal capturing process can be avoided, the accuracy of narrow-band signal extraction is improved, and the reliability and signal fidelity of filter output are enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of narrow-band filter, in particular to an implementation method of ultra-low-power narrow-band filter based on digital phased array system. BACKGROUND

[0002] A narrow-band filter is a filter that only allows single-wavelength light to pass through, which plays an important role in the field of spectroscopy and optical communication. The narrow-band filter is a frequency selection device designed based on high-Q element or resonance principle in electronics. The device produces sharp frequency response through LC resonant circuit, crystal oscillator or surface acoustic wave device, which can extract weak signals in communication systems and suppress out-of-band interference.

[0003] At present, due to the complex electromagnetic interference of the digital phased array system in the multi-target scene, when performing dynamic power optimization, it is impossible to determine in real time whether there is a nonlinear power mutation in the signal processing link. When the transient current overshoot phenomenon occurs in the array beam switching process, the voltage frequency adjustment of the power control model DVFS will fail, resulting in filter performance collapse in a low signal-to-noise ratio environment, and the accuracy of narrow-band filtering cannot be guaranteed.

[0004] Therefore, the present application provides an implementation method of ultra-low-power narrow-band filter based on digital phased array system to solve the above problems. SUMMARY

[0005] Technical problems solved

[0006] In view of the deficiencies in the prior art, the present application provides an implementation method of ultra-low-power narrow-band filter based on digital phased array system to solve the problems raised in the background art.

[0007] (II) Technical solutions

[0008] To achieve the above purposes, the present application provides the following technical solutions: an implementation method of ultra-low-power narrow-band filter based on digital phased array system, the method comprising the following steps:

[0009] S1, collecting input signal data and performing adaptive preprocessing to generate preprocessed signal data;

[0010] S2, performing digital beam forming processing based on the preprocessed signal data to generate digital beam forming data;

[0011] S3, performing narrow-band filter parameter matching processing according to the digital beam forming data to generate narrow-band filter parameter data;

[0012] S4, narrow-band filtering the digital beamforming data by using a hybrid filtering algorithm combined with the narrow-band filtering parameter data, to generate initial filtering output data;

[0013] S5, performing power consumption optimization processing on the initial filtering output data based on a dynamic power consumption control model, to generate power consumption optimized filtering data, wherein the dynamic power consumption control model uses a dynamic voltage and frequency scaling (DVFS) technique to generate the power consumption optimized filtering data;

[0014] S6, performing filtering output calibration processing, to generate calibrated filtering output data;

[0015] S7, outputting the calibrated filtering output data as a final narrow-band filtering result.

[0016] Preferably, the S1 comprises the following steps:

[0017] S11, collecting multi-channel input signal data by using a digital phased array receiving antenna array, wherein the input signal data comprises signal frequency parameters, amplitude parameters, and phase parameters, and the signal frequency parameters range from 1 MHz to 10 GHz;

[0018] S12, performing noise suppression processing on the input signal data by using an adaptive noise reduction algorithm, to generate noise-reduced signal data;

[0019] S13, performing signal normalization processing on the noise-reduced signal data based on signal integrity analysis, to generate normalized signal data as the preprocessed signal data.

[0020] Preferably, the S2 comprises the following steps:

[0021] S21, importing the preprocessed signal data into a digital signal processing platform, and calculating beam weight parameters by using a weighted least square algorithm, to generate beam weight data, wherein the beam weight is calculated by the following formula:

[0022] ;

[0023] wherein is a beam weight vector, is an input signal covariance matrix, is a directional vector, is a conjugate transpose operator;

[0024] S22, performing array antenna phase adjustment processing based on the beam weight data, to generate phase adjustment data;

[0025] S23, applying a digital beam synthesis algorithm combined with the phase adjustment data to generate digital beam forming data, wherein the digital beam forming data represents beam pattern parameters with spatial angle as coordinates.

[0026] Preferably, the S3 comprises the following steps:

[0027] S31, obtaining target frequency parameters in the digital beam forming data;

[0028] S32, matching the target frequency parameters with a preset narrowband filtering frequency band library to generate frequency band matching data;

[0029] S33, generating narrowband filtering parameter data according to the frequency band matching result, wherein the narrowband filtering parameter data comprises center frequency parameters, bandwidth parameters and filtering order parameters.

[0030] Preferably, the S4 comprises the following steps:

[0031] S41, combining the digital beam forming data with the narrowband filtering parameter data by using a hybrid filtering algorithm, wherein the hybrid filtering algorithm comprises a combination of FIR filter and IIR filter, and the transfer function thereof is:

[0032] ;

[0033] wherein is the transfer function of the hybrid filter in the z domain, is a complex frequency domain variable, is a filter tap number, is the order of the FIR filter, is the coefficient of the FIR filter at the kth tap, is the order of the IIR filter numerator polynomial, is the kth coefficient of the IIR filter numerator part, is the order of the IIR filter denominator polynomial, is the Kth coefficient of the IIR filter denominator part;

[0034] S42, performing real-time filtering calculation processing to generate initial filtering output data, wherein the filtering calculation processing is optimized by using floating point operation to reduce the calculation power consumption.

[0035] Preferably, the S5 comprises the following steps:

[0036] S51, constructing a dynamic power consumption control model, wherein the model dynamically adjusts the filter working mode based on signal power parameters and environmental noise parameters;

[0037] S52, performing power consumption grading control processing according to the signal strength parameter of the initial filtering output data, generating power consumption optimization filtering data, wherein the power consumption optimization processing includes dynamic voltage frequency scaling (DVFS) technology.

[0038] Preferably, the S6 comprises the following steps:

[0039] S61, performing error correction processing on the power consumption optimization filtering data based on a filtering error feedback mechanism, to generate correction intermediate data;

[0040] S62, performing phase alignment processing on the correction intermediate data by applying a phase compensation algorithm, to generate calibration filtering output data.

[0041] Preferably, the S7 comprises the following steps:

[0042] S71, converting the calibration filtering output data into a standard output format;

[0043] S72, outputting the final narrowband filtering result through a digital interface, and recording power consumption log data.

[0044] Preferably, the hybrid filtering algorithm in the S4 comprises the following sub-steps:

[0045] S91, initializing FIR filter coefficients and IIR filter coefficients;

[0046] S92, performing parallel filtering calculation, wherein the FIR filter processes high-frequency components, and the IIR filter processes low-frequency components;

[0047] S93, fusing FIR and IIR output data to generate initial filtering output data.

[0048] Preferably, the dynamic power consumption control model in the S5 comprises the following sub-steps:

[0049] S101, monitoring environmental noise parameters and signal power parameters;

[0050] S102, when the signal power is lower than a threshold value, switching to a low-power consumption mode and reducing a sampling rate;

[0051] S103, when the environmental noise is higher than a threshold value, activating a high-precision mode and increasing a filtering order.

[0052] (Three) beneficial effects

[0053] Compared with the prior art, the present application provides an ultra-low power consumption narrowband filter implementation method based on a digital phased array system, which has the following beneficial effects:

[0054] 1. In the present application, in the process of narrowband filtering, through adaptive noise suppression and multi-dimensional signal preprocessing mechanism, real-time identification and elimination of environmental electromagnetic interference disturbance to target signal is realized, so that the system can guarantee the pointing accuracy of digital beam forming, avoid the pointing deviation problem in spatial signal capture process, and improve the accuracy of narrowband signal extraction; at the same time, combined with the dynamic synergy mechanism of frequency band matching and hybrid filtering algorithm, the target narrowband signal is separated in complex spectrum environment, and the signal distortion problem caused by frequency response mismatch of traditional filter is overcome, and the reliability and signal fidelity of filtering output are enhanced.

[0055] 2. In the present application, in the power consumption optimization control link, through the closed-loop linkage mechanism of dynamic power consumption model and real-time signal strength detection, the system working mode is automatically adjusted according to the input signal characteristics, so that the system can actively suppress the nonlinear power consumption mutation while guaranteeing the filtering accuracy; when the instantaneous current anomaly and environmental noise mutation are detected, the calculation resource redistribution strategy is automatically triggered, the performance collapse risk caused by voltage frequency regulation failure is eliminated, the running stability of the system in low power consumption mode is improved, and the core contradiction between energy consumption and performance in the traditional scheme is solved.

[0056] 3. In the present application, at the system synergy optimization level, through the coupling feedback mechanism of phase error real-time correction and energy efficiency evaluation index, the timing matching relationship of beam synthesis and filtering processing is optimized synchronously, so that the system can adaptively compensate the phase deviation caused by unit mutual coupling and sampling rate fluctuation; when the beam pointing misalignment and response characteristic anomaly are detected, the multi-module joint calibration process is automatically activated, the spectral aliasing and filter oscillation risk is eliminated, the system robustness in complex electromagnetic environment is improved, and the energy efficiency balance and output consistency of narrowband filtering under all working conditions are guaranteed. BRIEF DESCRIPTION OF DRAWINGS

[0057] Figure 1 The flowchart of the present application is shown in the figure. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0059] Specific embodiment: please refer to Figure 1 A narrowband filter implementation method based on a digital phased array system, the method comprising the following steps:

[0060] S1, collect input signal data and perform adaptive preprocessing to generate preprocessed signal data;

[0061] S2, perform digital beamforming processing based on the preprocessed signal data to generate digital beamforming data;

[0062] S3, perform narrowband filter parameter matching processing according to the digital beamforming data to generate narrowband filter parameter data;

[0063] S4, perform narrowband filtering processing on the digital beamforming data using a hybrid filtering algorithm combined with the narrowband filter parameter data to generate initial filter output data;

[0064] S5, perform power consumption optimization processing on the initial filter output data based on a dynamic power consumption control model to generate power consumption optimized filter data, wherein the dynamic power consumption control model uses dynamic voltage frequency scaling (DVFS) technology to generate power consumption optimized filter data;

[0065] S6, perform filter output calibration processing to generate calibrated filter output data;

[0066] S7, output the calibrated filter output data as the final narrowband filtering result.

[0067] S1 includes the following steps:

[0068] S11, collect multi-channel input signal data through a digital phased array receiving antenna array, the input signal data including signal frequency parameters, amplitude parameters, and phase parameters, wherein the signal frequency parameters range from 1 MHz to 10 GHz;

[0069] S12, perform noise suppression processing on the input signal data using an adaptive noise reduction algorithm to generate noise-reduced signal data;

[0070] S13, perform signal normalization processing on the noise-reduced signal data based on signal integrity analysis to generate normalized signal data as preprocessed signal data.

[0071] S2 includes the following steps:

[0072] S21, import the preprocessed signal data into a digital signal processing platform and calculate beam weight parameters using a weighted least squares algorithm to generate beam weight data, wherein the beam weight is calculated by the following formula:

[0073]

[0074] wherein is a beam weight vector, is an input signal covariance matrix, is a directional vector, ​It is the conjugate transpose operator;

[0075] S22. Perform phase adjustment processing on the array antenna based on the beam weight data to generate phase adjustment data;

[0076] S23. Combining phase adjustment data with a digital beamforming algorithm, digital beamforming data is generated, and its beamforming output function is:

[0077] ;

[0078] in For time-domain beamforming output signal, For the first The weighting coefficients of each array antenna, For the first The received signal from each array antenna The center frequency of the carrier. Beam direction The corresponding number Array element delay, This represents the total number of phased array elements. This is the phase compensation term.

[0079] S3 includes the following steps:

[0080] S31. Obtain the target frequency parameters from the digital beamforming data;

[0081] S32. Match the target frequency parameters with the preset narrowband filter frequency band library to generate frequency band matching data;

[0082] S33. Generate narrowband filter parameter data based on the frequency band matching results. The narrowband filter parameter data includes center frequency parameters, bandwidth parameters, and filter order parameters.

[0083] S4 includes the following steps:

[0084] S41. A hybrid filtering algorithm is used to combine digital beamforming data with narrowband filtering parameter data. The hybrid filtering algorithm includes a combination of FIR and IIR filters, and its transfer function is:

[0085] ;

[0086] in Let be the transfer function of the hybrid filter in the z-domain. For complex frequency domain variables, This refers to the filter tap number. Let be the order of the FIR filter. Let be the coefficient of the FIR filter at the k-th tap. Let be the order of the numerator polynomial of the IIR filter. is the kth coefficient of the numerator polynomial of the IIR filter, is the order of the denominator polynomial of the IIR filter, is the kth coefficient of the denominator polynomial of the IIR filter;

[0087] S42, performing a real-time filtering calculation process to generate initial filtering output data, wherein the filtering calculation process is optimized by using floating-point operation to reduce calculation power consumption, and the operation power consumption model is:

[0088] ;

[0089] wherein is the dynamic power consumption of the floating-point operation unit is a process-related conversion factor, is the number of floating-point operations per second, is the processor operating frequency, is the core supply voltage.

[0090] S5 includes the following steps:

[0091] S51, constructing a dynamic power consumption control model, which dynamically adjusts the working mode of the filter based on the signal power parameter and the environmental noise parameter;

[0092] S52, performing power consumption hierarchical control processing according to the signal strength parameter of the initial filtering output data to generate power consumption optimized filtering data, wherein the power consumption optimization processing includes dynamic voltage frequency scaling (DVFS) technology, and the power consumption control function is:

[0093] ;

[0094] wherein is the optimized supply voltage, is the target power consumption threshold, is the optimized operating frequency, is the voltage frequency conversion coefficient;

[0095] ;

[0096] wherein is the input signal signal-to-noise ratio, is the signal-to-noise ratio-frequency adjustment factor.

[0097] S6 includes the following steps:

[0098] S61, performing error correction processing on the power consumption optimized filtering data based on a filtering error feedback mechanism to generate corrected intermediate data, wherein the phase error estimation model is:

[0099] ;

[0100] wherein is a phase deviation estimate, is an error complex vector, Im is a complex vector imaginary part operator, Re is a complex vector real part operator, is an arctangent function;

[0101] S62, applying a phase compensation algorithm to the intermediate correction data for phase alignment processing to generate calibration filter output data.

[0102] S7 includes the following steps:

[0103] S71, converting the calibration filter output data into a standard output format;

[0104] S72, outputting the final narrowband filtering result through a digital interface and recording power consumption log data, and the energy efficiency evaluation index is:

[0105] ;

[0106] wherein is the spectral efficiency / power consumption ratio, is the signal effective bandwidth, is the output signal-to-noise ratio, is the Shannon channel capacity, is the total system power consumption.

[0107] The hybrid filtering algorithm in S4 includes the following sub-steps:

[0108] S91, initializing the FIR filter coefficients and the IIR filter coefficients;

[0109] S92, performing parallel filtering calculation, wherein the FIR filter processes high frequency components and the IIR filter processes low frequency components;

[0110] S93, fusing the FIR and IIR output data to generate initial filter output data.

[0111] The dynamic power consumption control model in S5 includes the following sub-steps:

[0112] S101, monitoring the environmental noise parameters and signal power parameters;

[0113] S102, when the signal power is lower than the threshold, switching to a low power consumption mode and reducing the sampling rate;

[0114] S103, when the environmental noise is higher than the threshold, activating a high precision mode and increasing the filter order.

[0115] The running steps of the ultra-low power consumption narrowband filter implementation method based on the digital phased array system are as follows:

[0116] Step one, signal acquisition and adaptive preprocessing

[0117] The system captures multi-channel radio frequency signals, including frequency, amplitude and phase parameters, through a digital phased array antenna array. An adaptive noise reduction algorithm suppresses environmental electromagnetic interference in real time while preserving the core characteristics of the target signal. Subsequently, signal normalization is performed to dynamically compress the amplitude to a unified dimension, eliminating the intensity fluctuations caused by the position differences of the array elements. The preprocessing process monitors the integrity of the signal and activates the anti-saturation protection when abnormal distortion is detected, ensuring that the input signal is stable and pure, laying the foundation for subsequent processing.

[0118] Step two, digital beam forming and spatial filtering

[0119] Based on the preprocessed data, the system calculates the optimal weighting coefficients of each array element: the signal main direction is determined through eigenvalue decomposition of the covariance matrix, and the weight vector is solved by applying the minimum variance distortionless response algorithm. The core of spatial filtering is phase synchronization control, which compensates for the phase deviation caused by the wave path difference in real time, realizing super-directivity beam synthesis. The spatial spectrum is monitored during beam forming, and the null position is automatically adjusted when strong interference sources are encountered to ensure the separation of target signals.

[0120] Step three, dynamic matching of narrowband filtering parameters

[0121] For the beam output signal, the system extracts time-frequency domain features. A narrowband filter frequency band database is pre-set, and the real-time signal is matched with the standard template through a pattern recognition algorithm. The fuzzy decision mechanism handles the frequency band boundary scenarios, and selects the optimal filter parameters according to the modulation type. Online parameter updating is supported, and the filter parameters are immediately reconfigured when the signal jumps, ensuring the dynamic following ability of the filter characteristics.

[0122] Step four, real-time processing of hybrid filtering algorithm

[0123] FIR-IIR hybrid architecture is adopted: first, initialize the filter coefficient matrix, FIR order is set based on passband ripple, and IIR order is determined according to stopband suppression requirements. Double parallel processing: FIR path focuses on high-frequency components, maintaining linear phase; IIR path processes low frequencies, achieving steep roll-off. Floating-point operation optimization reduces computing power consumption, and the output signal is compensated and dynamically weighted after time domain alignment, ensuring smooth transition of passband group delay.

[0124] Step five, dynamic power consumption optimization control

[0125] The closed-loop power consumption model monitors the signal-to-noise ratio and noise power parameters, and constructs a pattern decision tree. In high signal-to-noise ratio environments, switch to low power consumption mode: reduce sampling rate, optimize processor frequency, and apply dynamic voltage frequency scaling to reduce energy consumption. When noise increases, activate high-precision mode: increase filter order, allocate computing resources. Built-in fuse protection mechanism limits peak power consumption when there is an abnormal current fluctuation, maintaining filtering accuracy.

[0126] Step six, output calibration and error suppression

[0127] Phase calibration extracts residual deviation through error feedback loop, offset is calculated by complex signal vector analysis, and precise phase realignment is achieved by digital phase shifter. Amplitude equalization compensates passband edge attenuation to avoid demodulation distortion. Environmental adaptability strategy recalibrates parameters under temperature and power fluctuations to eliminate external influences. Calibration process generates quality report, including signal-to-noise improvement and out-of-band suppression rate, quantifying system status.

[0128] Step seven, system synergy and energy efficiency optimization

[0129] Full-link synergy targets unit energy consumption information transmission, associated with power consumption control, beamforming and filtering algorithm constraints. State self-learning mechanism optimizes decision parameters: relax threshold during frequent mode switching, and preloads suppression scheme when interference recurs. Output standard narrowband signal stream, record instantaneous power consumption and noise level logs, forming energy efficiency closed-loop optimization.

[0130] It needs to be noted that the relative terms such as first and second and the like are used herein solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0131] While the embodiments of the present application have been illustrated and described, it will be understood by those skilled in the art that various changes, modifications, substitutions, and alterations can be made hereto without departing from the spirit and scope of the application in its broadest form. The scope of the present application is limited only by the claims and the equivalents thereof.

Claims

1. An ultra-low power narrowband filter implementation method based on a digital phased array system, characterized by: The method comprises the following steps: S1, collecting input signal data and performing adaptive preprocessing to generate preprocessed signal data; S2, performing digital beamforming processing based on the preprocessed signal data to generate digital beamforming data; S3, performing narrowband filter parameter matching processing according to the digital beamforming data to generate narrowband filter parameter data; S4, using a hybrid filter algorithm to combine the digital beamforming data with the narrowband filter parameter data to perform narrowband filter processing on the digital beamforming data, and generating initial filter output data; The hybrid filter algorithm combines the digital beamforming data with the narrowband filter parameter data, and the hybrid filter algorithm includes a combination of FIR filters and IIR filters, and the transfer function is: ； wherein is the transfer function of the hybrid filter in the z-domain, is a complex frequency domain variable, is a filter tap number, is the order of the FIR filter, is the coefficient of the FIR filter at the kth tap, is the order of the IIR filter numerator polynomial, is the kth coefficient of the IIR filter numerator part, is the order of the IIR filter denominator polynomial, is the kth coefficient of the IIR filter denominator part; Performing real-time filter calculation processing to generate initial filter output data, wherein the filter calculation processing uses floating-point operation optimization to reduce computing power consumption; S5, performing power consumption optimization processing on the initial filter output data based on a dynamic power consumption control model to generate power consumption optimized filter data, wherein the dynamic power consumption control model uses dynamic voltage and frequency scaling (DVFS) technology to generate power consumption optimized filter data; The dynamic power consumption control model includes: Monitoring environmental noise parameters and signal power parameters; When the signal power is lower than a threshold, switch to a low-power mode to reduce the sampling rate; When the environmental noise is higher than a threshold, activate a high-precision mode to increase the filter order; S6, performing filter output calibration processing to generate calibrated filter output data; S7, outputting the calibrated filter output data as the final narrowband filter result.

2. The method of claim 1, wherein the method is implemented by a digital phased array system. The S1 comprises the following steps: S11, collecting multi-channel input signal data through a digital phased array receiving antenna array, wherein the input signal data includes signal frequency parameters, amplitude parameters, and phase parameters, and the signal frequency parameters range from 1 MHz to 10 GHz; S12, using an adaptive noise reduction algorithm to perform noise suppression processing on the input signal data to generate noise reduction signal data; S13, performing signal normalization processing on the noise reduction signal data based on signal integrity analysis to generate normalized signal data as the preprocessed signal data.

3. The method of claim 1, wherein the method is implemented by a digital phased array system, and wherein the method further comprises: The S2 comprises the following steps: ​ S21, importing the preprocessed signal data into a digital signal processing platform and calculating beam weight parameters using a weighted least squares algorithm to generate beam weight data, wherein the beam weight is calculated by the following formula: ； wherein is a beam weight vector, is an input signal covariance matrix, is a direction of a steering vector, is a conjugate transpose operator; S22, performing array antenna phase adjustment processing based on the beam weight data to generate phase adjustment data; S23, applying a digital beam synthesis algorithm in combination with the phase adjustment data to generate digital beamforming data, wherein the digital beamforming data represents beam direction pattern parameters with spatial angle as coordinates.

4. The method of claim 1, wherein the method is implemented by a digital phased array system. The S3 comprises the following steps: S31, obtaining target frequency parameters in the digital beamforming data; S32, matching the target frequency parameters with a preconfigured narrowband filter frequency band library to generate frequency band matching data; S33, generating narrowband filter parameter data according to the frequency band matching result, wherein the narrowband filter parameter data includes center frequency parameters, bandwidth parameters, and filter order parameters.

5. The method of claim 1, wherein the method is implemented by a digital phased array system. The S5 comprises the following steps: S51, a dynamic power consumption control model is constructed, which dynamically adjusts the filter working mode based on the signal power parameter and the environmental noise parameter; S52, a power consumption hierarchical control process is performed according to the signal strength parameter of the initial filter output data, and power-optimized filter data is generated, wherein the power-optimized process includes dynamic voltage frequency scaling (DVFS) technology.

6. The method of claim 1, wherein the method is implemented by a digital phased array system. The S6 includes the following steps: S61, error correction processing is performed on the power-optimized filter data based on a filter error feedback mechanism, and corrected intermediate data is generated; S62, a phase compensation algorithm is applied to perform phase alignment processing on the corrected intermediate data, and calibrated filter output data is generated.

7. The method of claim 1, wherein the method is implemented by a digital phased array system. The S7 includes the following steps: S71, the calibrated filter output data is converted into a standard output format; S72, the final narrowband filtering result is output through a digital interface, and power consumption log data is recorded.

8. The method of claim 1, wherein the method is implemented by a digital phased array system. The hybrid filtering algorithm in the S4 includes the following sub-steps: S91, initializing the FIR filter coefficient and the IIR filter coefficient; S92, performing parallel filtering calculation, wherein the FIR filter processes high-frequency components, and the IIR filter processes low-frequency components; S93, fusing the FIR and IIR output data to generate initial filter output data.

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