Ultra-low power consumption narrow-band filter implementation method based on digital phased array system

Abstract

The invention relates to the technical field of narrow-band filters, and discloses an ultra-low power consumption narrow-band filter implementation method based on a digital phased array system, and the method comprises the following steps: S1, collecting input signal data, carrying out the adaptive preprocessing, and generating preprocessed signal data; s2, performing digital beam forming processing based on the preprocessed signal data to generate digital beam forming data; s3, performing narrowband filtering parameter matching processing according to the digital beam forming data to generate narrowband filtering parameter data; in the narrowband filtering processing process, disturbance of environmental electromagnetic interference to a target signal is identified and eliminated in real time through adaptive noise suppression and a multi-dimensional signal preprocessing mechanism, so that the system can guarantee the pointing precision of digital beam forming, the pointing offset problem in the space signal capturing process is avoided, the accuracy of narrowband signal extraction is improved, and the accuracy of narrowband signal extraction is improved. And the reliability of filtering output and the signal fidelity 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 consumption 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. It 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 system and suppress out-of-band interference.

[0003] At present, due to the complex electromagnetic interference of digital phased array system in multi-target scene, it is unable to determine whether there is nonlinear power consumption mutation in the signal processing link in real time when performing dynamic power consumption optimization. When the transient current overshoot phenomenon occurs in the array beam switching process, the voltage frequency adjustment of the power consumption control model DVFS will fail, resulting in filter performance collapse in low signal-to-noise ratio environment, and unable to guarantee the accuracy of narrow-band filtering.

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

[0005] (I) Technical problems solved In view of the deficiencies in the prior art, the present application provides an implementation method of ultra-low power consumption narrow-band filter based on digital phased array system to solve the problems proposed in the background art.

[0006] (II) Technical solutions To achieve the above purpose, the present application provides the following technical solutions: an implementation method of ultra-low power consumption narrow-band filter based on digital phased array system, comprising 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; S3, performing narrow-band filter parameter matching processing according to the digital beam forming data to generate narrow-band filter parameter data; S4, using a hybrid filter algorithm to combine the narrow-band filter parameter data to perform narrow-band filter processing on the digital beam forming data to generate initial filter output data; 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; S6, performing filtering output calibration processing to generate calibration filtering output data; S7, outputting the calibration filtering output data as a final narrowband filtering result.

[0007] Preferably, the S1 comprises the following steps: S11, acquiring multi-channel input signal data by a digital phased array receiving antenna array, the input signal data comprising signal frequency parameters, amplitude parameters and phase parameters, wherein the signal frequency parameters range from 1 MHz to 10 GHz; S12, performing noise suppression processing on the input signal data by using an adaptive noise reduction algorithm 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 pre-processing signal data.

[0008] Preferably, the S2 comprises the following steps: S21, importing the pre-processing signal data into a digital signal processing platform and calculating beam weight parameters by 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 directional 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 beam forming data, wherein the digital beam forming data represents beam direction pattern parameters with spatial angles as coordinates.

[0009] Preferably, the S3 comprises the following steps: S31, acquiring target frequency parameters in the digital beam forming data; S32, matching the target frequency parameters with a preset narrowband filtering frequency band library to generate frequency band matching data; 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.

[0010] Preferably, the S4 comprises the following steps: S41, combining the digital beamforming data and the narrowband filtering parameter data by using a hybrid filtering algorithm, the hybrid filtering algorithm comprising a combination of a FIR filter and an IIR filter, and a transfer function of the hybrid filter being: ; wherein is a transfer function of the hybrid filter in the z domain, is a complex frequency domain variable, is a filter tap number, is an order of the FIR filter, is a coefficient of the FIR filter at the kth tap, is an order of a numerator polynomial of the IIR filter, is a kth coefficient of the numerator part of the IIR filter, is an order of a denominator polynomial of the IIR filter, is a kth coefficient of the denominator part of the IIR filter; 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.

[0011] Preferably, the S5 comprises the following steps: S51, constructing a dynamic power consumption control model, the model dynamically adjusting a filter working mode based on a signal power parameter and an environmental noise parameter; S52, performing a power consumption grading control process according to a signal strength parameter of the initial filtering output data to generate power consumption optimized filtering data, wherein the power consumption optimized process comprises a dynamic voltage frequency scaling (DVFS) technology.

[0012] Preferably, the S6 comprises the following steps: S61, performing an error correction process on the power consumption optimized filtering data based on a filtering error feedback mechanism to generate correction intermediate data; S62, applying a phase compensation algorithm to the correction intermediate data to perform phase alignment processing to generate calibrated filtering output data.

[0013] Preferably, the S7 comprises the following steps: S71, converting the calibrated filtering output data into a standard output format; S72, outputting the final narrowband filtering result through a digital interface and recording power consumption log data.

[0014] Preferably, the hybrid filtering algorithm in the S4 comprises 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 filtering output data.

[0015] Preferably, the dynamic power consumption control model in S5 comprises the following sub-steps: S101, monitoring environmental noise parameters and signal power parameters; S102, switching to a low power consumption mode and reducing the sampling rate when the signal power is lower than a threshold value; S103, activating a high precision mode and increasing the filtering order when the environmental noise is higher than a threshold value.

[0016] (Three) beneficial effects 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: 1. In the present application, in the narrowband filtering process, through the adaptive noise suppression and multi-dimensional signal preprocessing mechanism, the disturbance of environmental electromagnetic interference on the target signal is identified and eliminated in real time, so that the system can guarantee the pointing accuracy of digital beam forming, avoid the pointing deviation problem in the 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, which overcomes the signal distortion problem caused by frequency response mismatch of traditional filter, and enhances the reliability and signal fidelity of filtering output.

[0017] 2. 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 nonlinear power consumption mutation while ensuring filtering accuracy; when instantaneous current anomaly and environmental noise mutation are detected, the calculation resource redistribution strategy is automatically triggered to eliminate the performance collapse risk caused by voltage frequency regulation failure, improve the running stability of the system in low power consumption mode, and solve the core contradiction between energy consumption and performance in the traditional scheme.

[0018] 3. In 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 for 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 to eliminate the risk of spectral aliasing and filter oscillation, improve the system robustness in complex electromagnetic environment, and ensure the energy efficiency balance and output consistency of narrowband filtering under all working conditions. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 A flowchart of an implementation method of an ultra-low-power narrow-band filter based on a digital phased array system according to the present application. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be clearly and completely described 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, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0021] Specific embodiments: please refer to Figure 1 An implementation method of an ultra-low-power narrow-band filter based on a digital phased array system, the method comprising 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; S3, performing narrow-band filter parameter matching processing according to the digital beam forming data to generate narrow-band filter parameter data; S4, performing narrow-band filter processing on the digital beam forming data using a hybrid filter algorithm combined with the narrow-band filter parameter data to generate initial filter output data; 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; S6, performing filter output calibration processing to generate calibrated filter output data; S7, outputting the calibrated filter output data as the final narrow-band filter result.

[0022] S1 comprises the following steps: S11, collecting multi-channel input signal data through a digital phased array receiving antenna array, the input signal data comprising signal frequency parameters, amplitude parameters and phase parameters, wherein the signal frequency parameters range from 1 MHz to 10 GHz; S12, performing noise suppression processing on the input signal data using an adaptive noise reduction algorithm 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 preprocessed signal data.

[0023] S2 comprises the following steps: S21, import the pretreatment signal data into the digital signal processing platform, and calculate the beam weight parameters by using the weighted least square 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 directional vector, is a conjugate transpose operator; S22, perform array antenna phase adjustment processing based on the beam weight data to generate phase adjustment data; S23, apply a digital beam synthesis algorithm in combination with the phase adjustment data to generate digital beam forming data, and the beam synthesis output function is: ; Wherein is a time domain beam synthesis output signal, is the weight coefficient of the th array antenna, is the received signal of the th array antenna, is the carrier center frequency, is the beam direction corresponding to the th element delay, is the total number of phased array elements, is a phase compensation term.

[0024] S3 includes the following steps: S31, obtain the target frequency parameter in the digital beam forming data; S32, match the target frequency parameter with the preset narrowband filtering frequency band library to generate frequency band matching data; S33, generate narrowband filtering parameter data according to the frequency band matching result, wherein the narrowband filtering parameter data includes center frequency parameter, bandwidth parameter and filter order parameter.

[0025] S4 includes the following steps: S41, combine the digital beam forming data with the narrowband filtering parameter data by using a hybrid filtering algorithm, wherein the hybrid filtering algorithm includes a combination of FIR filter and IIR filter, 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 numerator part of the IIR filter, is the order of the IIR filter denominator polynomial, is the Kth coefficient of the denominator of the IIR filter; S42: Execute real-time filtering calculation processing to generate initial filtering output data, wherein the filtering calculation processing uses floating-point operation optimization to reduce calculation power consumption. The calculation power consumption model is: ; in Dynamic power consumption of floating point unit is the process-related conversion factor, is the number of floating-point operations per second, is the processor operating frequency, Core supply voltage.

[0026] S5 includes the following steps: S51, constructing a dynamic power consumption control model, the model dynamically adjusts the filter working mode based on the signal power parameter and the environmental noise parameter; S52: Perform power consumption hierarchical control processing based on the signal strength parameter of the initial filtered output data to generate power consumption optimized filtered data, wherein the power consumption optimization processing includes dynamic voltage frequency scaling (DVFS) technology, and the power consumption control function is: ; in For the optimized supply voltage, is the target power consumption threshold, For the optimized working frequency, is the voltage-frequency conversion coefficient; ; in is the input signal-to-noise ratio, is the signal-to-noise ratio-frequency adjustment factor.

[0027] S6 includes the following steps: S61, performing error correction processing on the power consumption optimization filter data based on the filter error feedback mechanism to generate corrected intermediate data, wherein the phase error estimation model is: ; in is the estimated phase deviation, is the error complex vector, Im is the operator for taking the imaginary part of the complex vector, Re is the operator for taking the real part of the complex vector, is an inverse tangent function; S62, applying a phase compensation algorithm to the intermediate data for phase alignment processing, generating calibration filter output data.

[0028] S7 includes the following steps: S71, converting the calibration filter output data into a standard output format; S72, outputting the final narrowband filtering result through a digital interface, and recording power consumption log data, and the energy efficiency evaluation index is: ; wherein is the spectrum 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.

[0029] The hybrid filtering algorithm in S4 includes the following sub-steps: S91, initializing the FIR filter coefficients and the IIR filter coefficients; 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.

[0030] The dynamic power consumption control model in S5 includes the following sub-steps: S101, monitoring the environmental noise parameters and signal power parameters; S102, when the signal power is lower than the threshold, switching to a low power consumption mode and reducing the sampling rate; S103, when the environmental noise is higher than the threshold, activating a high precision mode and increasing the filter order.

[0031] The operation steps of the ultra-low power narrowband filter implementation method based on the digital phased array system are as follows: Step one, signal acquisition and adaptive preprocessing The system captures multi-channel radio frequency signals through a digital phased array antenna array, including frequency, amplitude and phase parameters. The adaptive noise reduction algorithm suppresses environmental electromagnetic interference in real time and preserves the core characteristics of the target signal. Then, signal normalization is performed to dynamically compress the amplitude to a unified dimension and eliminate the intensity fluctuations caused by the position difference of the array elements. The preprocessing process monitors the signal integrity and starts the anti-saturation protection when detecting abnormal distortion, ensuring the stability and purity of the input signal and laying a foundation for subsequent processing.

[0032] Step two, digital beam forming and spatial filtering Based on the pretreatment data, the system calculates the optimal weighting coefficient of each array element: through the eigenvalue decomposition of the covariance matrix to determine the main direction of the signal, and apply the minimum variance distortionless response algorithm to solve the weight vector. The core of spatial filtering is phase synchronization control, which compensates for the phase deviation caused by the wave path difference in real time, and realizes the super-directivity beam synthesis. In the process of beam forming, the spatial spectrum is monitored in real time, and when strong interference sources are encountered, the zero position is automatically adjusted to ensure the separation of target signals.

[0033] Step three, dynamic matching of narrowband filtering parameters For the output signal of the beam, the system extracts the time-frequency domain features. A database of narrowband filter frequency bands 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 scene, and selects the optimal filter parameter according to the modulation type. Online parameter update is supported, and the system is immediately reconfigured when the signal jumps, ensuring the dynamic following ability of the filtering characteristics.

[0034] Step four, real-time processing of hybrid filtering algorithm FIR-IIR hybrid architecture is adopted: first, initialize the filter coefficient matrix, the FIR order is set based on the passband ripple, and the IIR order is determined according to the stopband suppression requirement. Double-path parallel processing: FIR path focuses on high-frequency components, maintaining linear phase; IIR path processes low-frequency, 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.

[0035] Step five, dynamic power consumption optimization control 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 environment, switch to low power consumption mode: reduce sampling rate, optimize processor frequency, and apply dynamic voltage frequency scaling to reduce energy consumption. When the noise increases, activate the high-precision mode: increase the filter order, and allocate computing resources. Built-in fuse protection mechanism limits peak power consumption when there is an abnormal current fluctuation, maintaining filtering accuracy.

[0036] Step six, output calibration and error suppression Phase calibration extracts residual deviation through error feedback loop, calculates offset through complex signal vector analysis, and accurately realigns the phase through a digital phase shifter. Amplitude equalization compensates for passband edge attenuation to avoid demodulation distortion. Environmental adaptability strategy recalibrates parameters when temperature and power fluctuate, eliminating external influences. The calibration process generates a quality report, including signal-to-noise ratio improvement and out-of-band suppression rate, quantifying system status.

[0037] Step seven, system coordination and energy efficiency optimization The full-link cooperation takes the unit energy consumption information transmission amount as the objective function, and is associated with the power consumption control, beam forming and filtering algorithm constraints. The state self-learning mechanism optimizes the decision parameters: relaxes the threshold when the mode is frequently switched, and preloads the suppression scheme when the interference is reproduced. The output standard narrowband signal stream records the instantaneous power consumption and noise level log, and forms an energy efficiency closed loop optimization.

[0038] It should be noted that the relational terms herein such as first and second and the like are used solely to distinguish one from another entity or action without necessarily requiring or implying any actual 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.

[0039] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, combinations, and alterations can be made to the embodiments without departing from the principles and spirit of the present application. The scope of the present application is not to be limited to the embodiments shown and described, but is to be accorded the full scope of the claims and their equivalents.

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, performing narrowband filter processing on the digital beamforming data using a hybrid filter algorithm combined with the narrowband filter parameter data to generate initial filter output data; 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; 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, the input signal data comprising signal frequency parameters, amplitude parameters and phase parameters, wherein the signal frequency parameters range from 1 MHz to 10 GHz; S12, performing noise suppression processing on the input signal data using an adaptive noise reduction algorithm 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. 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 combined 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 preset narrowband filter frequency band library to generate frequency band matching data; S33, generating narrowband filter parameter data according to the frequency band matching result, the narrowband filter parameter data comprising 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, and wherein the method further comprises: The S4 comprises the following steps: ​ S41, combining the digital beamforming data with the narrowband filter parameter data using a hybrid filter algorithm, the hybrid filter algorithm comprising a combination of FIR filters and IIR filters, and the transfer function thereof being: ； wherein H(z) is a transfer function of the mixed filter in the z-domain, is a complex frequency domain variable, is a filter tap number, is an order of the FIR filter, is a coefficient of the FIR filter at the kth tap, is an order of the IIR filter numerator polynomial, is the kth coefficient of the IIR filter numerator part, is an order of the IIR filter denominator polynomial, is the kth coefficient of the IIR filter denominator part; S42, performing real-time filter calculation processing to generate initial filter output data, wherein the filter calculation processing uses floating point operation optimization to reduce calculation power consumption.

6. The method of claim 1, wherein the method is implemented by a digital phased array system. The S5 comprises the following steps: S51, constructing a dynamic power consumption control model, the model dynamically adjusting filter working modes based on signal power parameters and environmental noise parameters; 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.

7. The method of claim 1, wherein the method is implemented by a digital phased array system. The S6 includes the following steps: S61, performing error correction processing on the power consumption optimization filtering data based on a filtering error feedback mechanism, generating correction intermediate data; S62, applying a phase compensation algorithm to perform phase alignment processing on the correction intermediate data, generating calibration filtering output data.

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

9. The method of claim 5, wherein the method is implemented by a digital phased array system, and wherein the method further comprises: determining a frequency of the signal; and selecting the first and second filter coefficients based on the determined frequency. The hybrid filtering algorithm in the S4 includes the following sub-steps: S91, initializing FIR filter coefficients and IIR filter coefficients; S92, performing parallel filtering calculation, wherein the FIR filter processes high frequency components and the IIR filter processes low frequency components; S93, fusing FIR and IIR output data to generate initial filtering output data.

10. The method of claim 6, wherein the method is implemented by a digital phased array system. The dynamic power consumption control model in the S5 includes the following sub-steps: S101, monitoring environmental noise parameters and signal power parameters; S102, when the signal power is lower than a threshold value, switching to a low power consumption mode and reducing the sampling rate; S103, when the environmental noise is higher than a threshold value, activating a high precision mode and increasing the filtering order.

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