Signal processing method and device
By performing down-conversion and frequency domain analysis on radio frequency signals in an FM broadcast receiving system, noise and signal boundaries are accurately identified, solving the problems of out-of-band noise and adjacent channel interference in existing technologies, and achieving more efficient filtering and storage resource optimization.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- KTMICRO ELECTRONICS
- Filing Date
- 2026-01-15
- Publication Date
- 2026-04-10
AI Technical Summary
Existing technologies in FM broadcast receiving systems struggle to efficiently identify and eliminate out-of-band noise and interference signals from adjacent channels, resulting in high storage resource consumption and insufficient filtering accuracy.
After down-converting the radio frequency signal, a frequency domain conversion algorithm is used to calculate the spectrum value, determine the noise threshold and signal bandwidth region, accurately identify the boundary between noise and signal, filter the noise region, and convert it back to a time domain signal.
It achieves more precise filtering, reduces storage resource consumption, improves the accuracy and efficiency of filtering, and effectively suppresses interference from adjacent channels.
Smart Images
Figure CN121841913A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of wireless communication, in particular to a signal processing method and device. BACKGROUND
[0002] In the FM broadcast receiving system, the received radio frequency signal contains the desired signal and the out-of-band noise and the interference signal of the adjacent channel. The FM broadcast signal is first down-converted to obtain a baseband complex signal, and then filtered in the time domain by using a digital filter. The system pre-stores a plurality of sets of filter coefficients of different bandwidths, performs FM demodulation on the filter output signal, performs bandwidth determination on the demodulated signal, and dynamically selects the filter coefficients according to the determination result. This is a closed-loop feedback adjustment system, which adjusts the filter parameters by continuously comparing the quality indicators of the output signals. Since the actual bandwidth of the FM broadcast signal will dynamically fluctuate with the content of the voice signal, in the prior art, the baseband signal is filtered in the time domain, but different filters need to be set for different interference signals, which occupies a large amount of storage resources. SUMMARY
[0003] Some embodiments of the present application aim to provide a signal processing method and device. Through the technical solutions of the embodiments of the present application, a first radio frequency signal is acquired, and a baseband signal corresponding to the first radio frequency signal is obtained by performing down-conversion processing on the first radio frequency signal. A preset frequency domain conversion algorithm is used to convert the baseband signal into a frequency spectrum value in the frequency domain corresponding to the baseband signal. A noise threshold is determined according to the frequency spectrum value in the frequency domain. The noise threshold and the frequency spectrum value in the frequency domain are compared to determine a noise bandwidth region and a target signal bandwidth region. The noise signal in the noise bandwidth region is filtered to obtain a target signal in the frequency domain corresponding to the target signal bandwidth region, and the target signal in the frequency domain is converted into a baseband signal in the time domain. In the embodiments of the present application, the radio frequency signal in the time domain is acquired, and the radio frequency signal in the time domain is converted into the frequency domain to calculate the frequency spectrum value of each sampling point. Then, the noise threshold is set, the frequency spectrum value of each sampling point and the noise threshold are used to determine the noise bandwidth region and the target signal bandwidth region, and the noise in the noise bandwidth region is eliminated to obtain the final target signal. Through frequency domain analysis, the boundary between the signal and the noise can be accurately identified, more accurate filtering is realized, and the accuracy of the filtering is improved.
[0004] In a first aspect, some embodiments of the present application provide a signal processing method, comprising: acquiring a first radio frequency signal and performing down-conversion processing on the first radio frequency signal to obtain a baseband signal corresponding to the first radio frequency signal; convert the baseband signal into a spectrum value in a frequency domain corresponding to the baseband signal by using a preset frequency domain conversion algorithm; determine a noise threshold according to the spectrum value in the frequency domain; compare the noise threshold and the spectrum value in the frequency domain to determine a noise bandwidth region and a target signal bandwidth region; filter noise signals in the noise bandwidth region to obtain a target signal in the frequency domain corresponding to the target signal bandwidth region, and convert the target signal in the frequency domain into a baseband signal in time domain. Some embodiments of the present application can accurately identify the boundaries of signals and noise through frequency domain analysis to achieve more accurate filtering and improve the accuracy of filtering.
[0005] Optionally, the first radio frequency signal is obtained, and the first radio frequency signal is down-converted to obtain a baseband signal corresponding to the first radio frequency signal, including: The first radio frequency signal is obtained, and the first radio frequency signal is down-converted to obtain a baseband complex signal. According to a preset number of sample points N, the baseband complex signal is grouped to obtain a grouped baseband complex signal.
[0006] Some embodiments of the present application can obtain a digital baseband signal by down-converting the collected analog first radio frequency signal, and use different size window functions (such as Hanning window, Hamming window, etc.) for window processing to collect a baseband complex signal of a preset number of sample points N to reduce spectrum leakage.
[0007] Optionally, the baseband signal is converted into a spectrum value in a frequency domain corresponding to the baseband signal by using a preset frequency domain conversion algorithm, including: Any one of fast Fourier transform, discrete cosine transform or wavelet transform is used to convert the grouped baseband complex signal in time domain into a complex spectrum value in a frequency domain corresponding to the baseband complex signal. Some embodiments of the present application can convert the baseband signal in time domain into a spectrum value in frequency domain by using different preset frequency domain conversion algorithms, and can accurately identify the boundaries of signals and noise through frequency domain analysis to achieve more accurate filtering.
[0008] Optionally, the converting the grouped baseband complex signal in time domain into complex spectrum values in frequency domain corresponding to the baseband complex signal by using fast Fourier transform comprises: performing fast Fourier transform on the baseband complex signal with a preset sample point number to obtain complex spectrum values of frequency bins corresponding to the sample point number.
[0009] Optionally, the determining a noise threshold according to the spectrum values in the frequency domain comprises: determining the noise threshold by using any one of the minimum spectrum value of the frequency bins, the average value or the median value of the complex spectrum values of the frequency bins.
[0010] In some embodiments of the present application, the noise threshold can be determined by statistical analysis method, such as multiplying the average value or the median value of all bin energies by a coefficient, or using an adaptive statistical threshold algorithm. Optionally, the comparing the noise threshold with the spectrum values in the frequency domain to determine the noise bandwidth region and the target signal bandwidth region comprises: comparing the complex spectrum values of the frequency bins numbered 0 to N / 2 with the noise threshold respectively, and determining the last frequency bin smaller than the noise threshold as a first identification point; comparing the complex spectrum values of the frequency bins numbered N-1 to N / 2+1 with the noise threshold respectively, and determining the last frequency bin smaller than the noise threshold as a second identification point; determining the noise bandwidth region and the target signal bandwidth region according to the first identification point and the second identification point in the first radio frequency signal respectively.
[0011] In some embodiments of the present application, the noise bandwidth region and the target signal bandwidth region are determined by searching the signal boundary from the direct current carrier point to both sides by the method of finding the minimum value of FFT bin energy and multiplying a coefficient to obtain the noise threshold.
[0012] Optionally, the determining the noise bandwidth region and the target signal bandwidth region according to the first identification point and the second identification point in the first radio frequency signal comprises: determining the region between the first identification point and the second identification point in the first radio frequency signal as the noise bandwidth region; determining other regions in the first radio frequency signal except the noise bandwidth region as the target signal bandwidth region.
[0013] Optionally, the filtering of the noise signal in the noise bandwidth region to obtain the target signal in the frequency domain corresponding to the target signal bandwidth region comprises: zeroing or amplitude attenuation of the spectrum value of the noise signal in the noise bandwidth region to obtain the target signal in the frequency domain corresponding to the target signal bandwidth region.
[0014] In addition to directly zeroing the noise region bin, some embodiments of the present application can also use a gradual attenuation method, such as setting different attenuation coefficients according to the distance between the bin and the signal boundary to achieve a smoother transition.
[0015] Optionally, the method further comprises: obtaining a second radio frequency signal; calculating a first sum value of complex spectrum values of N sample points of the second radio frequency signal; calculating a second sum value of complex spectrum values of N sample points of the first radio frequency signal; calculating a spectrum ratio value according to the first sum value and the second sum value; comparing the spectrum ratio value with a preset threshold value; if the spectrum ratio value is greater than or equal to the preset threshold value, determining that the second radio frequency signal is adjacent channel interference of the first radio frequency signal.
[0016] Some embodiments of the present application effectively suppress adjacent channel interference through adjacent channel interference detection and adaptive bandwidth adjustment.
[0017] In a second aspect, some embodiments of the present application provide a signal processing device, comprising: an acquisition module configured to acquire a first radio frequency signal and perform down-conversion processing on the first radio frequency signal to obtain a baseband signal corresponding to the first radio frequency signal; a transformation module configured to convert the baseband signal into a spectrum value in a frequency domain corresponding to the baseband signal using a preset frequency domain conversion algorithm; a calculation module configured to determine a noise threshold value according to the spectrum value in the frequency domain; a comparison module configured to compare the noise threshold value with the spectrum value in the frequency domain to determine a noise bandwidth region and a target signal bandwidth region; a filtering module configured to filter a noise signal in the noise bandwidth region to obtain a target signal in the frequency domain corresponding to the target signal bandwidth region, and convert the target signal in the frequency domain into a baseband signal in time domain.
[0018] Some embodiments of this application acquire radio frequency signals in the time domain, convert the radio frequency signals in the time domain to the frequency domain, calculate the spectral values of each sampling point, and then set a noise threshold. Using the spectral values of each sampling point and the noise threshold, the noise bandwidth region and the target signal bandwidth region are determined, and the noise in the noise bandwidth region is eliminated to obtain the final target signal. Through frequency domain analysis, the boundary between signal and noise can be accurately identified, achieving more precise filtering and improving the accuracy of filtering.
[0019] Optionally, the acquisition module is used for: The first radio frequency signal is acquired, and the first radio frequency signal is down-converted to obtain a baseband complex signal; The baseband complex signal is grouped according to the preset number of sample points N to obtain the grouped baseband complex signal.
[0020] Some embodiments of this application obtain the baseband signal of the digital signal by downconverting the acquired analog first radio frequency signal, and then performing windowing processing using window functions of different sizes (such as Hanning window, Hamming window, etc.) to acquire a baseband complex signal with a pre-set number of sample points N, so as to reduce spectral leakage.
[0021] Optionally, the transformation module is used for: The baseband complex signal after grouping in the time domain is converted into a complex spectrum value in the frequency domain corresponding to the baseband complex signal by using any one of the Fast Fourier Transform, Discrete Cosine Transform, or Wavelet Transform. Some embodiments of this application can use different preset frequency domain conversion algorithms to convert the baseband signal in the time domain into the spectrum value in the frequency domain. Through frequency domain analysis, the boundary between signal and noise can be accurately identified, and more accurate filtering can be achieved.
[0022] Optionally, the transformation module is used for: The baseband complex signal is subjected to a fast Fourier transform with a preset number of sample points to obtain the complex spectrum value of the frequency bin corresponding to the number of sample points. Optionally, the computing module is used for: The noise threshold is determined by using any one of the minimum spectral value of the frequency bin, the average value of the complex spectral values of the frequency bin, or the median value.
[0023] In some embodiments of this application, the noise threshold can be determined by statistical analysis methods, such as multiplying the average or median of all bin energies by a coefficient, or by employing an adaptive statistical threshold algorithm. Optionally, the comparison module is used to: The complex spectral values of the frequency bins numbered 0 to N / 2 are compared with the noise threshold, and the last frequency bin that is less than the noise threshold is determined as the first identification point; The complex spectral values of the frequency bins numbered N-1 to N / 2+1 are compared with the noise threshold, and the last frequency bin that is less than the noise threshold is determined as the second identifier point; The noise bandwidth region and the target signal bandwidth region are determined based on the first and second marker points in the first radio frequency signal, respectively.
[0024] Some embodiments of this application determine the noise bandwidth region and the target signal bandwidth region by finding the minimum FFT bin energy and multiplying it by a coefficient to obtain the noise threshold, and by using a technique of searching for signal boundaries from the DC carrier point to both sides.
[0025] Optionally, the comparison module is used to: The region between the first marker point and the second marker point in the first radio frequency signal is defined as the noise bandwidth region; In the first radio frequency signal, the regions other than the noise bandwidth region are determined as the target signal bandwidth region.
[0026] Optionally, the filtering module is used for: The spectral value of the noise signal within the noise bandwidth region is set to zero or its amplitude is attenuated to obtain the target signal in the frequency domain corresponding to the target signal bandwidth region.
[0027] In addition to directly setting the noise region bin to zero, some embodiments of this application can also employ a gradual attenuation method, such as setting different attenuation coefficients according to the distance between bin and the signal boundary, to achieve a smoother transition.
[0028] Optionally, the filtering module is used for: Acquire the second radio frequency signal; Calculate the first sum of the complex spectral values of the N sample points of the second radio frequency signal; Calculate the second sum of the complex spectral values of the first radio frequency signal and the N sample points of the first radio frequency signal; Calculate the spectral ratio based on the first sum and the second sum; The spectral ratio is compared with a preset threshold. If the spectrum ratio is greater than or equal to the preset threshold, then the second radio frequency signal is determined to be adjacent channel interference of the first radio frequency signal.
[0029] Some embodiments of this application effectively suppress interference from nearby stations through adjacent station interference detection and adaptive bandwidth adjustment.
[0030] Thirdly, some embodiments of this application provide an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, can implement the signal processing method as described in any embodiment of the first aspect.
[0031] Fourthly, some embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, can implement the signal processing method as described in any embodiment of the first aspect.
[0032] Fifthly, some embodiments of this application provide a computer program product, the computer program product including a computer program, wherein when the computer program is executed by a processor, it can implement the signal processing method as described in any embodiment of the first aspect. Attached Figure Description
[0033] To more clearly illustrate the technical solutions of some embodiments of this application, the accompanying drawings used in some embodiments of this application will be briefly described below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0034] Figure 1 A schematic flowchart of a signal processing method provided in an embodiment of this application; Figure 2 A schematic flowchart illustrating another signal processing method provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a signal processing device provided in an embodiment of this application; Figure 4 This is a schematic diagram of an electronic device provided in an embodiment of this application. Detailed Implementation
[0035] The technical solutions of some embodiments of this application will now be described with reference to the accompanying drawings.
[0036] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.
[0037] In an FM radio reception system, the received radio frequency signal contains the desired signal as well as out-of-band noise and interference signals from adjacent channels. First, the FM radio signal is down-converted to obtain a baseband complex signal, and then filtered in the time domain using a digital filter. The system pre-stores multiple sets of filter coefficients with different bandwidths. By demodulating the filter output signal using FM, the bandwidth of the demodulated signal is determined, and the filter coefficients are dynamically selected based on the determination result. This is a closed-loop feedback adjustment system that adjusts the filter parameters by continuously comparing the quality indicators of the output signal. Since the actual bandwidth of the FM radio signal fluctuates dynamically with changes in the content of the voice signal, existing technologies filter the baseband signal in the time domain. However, different interference signals require corresponding filters, consuming significant storage resources. Therefore, some embodiments of this application provide a signal processing method. This method includes acquiring a first radio frequency signal and down-converting the first radio frequency signal to obtain a baseband signal corresponding to the first radio frequency signal; using a preset frequency domain conversion algorithm to convert the baseband signal into a frequency domain spectrum value corresponding to the baseband signal; determining a noise threshold based on the frequency domain spectrum value; and adjusting the noise threshold and... The frequency spectrum values are compared to determine the noise bandwidth region and the target signal bandwidth region. The noise signal within the noise bandwidth region is filtered to obtain the target signal in the frequency domain corresponding to the target signal bandwidth region. The target signal in the frequency domain is then converted into a baseband signal in the time domain. In this embodiment, radio frequency signals in the time domain are collected and converted to the frequency domain. The spectrum values of each sampling point are calculated, and a noise threshold is set. Using the spectrum values of each sampling point and the noise threshold, the noise bandwidth region and the target signal bandwidth region are determined. The noise within the noise bandwidth region is then eliminated to obtain the final target signal. Frequency domain analysis can accurately identify the boundary between signal and noise, achieving more precise filtering and improving the accuracy of filtering.
[0038] like Figure 1 As shown, an embodiment of this application provides a signal processing method, the method comprising: S101. Obtain the first radio frequency signal and perform down-conversion processing on the first radio frequency signal to obtain the baseband signal corresponding to the first radio frequency signal; This application embodiment is applied to a broadcast signal system, which includes a transmitting terminal and a receiving terminal. The receiving terminal receives a first radio frequency signal transmitted by the transmitting terminal. The first radio frequency signal is an analog signal in the time domain. The receiving terminal performs down-conversion processing on the first radio frequency signal to obtain a baseband signal corresponding to the first radio frequency signal. The baseband signal is a baseband complex signal.
[0039] That is, the receiving terminal performs down-conversion processing on the received FM broadcast signal, i.e. the first radio frequency signal. The down-conversion method is to multiply the received signal with the local oscillator signal generated by the local oscillator, and then obtain the frequency-converted signal through a low-pass filter. The two multiplied signals can be represented by real numbers or complex numbers and can be divided into real mixing and complex mixing to obtain the baseband complex signal.
[0040] S102. A preset frequency domain conversion algorithm is used to convert the baseband signal into a spectrum value in the frequency domain corresponding to the baseband signal. The receiving terminal pre-sets the number of sample points, that is, it performs windowing processing on the baseband complex signal by setting a window function, obtains N sample points in each window, and then uses a preset frequency domain conversion algorithm to convert the signal of the sample points in the time domain into the spectrum value in the frequency domain.
[0041] In practical implementation, the spectral values of multiple consecutive preset frequency domain transformation algorithm outputs (e.g., 16 FFT outputs) can be averaged to avoid threshold deviations caused by extreme noise points in a single frame and improve the stability of bandwidth estimation. The preset frequency domain transformation algorithm includes at least one of the following: Fast Fourier Transform, Discrete Cosine Transform, or Wavelet Transform.
[0042] S103. Determine the noise threshold based on the frequency spectrum value in the frequency domain; Further, the receiving terminal judges the spectral values of multiple sample points in the frequency domain, obtains the minimum or average value of the spectral values among the multiple sample points, and then uses m times the minimum or average value as the noise threshold, where m is greater than 1.2-2.0. S104, compare the noise threshold and the spectral values in the frequency domain to determine the noise bandwidth region and the target signal bandwidth region; The receiving terminal compares the noise threshold and the frequency spectrum values corresponding to multiple sample points in the frequency domain. For example, it compares the energy of bins numbered 0 to N / 2 with the noise threshold one by one and records the index number Kp of the last bin that is less than the noise threshold. The energy of bins numbered N-1 to N / 2+1 is compared with the noise threshold value one by one. The index number Kn of the last bin that is less than the threshold value is recorded. Based on the two index numbers, the noise bandwidth region and the target signal bandwidth region are determined.
[0043] S105. Filter the noise signal within the noise bandwidth region to obtain the target signal in the frequency domain corresponding to the target signal bandwidth region, and convert the target signal in the frequency domain into a baseband signal in the time domain.
[0044] Specifically, after determining the noise bandwidth region, the receiving terminal filters the noise within that region. This can be done by directly setting the spectral values to zero, or by setting a transition band (e.g., gradually attenuating from bin number Kp until it completely becomes zero at bin number Kp+Δ) to avoid ringing effects caused by steep transitions. In this way, a clean target signal can be obtained, and the target signal in the frequency domain can be converted into a baseband signal in the time domain, thus obtaining the desired signal.
[0045] Some embodiments of this application acquire radio frequency signals in the time domain, convert the radio frequency signals in the time domain to the frequency domain, calculate the spectral values of each sampling point, and then set a noise threshold. Using the spectral values of each sampling point and the noise threshold, the noise bandwidth region and the target signal bandwidth region are determined, and the noise in the noise bandwidth region is eliminated to obtain the final target signal. Through frequency domain analysis, the boundary between signal and noise can be accurately identified, achieving more precise filtering and improving the accuracy of filtering.
[0046] Another embodiment of this application further supplements the description of the signal processing method provided in the above embodiments.
[0047] Optionally, a first radio frequency (RF) signal is acquired, and the first RF signal is down-converted to obtain a baseband signal corresponding to the first RF signal, including: Acquire a first radio frequency signal and perform down-conversion processing on the first radio frequency signal to obtain a baseband complex signal; The baseband complex signal is grouped according to the preset number of sample points N to obtain the grouped baseband complex signal.
[0048] Some embodiments of this application obtain the baseband signal of the digital signal by downconverting the acquired analog first radio frequency signal, and then performing windowing processing using window functions of different sizes (such as Hanning window, Hamming window, etc.) to acquire a baseband complex signal with a pre-set number of sample points N, so as to reduce spectral leakage.
[0049] Optionally, a preset frequency domain conversion algorithm is used to convert the baseband signal into a spectrum value in the frequency domain corresponding to the baseband signal, including: The baseband complex signal after grouping in the time domain is converted into a complex spectrum value in the frequency domain corresponding to the baseband complex signal by using any one of the Fast Fourier Transform, Discrete Cosine Transform, or Wavelet Transform. Some embodiments of this application can use different preset frequency domain conversion algorithms to convert the baseband signal in the time domain into the spectrum value in the frequency domain. Through frequency domain analysis, the boundary between signal and noise can be accurately identified, and more accurate filtering can be achieved.
[0050] Optionally, a Fast Fourier Transform is employed to convert the grouped baseband complex signal in the time domain into a complex spectrum value in the frequency domain corresponding to the baseband complex signal, including: Perform a Fast Fourier Transform on the baseband complex signal with a pre-set number of sample points to obtain the complex spectrum value of the frequency bin corresponding to the number of sample points.
[0051] Optionally, the noise threshold is determined based on the spectral values in the frequency domain, including: The noise threshold is determined by using any one of the minimum spectral value of frequency bin, the average value of the complex spectral values of frequency bin, or the median value.
[0052] Specifically, the receiving terminal performs an N-point FFT (Fast Fourier Transform) on the windowed signal, which transforms the signal from the time domain to the frequency domain, obtaining complex spectral values of N frequency bins. In other words, each sample point corresponds to a spectrum, which is a complex spectral value. Here, the frequency bin is the interval or resolution on the frequency axis, and its size depends on the sampling rate and the number of sampling points. In the FFT result, each frequency component corresponds to a "frequency bin, frequency resolution".
[0053] In the embodiments of this application, the preset spectrum conversion algorithm includes at least one of Fast Fourier Transform, Discrete Cosine Transform (DCT) or Wavelet Transform (WT), and is not specifically limited in the embodiments of this application.
[0054] Find the minimum energy value Emin of all bins in the FFT output (the minimum value among the complex spectral values mentioned above), and multiply it by a coefficient α greater than 1 (usually 1.2-2.0) as the noise threshold: Threshold = α × Emin.
[0055] Furthermore, the energy of multiple adjacent bins can be accumulated to form an energy block, and bandwidth determination and filtering can be performed on the basis of energy blocks to improve the ability to resist fluctuations.
[0056] In some embodiments of this application, the noise threshold can be determined by statistical analysis methods, such as multiplying the average or median of all bin energies by a coefficient, or by employing an adaptive statistical threshold algorithm. Optionally, the noise threshold and the spectral value in the frequency domain are compared to determine the noise bandwidth region and the target signal bandwidth region, including: The complex spectral values of the frequency bins numbered 0 to N / 2 are compared with the noise threshold, and the last frequency bin that is less than the noise threshold is determined as the first marker point; The complex spectral values of the frequency bins numbered N-1 to N / 2+1 are compared with the noise threshold, and the last frequency bin that is less than the noise threshold is determined as the second marker point; Based on the first and second markers in the first radio frequency signal, the noise bandwidth region and the target signal bandwidth region are determined respectively.
[0057] Specifically, the receiving terminal compares the energy (complex spectral values) of the frequency bins numbered 0 to N / 2 one by one with the noise threshold, and records the index number Kp of the last bin that is less than the noise threshold, which is the first identification point; Compare the energy (complex spectrum value) of bins numbered N-1 to N / 2+1 one by one with the noise threshold, and record the index number Kn of the last bin that is less than the threshold, which is the second identifier point; The region between the first marker point Kp and the second marker point Kn is identified as the noise-dominant region, while the regions 0~Kp-1 and Kn~N-1 are identified as the signal-dominant regions (i.e., the target signal bandwidth regions).
[0058] Some embodiments of this application determine the noise bandwidth region and the target signal bandwidth region by finding the minimum FFT bin energy and multiplying it by a coefficient to obtain the noise threshold, and by using a technique of searching for signal boundaries from the DC carrier point to both sides.
[0059] Optionally, the noise bandwidth region and the target signal bandwidth region are determined based on the first and second marker points in the first radio frequency signal, including: The region between the first marker point and the second marker point in the first radio frequency signal is defined as the noise bandwidth region; In the first radio frequency signal, the regions other than the noise bandwidth region are defined as the target signal bandwidth region.
[0060] Optionally, the noise signal within the noise bandwidth region is filtered to obtain the target signal in the frequency domain corresponding to the target signal bandwidth region, including: The spectral values of the noise signal within the noise bandwidth region are set to zero or the amplitude is attenuated to obtain the target signal in the frequency domain corresponding to the target signal bandwidth region.
[0061] Specifically, after determining the noise bandwidth region, the receiving terminal sets the spectral values of the frequency bins numbered Kp to Kn to zero (or attenuates them), while keeping the spectral values of bins numbered 0~Kp-1 and Kn~N-1 unchanged, thus achieving a band-stop filtering effect.
[0062] In addition to directly setting the noise region bin to zero, some embodiments of this application can also employ a gradual attenuation method, such as setting different attenuation coefficients according to the distance between bin and the signal boundary, to achieve a smoother transition.
[0063] Optionally, the method further includes: Acquire the second radio frequency signal; Calculate the first sum of the complex spectral values of the N sample points of the second radio frequency signal; Calculate the second sum of the complex spectral values of the first radio frequency signal and the N sample points; Calculate the spectral ratio based on the first and second sums; Compare the spectral ratio with a preset threshold; If the spectrum ratio is greater than or equal to a preset threshold, the second radio frequency signal is determined to be adjacent interference of the first radio frequency signal.
[0064] Specifically, the receiving terminal receives the first radio frequency signal and the second radio frequency signal simultaneously. The second radio frequency signal is then down-converted to obtain a baseband complex signal. This baseband complex signal is grouped, with each group containing N sample points. An N-point FFT (Fast Fourier Transform) is performed on the windowed signal to convert it from the time domain to the frequency domain, resulting in complex spectral values for N frequency bins. The sum of these N spectral values is calculated to obtain a first sum. Simultaneously, a second sum of the complex spectral values of the N sample points of the first radio frequency signal is calculated. The ratio of the first and second sums is then calculated as the spectral ratio. This spectral ratio is compared to a preset threshold. If the spectral ratio is greater than or equal to the preset threshold, the second radio frequency signal is determined to be adjacent channel interference of the first radio frequency signal, and the filter bandwidth is appropriately reduced based on the interference intensity. Some embodiments of this application effectively suppress interference from nearby stations through adjacent station interference detection and adaptive bandwidth adjustment.
[0065] like Figure 2 As shown in the embodiment of this application, another signal processing method is provided, including: Step 1: Signal down-conversion and preprocessing; Step 2: Frequency domain transformation; Step 3: Noise threshold calculation; Step 4: Signal bandwidth determination; Step 5: Frequency domain filtering; Step 6: Inverse transform, perform IFFT (Inverse Fast Fourier Transform) on the processed frequency domain signal to transform it back to the time domain, completing the filtering of the FM baseband signal.
[0066] It should be noted that each of the implementable methods in this embodiment can be implemented individually or in any combination without conflict. This application does not limit this.
[0067] Another embodiment of this application provides a signal processing apparatus for performing the signal processing method provided in the above embodiments.
[0068] like Figure 3 The diagram shown is a schematic representation of the signal processing apparatus provided in an embodiment of this application. The signal processing apparatus includes an acquisition module 301, a transformation module 302, a calculation module 303, a comparison module 304, and a filtering module 305, wherein: The acquisition module 301 is used to acquire the first radio frequency signal and perform down-conversion processing on the first radio frequency signal to obtain the baseband signal corresponding to the first radio frequency signal; The conversion module 302 is used to convert the baseband signal into a spectrum value in the frequency domain corresponding to the baseband signal using a preset frequency domain conversion algorithm; The calculation module 303 is used to determine the noise threshold based on the spectral values in the frequency domain; Comparison module 304 is used to compare the noise threshold and the spectral value in the frequency domain to determine the noise bandwidth region and the target signal bandwidth region; The filtering module 305 is used to filter noise signals within the noise bandwidth region to obtain the target signal in the frequency domain corresponding to the target signal bandwidth region, and convert the target signal in the frequency domain into a baseband signal in the time domain.
[0069] Regarding the apparatus in this embodiment, the specific manner in which each module performs its operations has been described in detail in the embodiments related to the method, and will not be elaborated upon here.
[0070] Some embodiments of this application acquire radio frequency signals in the time domain, convert the radio frequency signals in the time domain to the frequency domain, calculate the spectral values of each sampling point, and then set a noise threshold. Using the spectral values of each sampling point and the noise threshold, the noise bandwidth region and the target signal bandwidth region are determined, and the noise in the noise bandwidth region is eliminated to obtain the final target signal. Through frequency domain analysis, the boundary between signal and noise can be accurately identified, achieving more precise filtering and improving the accuracy of filtering.
[0071] Another embodiment of this application further illustrates the signal processing apparatus provided in the above embodiments.
[0072] Optionally, the acquisition module is used for: Acquire a first radio frequency signal and perform down-conversion processing on the first radio frequency signal to obtain a baseband complex signal; The baseband complex signal is grouped according to the preset number of sample points N to obtain the grouped baseband complex signal.
[0073] Some embodiments of this application obtain the baseband signal of the digital signal by downconverting the acquired analog first radio frequency signal, and then performing windowing processing using window functions of different sizes (such as Hanning window, Hamming window, etc.) to acquire a baseband complex signal with a pre-set number of sample points N, so as to reduce spectral leakage.
[0074] Optionally, the transformation module is used for: The baseband complex signal after grouping in the time domain is converted into a complex spectrum value in the frequency domain corresponding to the baseband complex signal by using any one of the Fast Fourier Transform, Discrete Cosine Transform, or Wavelet Transform. Some embodiments of this application can use different preset frequency domain conversion algorithms to convert the baseband signal in the time domain into the spectrum value in the frequency domain. Through frequency domain analysis, the boundary between signal and noise can be accurately identified, and more accurate filtering can be achieved.
[0075] Optionally, the transformation module is used for: Perform a Fast Fourier Transform on the baseband complex signal with a pre-set number of sample points to obtain the complex spectrum value of the frequency bin corresponding to the number of sample points.
[0076] Optionally, the calculation module is used for: The noise threshold is determined by using any one of the minimum spectral value of frequency bin, the average value of the complex spectral values of frequency bin, or the median value.
[0077] In some embodiments of this application, the noise threshold can be determined by statistical analysis methods, such as multiplying the average or median of all bin energies by a coefficient, or by employing an adaptive statistical threshold algorithm. Optionally, the comparison module is used for: The complex spectral values of the frequency bins numbered 0 to N / 2 are compared with the noise threshold, and the last frequency bin that is less than the noise threshold is determined as the first marker point; The complex spectral values of the frequency bins numbered N-1 to N / 2+1 are compared with the noise threshold, and the last frequency bin that is less than the noise threshold is determined as the second marker point; Based on the first and second markers in the first radio frequency signal, the noise bandwidth region and the target signal bandwidth region are determined respectively.
[0078] Some embodiments of this application determine the noise bandwidth region and the target signal bandwidth region by finding the minimum FFT bin energy and multiplying it by a coefficient to obtain the noise threshold, and by using a technique of searching for signal boundaries from the DC carrier point to both sides.
[0079] Optionally, the comparison module is used for: The region between the first marker point and the second marker point in the first radio frequency signal is defined as the noise bandwidth region; In the first radio frequency signal, the regions other than the noise bandwidth region are defined as the target signal bandwidth region.
[0080] Optionally, the filtering module is used for: The spectral values of the noise signal within the noise bandwidth region are set to zero or the amplitude is attenuated to obtain the target signal in the frequency domain corresponding to the target signal bandwidth region.
[0081] In addition to directly setting the noise region bin to zero, some embodiments of this application can also employ a gradual attenuation method, such as setting different attenuation coefficients according to the distance between bin and the signal boundary, to achieve a smoother transition.
[0082] Optionally, the filtering module is used for: Acquire the second radio frequency signal; Calculate the first sum of the complex spectral values of the N sample points of the second radio frequency signal; Calculate the second sum of the complex spectral values of the first radio frequency signal and the N sample points; Calculate the spectral ratio based on the first and second sums; Compare the spectral ratio with a preset threshold; If the spectrum ratio is greater than or equal to a preset threshold, the second radio frequency signal is determined to be adjacent interference of the first radio frequency signal.
[0083] Some embodiments of this application effectively suppress interference from nearby stations through adjacent-channel interference detection and adaptive bandwidth adjustment. Regarding the apparatus in this embodiment, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.
[0084] It should be noted that each of the implementable methods in this embodiment can be implemented individually or in any combination without conflict. This application does not limit this.
[0085] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, can perform the operation of any of the methods corresponding to the signal processing methods provided in the above embodiments.
[0086] This application also provides a computer program product, which includes a computer program, wherein when the computer program is executed by a processor, it can implement the operation of any of the methods corresponding to the signal processing methods provided in the above embodiments.
[0087] like Figure 4As shown, some embodiments of this application provide an electronic device 400, which includes a memory 410, a processor 420, and a computer program stored in the memory 410 and executable on the processor 420. When the processor 420 reads the program from the memory 410 via a bus 430 and executes the program, it can implement any of the methods included in the above-described signal processing methods.
[0088] Processor 420 can process digital signals and may include various computing architectures. For example, it may be a complex instruction set computer architecture, a reduced instruction set computer architecture, or an architecture that implements multiple instruction set combinations. In some examples, processor 420 may be a microprocessor.
[0089] Memory 410 can be used to store instructions executed by processor 420 or data related to the execution of instructions. These instructions and / or data may include code for implementing some or all of the functions of one or more modules described in the embodiments of this application. The processor 420 of this disclosure embodiment can be used to execute instructions in memory 410 to implement the methods shown above. Memory 410 includes dynamic random access memory, static random access memory, flash memory, optical memory, or other memories well known to those skilled in the art.
[0090] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application. It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.
[0091] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
[0092] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
Claims
1. A signal processing method, characterized in that, The method includes: A first radio frequency signal is acquired, and the first radio frequency signal is down-converted to obtain a baseband signal corresponding to the first radio frequency signal. A preset frequency domain conversion algorithm is used to convert the baseband signal into a spectrum value in the frequency domain corresponding to the baseband signal; The noise threshold is determined based on the spectral values in the frequency domain. The noise threshold and the spectral value in the frequency domain are compared to determine the noise bandwidth region and the target signal bandwidth region. The noise signal within the noise bandwidth region is filtered to obtain the target signal in the frequency domain corresponding to the target signal bandwidth region, and the target signal in the frequency domain is converted into a baseband signal in the time domain.
2. The signal processing method according to claim 1, characterized in that, The step of acquiring a first radio frequency signal and performing down-conversion processing on the first radio frequency signal to obtain a baseband signal corresponding to the first radio frequency signal includes: The first radio frequency signal is acquired, and the first radio frequency signal is down-converted to obtain a baseband complex signal; The baseband complex signal is grouped according to the preset number of sample points N to obtain the grouped baseband complex signal.
3. The signal processing method according to claim 2, characterized in that, The step of using a preset frequency domain conversion algorithm to convert the baseband signal into a frequency spectrum value corresponding to the baseband signal includes: The baseband complex signal after grouping in the time domain is converted into a complex spectrum value in the frequency domain corresponding to the baseband complex signal by using any one of the Fast Fourier Transform, Discrete Cosine Transform, or Wavelet Transform.
4. The signal processing method according to claim 3, characterized in that, The step of employing Fast Fourier Transform to convert the grouped baseband complex signal in the time domain into a complex spectrum value in the frequency domain corresponding to the baseband complex signal includes: The baseband complex signal is subjected to a fast Fourier transform with a preset number of sample points to obtain the complex spectrum value of the frequency bin corresponding to the number of sample points.
5. The signal processing method according to claim 4, characterized in that, Determining the noise threshold based on the frequency domain spectrum values includes: The noise threshold is determined by using any one of the minimum spectral value of the frequency bin, the average value of the complex spectral values of the frequency bin, or the median value.
6. The signal processing method according to claim 4, characterized in that, The step of comparing the noise threshold and the spectral value in the frequency domain to determine the noise bandwidth region and the target signal bandwidth region includes: The complex spectral values of the frequency bins numbered 0 to N / 2 are compared with the noise threshold, and the last frequency bin that is less than the noise threshold is determined as the first identification point; The complex spectral values of the frequency bins numbered N-1 to N / 2+1 are compared with the noise threshold, and the last frequency bin that is less than the noise threshold is determined as the second identifier point; The noise bandwidth region and the target signal bandwidth region are determined based on the first and second marker points in the first radio frequency signal, respectively.
7. The signal processing method according to claim 6, characterized in that, The step of determining the noise bandwidth region and the target signal bandwidth region based on the first marker point and the second marker point in the first radio frequency signal includes: The region between the first marker point and the second marker point in the first radio frequency signal is defined as the noise bandwidth region; In the first radio frequency signal, the regions other than the noise bandwidth region are determined as the target signal bandwidth region.
8. The signal processing method according to claim 1, characterized in that, The step of filtering the noise signal within the noise bandwidth region to obtain the target signal in the frequency domain corresponding to the target signal bandwidth region includes: The spectral value of the noise signal within the noise bandwidth region is set to zero or its amplitude is attenuated to obtain the target signal in the frequency domain corresponding to the target signal bandwidth region.
9. The signal processing method according to claim 1, characterized in that, The method further includes: Acquire the second radio frequency signal; Calculate the first sum of the complex spectral values of the N sample points of the second radio frequency signal; Calculate the second sum of the complex spectral values of the first radio frequency signal and the N sample points of the first radio frequency signal; Calculate the spectral ratio based on the first sum and the second sum; The spectral ratio is compared with a preset threshold. If the spectrum ratio is greater than or equal to the preset threshold, then the second radio frequency signal is determined to be adjacent channel interference of the first radio frequency signal.
10. A signal processing apparatus, characterized in that, The device includes: An acquisition module is used to acquire a first radio frequency signal and perform down-conversion processing on the first radio frequency signal to obtain a baseband signal corresponding to the first radio frequency signal; The conversion module is used to convert the baseband signal into a spectrum value in the frequency domain corresponding to the baseband signal using a preset frequency domain conversion algorithm; The calculation module is used to determine the noise threshold based on the spectral values in the frequency domain. The comparison module is used to compare the noise threshold and the spectral value in the frequency domain to determine the noise bandwidth region and the target signal bandwidth region. The filtering module is used to filter the noise signal in the noise bandwidth region to obtain the target signal in the frequency domain corresponding to the target signal bandwidth region, and convert the target signal in the frequency domain into a baseband signal in the time domain.