Signal frequency domain detection and parameter estimation method, device, equipment, storage medium and program product

Abstract

This application discloses a signal frequency domain detection and parameter estimation method, apparatus, device, storage medium, and program product, comprising: performing a first-resolution Fast Fourier Transform on broadband in-phase quadrature signal data to obtain a full-band low-resolution spectrum; performing energy detection based on the full-band low-resolution spectrum to determine the energy concentration region corresponding to an independent signal carrier; performing a second-resolution Fast Fourier Transform on the local frequency band data corresponding to the energy concentration region, and performing adaptive parameter estimation based on the obtained corresponding local high-resolution spectrum to obtain estimated parameters. Since the first-resolution Fast Fourier Transform is used to coarsely scan the entire frequency band, the energy concentration region is quickly marked. Performing the second-resolution Fast Fourier Transform on the local frequency band data corresponding to the marked energy concentration region avoids the large amount of invalid computation caused by performing a high-resolution transform across the entire frequency band, improving real-time performance and reducing power consumption.

Description

Technical Field

[0001] This application relates to the field of signal processing technology, and in particular to methods, apparatus, devices, storage media and program products for signal frequency domain detection and parameter estimation. Background Technology

[0002] In 5G New Radio (NR) systems, identifying the center frequency and bandwidth of the signal is a key step in achieving air interface synchronization and access.

[0003] Existing solutions perform large-scale Fast Fourier Transform (FFT) and energy detection in a central processing unit (CPU) or graphics processing unit (GPU). This approach suffers from poor real-time performance and high power consumption due to the need to process the entire dataset, making it unsuitable for the high-bandwidth scenarios of edge devices. Summary of the Invention

[0004] The main objective of this application is to provide a signal frequency domain detection and parameter estimation method, apparatus, device, storage medium, and program product, which aims to solve the technical problem that existing signal processing solutions are difficult to adapt to the high-speed bandwidth scenarios of edge devices.

[0005] To achieve the above objectives, this application proposes a signal frequency domain detection and parameter estimation method, which includes: Acquire broadband in-phase quadrature signal data, and perform a fast Fourier transform with a first resolution on the broadband in-phase quadrature signal data to obtain a full-band low-resolution spectrum; Energy detection is performed based on the full-band low-resolution spectrum to determine the energy concentration area corresponding to each independent signal carrier. Based on the local frequency band data corresponding to each of the energy concentration regions, a second-resolution fast Fourier transform is performed to obtain the corresponding local high-resolution spectrum; wherein, the second resolution is higher than the first resolution; Based on the local high-resolution spectrum of each of the above, adaptive parameter estimation is performed to obtain the estimated parameters corresponding to each of the independent signal carriers.

[0006] In one embodiment, the step of determining the energy concentration region corresponding to each independent signal carrier based on the full-band low-resolution spectrum includes: One-dimensional density clustering is performed based on the full-band low-resolution spectrum to obtain the signal cluster center and signal cluster boundary corresponding to each independent signal carrier in the broadband in-phase orthogonal signal data. Based on the signal cluster, determine the carrier parameters corresponding to each independent signal carrier in the broadband in-phase quadrature signal data; The energy concentration region corresponding to each independent signal carrier is determined based on the full-band low-resolution spectrum and the carrier parameters.

[0007] In one embodiment, the step of performing one-dimensional density clustering based on the full-band low-resolution spectrum to obtain the signal cluster centers and signal cluster boundaries corresponding to each independent signal carrier in the broadband in-phase quadrature signal data includes: The energy peak location was determined in the full-band low-resolution spectrum. One-dimensional density clustering is performed based on the energy peak positions to determine the signal cluster center and signal cluster boundary corresponding to each independent signal carrier in the broadband in-phase orthogonal signal data.

[0008] In one embodiment, the step of adaptively estimating parameters based on each of the local high-resolution spectra to obtain the estimated parameters corresponding to each of the independent signal carriers includes: For any local high-resolution spectrum, perform first-order difference processing on the local high-resolution spectrum to determine the power difference between adjacent frequency points in the local high-resolution spectrum. Based on the power difference, mutation point detection is performed to determine candidate mutation points; The dynamic threshold of the local high-resolution spectrum is obtained, and the effective breakthrough point is determined among the candidate mutation points based on the dynamic threshold. The lower boundary of the independent signal carrier corresponding to the local high-resolution spectrum is determined based on the frequency position corresponding to the positive breakthrough point among the effective breakthrough points, and the upper boundary of the independent signal carrier corresponding to the local high-resolution spectrum is determined based on the frequency position corresponding to the negative breakthrough point among the effective breakthrough points. The estimated parameters of the independent signal carrier corresponding to the local high-resolution spectrum are determined based on the frequency difference between the upper boundary and the lower boundary.

[0009] In one embodiment, the estimation parameters include a bandwidth estimate and an estimated center frequency. The step of determining the estimated parameters of the independent signal carrier corresponding to the local high-resolution spectrum based on the frequency difference between the upper boundary and the lower boundary includes: The boundary difference between the upper boundary and the lower boundary is used as the bandwidth estimate of the independent signal carrier corresponding to the local high-resolution spectrum. Based on the spectrum data, determine whether the spectral power distribution within the bandwidth of the independent signal carrier is uniform; When the spectral power distribution is uniform, the estimated center frequency of the independent signal carrier is determined based on the mean of the upper and lower boundaries; When the spectral power distribution is non-uniform, the estimated center frequency of the independent signal carrier is determined based on the energy-weighted centroid method.

[0010] In one embodiment, the step of performing a first-resolution Fast Fourier Transform on the broadband in-phase quadrature signal data to obtain a full-band low-resolution spectrum includes: The broadband in-phase quadrature signal data is preprocessed by windowing and buffering to obtain preprocessed data; The preprocessed data is subjected to a fast Fourier transform at a first resolution to obtain a full-band low-resolution spectrum.

[0011] Furthermore, to achieve the above objectives, this application also proposes a signal frequency domain detection and parameter estimation device, which includes: The coarse processing module acquires broadband in-phase quadrature signal data and performs a fast Fourier transform with a first resolution on the broadband in-phase quadrature signal data to obtain a full-band low-resolution spectrum. The energy detection module performs energy detection based on the full-band low-resolution spectrum to determine the energy concentration area corresponding to each independent signal carrier. The fine processing module performs a fast Fourier transform with a second resolution based on the local frequency band data corresponding to each of the energy concentration regions to obtain the corresponding local high-resolution spectrum; wherein, the second resolution is higher than the first resolution; The adaptive estimation module performs adaptive parameter estimation based on each of the local high-resolution spectra to obtain the estimated parameters corresponding to each of the independent signal carriers.

[0012] In addition, to achieve the above objectives, this application also proposes a signal frequency domain detection and parameter estimation device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the signal frequency domain detection and parameter estimation method described above.

[0013] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the signal frequency domain detection and parameter estimation method described above.

[0014] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the signal frequency domain detection and parameter estimation method described above.

[0015] One or more technical solutions proposed in this application have at least the following technical effects: This application acquires broadband in-phase quadrature signal data and performs a first-resolution Fast Fourier Transform (FFT) on the data to obtain a full-band low-resolution spectrum. Based on this low-resolution spectrum, energy detection is performed to determine the energy concentration regions corresponding to each independent signal carrier. Then, based on the local frequency band data corresponding to each energy concentration region, a second-resolution FFT is performed to obtain the corresponding local high-resolution spectrum. The second resolution is higher than the first resolution. Finally, adaptive parameter estimation is performed based on each local high-resolution spectrum to obtain the estimated parameters for each independent signal carrier. Because a coarse-to-fine cascaded search strategy is adopted, the first-resolution FFT is used to coarsely scan the entire band. The first-resolution FFT has fewer transformation points and lower computational cost, allowing for full-band coverage in a very short time and rapid identification of target areas with signal energy. A high-resolution second-resolution FFT is then performed on the local frequency band data corresponding to the identified energy concentration regions. The computational power of the high-resolution transform is concentrated on at least a few local regions, rather than being uniformly consumed across the entire broadband, avoiding the large amount of unnecessary computation that would result from performing the high-resolution transform across the entire band. Furthermore, the first and second stages of the Fast Fourier Transform can share the same set of FFT hardware units through time-division multiplexing, with the state machine controlling parameter switching and mode conversion. This avoids the need to fix separate hardware resources for the two stages of the transform, further reducing the overall resource consumption. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating an embodiment of the signal frequency domain detection and parameter estimation method of this application. Figure 2 This is a system processing flowchart of an example embodiment of the signal frequency domain detection and parameter estimation method of this application; Figure 3 This is a flowchart illustrating Embodiment 2 of the signal frequency domain detection and parameter estimation method of this application; Figure 4 This is a block diagram of a multi-carrier identification module in one implementation of an embodiment of this application; Figure 5 This is a flowchart illustrating Embodiment 3 of the signal frequency domain detection and parameter estimation method of this application; Figure 6 This is a block diagram showing the joint operation of the multi-resolution spectrum search module and the multi-carrier identification module; Figure 7 This is a schematic diagram of the module structure of the signal frequency domain detection and parameter estimation device according to an embodiment of this application; Figure 8 This is a schematic diagram of the device structure of the hardware operating environment involved in the signal frequency domain detection and parameter estimation method in the embodiments of this application.

[0019] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0021] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0022] The main solution of this application embodiment is as follows: acquire broadband in-phase quadrature signal data, and perform a fast Fourier transform of the broadband in-phase quadrature signal data at a first resolution to obtain a full-band low-resolution spectrum; perform energy detection based on the full-band low-resolution spectrum to determine the energy concentration region; perform a fast Fourier transform of the local frequency band data corresponding to the energy concentration region at a second resolution to obtain a local high-resolution spectrum; wherein, the second resolution is higher than the first resolution; and perform adaptive parameter estimation based on the local high-resolution spectrum to obtain the estimated parameters of the broadband in-phase quadrature signal data.

[0023] This application provides a scheme for frequency domain detection and parameter estimation of 5G NR signals based on FPGA. It realizes search and bandwidth boundary detection, center frequency estimation and clustering algorithm through multi-resolution FFT, so as to achieve fast and accurate estimation of the center frequency and bandwidth of NR signals in space.

[0024] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, server, etc., or an electronic device or virtual device capable of realizing the above functions. The following description uses a signal frequency domain detection and parameter estimation device (hereinafter referred to as the estimation device) as an example to illustrate this embodiment and the following embodiments.

[0025] Based on this, embodiments of this application provide a signal frequency domain detection and parameter estimation method, referring to... Figure 1 and Figure 2 , Figure 1 This is a flowchart illustrating an embodiment of the signal frequency domain detection and parameter estimation method of this application. Figure 2 This is a flowchart illustrating the system processing in an example of the signal frequency domain detection and parameter estimation method of this application.

[0026] In this embodiment, the signal frequency domain detection and parameter estimation method includes steps S10 to S40: Step S10: Acquire broadband in-phase quadrature signal data, and perform a fast Fourier transform at a first resolution on the broadband in-phase quadrature signal data to obtain a full-band low-resolution spectrum.

[0027] It should be noted that the aforementioned broadband in-phase and quadrature signal (IQ signal) data can be a sampled data stream of in-phase and quadrature signals covering a certain frequency range, such as IQ sampled data covering a bandwidth of 245.76MHz from an analog-to-digital converter (ADC), where I represents the in-phase component of the signal and Q represents the quadrature component. The broadband in-phase and quadrature signal may contain several independent signal carriers from different base stations, various interference signals, background noise, etc.

[0028] In this embodiment, the aforementioned first resolution can be the resolution used for coarse-grained Fast Fourier Transform (FFT), which is used to quickly complete full-band scanning with a small computational load. The first resolution can be achieved by selecting a smaller number of FFT points; for example, a 2048-point coarse-resolution FFT can be used to process broadband in-phase quadrature signal data to obtain a full-band low-resolution spectrum.

[0029] It should be noted that the aforementioned full-band low-resolution spectrum is the spectrum obtained through a coarse-resolution Fast Fourier Transform (FFT). Each frequency point in the full-band low-resolution spectrum corresponds to a power value. Because this spectrum has a relatively coarse frequency resolution, it can reflect the overall distribution of signal energy across the entire band without requiring large-scale, fine-grained FFTs and energy detection across the entire signal band. This reduces power consumption and improves real-time processing capabilities.

[0030] In some embodiments of this application, to improve the accuracy of detection and estimation, the acquired broadband in-phase quadrature signal data can be preprocessed using a signal preprocessing module. Specifically, the step of performing a first-resolution Fast Fourier Transform on the broadband in-phase quadrature signal data to obtain a full-band low-resolution spectrum includes: performing windowing and buffering preprocessing on the broadband in-phase quadrature signal data to obtain preprocessed data; and performing a first-resolution Fast Fourier Transform on the preprocessed data to obtain a full-band low-resolution spectrum.

[0031] It is understandable that applying a window function before the Fast Fourier Transform can reduce spectral leakage caused by data truncation, making the signal energy more concentrated in the spectrum. In this embodiment, the window function used may include the Hanning window, Hamming window, Blackman window, etc., and this embodiment is not limited thereto.

[0032] In this embodiment, to achieve parallel data read and write, wideband in-phase quadrature signal data can also be buffered. Specifically, a First-In / First-Out (FIFO) memory combined with a ping-pong buffer structure can be used to buffer and reassemble the data stream, thereby ensuring the continuity of the data stream. For example, a dual-buffer structure of FIFO plus Ping-PongRAM can be used, where while one RAM is writing new data, the other RAM can be read simultaneously for subsequent Fast Fourier Transform processing, with the two RAMs switching alternately.

[0033] It should be understood that the above-described windowing and buffering preprocessing do not constitute a limitation on the preprocessing procedure for wideband in-phase quadrature signal data in this application. In practical applications, embodiments of this application may employ any preprocessing method or any combination of preprocessing methods, such as filtering, downsampling, imbalance correction, buffering, windowing, etc.

[0034] In its implementation, the estimation device first acquires the broadband in-phase quadrature signal data output by the analog-to-digital converter. Then, the estimation device performs a first-resolution Fast Fourier Transform on the broadband in-phase quadrature signal data using a multi-resolution frequency search module, converting the time-domain in-phase quadrature signal data into a full-band low-resolution spectrum in the frequency domain (i.e., a coarse-resolution FFT result), providing a basis for subsequent identification of energy-concentrated target regions.

[0035] In some implementations, before performing a Fast Fourier Transform, the estimation device can first perform a windowing operation by multiplying the broadband in-phase quadrature signal data point by point with the window function coefficients through a signal preprocessing module, thereby reducing spectral leakage in subsequent spectrum analysis. Subsequently, the estimation device sends the windowed data into a buffer structure, and through a ping-pong buffer alternating read-write mechanism, ensures that no conflicts occur between writing and reading data, thus obtaining continuous and ordered preprocessed data.

[0036] Step S20: Perform energy detection based on the full-band low-resolution spectrum to determine the energy concentration area corresponding to each independent signal carrier; Step S30: Perform a fast Fourier transform with second resolution based on the local frequency band data corresponding to each energy concentration region to obtain the corresponding local high-resolution spectrum. The second resolution is higher than the first resolution.

[0037] It should be noted that the aforementioned independent signal carriers are radio frequency signals that can independently transmit information. They can be transmitted by 5G base stations, occupy a continuous frequency range in the spectrum, and have a center frequency and bandwidth. Each independent signal carrier can occupy a continuous frequency range, and multiple independent signal carriers in the full-band low-resolution spectrum can correspond to multiple unconnected energy concentration areas.

[0038] It should be explained that the aforementioned local frequency band data refers to a portion of the time-domain data corresponding to the frequency range of the energy concentration area in the broadband in-phase quadrature signal data. The second resolution mentioned above refers to the resolution used in the fine-grained Fast Fourier Transform (FFT). The second resolution is higher than the first resolution and is used to more precisely resolve spectral details. Specifically, the second resolution can be achieved by using a FFT with more points than the first resolution, for example, increasing it from 2048 points to 8192 points, thus correspondingly improving the frequency resolution.

[0039] It is understandable that the aforementioned local high-resolution spectrum is the spectrum data obtained by performing a high-resolution fast Fourier transform on local frequency band data. This spectrum data only covers the frequency band corresponding to the energy concentration area, and can more clearly show the fine structure of the signal spectrum in this area, including the specific boundary shape and power distribution details of the signal.

[0040] In practical applications, the estimation device in this embodiment can perform energy detection on the coarse FFT result through a multi-carrier identification module to obtain the multi-carrier identification result (energy concentration region). After determining the energy concentration region, a second-resolution fast Fourier transform can be performed on the local frequency band data corresponding to the energy concentration region, thereby achieving a finer frequency division of the local frequency band data and obtaining the local high-resolution spectrum (fine-resolution FFT result) corresponding to each independent signal carrier. By limiting high-resolution computation to local frequency bands, the large amount of invalid computation caused by performing high-resolution fast Fourier transforms across the entire frequency band is avoided, reducing the system's resource consumption and power consumption. The solution in this application can be deployed in a Field Programmable Gate Array (FPGA). Utilizing the FPGA's streaming pipeline processing architecture, data is processed step-by-step as it flows into the processing module, without waiting for the entire frame of data to be acquired, further eliminating the waiting delay caused by batch processing. The combination of the speed of coarse scanning and the locality of high-resolution processing significantly compresses the overall time consumption of signal search and parameter estimation.

[0041] Step S40: Based on the local high-resolution spectrum of each of the above, perform adaptive parameter estimation to obtain the estimated parameters corresponding to each of the independent signal carriers.

[0042] It should be noted that the above estimated parameters are the parameters extracted from the broadband in-phase quadrature signal data to describe the characteristics of each independent signal carrier, such as the estimated center frequency and estimated bandwidth of each independent signal carrier.

[0043] In its implementation, when obtaining a local high-resolution spectrum, the estimation device can adaptively estimate the parameters of the local high-resolution spectrum corresponding to each independent signal carrier using both a bandwidth estimation module and a center frequency estimation module. This determines the bandwidth estimate, and the center frequency estimation module determines the estimated center frequency of the signal based on the boundary location or spectral energy distribution. Finally, the estimation device outputs the bandwidth estimate and center frequency of each independent signal carrier as estimation parameters, completing the frequency domain detection and parameter estimation of broadband in-phase quadrature signal data.

[0044] This application embodiment acquires broadband in-phase quadrature signal data and performs a first-resolution Fast Fourier Transform (FFT) on the data to obtain a full-band low-resolution spectrum. Energy detection is then performed based on this low-resolution spectrum to determine the energy concentration regions corresponding to each independent signal carrier. A second-resolution FFT is then performed on the local frequency band data corresponding to each energy concentration region to obtain the corresponding local high-resolution spectrum. The second resolution is higher than the first resolution. Parameter adaptive estimation is then performed based on each local high-resolution spectrum to obtain the estimated parameters for each independent signal carrier. Because a coarse-to-fine cascaded search strategy is adopted, a coarse scan of the entire band is first performed using the first-resolution FFT. The first-resolution transform has fewer transformation points and lower computational load, allowing for full-band coverage to be completed in a very short time, quickly marking target areas with signal energy. A high-resolution second-resolution FFT is then performed on the local frequency band data corresponding to the marked energy concentration regions. The computational power of the high-resolution transform is concentrated on at least a few local regions, rather than being uniformly consumed across the entire broadband, avoiding the large amount of invalid computation that would result from performing the high-resolution transform across the entire band. Furthermore, the first and second stages of the Fast Fourier Transform can share the same set of FFT hardware units through time-division multiplexing, with the state machine controlling parameter switching and mode conversion. This avoids the need to fix separate hardware resources for the two stages of the transform, further reducing the overall resource consumption.

[0045] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 , Figure 3 This is a flowchart illustrating Embodiment 2 of the signal frequency domain detection and parameter estimation method of this application.

[0046] like Figure 3 As shown in the embodiment of this application, the step of determining the energy concentration region based on the full-band low-resolution spectrum includes: The step of determining the energy concentration region corresponding to each independent signal carrier by performing energy detection based on the full-band low-resolution spectrum includes: Step S21: Perform one-dimensional density clustering based on the full-band low-resolution spectrum to obtain the signal cluster center and signal cluster boundary corresponding to each independent signal carrier in the broadband in-phase orthogonal signal data; Step S22: Determine the carrier parameters corresponding to each independent signal carrier in the broadband in-phase quadrature signal data based on the signal cluster; Step S23: Determine the energy concentration region corresponding to each of the independent signal carriers based on the full-band low-resolution spectrum and the carrier parameters.

[0047] It should be noted that, in this embodiment of the application, by performing a density clustering algorithm based on point density distribution on the full-band low-resolution spectrum, the frequency points on the spectrum can be automatically divided into several signal clusters according to the distance between them. Each independent signal carrier can correspond to one of these signal clusters, and these signal clusters can correspond to a signal cluster center and a signal cluster boundary. The signal cluster center and signal cluster boundary can be regarded as the boundary corresponding to the independent signal carrier from a coarse resolution perspective.

[0048] In some embodiments of this application, the step of performing one-dimensional density clustering based on the full-band low-resolution spectrum to obtain the signal cluster center and signal cluster boundary corresponding to each independent signal carrier in the broadband in-phase quadrature signal data includes: determining the energy peak position in the full-band low-resolution spectrum; and performing one-dimensional density clustering based on each energy peak position to determine the signal cluster center and signal cluster boundary corresponding to each independent signal carrier in the broadband in-phase quadrature signal data.

[0049] It should be noted that in the full-band low-resolution spectrum, an independent signal carrier can correspond to a series of frequency points with prominent power; these frequency points constitute the energy peak positions corresponding to that independent signal carrier. By determining the energy peak positions in the full-band low-resolution spectrum and performing one-dimensional density clustering based on these energy peak positions, the signal clusters corresponding to each independent carrier under the coarse-resolution view can be determined. For each signal cluster, the average frequency of all frequency points within the cluster can be used as the initial cluster center position of that independent signal carrier under coarse resolution, and the signal cluster boundaries under coarse-resolution processing can be determined based on the lowest and highest frequencies within the cluster.

[0050] It should be understood that one-dimensional density clustering is a density clustering algorithm performed on a one-dimensional, full-band, low-resolution spectrum based on the distribution density between frequency points. Density clustering algorithms are algorithms that can automatically identify signal clusters and noise points based on the density relationship between frequency points without requiring a pre-specified number of clusters.

[0051] In some embodiments of this application, a simplified version of the density-based spatial clustering algorithm (DBSCAN) is used to cluster the detected energy peak locations on the spectrum. Continuously dense frequency points are grouped into an independent signal carrier, while sparse and isolated frequency points are marked as noise. Compared to traditional methods that use fixed thresholds for simple classification, which may lead to over-merging or misclassification of noise, the DBSCAN algorithm has the advantage of being able to discover clusters of arbitrary shapes without needing to pre-specify the number of classes. It can automatically mark low-density points as noise, such as isolated interference and / or spikes, effectively avoiding misclassification. This application does not restrict the specific method for determining continuously dense clusters; frequency points within a neighborhood radius (e.g., between 120kHz and 360kHz) can be selected as frequency points within the same signal cluster according to the needs of the actual application.

[0052] It should be noted that the carrier parameters mentioned above refer to the parameters corresponding to each independent carrier signal after coarse resolution processing. Specifically, these parameters can be the frequency range corresponding to the independent carrier signal. This frequency range can be regarded as the signal cluster boundary of the signal cluster corresponding to the independent carrier signal, that is, the energy concentration region corresponding to the independent carrier signal in the full-band low-resolution spectrum. By extracting the full-band low-resolution spectrum based on this energy concentration region, the local frequency band data corresponding to the independent carrier signal can be determined.

[0053] In some embodiments of this application, the multi-carrier identification processing procedure of this application can be as follows: Figure 4 As shown, Figure 4 This is a block diagram of a multi-carrier identification module in one implementation of an embodiment of this application.

[0054] Reference Figure 4 In this embodiment, energy peak preprocessing can be performed based on coarse resolution FFT results to determine the energy peak position in broadband in-phase orthogonal signal data. Based on the energy peak position, one-dimensional DBSCAN clustering can be performed, and cluster center and cluster boundary calculations can be performed based on the signal clusters obtained by clustering, thereby realizing the generation of multi-carrier parameters and determining the carrier information (such as carrier 1 information, carrier 2 information, and carrier 3 information) corresponding to these carriers.

[0055] This application embodiment obtains the signal cluster center and signal cluster boundary corresponding to each independent signal carrier in broadband in-phase quadrature signal data by performing one-dimensional density clustering based on the full-band low-resolution spectrum; determines the carrier parameters corresponding to each independent signal carrier in the broadband in-phase quadrature signal data based on the signal clusters; and determines the energy concentration region corresponding to each independent signal carrier based on the full-band low-resolution spectrum and carrier parameters. Since clusters of arbitrary shapes are discovered through one-dimensional density clustering, there is no need for simple classification according to a fixed threshold, avoiding excessive merging of different energy concentration regions or misjudgment of noise. It also eliminates the need to pre-specify the number of categories, automatically marking low-density points as noise, including isolated interference and / or spikes, further avoiding misjudgment.

[0056] Based on the first and / or second embodiments of this application, in the third embodiment of this application, the content that is the same as or similar to the first and / or second embodiments described above can be referred to the above description and will not be repeated hereafter. Based on this, please refer to... Figure 5 , Figure 5 This is a flowchart illustrating Embodiment 3 of the signal frequency domain detection and parameter estimation method of this application.

[0057] like Figure 5 As shown in the embodiments of this application, the step of performing adaptive parameter estimation based on each of the local high-resolution spectra to obtain the estimated parameters corresponding to each of the independent signal carriers includes: Step S41: For any local high-resolution spectrum, perform first-order difference processing on the local high-resolution spectrum to determine the power difference between adjacent frequency points in the local high-resolution spectrum. Step S42: Detect abrupt change points based on the power difference to determine candidate abrupt change points; Step S43: Obtain the dynamic threshold of the local high-resolution spectrum, and determine the effective breakthrough point among the candidate mutation points according to the dynamic threshold; Step S44: Determine the lower boundary of the independent signal carrier corresponding to the local high-resolution spectrum based on the frequency position corresponding to the positive breakthrough point among the effective breakthrough points, and determine the upper boundary of the independent signal carrier corresponding to the local high-resolution spectrum based on the frequency position corresponding to the negative breakthrough point among the effective breakthrough points. Step S45: Determine the estimated parameters of the independent signal carrier corresponding to the local high-resolution spectrum based on the frequency difference between the upper boundary and the lower boundary.

[0058] It should be noted that for any independent signal carrier corresponding to a local high-resolution spectrum, the power difference between adjacent frequency points can be determined through first-order differential processing. This power difference can be used to characterize the magnitude and direction of power change between two adjacent frequency points; a positive difference indicates an increase in power, a negative difference indicates a decrease in power, and the magnitude of the absolute value indicates the degree of change. Based on the difference sequence formed by these power differences, positive abrupt change points with positive difference values ​​and negative abrupt change points with negative difference values ​​can be identified. Positive abrupt change points can be used to characterize the possible lower boundary of the signal carrier, and negative abrupt change points can be used to characterize the possible upper boundary of the signal carrier.

[0059] In some embodiments of this application, a dynamic threshold can be used to determine effective breakthrough points from positive and negative mutation points. Specifically, the dynamic threshold can be determined as follows: ; in, Indicates a dynamic threshold. Indicates the noise mean. Indicates the noise variance; This is an adjustment coefficient, and its value can be set according to the actual application, such as 4-6.

[0060] It should be noted that the dynamic threshold in this embodiment can be modulated in real time according to the noise power, which improves the recognition rate under weak signals compared to the traditional fixed threshold method. Candidate abrupt change points where the absolute value of the power difference is greater than the dynamic threshold can be used as effective breakthrough points.

[0061] Understandably, the frequency position corresponding to the positive breakthrough point among the effective breakthrough points can be used as the lower boundary of the independent carrier signal, and the frequency position corresponding to the negative breakthrough point can be used as the upper boundary of the independent signal carrier. The frequency difference between the upper and lower boundaries is used as the bandwidth estimate of the independent signal carrier. This allows us to determine whether the spectral data of the local high-resolution spectrum corresponding to the independent signal carrier is uniformly distributed within the bandwidth of the bandwidth estimate. When the spectral power distribution is uniform, the mean between the upper and lower boundaries can be directly used as the estimated center frequency of the independent signal carrier. If the distribution is uneven, the centroid position can be determined using the energy-weighted centroid method, and then the centroid position can be used as the location of the estimated center frequency of the independent signal carrier. That is, the estimation parameters include a bandwidth estimate and an estimated center frequency; the step of determining the estimation parameters of the independent signal carrier corresponding to the local high-resolution spectrum based on the frequency difference between the upper boundary and the lower boundary includes: using the boundary difference between the upper boundary and the lower boundary as the bandwidth estimate of the independent signal carrier corresponding to the local high-resolution spectrum; determining whether the spectral power distribution within the bandwidth of the independent signal carrier is uniform based on the spectral data; when the spectral power distribution is uniform, determining the estimated center frequency of the independent signal carrier based on the mean of the upper boundary and the lower boundary; when the spectral power distribution is non-uniform, determining the estimated center frequency of the independent signal carrier based on the energy-weighted centroid method.

[0062] It should be noted that for independent signal carriers with uniform spectral power distribution, the spectral power in their spectral data will be concentrated near the center of the bandwidth, and the power on both sides will be roughly symmetrically distributed. Therefore, the center frequency of the independent signal carrier can be determined based on the average of the upper and lower boundaries. However, due to interference from factors such as multipath fading, adjacent channel interference, or channel distortion, the power distribution within the bandwidth of some independent signal carriers may show a significant tilt, with one side having significantly higher energy than the other, and the spectral shape no longer being symmetrical. For such independent signal carriers with non-uniform spectral power distribution, the corresponding center frequency can be determined using the energy-weighted centroid method.

[0063] It should be understood that the energy-weighted centroid method described above refers to a method that uses spectral power values ​​as weights to calculate the centroid position of the power distribution within the signal bandwidth. By calculating the centroid position and using the corresponding frequency point as the center frequency, the center frequency of the signal carrier can be accurately identified, reducing the probability of misjudgment. Specifically, the center frequency can be determined as follows: ; in, Indicates the position of the center of mass. The frequencies corresponding to the frequency points within the range from the upper boundary to the lower boundary. This represents the power corresponding to the frequency points within the range from the upper boundary to the lower boundary.

[0064] In some embodiments of this application, such as Figure 6 As shown, Figure 6 This is a block diagram showing the joint operation of the multi-resolution spectrum search module and the multi-carrier identification module.

[0065] Reference Figure 6 In this embodiment, the broadband in-phase quadrature signal data can be preprocessed to obtain preprocessed data. A coarse-resolution FFT full-bandwidth scan is performed on the preprocessed data, and the coarse-resolution FFT identification result is sent to a multi-carrier identification module for carrier parameter identification. The multi-carrier identification module performs multi-carrier identification on the coarse-resolution FFT identification result to determine the carrier parameters corresponding to different independent signal carriers. Then, based on these carrier parameters, signal decimation and upper / lower boundary frequency conversion operations are performed in the full-band low-resolution spectrum to determine the local frequency band data corresponding to different independent signal carriers. A fine-resolution FFT local fine search is performed on these local frequency band data, and the resulting fine spectrum (local high-resolution spectrum) is output.

[0066] This application embodiment determines the power difference between adjacent frequency points in an arbitrary local high-resolution spectrum by performing first-order differential processing on the local high-resolution spectrum; it then detects abrupt change points based on the power difference to identify candidate abrupt change points; it obtains the dynamic threshold of the local high-resolution spectrum and identifies effective breakthrough points among the candidate abrupt change points based on the dynamic threshold; it determines the lower boundary of the independent signal carrier corresponding to the local high-resolution spectrum based on the frequency position corresponding to the positive breakthrough point among the effective breakthrough points, and determines the upper boundary of the independent signal carrier corresponding to the local high-resolution spectrum based on the frequency position corresponding to the negative breakthrough point among the effective breakthrough points; and it determines the estimated parameters of the independent signal carrier corresponding to the local high-resolution spectrum based on the frequency difference between the upper and lower boundaries. Since the dynamic threshold is calculated in real time based on the noise statistical characteristics (noise mean and noise variance) of the differential value sequence, when the noise increases, the variance automatically increases, and the dynamic threshold rises accordingly, effectively suppressing false abrupt change points caused by noise; when the noise decreases, the variance decreases, and the dynamic threshold falls accordingly, ensuring that boundary abrupt change points of weak signals can still be captured. This adaptive mechanism enables boundary detection to maintain stable accuracy under different noise environments.

[0067] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the signal frequency domain detection and parameter estimation method of this application. Any simple transformations based on this technical concept are within the protection scope of this application.

[0068] This application also provides a signal frequency domain detection and parameter estimation device, please refer to... Figure 7 , Figure 7This is a schematic diagram of the module structure of the signal frequency domain detection and parameter estimation device according to an embodiment of this application. The signal frequency domain detection and parameter estimation device includes: The coarse processing module 10 acquires broadband in-phase quadrature signal data and performs a fast Fourier transform with a first resolution on the broadband in-phase quadrature signal data to obtain a full-band low-resolution spectrum. The energy detection module 20 performs energy detection based on the full-band low-resolution spectrum to determine the energy concentration area corresponding to each independent signal carrier. The fine processing module 30 performs a fast Fourier transform with a second resolution based on the local frequency band data corresponding to each of the energy concentration regions to obtain the corresponding local high-resolution spectrum; wherein, the second resolution is higher than the first resolution; The adaptive estimation module 40 performs adaptive parameter estimation based on each of the local high-resolution spectra to obtain the estimated parameters corresponding to each of the independent signal carriers.

[0069] The signal frequency domain detection and parameter estimation device provided in this application, employing the signal frequency domain detection and parameter estimation method described in the above embodiments, can solve the technical problem that existing signal processing solutions are difficult to adapt to high-speed bandwidth scenarios of edge devices. Compared with the prior art, the beneficial effects of the signal frequency domain detection and parameter estimation device provided in this application are the same as those of the signal frequency domain detection and parameter estimation method described in the above embodiments, and other technical features in the signal frequency domain detection and parameter estimation device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0070] This application provides a signal frequency domain detection and parameter estimation device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the signal frequency domain detection and parameter estimation method in the above embodiment 1.

[0071] The following is for reference. Figure 8This document illustrates a schematic diagram of a signal frequency domain detection and parameter estimation device suitable for implementing embodiments of this application. The signal frequency domain detection and parameter estimation device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 8 The signal frequency domain detection and parameter estimation device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0072] like Figure 8 As shown, the signal frequency domain detection and parameter estimation device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 1002 or a program loaded from storage device 1003 into random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the signal frequency domain detection and parameter estimation device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the signal frequency domain detection and parameter estimation device to exchange data with other devices wirelessly or via wired communication. Although signal frequency domain detection and parameter estimation devices with various systems are shown in the figure, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0073] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0074] The signal frequency domain detection and parameter estimation device provided in this application, employing the signal frequency domain detection and parameter estimation method described in the above embodiments, can solve the technical problem that existing signal processing solutions are difficult to adapt to high-speed bandwidth scenarios of edge devices. Compared with the prior art, the beneficial effects of the signal frequency domain detection and parameter estimation device provided in this application are the same as those of the signal frequency domain detection and parameter estimation method described in the above embodiments, and other technical features in this signal frequency domain detection and parameter estimation device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0075] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0076] 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.

[0077] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the signal frequency domain detection and parameter estimation method described in the above embodiments.

[0078] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or flash memory, optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0079] The aforementioned computer-readable storage medium may be included in the signal frequency domain detection and parameter estimation device; or it may exist independently and not be assembled into the signal frequency domain detection and parameter estimation device.

[0080] The aforementioned computer-readable storage medium carries one or more programs that, when executed by the signal frequency domain detection and parameter estimation device, cause the signal frequency domain detection and parameter estimation device to: Acquire broadband in-phase quadrature signal data, and perform a fast Fourier transform with a first resolution on the broadband in-phase quadrature signal data to obtain a full-band low-resolution spectrum; Energy detection is performed based on the full-band low-resolution spectrum to determine the energy concentration area corresponding to each independent signal carrier. Based on the local frequency band data corresponding to each of the energy concentration regions, a second-resolution fast Fourier transform is performed to obtain the corresponding local high-resolution spectrum; wherein, the second resolution is higher than the first resolution; Based on the local high-resolution spectrum of each of the above, adaptive parameter estimation is performed to obtain the estimated parameters corresponding to each of the independent signal carriers.

[0081] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0082] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation that may be implemented in systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0083] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0084] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described signal frequency domain detection and parameter estimation method. This addresses the technical problem that existing signal processing schemes are ill-suited to the high-speed bandwidth scenarios of edge devices. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the signal frequency domain detection and parameter estimation method provided in the above embodiments, and will not be elaborated upon here.

[0085] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the signal frequency domain detection and parameter estimation method described above.

[0086] The computer program product provided in this application can solve the technical problem that existing signal processing solutions are difficult to adapt to the high-speed bandwidth scenarios of edge devices. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the signal frequency domain detection and parameter estimation methods provided in the above embodiments, and will not be repeated here.

[0087] The above description is only a part of the embodiments of this application and does not limit the scope of protection of this application. All equivalent structural transformations made under the technical concept of this application and using the content of this application specification and drawings, or direct / indirect applications in other related technical fields, are included in the scope of protection of this application.

Claims

1. A method for signal frequency domain detection and parameter estimation, characterized in that, The method includes: Acquire broadband in-phase quadrature signal data, and perform a fast Fourier transform with a first resolution on the broadband in-phase quadrature signal data to obtain a full-band low-resolution spectrum; Energy detection is performed based on the full-band low-resolution spectrum to determine the energy concentration area corresponding to each independent signal carrier. Based on the local frequency band data corresponding to each of the energy concentration regions, a second-resolution fast Fourier transform is performed to obtain the corresponding local high-resolution spectrum; wherein, the second resolution is higher than the first resolution; Based on the local high-resolution spectrum of each of the above, adaptive parameter estimation is performed to obtain the estimated parameters corresponding to each of the independent signal carriers.

2. The signal frequency domain detection and parameter estimation method as described in claim 1, characterized in that, The step of determining the energy concentration region corresponding to each independent signal carrier by performing energy detection based on the full-band low-resolution spectrum includes: One-dimensional density clustering is performed based on the full-band low-resolution spectrum to obtain the signal cluster center and signal cluster boundary corresponding to each independent signal carrier in the broadband in-phase orthogonal signal data. Based on the signal cluster, determine the carrier parameters corresponding to each independent signal carrier in the broadband in-phase quadrature signal data; The energy concentration region corresponding to each independent signal carrier is determined based on the full-band low-resolution spectrum and the carrier parameters.

3. The signal frequency domain detection and parameter estimation method as described in claim 2, characterized in that, The step of performing one-dimensional density clustering based on the full-band low-resolution spectrum to obtain the signal cluster center and signal cluster boundary corresponding to each independent signal carrier in the broadband in-phase quadrature signal data includes: The energy peak location was determined in the full-band low-resolution spectrum. One-dimensional density clustering is performed based on the energy peak positions to determine the signal cluster center and signal cluster boundary corresponding to each independent signal carrier in the broadband in-phase orthogonal signal data.

4. The signal frequency domain detection and parameter estimation method as described in claim 1, characterized in that, The step of adaptively estimating parameters based on each of the local high-resolution spectra to obtain the estimated parameters corresponding to each of the independent signal carriers includes: For any local high-resolution spectrum, perform first-order difference processing on the local high-resolution spectrum to determine the power difference between adjacent frequency points in the local high-resolution spectrum. Based on the power difference, mutation point detection is performed to determine candidate mutation points; The dynamic threshold of the local high-resolution spectrum is obtained, and the effective breakthrough point is determined among the candidate mutation points based on the dynamic threshold. The lower boundary of the independent signal carrier corresponding to the local high-resolution spectrum is determined based on the frequency position corresponding to the positive breakthrough point among the effective breakthrough points, and the upper boundary of the independent signal carrier corresponding to the local high-resolution spectrum is determined based on the frequency position corresponding to the negative breakthrough point among the effective breakthrough points. The estimated parameters of the independent signal carrier corresponding to the local high-resolution spectrum are determined based on the frequency difference between the upper boundary and the lower boundary.

5. The signal frequency domain detection and parameter estimation method as described in claim 4, characterized in that, The estimation parameters include bandwidth estimates and estimated center frequency. The step of determining the estimated parameters of the independent signal carrier corresponding to the local high-resolution spectrum based on the frequency difference between the upper boundary and the lower boundary includes: The boundary difference between the upper boundary and the lower boundary is used as the bandwidth estimate of the independent signal carrier corresponding to the local high-resolution spectrum. Based on the spectrum data, determine whether the spectral power distribution within the bandwidth of the independent signal carrier is uniform; When the spectral power distribution is uniform, the estimated center frequency of the independent signal carrier is determined based on the mean of the upper and lower boundaries; When the spectral power distribution is non-uniform, the estimated center frequency of the independent signal carrier is determined based on the energy-weighted centroid method.

6. The signal frequency domain detection and parameter estimation method as described in claim 1, characterized in that, The step of performing a fast Fourier transform of the broadband in-phase quadrature signal data at a first resolution to obtain a full-band low-resolution spectrum includes: The broadband in-phase quadrature signal data is preprocessed by windowing and buffering to obtain preprocessed data; The preprocessed data is subjected to a fast Fourier transform at a first resolution to obtain a full-band low-resolution spectrum.

7. A signal frequency domain detection and parameter estimation device, characterized in that, The signal frequency domain detection and parameter estimation device includes: The coarse processing module acquires broadband in-phase quadrature signal data and performs a fast Fourier transform with a first resolution on the broadband in-phase quadrature signal data to obtain a full-band low-resolution spectrum. The energy detection module performs energy detection based on the full-band low-resolution spectrum to determine the energy concentration area corresponding to each independent signal carrier. The fine processing module performs a fast Fourier transform with a second resolution based on the local frequency band data corresponding to each of the energy concentration regions to obtain the corresponding local high-resolution spectrum; wherein, the second resolution is higher than the first resolution; The adaptive estimation module performs adaptive parameter estimation based on each of the local high-resolution spectra to obtain the estimated parameters corresponding to each of the independent signal carriers.

8. A signal frequency domain detection and parameter estimation device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the signal frequency domain detection and parameter estimation method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the signal frequency domain detection and parameter estimation method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the signal frequency domain detection and parameter estimation method as described in any one of claims 1 to 6.

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