Signal frequency estimation method and device for adaptive non-uniform sampling Chirp-Z transformation and medium
By using adaptive non-uniform sampling Chirp-Z transform and frequency deviation estimation and dynamic adjustment of the mapping function, the problem of insufficient signal frequency estimation accuracy in traditional methods is solved, and high-precision signal frequency estimation is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-16
- Publication Date
- 2026-03-17
AI Technical Summary
Traditional signal frequency estimation algorithms struggle to achieve high-precision frequency estimation under conditions of low signal-to-noise ratio or small sample size. Furthermore, methods based on Chirp-Z transform cannot adapt to the frequency deviations of different signals, resulting in insufficient resolution in key frequency bands.
An adaptive non-uniform sampling Chirp-Z transform is adopted, and the amplitude spectrum is obtained through fast Fourier transform. The frequency deviation is calculated and a mapping function is defined. The algorithm is dynamically adjusted to improve the signal frequency estimation accuracy.
It achieves high-precision signal frequency estimation in complex signal environments and can dynamically adjust according to frequency deviation, thereby improving the accuracy of signal frequency estimation.
Smart Images
Figure CN121679119A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of signal frequency estimation, and more specifically, to a signal frequency estimation method, device, and medium based on adaptive non-uniform sampling Chirp-Z transform. Background Technology
[0002] Accurate frequency estimation of signals is a crucial fundamental task in many fields such as communications, radar, and telemetry. Traditional signal frequency estimation algorithms, such as spectral analysis based on Fast Fourier Transform (FFT), are widely used due to their simplicity. However, this method is limited by the signal truncation length and window function selection, making it difficult to achieve high-precision frequency estimation under low signal-to-noise ratio (SNR) or small sample size conditions. A two-step frequency estimation method based on Chirp-Z transform estimates the signal frequency through two steps: coarse estimation, which uses FFT to obtain the amplitude spectrum and performs smoothing to find an interval containing the frequency to be estimated; and fine estimation, which uses Chirp-Z transform within the defined frequency interval. However, this method cannot adapt to the frequency deviations of different signals, and it uses uniform sampling within the analyzed frequency band, meaning the accuracy is the same throughout the analyzed band, resulting in insufficient resolution in key frequency bands. Although the estimation accuracy is higher than traditional FFT and interpolation frequency estimation methods, there is still room for improvement in estimation performance under complex and dynamic signal environments. Summary of the Invention
[0003] To address the aforementioned problems, this invention provides an adaptive non-uniform sampling Chirp-Z signal frequency estimation method, device, and medium to maximize the accuracy of signal frequency estimation and dynamically adjust the algorithm based on different frequency deviations.
[0004] In a first aspect, the present invention provides a signal frequency estimation method based on adaptive non-uniform sampling Chirp-Z transform, comprising: Step 1: After sampling the signal to be estimated, perform a fast Fourier transform to obtain its amplitude spectrum and search for the maximum value index of the amplitude spectrum. Step 2: Calculate the minimum and maximum frequency points of the adaptive non-uniform sampling Chirp-Z transform based on the sampling frequency and the maximum value index; Step 3: Calculate the frequency deviation estimate based on the spectral value corresponding to the maximum value and its two adjacent spectral values; Step 4: Define a mapping function based on the frequency deviation estimation, and calculate the frequency sampling points of the adaptive non-uniform sampling Chirp-Z transform based on the minimum frequency point, the maximum frequency point, and the mapping function; Step 5: Calculate the sampling points of the adaptive non-uniform sampling Chirp-Z transform on the unit circle based on the sampling frequency and the frequency sampling points; Step 6: Calculate the amplitude spectrum of the adaptive non-uniform sampling Chirp-Z transform based on the sampling points on the unit circle, search for the maximum value index of the amplitude spectrum, and obtain the signal frequency estimation result based on the maximum value index.
[0005] In a preferred embodiment, the step of sampling the signal to be estimated, performing a Fast Fourier Transform to obtain its amplitude spectrum, and searching for the maximum value index of the amplitude spectrum includes: The signal to be estimated is sampled to obtain a sampling sequence; where the sampling frequency is... The number of sampling points is ; The spectrum is obtained by performing a Fast Fourier Transform on the sampled sequence. and amplitude spectrum ,in, ; Find the amplitude spectrum by peak search. maximum value ,in, For this maximum value The corresponding sequence number is also the maximum value sequence number mentioned in step 1.
[0006] In a preferred embodiment, the calculation of the minimum and maximum frequency points of the adaptive non-uniform sampling Chirp-Z transform based on the sampling frequency and the maximum value index includes: Based on the sampling frequency The number of sampling points is and maximum value index Calculate the center frequency of the adaptive non-uniform sampling Chirp-Z transform. ; The bandwidth analyzed by the adaptive non-uniform sampling Chirp-Z transform is set. ; Based on the center frequency and bandwidth Calculate the minimum frequency point and the maximum frequency point .
[0007] In a preferred embodiment, the calculation of the frequency deviation estimate based on the spectral value corresponding to the maximum value index and its two adjacent spectral values is expressed as follows:
[0008] in, This indicates finding the real part of a complex number.
[0009] In a preferred embodiment, the mapping function is expressed as:
[0010] in, ; , This is the coefficient of variation.
[0011] In a preferred embodiment, the frequency sampling point is represented as .
[0012] In a preferred embodiment, the sampling points on the unit circle are represented as follows:
[0013] in, .
[0014] In a preferred embodiment, the step of calculating the amplitude spectrum of the adaptive non-uniform sampling Chirp-Z transform based on the sampling points on the unit circle, searching for the maximum value index of the amplitude spectrum, and obtaining the signal frequency estimation result based on the maximum value index includes: The amplitude spectrum of the adaptive non-uniform sampling Chirp-Z transform is calculated based on sampling points on the unit circle. ; Find the amplitude spectrum by peak search. maximum value ,in For this maximum value The corresponding sequence number, which is also the maximum value sequence number mentioned in step 6; The frequency estimation result is the maximum value index. corresponding frequency .
[0015] In a second aspect, the present invention provides an electronic device, comprising: At least one processor; and a memory communicatively connected to said at least one processor; The memory stores instructions that can be executed by the at least one processor, and the at least one processor executes the instructions stored in the memory to perform the above-described method.
[0016] Thirdly, the present invention provides a computer-readable storage medium for storing instructions that, when executed, enable the above-described method to be implemented.
[0017] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: In digital signal processing systems, this invention utilizes signal frequency offset estimation, nonlinear mapping, and Chirp-Z transform with non-uniform sampling to achieve adaptive and high-precision signal frequency estimation. This can maximize the accuracy of signal frequency estimation and dynamically adjust the algorithm according to different frequency deviations. Attached Figure Description
[0018] Figure 1 The flowchart illustrates an adaptive non-uniform sampling Chirp-Z signal frequency estimation method provided in this embodiment of the invention.
[0019] Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation
[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.
[0021] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.
[0022] like Figure 1 As shown, this embodiment of the invention provides an adaptive non-uniform sampling Chirp-Z signal frequency estimation method, including: Step 1: After sampling the signal to be estimated, perform a fast Fourier transform to obtain its amplitude spectrum and search for the maximum value index of the amplitude spectrum. Step 2: Calculate the minimum and maximum frequency points of the adaptive non-uniform sampling Chirp-Z transform based on the sampling frequency and the maximum value index; Step 3: Calculate the frequency deviation estimate based on the spectral value corresponding to the maximum value and its two adjacent spectral values; Step 4: Define a mapping function based on the frequency deviation estimation, and calculate the frequency sampling points of the adaptive non-uniform sampling Chirp-Z transform based on the minimum frequency point, the maximum frequency point, and the mapping function; Step 5: Calculate the sampling points of the adaptive non-uniform sampling Chirp-Z transform on the unit circle based on the sampling frequency and the frequency sampling points; Step 6: Calculate the amplitude spectrum of the adaptive non-uniform sampling Chirp-Z transform based on the sampling points on the unit circle, search for the maximum value index of the amplitude spectrum, and obtain the signal frequency estimation result based on the maximum value index.
[0023] The following details the specific implementation of the above-mentioned adaptive non-uniform sampling Chirp-Z transform signal frequency estimation method.
[0024] Step 1: After sampling the signal to be estimated, perform a Fast Fourier Transform to obtain its amplitude spectrum and search for the maximum value index of the amplitude spectrum: The signal to be estimated is sampled to obtain a sampling sequence; where the sampling frequency is... The number of sampling points is ; The spectrum is obtained by performing a Fast Fourier Transform on the sampled sequence. and amplitude spectrum ,in, ; Find the amplitude spectrum by peak search. maximum value ,in, For this maximum value The corresponding sequence number is also the maximum value sequence number mentioned in step 1.
[0025] Step 2: Calculate the minimum and maximum frequency points of the adaptive non-uniform sampling Chirp-Z transform based on the sampling frequency and the maximum value index. Based on the sampling frequency The number of sampling points is and maximum value index Calculate the center frequency of the adaptive non-uniform sampling Chirp-Z transform. ; The bandwidth analyzed by the adaptive non-uniform sampling Chirp-Z transform is set. ; Based on the center frequency and bandwidth Calculate the minimum frequency point and the maximum frequency point .
[0026] Step 3: Calculate the frequency deviation estimate based on the spectral value corresponding to the maximum value and its two adjacent spectral values, expressed as:
[0027] in, This indicates finding the real part of a complex number. Step 4: Define a mapping function based on the frequency bias estimation, and calculate the frequency sampling points of the adaptive non-uniform sampling Chirp-Z transform based on the minimum frequency point, the maximum frequency point, and the mapping function: Defined mapping function
[0028] in, ; , The coefficient of variation, The larger the value, the higher the frequency deviation estimation. The more significant the impact on the sampling distribution, The smaller the value, the closer the sampling distribution is to a uniform distribution. To balance the non-uniform effect and stability, the coefficient of variation is chosen. That's quite suitable.
[0029] Calculate the adaptive non-uniform sampling Chirp-Z transform within the analyzed frequency band bandwidth. The frequency sampling points within are ; Step 5, based on the sampling frequency... and frequency sampling points Calculate the sampling points of the adaptive non-uniform sampling Chirp-Z transform on the unit circle, denoted as: ,in ; Step 6: Calculate the amplitude spectrum of the adaptive non-uniform sampling Chirp-Z transform based on the sampling points on the unit circle, search for the maximum value index of the amplitude spectrum, and obtain the signal frequency estimation result based on the maximum value index. Based on sampling points on the unit circle Calculate the amplitude spectrum of the adaptive non-uniform sampling Chirp-Z transform. ; Find the maximum value by searching for spectral peaks. ,in This is the index corresponding to the maximum value, that is, the index of the maximum value; The frequency estimation result is the maximum value index. corresponding frequency This enables signal frequency estimation using adaptive non-uniform sampling Chirp-Z transform.
[0030] This invention, within a digital signal processing system, utilizes signal frequency offset estimation, nonlinear mapping, and Chirp-Z transform with non-uniform sampling to achieve adaptive, high-precision signal frequency estimation. It maximizes the accuracy of signal frequency estimation and dynamically adjusts the algorithm based on varying frequency deviations. This invention can be applied to fields such as communications, radar, and telemetry and control that require signal frequency estimation.
[0031] Based on the same technical concept, embodiments of the present invention also provide an electronic device that can implement the signal frequency estimation method flow of the adaptive non-uniform sampling Chirp-Z transform provided in the above embodiments of the present invention. In one embodiment, the electronic device can be a server, a terminal device, or other electronic device. Figure 2 As shown, the electronic device may include: At least one processor and a memory connected to the at least one processor. In this embodiment of the invention, the specific connection medium between the processor and the memory is not limited. Figure 2 The example used is the connection between the processor and memory via a bus. The bus... Figure 2 The connections between other components are indicated by thick lines and are for illustrative purposes only, not as limiting information. Buses can be divided into address buses, data buses, control buses, etc., but for ease of representation, [the specific bus type is not shown here]. Figure 2 The processor is represented by a single thick line, but this does not imply that there is only one bus or one type of bus. Alternatively, a processor can also be called a controller; there are no restrictions on the name.
[0032] In this embodiment of the invention, the memory stores instructions that can be executed by at least one processor. By executing the instructions stored in the memory, at least one processor can perform a signal frequency estimation method for adaptive non-uniform sampling Chirp-Z transform as described above.
[0033] The processor is the control center of the device. It can connect to various parts of the control device through various interfaces and lines. By running or executing instructions stored in memory and calling data stored in memory, it can monitor the device's various functions and process data, thereby enabling overall monitoring of the device.
[0034] In an alternative design, the processor may include one or more processing units. The processor may integrate an application processor and a modem processor, wherein the application processor primarily handles the operating system, user interface, and applications, while the modem processor primarily handles wireless communication. It is understood that the modem processor may also not be integrated into the processor. In some embodiments, the processor and memory may be implemented on the same chip; in some embodiments, they may also be implemented separately on separate chips.
[0035] The processor can be a general-purpose processor, such as a CPU, digital signal processor, application-specific integrated circuit, field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the adaptive non-uniform sampling Chirp-Z transform signal frequency estimation method disclosed in the embodiments of this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.
[0036] Memory, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. Memory can include at least one type of storage medium, such as flash memory, hard disk, multimedia card, card-type memory, random access memory (RAM), static random access memory (SRAM), programmable read-only memory (PROM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), magnetic memory, magnetic disk, optical disk, etc. Memory is any other medium capable of carrying or storing desired program code in the form of instructions or data structures, and accessible by a computer, but is not limited thereto. In embodiments of the present invention, memory can also be a circuit or any other device capable of implementing storage functions, used to store program instructions and / or data.
[0037] By designing and programming the processor, the code corresponding to the adaptive non-uniform sampling Chirp-Z transform signal frequency estimation method described in the foregoing embodiments can be embedded into the chip, enabling the chip to execute the steps of the method described in the foregoing embodiments during operation. How to design and program the processor is a technique well-known to those skilled in the art and will not be elaborated upon here.
[0038] Based on the same inventive concept, embodiments of the present invention also provide a storage medium storing computer instructions that, when executed on a computer, cause the computer to perform a signal frequency estimation method for adaptive non-uniform sampling Chirp-Z transform as described above.
[0039] In some alternative embodiments, the present invention also provides a method for estimating the signal frequency of an adaptive non-uniform sampling Chirp-Z transform, which can also be implemented as a program product comprising program code that, when the program product is run on a device, causes the control device to perform the steps in the method for estimating the signal frequency of an adaptive non-uniform sampling Chirp-Z transform according to various exemplary embodiments of the present invention as described above.
[0040] It should be noted that although several units or sub-units of the apparatus have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the invention, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units. Furthermore, although the operation of the method of the invention is described in a specific order in the drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.
[0041] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0042] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a server, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0043] Program code for performing the operations of this invention can be written using any combination of one or more programming languages, including object-oriented programming languages such as Java and C++, as well as conventional procedural programming languages such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.
[0044] In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).
[0045] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0046] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0047] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A method for signal frequency estimation of adaptive non-uniformly sampled Chirp-Z transform, characterized in that, The method comprises the following steps: Step 1: sampling the signal to be estimated, performing fast Fourier transform on the signal to obtain an amplitude spectrum of the signal, and searching for a maximum value sequence number of the amplitude spectrum; Step 2: calculating a minimum frequency point and a maximum frequency point of adaptive non-uniform sampling Chirp-Z transform based on a sampling frequency and the maximum value sequence number; Step 3: calculating a frequency deviation estimation based on a spectrum value corresponding to the maximum value sequence number and two adjacent spectrum values of the maximum value sequence number; Step 4: defining a mapping function based on the frequency deviation estimation, and calculating frequency sampling points of the adaptive non-uniform sampling Chirp-Z transform based on the minimum frequency point, the maximum frequency point and the mapping function; Step 5: calculating sampling points of the adaptive non-uniform sampling Chirp-Z transform on a unit circle based on the sampling frequency and the frequency sampling points; Step 6: calculating an amplitude spectrum of the adaptive non-uniform sampling Chirp-Z transform based on the sampling points on the unit circle, searching for a maximum value sequence number of the amplitude spectrum, and obtaining a signal frequency estimation result based on the maximum value sequence number.
2. The method of claim 1, wherein, The method of sampling the signal to be estimated, performing fast Fourier transform on the signal to obtain an amplitude spectrum of the signal, and searching for a maximum value sequence number of the amplitude spectrum comprises the following steps: Sample the to-be-estimated signal to obtain a sample sequence; wherein a sampling frequency is , and a number of sampling points is ; A fast Fourier transform is performed on the sampled sequence to obtain its frequency spectrum and the amplitude spectrum wherein ; Find the amplitude spectrum by searching for peaks. maximum value ,in, For this maximum value The corresponding sequence number is also the maximum value sequence number mentioned in step 1.
3. The method of adaptive non-uniform sampling Chirp-Z transform based signal frequency estimation according to claim 2, characterized in that, The method of calculating a minimum frequency point and a maximum frequency point of adaptive non-uniform sampling Chirp-Z transform based on a sampling frequency and the maximum value sequence number comprises the following steps: According to a sampling frequency , a sampling point number , and a maximum value serial number , a center frequency point of adaptive non-uniform sampling Chirp-Z transform is calculated ; Setting the frequency band bandwidth analyzed by adaptive non-uniform sampling chirp-z transform ; According to the center frequency point and the frequency band bandwidth , the minimum frequency point and the maximum frequency point are calculated.
4. The method of adaptive non-uniform sampling Chirp-Z transform based signal frequency estimation according to claim 3, wherein, The method of calculating a frequency deviation estimation based on a spectrum value corresponding to the maximum value sequence number and two adjacent spectrum values of the maximum value sequence number is expressed as: wherein denotes the real part of a complex number.
5. The method of adaptive non-uniform sampling Chirp-Z transform based signal frequency estimation according to claim 4, characterized in that, The mapping function is expressed as: wherein ; , is the coefficient of variation.
6. The adaptive non-uniform sampling Chirp-Z signal frequency estimation method of claim 5, wherein, The frequency sampling points are represented as .
7. The method of adaptive non-uniform sampling Chirp-Z transform based signal frequency estimation according to claim 6, characterized in that, The sampling points on the unit circle are expressed as: wherein .
8. The method of adaptive non-uniform sampling Chirp-Z transform based signal frequency estimation according to claim 7, wherein, The method of calculating an amplitude spectrum of the adaptive non-uniform sampling Chirp-Z transform based on the sampling points on the unit circle, searching for a maximum value sequence number of the amplitude spectrum, and obtaining a signal frequency estimation result based on the maximum value sequence number comprises the following steps: Adaptive non-uniform sampling chirp-z transform amplitude spectrum based on sampling points on a unit circle ; Find the amplitude spectrum by searching for peaks. maximum value ,in For this maximum value The corresponding sequence number, which is also the maximum value sequence number mentioned in step 6; Obtaining a frequency estimation result as a maximum index Corresponding frequency .
9. An electronic device, comprising: The method comprises: at least one processor; and a memory connected in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the at least one processor, by executing the instructions stored in the memory, performs the method of any one of claims 1-8.
10. A computer-readable storage medium, characterized in that, The computer readable storage medium is used to store instructions, when the instructions are executed, the method of any one of claims 1-8 is realized.