Generalized hyperbolic cordic implementation method for expanding convergence range and related device
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
- Filing Date
- 2026-05-14
- Publication Date
- 2026-08-07
AI Technical Summary
传统广义双曲CORDIC（GH-CORDIC）受限于其自身的收敛条件，输入变量的有效范围极为有限，在计算任意底数指数或对数时，其收敛域远无法覆盖双曲函数全实数域的定义范围，这导致在雷达波形生成、神经网络大动态信号模拟、惯性导航非线性误差修正等工程实践中，算法常常因输入超出范围而失效或精度骤降
[0015]本申请根据旋转方向、负向迭代参数和旋转角度确定负向迭代公式，从而构建硬件友好的反向迭代角度集，在不显著增加迭代次数与硬件开销的前提下，将GH-CORDIC的输入范围提升至数倍乃至数十倍，从根本上打破了该算法因收敛域狭窄而长期受限的应用瓶颈，使其能够无缝适配于各类需要大动态范围超越函数计算的实时系统，兼具理论完备性与工程实用性。
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Abstract
Description
Technical Field
[0001] This invention relates to the field of data processing technology, and more specifically, to a method and related apparatus for implementing generalized hyperbolic CORDIC with an expanded convergence range. Background Technology
[0002] The Coordinate Rotation Digital Computer (CORDIC) is a hardware algorithm that approximates transcendental functions through iterative shift-addition operations. Its core idea is to decompose the target angle into a series of preset standard angles, and by continuously rotating the vector and accumulating the directions, it eventually converges to the desired result. This algorithm requires no multipliers, only shift and addition operations, and can efficiently perform various operations such as trigonometric functions, hyperbolic functions, logarithms, and exponential functions. It is an ideal choice for resource-constrained scenarios such as digital signal processing, artificial intelligence accelerators, and communication systems.
[0003] The Generalized Hyperbolic CORDIC algorithm replaces the exponent *e* in the inverse hyperbolic tangent function of the traditional hyperbolic CORDIC algorithm with an arbitrary number *b*, transforming it into a generalized inverse hyperbolic tangent function. This substitution is then directly applied to the original iterative formula, allowing the algorithm to compute results for functions with arbitrary bases and exponents. Traditional Generalized Hyperbolic CORDIC (GH-CORDIC) is limited by its own convergence conditions, resulting in a very limited effective range of input variables. When computing arbitrary base exponents or logarithms, its convergence domain falls far short of the defined range of the hyperbolic function in the real number domain. This leads to frequent algorithm failures or sharp drops in accuracy in engineering applications such as radar waveform generation, large dynamic range signal simulation in neural networks, and nonlinear error correction in inertial navigation due to inputs exceeding the range. To overcome these limitations, the hardware implementation must incorporate complex modules such as multipliers, lookup tables, or piecewise approximations, significantly increasing resource overhead and design complexity. Summary of the Invention
[0004] To address the aforementioned problems in existing technologies, the present invention aims to provide a method and related apparatus for implementing generalized hyperbolic CORDIC with an expanded convergence range. Without significantly increasing the number of iterations and hardware overhead, the input range of GH-CORDIC is increased by several times or even tens of times, fundamentally breaking the application bottleneck of this algorithm that has long been limited by its narrow convergence domain. This allows it to be seamlessly adapted to various real-time systems that require the calculation of transcendental functions with large dynamic ranges, possessing both theoretical completeness and engineering practicality.
[0005] The first aspect of this application provides a method for implementing a generalized hyperbolic CORDIC with an expanded convergence range, the method comprising: Obtain the initial iteration variables, and determine the negative iteration formula based on the rotation direction, negative iteration parameters, and rotation angle; The initial iteration variable is used as the negative initial iteration variable, and the negative initial iteration variable and the preset negative iteration parameter value are substituted into the negative iteration formula to perform negative iteration and obtain the negative iteration result. The negative iteration result is substituted into the positive iteration formula as the initial positive iteration variable to obtain the positive iteration result; The generalized hyperbolic CORDIC result is determined based on the forward iteration result.
[0006] Optionally, determining the generalized hyperbolic CORDIC result based on the forward iteration result includes: The scaling factor is determined based on the total number of positive iterations, the total number of negative iterations, and the negative iteration parameters; The generalized hyperbolic CORDIC result is determined based on the forward iteration result and the scaling factor.
[0007] Optionally, the negative iteration formula is: , in, , , Let x, y, and y be the cumulative variables of the Cartesian coordinate system in the i-th iteration, representing the x-coordinate, y-coordinate, and angle, respectively. , , Let x, y, and y be the sum of the x-coordinate, y-coordinate, and angle in the Cartesian coordinate system for the (i+1)th iteration, respectively. Let i be the iteration direction variable for the i-th iteration. Let be the rotation angle at the i-th iteration. As the base, For negative iteration parameters.
[0008] Optionally, the scaling factor is: , in, Scaling factor denoted as , where n is the total number of negative iterations and n is the total number of positive iterations.
[0009] Optionally, the negative iteration formula is: , in, , , Let x, y, and y be the cumulative variables of the Cartesian coordinate system in the i-th iteration, representing the x-coordinate, y-coordinate, and angle, respectively. , , Let x, y, and y be the sum of the x-coordinate, y-coordinate, and angle in the Cartesian coordinate system for the (i+1)th iteration, respectively. Let be the iteration direction variable for the i-th iteration, and be the inverse hyperbolic tangent function. Let be the rotation angle at the i-th iteration. As the base, For negative iteration parameters.
[0010] Optionally, the scaling factor is: , in, Scaling factor denoted as , where n is the total number of negative iterations and n is the total number of positive iterations.
[0011] Optional, in When, the inverse hyperbolic tangent function Approximate implementation based on the Taylor expansion of the first i terms of the inverse hyperbolic tangent function; exist When, the inverse hyperbolic tangent function Approximately accomplish; Where n is the total number of forward iterations, and X is the inverse hyperbolic tangent function. The independent variable in the equation.
[0012] A second aspect of this application provides a generalized hyperbolic CORDIC implementation apparatus with an expanded convergence range, the apparatus comprising: The data acquisition unit is used to acquire the initial iteration variables and determine the negative iteration formula based on the rotation direction, negative iteration parameters, and rotation angle. The negative iteration unit is used to take the initial iteration variable as the negative initial iteration variable, and to substitute the negative initial iteration variable and the preset negative iteration parameter value into the negative iteration formula to perform negative iteration and obtain the negative iteration result. A forward iteration unit is used to substitute the negative iteration result as a forward initial iteration variable into the forward iteration formula for iteration to obtain a forward iteration result; The result determination unit is used to determine the generalized hyperbolic CORDIC result based on the forward iteration result.
[0013] A third aspect of this application provides an electronic device, including: a processor and a memory; The processor is connected to a memory, wherein the memory is used to store computer programs and the processor is used to invoke the computer programs to execute the methods as described in the first aspect of the embodiments of this application.
[0014] A fourth aspect of this application provides a computer-readable storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor, perform the method as described in the first aspect of this application.
[0015] This application determines the negative iteration formula based on the rotation direction, negative iteration parameters, and rotation angle, thereby constructing a hardware-friendly set of reverse iteration angles. Without significantly increasing the number of iterations and hardware overhead, it increases the input range of GH-CORDIC by several times or even tens of times, fundamentally breaking the application bottleneck of this algorithm that has long been limited by its narrow convergence domain. This allows it to be seamlessly adapted to various real-time systems that require large dynamic range transcendental function calculations, combining theoretical completeness with engineering practicality. Attached Figure Description
[0016] Figure 1 A flowchart illustrating a method for implementing a generalized hyperbolic CORDIC with an expanded convergence range according to an embodiment of this application is shown. Figure 2 This paper shows a schematic diagram of the structure of a generalized hyperbolic CORDIC implementation device with an extended convergence range provided in one embodiment of this application; Figure 3 A schematic diagram of the structure of a computer device provided in one embodiment of this application is shown. Detailed Implementation
[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.
[0018] Please refer to Figure 1 This illustration shows a flowchart of a method for implementing a generalized hyperbolic CORDIC model with expanded convergence range, provided in one embodiment of this application. The method may include the following steps: Step 101: Obtain the initial iteration variables.
[0019] Specifically, obtaining the initial iteration variables includes: receiving an indication signal and input data, wherein the indication signal indicates the mode type used, and the mode type includes a rotation mode and a vector mode, wherein the rotation mode is used to calculate the exponential function, and the vector mode is used to calculate the logarithmic function. For example, the indication signal can be, for instance, used as... express, When, it indicates the rotation mode; In this case, it indicates vector mode. In rotation mode, the input data is an exponent; in vector mode, the input data is an argument. If using... Given the input data, in rotation mode, the initial iteration variables are respectively In vector mode, the initial iteration variables are respectively .
[0020] Step 102: Determine the negative iteration formula based on the rotation direction, negative iteration parameters, and rotation angle.
[0021] In a specific embodiment provided in this application, the negative iteration formula is: , in, , , Let x, y, and y be the cumulative variables of the Cartesian coordinate system in the i-th iteration, representing the x-coordinate, y-coordinate, and angle, respectively. , , Let x, y, and y be the sum of the x-coordinate, y-coordinate, and angle in the Cartesian coordinate system for the (i+1)th iteration, respectively. Let i be the iteration direction variable for the i-th iteration. Let be the rotation angle at the i-th iteration. As the base, For negative iteration parameters.
[0022] In another specific embodiment provided in this application, the negative iteration formula is: , in, , , Let x, y, and y be the cumulative variables of the Cartesian coordinate system in the i-th iteration, representing the x-coordinate, y-coordinate, and angle, respectively. , , Let x, y, and y be the sum of the x-coordinate, y-coordinate, and angle in the Cartesian coordinate system for the (i+1)th iteration, respectively. Let be the iteration direction variable for the i-th iteration, and be the inverse hyperbolic tangent function. Let be the rotation angle at the i-th iteration. As the base, For negative iteration parameters.
[0023] Step 103: Use the initial iteration variable as the negative initial iteration variable, and substitute the negative initial iteration variable and the preset negative iteration parameter value into the negative iteration formula to perform negative iteration and obtain the negative iteration result.
[0024] Specifically, or Substitute one of the two negative iteration formulas above as the negative initial iteration variable, and then set... or The value of can be used to perform negative iteration. For example, if the total number of negative iterations is set to M, then the negative iteration result can be obtained. .
[0025] Of course, it can also make or That is, the negative initial iteration is , , Iterate M times to make the negative iteration result .
[0026] Step 104: Substitute the negative iteration result as the positive initial iteration variable into the positive iteration formula to perform iteration and obtain the positive iteration result.
[0027] Specifically, depending on the different negative initial iteration variables, or As in the forward iterative formula Then, substitute the values into the forward iteration formula and iterate. For example, if the total number of forward iterations is set to n, then the forward iteration result can be obtained. .
[0028] For example, the forward iteration formula is: , in, , , Let x, y, and y be the cumulative variables of the Cartesian coordinate system in the i-th iteration, representing the x-coordinate, y-coordinate, and angle, respectively. , , Let x, y, and y be the sum of the x-coordinate, y-coordinate, and angle in the Cartesian coordinate system for the (i+1)th iteration, respectively. Let be the iteration direction variable for the i-th iteration, and be the inverse hyperbolic tangent function. Let be the rotation angle at the i-th iteration. The base is .
[0029] Furthermore, it can be seen that both the negative and positive iteration formulas have the same form, thus enabling hardware reuse. The same iterative circuit can be used, and only the registers within it need to store different initial values. During the design, there is no need to introduce complex modules such as multipliers, lookup tables, or piecewise approximations in the hardware implementation, which reduces resource overhead and design difficulty.
[0030] Step 105: Determine the generalized hyperbolic CORDIC result based on the forward iteration result.
[0031] Specifically, determining the generalized hyperbolic CORDIC result based on the forward iteration result includes: The scaling factor is determined based on the total number of positive iterations, the total number of negative iterations, and the negative iteration parameters; The generalized hyperbolic CORDIC result is determined based on the forward iteration result and the scaling factor.
[0032] Specifically, when the negative iteration formula is: , The scaling factor is: , in, Scaling factor denoted as , where n is the total number of negative iterations and n is the total number of positive iterations.
[0033] Specifically, when the negative iteration formula is: , The scaling factor is: , in, Scaling factor denoted as , where n is the total number of negative iterations and n is the total number of positive iterations.
[0034] In the exponential mode, the generalized hyperbolic CORDIC results Generalized hyperbolic CORDIC results in vector mode .
[0035] The inverse hyperbolic tangent function cannot be directly implemented in a computer. The calculation requires approximation. The following approximation method can be used in this application: exist When, the inverse hyperbolic tangent function Approximate implementation based on the Taylor expansion of the first i terms of the inverse hyperbolic tangent function; exist When, the inverse hyperbolic tangent function Approximately accomplish; Where n is the total number of forward iterations, and X is the inverse hyperbolic tangent function. The independent variable in the equation.
[0036] For example, if n=15, then when i=±1, When i = ±2, , hour, In this application, Values can be retrieved , , The approximate functions are as follows: 、 、 The approximate iteration variables are as follows: 、 、 .
[0037] It can be seen that the approximated functions can all be expressed as constants. The combination of the product ...
[0038] To verify the effectiveness of the embodiments in this application, please refer to Tables 1 to 3. Table 1 uses the vector pattern as an example, employing the negative iteration formula from the first embodiment and setting the negative iteration parameter c to 1, for different total negative iteration counts M and maximum iteration angles. and The product is listed The range of values for is shown. It can be seen that the larger the iteration angle, the larger the convergence range of the logarithmic function result. It can also be seen that the more negative iterations introduced, the larger the range of values for the logarithmic function. This invention chooses to introduce four negative iterations. The data in the table shows that this design can meet most requirements.
[0039] Table 1 The range of values for -M and Relationship table Table 2 takes the rotation mode as an example and uses the negative iteration formula in the first embodiment to explore the relationship between the number of negative iterations and the maximum value of the iteration angle when different bases are introduced. It can be seen that the improvement of the present invention is applicable to different bases, and the number of negative iterations introduced is positively correlated with the convergence range.
[0040] Table 2. Relationship between the number of negative iterations M and the maximum iteration angle for different bases b. Relationship table Table 3, using the rotation mode as an example, employs the negative iteration formula from the second embodiment to explore the ratio of the convergence range of the negative iteration angle set after introducing different numbers of negative iterations under different parameter conditions. This ratio is the ratio of the convergence range of the improved negative iteration angle set to the convergence range of the negative iteration angle set itself. The table data shows that the ratio is greater than 1 after introducing a larger number of negative iterations. This indicates that the improved negative iteration angle set achieves a better convergence range expansion effect with the same number of negative iterations, and also indicates that when the required convergence range expansion effect is constant, the improved negative iteration angle set requires fewer negative iterations.
[0041] Table 3. Relationship between the convergence range of the negative iteration angle set under different parameter conditions and after introducing different numbers of negative iterations. In summary, this application determines the negative iteration formula based on the rotation direction, negative iteration parameters, and rotation angle, thereby constructing a hardware-friendly set of reverse iteration angles. Without significantly increasing the number of iterations and hardware overhead, the input range of GH-CORDIC can be increased by several times or even tens of times. This fundamentally breaks the application bottleneck of the algorithm, which has long been limited by its narrow convergence domain, enabling it to be seamlessly adapted to various real-time systems that require large dynamic range transcendental function calculations. It combines theoretical completeness with engineering practicality.
[0042] Figure 2 A schematic diagram of a generalized hyperbolic CORDIC implementation apparatus with expanded convergence range provided in one embodiment of this application is shown. The apparatus includes: The data acquisition unit 201 is used to acquire the initial iteration variables and determine the negative iteration formula based on the rotation direction, negative iteration parameters and rotation angle; The negative iteration unit 202 is used to take the initial iteration variable as the negative initial iteration variable, and to substitute the negative initial iteration variable and the preset negative iteration parameter value into the negative iteration formula to perform negative iteration and obtain the negative iteration result. The forward iteration unit 203 is used to substitute the negative iteration result as the positive initial iteration variable into the forward iteration formula for iteration to obtain the forward iteration result; Result determination unit 204 is used to determine the generalized hyperbolic CORDIC result based on the forward iteration result.
[0043] Figure 3 The diagram illustrates the structure of a computer device according to an embodiment of this application, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the functions of a computer system that implements the generalized hyperbolic CORDIC implementation method for expanding the convergence range in any of the above embodiments.
[0044] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a computer, causes the computer to perform the functions of a computer system implementing the generalized hyperbolic CORDIC implementation method with expanded convergence range in any of the above embodiments.
[0045] This application also provides a computer program product containing instructions that, when executed by a computer, cause the computer to perform the functions of a computer system implementing the generalized hyperbolic CORDIC method for expanding the convergence range in any of the above embodiments.
[0046] It is understood that the specific examples in this application are only intended to help those skilled in the art better understand the implementation methods of this application, and are not intended to limit the scope of the invention.
[0047] It is understood that in the various embodiments of this application, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not limit the implementation process of the embodiments of this application in any way.
[0048] It is understood that the various implementation methods described in this application can be implemented individually or in combination, and the implementation methods in this application are not limited in this respect.
[0049] Unless otherwise stated, all technical and scientific terms used in the embodiments of this application have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The term "and / or" as used in this application includes any and all combinations of one or more of the associated listed items. The singular forms "a," "the," and "the" as used in the embodiments of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.
[0050] It is understood that the processor in the embodiments of this application can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method embodiments can be completed by the integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.
[0051] It is understood that the memory in the embodiments of this application may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. Specifically, non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may be random access memory (RAM). It should be noted that the memory in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.
[0052] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0053] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the aforementioned method implementations, and will not be repeated here.
[0054] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.
[0055] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.
[0056] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.
[0057] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0058] The above are merely specific embodiments of this application, but the scope of protection of this invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this invention should be determined by the scope of the claims.
Claims
1. A method for implementing generalized hyperbolic CORDIC with expanded convergence range, characterized in that, The method includes: Obtain the initial iteration variables, and determine the negative iteration formula based on the rotation direction, negative iteration parameters, and rotation angle; The initial iteration variable is used as the negative initial iteration variable, and the negative initial iteration variable and the preset negative iteration parameter value are substituted into the negative iteration formula to perform negative iteration and obtain the negative iteration result. The negative iteration result is substituted into the positive iteration formula as the initial positive iteration variable to obtain the positive iteration result; The generalized hyperbolic CORDIC result is determined based on the forward iteration result.
2. The method according to claim 1, characterized in that, The determination of the generalized hyperbolic CORDIC result based on the forward iteration result includes: The scaling factor is determined based on the total number of positive iterations, the total number of negative iterations, and the negative iteration parameters; The generalized hyperbolic CORDIC result is determined based on the forward iteration result and the scaling factor.
3. The method according to claim 2, characterized in that, The negative iteration formula is: ， in, , , Let x, y, and y be the cumulative variables of the Cartesian coordinate system in the i-th iteration, representing the x-coordinate, y-coordinate, and angle, respectively. , , Let x, y, and y be the sum of the x-coordinate, y-coordinate, and angle in the Cartesian coordinate system for the (i+1)th iteration, respectively. Let i be the iteration direction variable for the i-th iteration. Let be the rotation angle at the i-th iteration. As the base, For negative iteration parameters.
4. The method according to claim 3, characterized in that, The scaling factor is: ， in, Scaling factor denoted as , where n is the total number of negative iterations and n is the total number of positive iterations.
5. The method according to claim 2, characterized in that, The negative iteration formula is: ， in, , , Let x, y, and y be the cumulative variables of the Cartesian coordinate system in the i-th iteration, representing the x-coordinate, y-coordinate, and angle, respectively. , , Let x, y, and y be the sum of the x-coordinate, y-coordinate, and angle in the Cartesian coordinate system for the (i+1)th iteration, respectively. Let be the iteration direction variable for the i-th iteration, and be the inverse hyperbolic tangent function. Let be the rotation angle at the i-th iteration. As the base, For negative iteration parameters.
6. The method according to claim 5, characterized in that, The scaling factor is: ， in, Scaling factor denoted as , where n is the total number of negative iterations and n is the total number of positive iterations.
7. The method according to any one of claims 3-6, characterized in that, exist When, the inverse hyperbolic tangent function Approximate implementation based on the Taylor expansion of the first i terms of the inverse hyperbolic tangent function; exist When, the inverse hyperbolic tangent function Approximately accomplish; Where n is the total number of forward iterations, and X is the inverse hyperbolic tangent function. The independent variable in the equation.
8. A generalized hyperbolic CORDIC implementation device with expanded convergence range, characterized in that, The device includes: The data acquisition unit is used to acquire the initial iteration variables and determine the negative iteration formula based on the rotation direction, negative iteration parameters, and rotation angle. The negative iteration unit is used to take the initial iteration variable as the negative initial iteration variable, and to substitute the negative initial iteration variable and the preset negative iteration parameter value into the negative iteration formula to perform negative iteration and obtain the negative iteration result. A forward iteration unit is used to substitute the negative iteration result as a forward initial iteration variable into the forward iteration formula for iteration to obtain a forward iteration result; The result determination unit is used to determine the generalized hyperbolic CORDIC result based on the forward iteration result.
9. An electronic device, characterized in that, include: Processor and memory; The processor is connected to a memory, wherein the memory is used to store a computer program, and the processor is used to invoke the computer program to perform the method as described in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions that, when executed by a processor, perform the method as described in any one of claims 1-7.