A combined scattering center estimation and Harris Eagle optimization method for precession parameter estimation

Abstract

This invention discloses a method for estimating precession parameters using a combination of scattering center estimation and Harris Eagle optimization. The method includes: estimating the radial distance information of the target's scattering centers based on frequency domain echo signals; estimating the target's initial precession angular frequency and initial phase based on the radial distance information; and using the Harris Eagle optimization algorithm to perform multiple rounds of optimization on the estimated values ​​of the precession characteristic parameters based on a first radial distance estimation model to obtain the optimal values ​​of the target's precession characteristic parameters. The first radial distance estimation model is constructed based on the quantitative relationship between the radial distances of the target's scattering centers and can be represented by a polynomial including all precession characteristic parameters, one of which is calculated based on the initial precession angular frequency and initial phase. This invention improves estimation accuracy by using frequency domain data for parameter estimation.

Description

Technical Field

[0001] This invention belongs to the field of signal processing technology, specifically relating to a method for estimating precession parameters by combining scattering center estimation and Harris Eagle optimization. Background Technology

[0002] Precession is a common phenomenon in radar detection. Radar echoes of precessing targets contain a wealth of detailed features about the target's structure and motion, making precession parameter estimation a hot topic in radar target identification research. The scattering center model is an effective tool for characterizing target reflection properties. However, for targets with complex motion behaviors, traditional scattering center estimation methods have certain limitations, making it difficult to accurately capture the target's dynamic characteristics, especially in the presence of non-rigid body motions such as precession.

[0003] Specifically, traditional methods typically convert the obtained signal into an image and then estimate it based on the Doppler image. However, in practical applications, due to the small size of the target and the presence of occlusion, the acquired image domain information is insufficient to support the estimation of small precession angle parameters, resulting in poor estimation accuracy of traditional methods. Summary of the Invention

[0004] This invention provides a method for estimating precession parameters by combining scattering center estimation and Harris Eagle optimization, which can solve the problem of poor estimation accuracy of traditional methods.

[0005] In a first aspect, embodiments of the present invention provide a method for estimating precession parameters by combining scattering center estimation and Harris Eagle optimization, the method comprising:

[0006] Based on the frequency domain echo signal, estimate the radial distance information of the target's scattering center;

[0007] Based on the radial distance information, estimate the target's initial precession angular frequency and initial phase;

[0008] Based on the first radial distance estimation model, the Harris Eagle optimization algorithm is used to optimize the estimated values ​​of the precession characteristic parameters in multiple rounds to obtain the optimal values ​​of the target's precession characteristic parameters;

[0009] The first radial distance estimation model is constructed based on the quantitative relationship between the radial distances of the target's scattering centers. The first radial distance estimation model can be represented by a polynomial that includes all precession characteristic parameters, one of which is calculated based on the initial precession angular frequency and the initial phase.

[0010] Secondly, embodiments of the present invention provide a precession parameter estimation apparatus that combines scattering center estimation and Harris Eagle optimization, the apparatus comprising:

[0011] The frequency domain data processing module is used to estimate the radial distance information of the target's scattering center based on the frequency domain echo signal.

[0012] The initial parameter estimation module is used to estimate the target's initial precession angular frequency and initial phase based on the radial distance information.

[0013] The precession feature parameter optimization module is used to optimize the estimated values ​​of the precession feature parameters of the target through multiple rounds of optimization based on the first radial distance estimation model and the Harris Eagle optimization algorithm.

[0014] The first radial distance estimation model is constructed based on the quantitative relationship between the radial distances of the target's scattering centers. The first radial distance estimation model can be represented by a polynomial that includes all precession characteristic parameters, one of which is calculated based on the initial precession angular frequency and the initial phase.

[0015] Thirdly, embodiments of the present invention provide an electronic device, including a processor and a memory, wherein the memory is used to store a computer program; the processor can be used to execute a calculator program (instructions) stored in the memory to implement the method of the first aspect described above.

[0016] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed, can implement the method described in the first aspect above.

[0017] The beneficial effects of the embodiments of the present invention compared with the prior art are as follows: According to the method provided by the present invention, the initial parameters of the target are estimated by frequency domain data to reduce the dimensionality of the subsequent optimization problem, which can improve the estimation accuracy and reduce the computational load of subsequent high-dimensional estimation, thus getting rid of the high dependence of traditional parameter estimation methods on image quality; the Harris Eagle optimization algorithm can solve the high-dimensional estimation problem, realize the comprehensive and accurate estimation of precession feature parameters, and further improve the estimation accuracy. Attached Figure Description

[0018] Figure 1 A flowchart illustrating the implementation of a method for estimating precession parameters using a combination of scattering center estimation and Harris Eagle optimization, provided in an embodiment of the present invention.

[0019] Figure 2 A schematic diagram illustrating an implementation scenario of the Harris Hawk algorithm provided in an embodiment of the present invention;

[0020] Figure 3 A flowchart illustrating the implementation of an optimization method for precession characteristic parameters based on the Harris Eagle optimization algorithm, provided in an embodiment of the present invention.

[0021] Figure 4 A schematic diagram of a blunt-headed chamfered cone model provided in an embodiment of the present invention;

[0022] Figure 5 A schematic diagram of a precession parameter estimation device for joint scattering center estimation and Harris Eagle optimization provided in an embodiment of the present invention;

[0023] Figure 6 A schematic diagram illustrating the estimation error of a precession characteristic parameter provided in an embodiment of the present invention;

[0024] Figure 7 A schematic diagram illustrating the estimation error of another precession characteristic parameter provided in an embodiment of the present invention;

[0025] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0026] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0027] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0028] It should also be understood that the term “and / or” as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0029] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if [described condition or event] is detected" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once [described condition or event] is detected," or "in response to detection of [described condition or event]."

[0030] Furthermore, in the description of this invention and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0031] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of the invention include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0032] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0033] The wireless blockchain network sharding method provided in this embodiment of the invention can be applied to electronic devices such as mobile terminals, personal laptops, and supercomputers. This embodiment of the invention does not impose any restrictions on the specific type of electronic device.

[0034] Figure 1 The diagram illustrates a flowchart of a method for estimating precession parameters using a combination of scattering center estimation and Harris Eagle optimization, provided by an embodiment of the present invention. This estimation method is intended as an example and not a limitation, and can be applied to the aforementioned electronic device. The method may include steps S101-S103, which are described below.

[0035] S101, based on the frequency domain echo signal, estimate the radial distance information of the target's scattering center.

[0036] For example, a frequency domain echo signal can satisfy the following formula:

[0037] (1.1)

[0038] in, It is a frequency domain echo signal. The first goal i The amplitude of each scattering center The total number of scattering centers of the target. The imaginary unit, The number of steps in the frequency domain echo signal. For frequency intervals, The initial frequency, For the first iThe type of scattering center, For the first i The radial distance of each scattering center It is a Gaussian white noise signal. It is the speed of light.

[0039] To facilitate subsequent calculations of the initial phase and initial precession angular frequency, a binomial expansion of the frequency domain echo signal can be performed, and based on Taylor series, the following can be obtained:

[0040] (1.2)

[0041] in:

[0042] (1.3)

[0043] (1.4).

[0044] In one example, the radial distance information of the scattering center can be estimated based on the second radial distance model.

[0045] For example, the second radial distance model can be based on the Time-Limited Self-Supervised PhasePrediction and Instant Translation (TLS-ESPPIT) algorithm or an improved TLS-ESPPIT algorithm, treating frequency and displacement as spatial angular changes of the signal, transforming the parameter extraction problem into a spatial spectrum estimation problem, and obtaining it by decomposing the autocorrelation function of the frequency domain echo signal.

[0046] S102, based on the radial distance information, estimate the target's initial precession angular frequency and initial phase.

[0047] In one possible implementation, a radial distance curve can be fitted based on the target's radial distance information, and then the target's initial precession angular frequency and initial phase can be determined based on the fitted radial distance curve.

[0048] Since the target has a large number of precession characteristic parameters, the precession angular frequency and initial phase, which are easily obtainable, can be estimated first using frequency domain data. This reduces the dimension / number of characteristic parameters to be estimated in the subsequent estimation process and reduces the amount of computation.

[0049] In one example, it is statistically known that the radial distance curve of a target generally follows a sinusoidal variation law. Therefore, the radial distance curve can be fitted to a sine curve, and then the Gauss-Newton method of nonlinear least squares problem can be used to determine the initial precession angular frequency and initial phase of the target.

[0050] For example, the radial distance curve can satisfy the following formula:

[0051] (1.5)

[0052] in, The radial distance of the target's precession. To initialize the distance, For precession amplitude, The initial precession angular frequency, This is the initial phase.

[0053] S103, based on the first radial distance estimation model, the Harris Eagle optimization algorithm is used to optimize the estimated values ​​of the precession characteristic parameters in multiple rounds to obtain the optimal values ​​of the target's precession characteristic parameters.

[0054] For example, the number of precession characteristic parameters of a target is large. Taking a blunt-headed chamfered cone as an example, it can include radar line-of-sight elevation angle, cone height, ground radius, spherical cap radius, and chamfer radius calculated from the line-of-sight angle, precession angle, initial precession angle frequency, and initial phase.

[0055] In one example, due to the high dimensionality of the parameters to be estimated, direct estimation using frequency domain data is difficult and high-dimensional optimization is limited. Using Gauss-Newton's method for nonlinear squared problems can lead to non-convergence or local convergence of the function. Currently popular methods for solving such complex nonlinear optimization problems often employ global optimum approaches: genetic algorithms, simulated annealing, particle swarm optimization, etc. Here, the parameter extraction problem can be transformed into a high-dimensional optimization problem, which can be solved using the Harris Eagle optimization algorithm.

[0056] For example, when optimizing precession feature parameters using the Harris Eagle algorithm, the fitness of each individual can be calculated based on a first radial distance estimation model. This first radial distance estimation model can be constructed based on the quantitative relationship between the radial distances between the target's scattering centers and can be represented by a polynomial that includes all precession feature parameters, allowing for comprehensive optimization of each precession feature parameter in each round of optimization.

[0057] According to the method provided by the present invention, the initial parameters of the target are estimated by frequency domain data to reduce the dimensionality of the subsequent optimization problem, which can improve the estimation accuracy and reduce the computational cost of subsequent high-dimensional estimation, thus getting rid of the high dependence of traditional parameter estimation methods on image quality; the Harris Eagle optimization algorithm can solve the high-dimensional estimation problem, realize the comprehensive and accurate estimation of precession feature parameters, and further improve the estimation accuracy.

[0058] In some embodiments, the autocorrelation function of the frequency domain echo signal can be decomposed using the improved TLS-ESPPIT algorithm, and a second radial distance estimation model can be constructed specifically through the following steps S1011-S101.

[0059] S1011, calculate the autocorrelation function of the radar's frequency domain echo signal to analyze the frequency domain characteristics of the frequency domain echo signal.

[0060] For example, the autocorrelation function of a frequency domain echo signal can satisfy the following formula:

[0061] (1.6)

[0062] in, Let be the autocorrelation function of the frequency domain echo signal. The length of the signal. For delay parameters, For the first Each frequency domain echo signal, This indicates the complex conjugate operation.

[0063] S1012, Constructing the autocorrelation matrix based on the autocorrelation function and cross-correlation matrix .

[0064] S1013, Perform singular value decomposition on the autocorrelation matrix to decompose the signal into signal subspaces. and noise subspace .in, Let be the singular value matrix of the signal subspace. , is the eigenvector matrix of the signal subspace; Let be the singular value matrix of the noise subspace. , Let be the eigenvector matrix of the noise subspace.

[0065] For example, the decomposition process can be represented as:

[0066] (1.7)

[0067] S1014, Determine the minimum singular value of the signal subspace. ,structure .

[0068] in:

[0069] (1.8).

[0070] S1015, Constructing the matrix , ,in, The generalized inverse matrix is Through eigenvalue decomposition The second radial distance model can be obtained.

[0071] For example, the second radial distance estimation model can satisfy the following formula:

[0072] (1.9)

[0073] in, The radial distance of the i-th scattering center. For frequency intervals, At the speed of light, Representing vectors phase, Let be the first decomposition matrix of the i-th scattering center. For the specific calculation method, please refer to the above formula (1.4).

[0074] Optionally, the amplitude and type of the scattering center can be estimated further, and then the radar frequency domain echo signal can be reconstructed.

[0075] For example, vectors can be Sort the matrix from smallest to largest to construct a new matrix .

[0076] For example, the amplitude and type of the scattering center can satisfy the following formulas:

[0077]

[0078]

[0079] in:

[0080]

[0081] in, The number of targets. It is a frequency domain echo signal.

[0082] In some embodiments, see Figure 2 The Harris optimization algorithm can include a trap phase, an energy decrease phase, and a sprint phase.

[0083] In one possible implementation, during the encirclement phase, individual Harris eagles randomly roost in various locations, using their keen eyesight to track and detect prey in the desert space. They employ two strategies to conduct a global search for prey with equal probability, gradually reducing the distance to the prey.

[0084] In one example, at this stage, the position of the Harris Hawk in round k+1 can be calculated using the following formula:

[0085] (1.10)

[0086] in, The position of Harris Hawk in round k+1. For the position of Harris Hawks in round k, This represents the location of a random eagle within the Harris Eagle population. For the location of the prey, This represents the current average position of the Harris Eagle population. , , , , All are random numbers. , These are the upper and lower limits of the variable.

[0087] In one possible implementation, the energy decline phase is the transition phase between the encirclement phase and the sprint phase, simulating the phenomenon that when a Harris eagle is chasing its prey, the prey's energy is greatly reduced during the escape process.

[0088] In one example, the prey's energy satisfies the following formula:

[0089] (1.11)

[0090] in, The energy that the prey escapes in round k. The initial energy for the prey This represents the maximum number of iterations. During each iteration, In the interval Internal random variation. The dynamic escape energy of the prey. There is a decreasing trend during the iteration process.

[0091] In one possible implementation, the Harris Eagle algorithm proposes four sprint strategies to simulate realistic attack behavior during the sprint phase. These are soft encirclement, hard encirclement, soft encirclement and progressive rapid dive, hard encirclement and progressive rapid dive.

[0092] In one example, if and The Harris Eagle uses a soft-surrounding tactic to capture prey, and it can update its position using the following formula:

[0093] (1.12)

[0094] in, Given a random number between (0, 1), This is a random number between (0,1) generated during each update.

[0095] In another example, if and As the prey begins to tire, the Harris Eagle employs a hard encirclement tactic to capture it.

[0096] For example, the position of the Harris Hawk during hard enclosure can be updated using the following formula:

[0097] (1.13)

[0098] In yet another example, if and The prey has enough energy to escape successfully, but still establishes a soft encirclement before the ambush. The Harris Eagle uses a soft encirclement and a gradual, rapid dive to capture its prey.

[0099] For example, the position of the Harris Hawk during a soft encirclement and a gradual rapid dive can be updated using the following formula:

[0100] (1.14)

[0101] (1.15)

[0102] in, For those in (0,1) 3D random vector, For flight function, The dimension of the solution space (in this invention, the number of precession feature parameters), This is the fitness function.

[0103] Specifically:

[0104] (1.16)

[0105] (1.17)

[0106] in, and All are random numbers between (0,1). The default value is 1.5.

[0107] In another example, if and When the prey doesn't have enough energy to escape, the Harris Eagle builds a hard encirclement before its sudden swoop, using a combination of hard encirclement and gradual, rapid dives to capture and kill its prey.

[0108] For example, during hard encirclement and gradual rapid dive, the Harris Eagle can update its position using the following formula:

[0109] (1.18)

[0110] (1.19).

[0111] Figure 3 The diagram illustrates an implementation flowchart of a precession feature parameter optimization method based on the Harris Eagle optimization algorithm, provided by an embodiment of the present invention. As an example and not a limitation, this optimization method can be a specific possible implementation of step S103 in the aforementioned precession parameter estimation method. This optimization method may include steps S301-S305, which are described below.

[0112] S301, based on the first radial distance estimation model, determine the fitness of the estimated values ​​of the precession characteristic parameters in the kth round according to the observed values ​​of the precession characteristic parameters.

[0113] In some embodiments, see Figure 4 If the target is a blunt-nosed chamfered cone, then the target's precession characteristic parameters may include: radar line-of-sight elevation angle. Vertebral height Base radius spherical crown radius and chamfer radius The target may include two scattering centers, A and B.

[0114] For example, the radar line-of-sight elevation angle can be calculated based on the initial precession frequency and the initial phase. The radar line-of-sight elevation angle can satisfy the following formula:

[0115] (1.20)

[0116] in, For example Figure 4 The shown viewing angle This is the precession angle.

[0117] In one possible implementation, the output value of the first radial distance estimation model can be the difference between the first projection and the second projection of the target.

[0118] For example, see Figure 4 First projection A scattering center A of the target can be located in the radar line-of-sight direction (see...) Figure 4 The projection of the second projection (in the direction indicated by the middle arrow LOS). It can be the projection of another scattering center B of the target in the radar line-of-sight direction.

[0119] For example, the first projection can satisfy the following formula:

[0120] (1.21).

[0121] For example, the second projection can satisfy the following formula:

[0122] (1.22).

[0123] For example, since both the first and second projections include the distance from the centroid to the ground surface... h To eliminate unnecessary parameters and reduce the dimensionality of the parameters to be estimated, we can... h Remove the first projection and use the difference between the first and second projections to determine the fitness of a set of precession characteristic parameter estimates. Therefore, the first radial distance estimation model can satisfy the following formula:

[0124] (1.23)

[0125] in, This is the output value of the first radial distance estimation model. See also... Figure 4 Its actual physical meaning is That is, the difference between the first projection and the second projection.

[0126] In one example, the optimal values ​​for the precession characteristic parameters are considered found when the optimal combination of parameters minimizes the residual between the output of the first radial distance estimation model and the observed values. This is because the residual between the first radial distance estimation model (a nonlinear model) and the observed values... It can be represented as: ,in, Therefore, the parameter estimation problem can be transformed into an optimization problem: .

[0127] Therefore, the fitness of the estimated precession characteristic parameters in the k-th round can satisfy the following formula:

[0128] (1.24)

[0129] in, The fitness of the estimated precession characteristic parameters for the i-th group in the k-th round. The total number of precession characteristic parameters. for The observed values.

[0130] For example, a smaller fitness value indicates a better fitness.

[0131] S302, the estimated precession feature parameter of the kth round with poor fitness is taken as the position of the Harris Eagle in the kth round, and the estimated precession feature parameter of the kth round with the best fitness is taken as the position of the prey in the kth round.

[0132] In one example, before optimization, the size of the Harris Eagle population (i.e., the number of groups of precession feature parameter estimates in each round of optimization) and the maximum number of iterations can be set, and the precession feature parameter estimates for round 0 can be randomly generated.

[0133] For example, when estimating precession characteristic parameters using the Harris Eagle algorithm, the estimated values ​​of a set of precession characteristic parameters from each round of optimization can be used as the position of a Harris Eagle in the current round. For instance, a set of estimated precession characteristic parameters in this round might be... , , , , Then the position of a Harris Hawk can be (1,2,0.5,0.22,0.21).

[0134] For example, the fitness of each set of precession characteristic parameter estimates can be calculated by substituting them into formula (1.24) above. Then, the estimate with the highest fitness is taken as the position of the prey in the kth round, and the remaining estimates are taken as the positions of the Harris Eagle.

[0135] S303, determine whether the stopping condition has been met.

[0136] In one example, if the stopping condition is not met, step S304 can be performed.

[0137] For example, the stopping condition could be whether the maximum number of iterations has been reached, or whether the value of the current best fitness is less than a preset fitness threshold.

[0138] In another example, if the stopping condition is met, step S305 can be performed.

[0139] S304, based on the Harris Eagle algorithm, updates the position of the Harris Eagle in round k based on the position of the Harris Eagle in round k and the position of the prey in round k, and obtains the estimated value of the precession feature parameters in round k+1.

[0140] In one example, if the hunt is underway, the position of the Harris Hawk can be updated according to the formula (1.10) above.

[0141] In another example, if it is in the sprint phase, it can be based on and The value of is updated according to the formulas (1.12)-(1.19) above to update the position of the Harris Hawk.

[0142] S305, the estimated value of the precession characteristic parameter with the best fitness of the first K wheels is taken as the optimal value of the precession characteristic parameter.

[0143] For example, K can be the total number of optimization rounds, i.e., the maximum number of iterations.

[0144] Figure 5The diagram shown illustrates the structure of a precession parameter estimation device combining joint scattering center estimation and Harris Eagle optimization, as provided in an embodiment of the present invention. By way of example and not limitation, the device 500 may include a frequency domain data processing module 510, an initial parameter estimation module 520, and a precession characteristic parameter optimization module 530.

[0145] Specifically, the frequency domain data processing module 510 can be used to estimate the radial distance information of the target's scattering center based on the frequency domain echo signal; the initial parameter estimation module 520 can be used to estimate the target's initial precession angular frequency and initial phase based on the radial distance information; and the precession characteristic parameter optimization module 530 can be used to optimize the estimated values ​​of the precession characteristic parameters of the target through multiple rounds of optimization based on the first radial distance estimation model and the Harris Eagle optimization algorithm to obtain the optimal values ​​of the target's precession characteristic parameters.

[0146] To better illustrate the beneficial effects of the present invention, the following simulation experiments were conducted:

[0147] For example, a simulation experiment can be conducted using the software CompukerSimulakion Technology (CST) with targets and radars whose parameters are shown in Table 1 below.

[0148] Table 1 Radar System Parameter Information

[0149]

[0150] Figure 6 The diagram shown illustrates the estimation error of a precession characteristic parameter provided in an embodiment of the present invention.

[0151] For example, Gaussian white noise can be added to the broadband echo generated by CST, with a signal-to-noise ratio of 20dB, a precession angle range of 2°-10°, a degree interval of 0.1°, and a precession frequency of 0.6Hz. Then, based on the simulated frequency domain echo signal data, the precession angle and precession frequency are estimated using the precession characteristic parameter estimation method provided by this invention, obtaining estimated values ​​for the precession angle and precession frequency. Finally, the estimation error is obtained by comparing the preset precession angle and precession frequency with the estimated precession angle and precession frequency, and plotted. Figure 6 .

[0152] See Figure 6 As can be seen, the estimated precession angle and precession frequency have errors of less than 5%.

[0153] Figure 7 The diagram shown illustrates the estimation error of another precession characteristic parameter provided in an embodiment of the present invention.

[0154] Similarly, Gaussian white noise can be added to the broadband echo generated by CST, with a signal-to-noise ratio of 20dB, a precession angle of 4°, a precession frequency range of 0.5~1.5Hz, and a frequency interval of 0.1Hz. Then, based on the simulated frequency domain echo signal data, the precession angle and precession frequency are estimated using the precession characteristic parameter estimation method provided by this invention, obtaining estimated values ​​for the precession angle and precession frequency. Finally, the estimation error is obtained by comparing the preset precession angle and precession frequency with the estimated precession angle and precession frequency, and plotted. Figure 7 .

[0155] Similarly, see Figure 7 As can be seen, the estimated precession angle and precession frequency have errors of less than 5%.

[0156] According to the method provided by the present invention, the initial parameters of the target are estimated by frequency domain data to reduce the dimensionality of the subsequent optimization problem, which can improve the estimation accuracy and reduce the computational cost of subsequent high-dimensional estimation, thus getting rid of the high dependence of traditional parameter estimation methods on image quality; the Harris Eagle optimization algorithm can solve the high-dimensional estimation problem, realize the comprehensive and accurate estimation of precession feature parameters, and further improve the estimation accuracy.

[0157] Figure 8 The diagram shown is a structural schematic of an electronic device provided in an embodiment of the present invention. Figure 8 The illustrated electronic device 800 may include: at least one processor 810 ( Figure 8 The diagram shows only one processor, a memory 820, and a computer program 830 stored in the memory 820 and executable on the at least one processor 810, wherein the processor 810 executes the computer program 830 to implement the steps in any of the above method embodiments.

[0158] The electronic device 800 may be a robot or other processing device capable of implementing the above methods. This embodiment of the invention does not impose any restrictions on the specific type of electronic device.

[0159] Those skilled in the art will understand that Figure 8 This is merely an example of electronic device 800 and does not constitute a limitation on the electronic device. It may include more or fewer components than shown, or combine certain components, or use different components. For example, the electronic device 800 may also include input / output interfaces.

[0160] The processor 810 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0161] In some embodiments, the memory 820 may be an internal storage unit, such as a hard disk or RAM. In other embodiments, the memory 820 may be an external storage device, such as a plug-in hard disk, a smart memory card (SMC), a secure digital card (SD), or a flash card. Furthermore, the memory 820 may include both internal and external storage units. The memory 820 is used to store the operating system, applications, a boot loader, data, and other programs, such as the program code of the computer program. The memory 820 can also be used to temporarily store data that has been output or will be output.

[0162] It should be understood that the sequence number of each step in the above embodiments 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 constitute any limitation on the implementation process of the embodiments of the present invention.

[0163] Those skilled in the art will understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the functions described above can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments 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. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0164] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0165] This invention provides a computer program product that, when run on an electronic device, enables the electronic device to perform the steps described in the various method embodiments above.

[0166] If the integrated unit 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, all or part of the processes in the methods of the above embodiments of the present invention can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0167] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0168] 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 implementations should not be considered beyond the scope of this invention.

Claims

1. A method for estimating precession parameters by combining scattering center estimation and Harris Eagle optimization, characterized in that, include: Based on the frequency domain echo signal, estimate the radial distance information of the target's scattering center; Based on the radial distance information, the initial precession angular frequency and initial phase of the target are estimated; Based on the first radial distance estimation model, the Harris Eagle optimization algorithm is used to optimize the estimated values ​​of the precession characteristic parameters in multiple rounds to obtain the optimal values ​​of the precession characteristic parameters of the target. The first radial distance estimation model is constructed based on the quantitative relationship between the radial distances of the target's scattering centers. The first radial distance estimation model can be represented by a polynomial including all precession characteristic parameters, one of which is calculated based on the initial precession angular frequency and the initial phase. The step of using the Harris Eagle optimization algorithm to perform multiple rounds of optimization on the estimated values ​​of the precession characteristic parameters based on the first radial distance estimation model to obtain the optimal values ​​of the precession characteristic parameters of the target includes: Based on the first radial distance estimation model, the fitness of the estimated values ​​of the precession characteristic parameters in the kth round is determined according to the observed values ​​of the precession characteristic parameters, where k is a non-negative integer; The precession feature parameter estimate of the kth round with poor fitness is taken as the position of the Harris Eagle in the kth round, and the precession feature parameter estimate of the kth round with the best fitness is taken as the position of the prey in the kth round. Determine whether the stopping conditions have been met; If the stopping condition is not met, the Harris Eagle position is updated based on the Harris Eagle position in the kth round and the prey position in the kth round, and the estimated value of the precession characteristic parameter in the (k+1)th round is obtained. If the stopping condition is met, the estimated value of the precession characteristic parameter with the best fitness in the first K rounds is taken as the optimal value of the precession characteristic parameter, where K is the total number of optimization rounds; The target is a blunt-headed chamfered cone, and the precession characteristic parameters include: radar line-of-sight elevation angle, cone height, base radius, spherical cap radius, and chamfer radius. The output value of the first radial distance estimation model is the difference between the first projection and the second projection of the target, wherein the first projection is the projection of one scattering center of the target in the radar line-of-sight direction, and the second projection is the projection of the other scattering center of the target in the radar line-of-sight direction; The first radial distance estimation model is expressed as: in, The output value of the first radial distance estimation model. The height of the vertebral body, The chamfer radius is... Let the radius of the spherical cap be . The radius of the base surface is... The radar line-of-sight elevation angle is calculated based on the initial precession frequency and the initial phase.

2. The method according to claim 1, characterized in that, The step of estimating the radial distance information of the target's scattering center based on the frequency domain echo signal includes: Based on the second radial distance estimation model, the radial distance information of the target's scattering center is estimated according to the frequency domain echo signal; The second radial distance estimation model is obtained by decomposing the autocorrelation function of the frequency domain echo signal based on the improved TLS-ESPPIT algorithm, and the second radial distance estimation model satisfies the following formula: in, The radial distance of the i-th scattering center. For frequency intervals, At the speed of light, Representing vectors phase, Let be the first decomposition matrix of the i-th scattering center.

3. The method according to claim 1, characterized in that, The step of estimating the initial precession angular frequency and initial phase of the target based on the radial distance information includes: A radial distance curve is fitted based on the radial distance information, and the initial precession angular frequency and the initial phase are determined based on the radial distance curve.

4. The method according to claim 1, characterized in that, The fitness of the estimated precession characteristic parameter value in the k-th round satisfies the following formula: in, The fitness of the estimated precession characteristic parameters for the i-th group in the k-th round. The total number of a set of precession characteristic parameters. The first radial distance estimation model obtains its output value based on the estimated value of the i-th group of precession characteristic parameters in the k-th round. The observed value is the difference between the first projection and the second projection.

5. A device for estimating precession parameters by combining scattering center estimation and Harris Eagle optimization, characterized in that, include: A frequency domain data processing module is used to estimate the radial distance information of the target's scattering center based on the frequency domain echo signal. An initial parameter estimation module is used to estimate the initial precession angular frequency and initial phase of the target based on the radial distance information. The precession feature parameter optimization module is used to optimize the estimated values ​​of the precession feature parameters of the target through multiple rounds of optimization based on the first radial distance estimation model and the Harris Eagle optimization algorithm. The first radial distance estimation model is constructed based on the quantitative relationship between the radial distances of the target's scattering centers. The first radial distance estimation model can be represented by a polynomial including all precession characteristic parameters, one of which is calculated based on the initial precession angular frequency and the initial phase. Specifically, the precession characteristic parameter optimization module is used for: Based on the first radial distance estimation model, the fitness of the estimated values ​​of the precession characteristic parameters in the kth round is determined according to the observed values ​​of the precession characteristic parameters, where k is a non-negative integer; The precession feature parameter estimate of the kth round with poor fitness is taken as the position of the Harris Eagle in the kth round, and the precession feature parameter estimate of the kth round with the best fitness is taken as the position of the prey in the kth round. Determine whether the stopping conditions have been met; If the stopping condition is not met, the Harris Eagle position is updated based on the Harris Eagle position in the kth round and the prey position in the kth round, and the estimated value of the precession characteristic parameter in the (k+1)th round is obtained. If the stopping condition is met, the estimated value of the precession characteristic parameter with the best fitness in the first K rounds is taken as the optimal value of the precession characteristic parameter, where K is the total number of optimization rounds; The target is a blunt-nosed chamfered cone, and the precession characteristic parameters include: radar line-of-sight elevation angle, cone height, base radius, spherical cap radius, and chamfer radius; the output value of the first radial distance estimation model is the difference between the first projection and the second projection of the target; the first projection is the projection of one scattering center of the target in the radar line-of-sight direction, and the second projection is the projection of the other scattering center of the target in the radar line-of-sight direction; The first radial distance estimation model is expressed as: in, The output value of the first radial distance estimation model. The height of the vertebral body, The chamfer radius is... Let the radius of the spherical cap be . The radius of the base surface is... The radar line-of-sight elevation angle is calculated based on the initial precession frequency and the initial phase.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1-5.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by an electronic device, it implements the method as described in any one of claims 1-5.

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