Single-shot direction of arrival estimation method and system for quantum honey bear foraging mechanism under impulsive noise

Abstract

The application discloses a single-snapshot direction of arrival (DOA) estimation method and system for quantum honey bear foraging mechanism under impact noise, and relates to the field of array signal processing. In order to solve the problem that the performance of most existing algorithms will significantly decline and the DOA estimation work is difficult to implement under the impact noise environment, the technical points of the application include: constructing a signal model under single-snapshot sampling for a uniform linear array, establishing a low-order matrix based on a hyperbolic tangent kernel median deviation correlation entropy, and further obtaining a single-snapshot weighted signal subspace fitting equation based on the hyperbolic tangent kernel correlation entropy; and constructing a quantum honey bear foraging mechanism to efficiently solve the subspace fitting equation based on the hyperbolic tangent kernel median deviation correlation entropy. The single-snapshot DOA estimation method provided by the application can effectively measure the direction of arrival of independent sources or coherent sources under the impact noise environment.

Description

Technical Field

[0001] This invention relates to the field of array signal processing technology, and more specifically, to a method and system for estimating the direction of arrival of a single snapshot of a quantum honey bear foraging mechanism under impulse noise. Background Technology

[0002] In the field of array signal processing, Direction of Arrival (DOA) estimation is of great research value, playing a crucial role in many areas such as spectrum analysis, radar detection, satellite navigation, and wireless communication. Traditional DOA estimation algorithms are mostly designed for Gaussian noise environments, specifically categorized into multiple signal classification (MUSIC) methods and the ESPRIT method based on subspace rotation invariance. However, while these eigenvalue decomposition-based algorithms offer high estimation accuracy, they also have significant drawbacks: they require a large amount of snapshot data to ensure accuracy, and the numerous complex matrix operations result in high computational cost and poor real-time performance. Current research often employs single-snapshot DOA estimation algorithms to accelerate system response speed. However, most current single-snapshot estimation algorithms are only suitable for Gaussian noise environments, exhibiting poor robustness and ineffective estimation in environments with low signal-to-noise ratios or impulsive noise. Therefore, developing a single-snapshot DOA estimation algorithm with high accuracy, high robustness, and applicability in complex impulsive noise environments is essential.

[0003] According to existing technical literature, in 2009, Arpita Thakre et al. proposed in IEEE Signal Processing Letters (2009, Vol. 16(6)) to construct an exchange matrix from the received single-shot data, then combine it with the eigenvalue matrix of the output matrix to form an augmented matrix, and finally perform decoherent processing on the augmented matrix. Zhang Heyong et al., in their paper "A Single-Shot DOA Estimation Method Based on Spatial Smoothing" published in Firepower and Control Command (2021, 46(06)), proposed using a spatial smoothing method to perform decoherent processing on the received single-shot data, then performing Toeplitz transformation through the covariance matrix, followed by eigenvalue decomposition, and finally integrating the MUSIC and ESPRIT algorithms to achieve effective DOA estimation. This method eliminates the need for multiple snapshots, greatly reducing computation. However, this algorithm cannot determine direction in impulsive noise environments, which has certain limitations. In 2022, Zhu Hangui et al. published "A Deep Learning Sparse Single-Snapshot DOA Estimation Method" in Signal Processing (2022, 38(10)). This method integrates deep learning algorithms with the SL0 method to solve the sparse recovery model, achieving the goal of high-resolution DOA estimation of relevant signal sources using single-snapshot data.

[0004] Existing research shows that using single-shot DOA estimation can improve the real-time response capability of the system and reduce the computational load. However, reducing the number of shots can affect the accuracy of DOA estimation. Furthermore, most current single-shot DOA estimation methods are designed for Gaussian white noise environments, which are no longer applicable to today's complex electromagnetic environments. Especially in electronic warfare scenarios, the influence of different types of interference results in a relatively small number of shots obtainable per unit time. Therefore, in such situations, the performance of most algorithms will significantly decline, making DOA estimation difficult. Thus, developing a high-performance single-shot DOA estimation method for impulsive noise environments is of great significance. Summary of the Invention

[0005] The technical problem to be solved by this invention is:

[0006] In environments with impulsive noise, the performance of most existing algorithms degrades significantly, making DOA estimation difficult.

[0007] The technical solution adopted by the present invention to solve the above-mentioned technical problems is as follows:

[0008] This invention proposes a single-shot DOA estimation method suitable for impulse noise environments. Specifically, it involves constructing a signal model for a uniform linear array under single-shot sampling, establishing a low-order matrix based on the hyperbolic tangent kernel median deviation correlation entropy, and thereby deriving a single-shot weighted signal subspace fitting equation based on the hyperbolic tangent kernel correlation entropy. Kernel functions possess unique advantages; by utilizing the regenerating kernel Hilbert space, they can transform the original nonlinear problem into a linear one, and can rapidly calculate high-dimensional inner products using mathematical formulas. Since the kernel length is closely related to specific applications, this invention determines the optimal kernel length through performance simulation and designs a quantum honey bear foraging mechanism for efficiently solving the subspace fitting equation based on the hyperbolic tangent kernel median deviation correlation entropy. The proposed single-shot DOA estimation method can achieve effective direction finding for both independent and coherent sources under impulse noise environments.

[0009] This invention provides a single-shot direction-of-arrival estimation method for the quantum honey bear foraging mechanism under impact noise, comprising the following steps:

[0010] Step 1: Establish a single snapshot sampling signal model under impact noise environment:

[0011] Step Two: For those from A linear array consisting of 100 elements with uniform spacing between them is divided into 100 elements by a forward sliding operation. A subarray structure with overlapping regions;

[0012] Step 3: Utilize The received data from each subarray is used to construct the receiving matrix. Covariance processing is used to construct the matrix, and median filtering is used to process the diagonal elements to construct the Toeplitz matrix. The Toeplitz matrix is ​​then processed using the hyperbolic tangent kernel median deviation correlation entropy to obtain a low-order moment matrix based on the hyperbolic tangent kernel median deviation correlation entropy.

[0013] Step 4: Construct an orthogonal projection matrix using the guiding matrix of a uniform linear array, and finally obtain the single-shot weighted signal subspace fitting equation based on hyperbolic tangent kernel correlation entropy by constructing a fitting relationship.

[0014] Step 5: Construct a fitness function based on the single-shot weighted signal subspace fitting equation based on hyperbolic tangent kernel correlation entropy, initialize the quantum honey bear population and set parameters;

[0015] Step Six: During the exploration phase, the Quantum Honey Bears use a golden sine wave method to search for prey, and use greedy selection to update the location of each Quantum Honey Bear.

[0016] Step 7: During the development phase, the Quantum Honey Bear uses a combination of soft and hard encirclement and rapid attack strategies to dynamically adjust its position and orientation; it also employs a greedy selection approach to update the quantum position of each Quantum Honey Bear.

[0017] Step 8: Update the population using a crossover and mutation strategy;

[0018] Step 9: Repeat steps 6 to 8 until the position of the globally optimal quantum honey bear is output, and output it as the direction of arrival estimation result of single snapshot direction finding.

[0019] Furthermore, step one includes the following steps:

[0020] Assume an equidistant uniform linear array containing There are 1 array element, and the distance between any two adjacent array elements is 1. A far-field narrowband signal source emits an incident signal to the array, and the signal is independent of the noise. The incident angle of each signal is , The wavelength of the signal is Select the first array element as the reference array element, and in the... Time of the first The signals incident on the array from each signal source are denoted as follows: In the Time of the first There exists a matching element at each array element. Stable impulse noise is expressed as follows: In the Time of the first The received signal of each array element is represented as , For complex units, The received signal of a single snapshot of the array is represented as: ,in , For the guiding matrix, For the first in the matrix The steering vector corresponding to the direction of the incoming wave. The incident direction vector, For a single snapshot signal vector, The superscript represents the impulse noise vector received by the array. This represents transposition.

[0021] Furthermore, step two includes the following steps:

[0022] For by A linear array consisting of 100 elements with uniform spacing between them is divided into 100 elements by a forward sliding operation. There are several subarray structures with overlapping regions, where the number of array elements in each subarray is set to be... One, then When the reference element is the first subarray on the left, then the... The single snapshot signal received by each subarray is represented as:

[0023]

[0024] in, For the first Sub-matrix guiding matrix For the first The first in the sub-array The steering vector corresponding to the direction of the incoming wave. Indicates the first The noise vector corresponding to each subarray .

[0025] Furthermore, step three includes the following steps:

[0026] use The data received by each subarray is used to construct the following reception matrix:

[0027]

[0028] Based on the single-shot signal model received by the subarray, a matrix is ​​constructed using covariance processing. Addressing the issues of noise interference and insufficient feature decomposition efficiency:

[0029]

[0030] Median filtering was applied to the diagonal elements to obtain the Toeplitz matrix shown below:

[0031]

[0032] Using hyperbolic tangent kernel median deviation correlation entropy to pair the matrix After processing, a low-order moment matrix based on the hyperbolic tangent kernel median deviation correlation entropy is obtained:

[0033]

[0034] The first in Line number Column elements This can be specifically expressed as follows:

[0035]

[0036] It is the kernel function of the hyperbolic tangent kernel. It is the kernel length of the kernel function. Indicates conjugate. This indicates the conjugate transpose. represent The Middle List all elements.

[0037] Furthermore, step four includes the following steps:

[0038] right Perform feature decomposition to obtain ,in and These correspond to the signal subspace constructed from eigenvectors associated with large eigenvalues ​​and the signal subspace constructed from eigenvectors associated with large eigenvalues, respectively. A diagonal matrix composed of large eigenvalues. and Corresponding to the noise subspace constructed from feature vectors associated with small eigenvalues ​​and the diagonal matrix composed of small eigenvalues, respectively, the estimated angle of the single-shot weighted signal subspace fitting equation based on the hyperbolic tangent kernel correlation entropy is: ,in It is an orthogonal projection matrix. , yes The average of the small eigenvalues, It is the identity matrix. A function for finding the trace of a matrix.

[0039] Furthermore, step five includes the following steps:

[0040] First, set the quantum honey bear population size to [value missing]. The search space dimension of each quantum honey bear is The maximum number of iterations is The number of iterations is , No. The generation The quantum position of the quantum honey bear is The mapping rule is ,in , This represents the lower boundary of the mapping space. It represents the upper boundary of the mapping space. The probability of prey escaping. For the first The prey escape energy is defined as: , Represents a random number between (0,1). ;

[0041] when At time, the quantum position of each quantum honey bear is randomly initialized, the fitness value of the mapped state position of each quantum honey bear is calculated, and the value is substituted into the fitness function expression, where the th... The generation The expressions for the quantum position and position fitness value of a quantum honey bear are as follows: Remember the search results for the first time. Position with the highest fitness value up to generation The corresponding optimal quantum position is .

[0042] Furthermore, step six includes the following steps:

[0043] During the exploration phase, the first The first quantum honey bear We use the golden sine wave method to search for prey, making... Its corresponding quantum rotation angle is , , According to the quantum position update formula of the simulated quantum rotating gate, In the formula, , , , , , , for Uniformly distributed random numbers in an interval and These are the weighting coefficients and multiplier coefficients. Represents a random integer in the set {1, 2};

[0044] Once prey appears, its initial position is randomly selected within the search space, and the quantum honey bear on the ground changes its position accordingly within the search space. To search for random quantum locations within the search space, let Its corresponding quantum rotation angle is According to the quantum position update formula of the simulated quantum rotating gate, In the formula, , , , , , Represents a random integer in the set {1, 2};

[0045] Calculate the fitness value corresponding to the new quantum position mapping state position for each quantum honey bear; for the th The quantum honey bear uses a greedy selection approach: if the calculated new quantum position optimizes the objective function, then it accepts the new quantum position. Conversely, the quantum honey bear retains its previous quantum position. The optimal quantum position is then obtained as .

[0046] Furthermore, step seven includes the following steps:

[0047] During the development phase, a choice is made between soft and hard encirclement strategies based on the energy contained in the prey, breaking through the constraints of local optima; for the first... Only quantum honey bear, when and At that time, a soft encirclement strategy was adopted. Quantum Honey Bear The quantum rotation angle is , , , ;

[0048] when and At that time, a hard encirclement strategy was adopted. Quantum Honey Bear The quantum rotation angle is ;

[0049] when and During the capture process, the quantum honey bear pack employs a coordinated tactic combining gradual, rapid dives with soft encirclement. Based on the prey's deceptive movements, they dynamically adjust their position and direction to select the optimal capture location. Quantum Honey Bear The quantum rotation angle is ,in for Random numbers between A constant between [0,1];

[0050] when and At that time, the quantum honey bear employed a tactic combining gradual, rapid hunting with hard encirclement, attempting to close the average distance between itself and its target prey. Quantum Honey Bear The quantum rotation angle is In the formula, For the first The generation Quantum Honey Bear The average position; according to the quantum position update formula of the simulated quantum rotation gate, it is: and ;

[0051] Calculate the fitness value corresponding to the quantum position mapping state position of each quantum honey bear; for the th The quantum honey bear uses a greedy selection approach: if the calculated new quantum position optimizes the objective function, then it accepts the new quantum position. Conversely, the quantum honey bear retains its previous quantum position. The optimal quantum position is .

[0052] Furthermore, step eight includes the following steps:

[0053] For the Only with the first A quantum honey bear has a crossover probability of 1. , Generate uniformly random numbers between [0,1]. , No. Only with the first Quantum Honey Bear The quantum positions of the two dimensions evolve as follows:

[0054]

[0055] in, yes Random numbers between;

[0056] The mutation probability of the quantum honey bear is Generate uniformly random numbers between [0,1]. , No. Quantum Honey Bear The quantum position evolves as follows:

[0057]

[0058] Calculate the fitness value corresponding to the new quantum position mapping state position for each quantum honey bear; for the th The quantum honey bear uses a greedy selection approach: if the calculated new quantum position optimizes the objective function, then it accepts the new quantum position. Conversely, the quantum position of the quantum honey bear. If it remains unchanged, then the optimal quantum position is obtained as follows: .

[0059] This invention provides a single-shot direction-of-arrival estimation system for the quantum honey bear foraging mechanism under impact noise. The system has a program module corresponding to the steps of any of the above-described technical solutions, and executes the steps in the single-shot direction-of-arrival estimation method for the quantum honey bear foraging mechanism under impact noise during operation.

[0060] Compared with the prior art, the beneficial effects of the present invention are:

[0061] To address the limitations of existing single-shot direction-of-arrival (DOA) estimation methods in complex multipath electromagnetic environments, and their poor robustness and decoherence capabilities under low signal-to-noise ratio (SNR) and strong impact noise conditions, this invention proposes a weighted signal subspace fitting method based on hyperbolic tangent kernel median deviation correlation entropy. This method effectively estimates the DOA of coherent source signals under low SNR, single-shot, and strong impact noise conditions. Furthermore, a quantum honey bear foraging mechanism is designed to solve the complex weighted signal subspace fitting equation based on hyperbolic tangent kernel median deviation correlation entropy. This improves estimation accuracy and stability while maintaining computational efficiency, and enhances the algorithm's robustness and decoherence capabilities. Attached Figure Description

[0062] Figure 1 This is a flowchart of the single-shot direction-of-arrival estimation method for the quantum honey bear foraging mechanism under impact noise in an embodiment of the present invention;

[0063] Figure 2 This is a schematic diagram of the spatial smoothing structure in an embodiment of the present invention;

[0064] Figure 3 This is a graph showing the relationship between the direction finding success probability and the generalized signal-to-noise ratio when using different low-order matrices in this embodiment of the invention.

[0065] Figure 4 This is a graph showing the relationship between the root mean square error of direction finding and the generalized signal-to-noise ratio when using different low-order matrices in this embodiment of the invention.

[0066] Figure 5 This is a graph showing the relationship between the success probability of direction finding and the characteristic index when using different low-order matrices in this embodiment of the invention.

[0067] Figure 6 This is a graph showing the relationship between the root mean square error of the direction finding and the characteristic index when using different low-order matrices in an embodiment of the present invention.

[0068] Figure 7 This is a DOA estimation diagram of three coherent sources under a strong impact noise environment in an embodiment of the present invention. Detailed Implementation

[0069] To enable those skilled in the art to better understand the present invention, exemplary embodiments or examples of the present invention will be described below in conjunction with the accompanying drawings. Obviously, the described embodiments or examples are merely some, not all, of the embodiments or examples of the present invention. All other embodiments or examples obtained by those skilled in the art based on the embodiments or examples of the present invention without inventive effort should fall within the scope of protection of the present invention.

[0070] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0071] Specific Implementation Plan 1: Combining Figure 1 As shown, this invention provides a single-shot direction-of-arrival estimation method for the quantum honey bear foraging mechanism under impact noise, comprising the following steps:

[0072] Step 1: Establish a single-shot sampling signal model under impact noise environment.

[0073] Assume an equidistant uniform linear array containing There are 1 array element, and the distance between any two adjacent array elements is 1. A far-field narrowband signal source emits an incident signal to the array, and the signal is independent of the noise. The incident angle of each signal is , The wavelength of the signal is The first array element is selected as the reference element, and in the second array element... Time of the first The signals incident on the array from each signal source are denoted as follows: In the Time of the first There exists a matching element at each array element. Stable impulse noise is expressed as follows: In the Time of the first The received signal of each array element is represented as , For complex units, The received signal of a single snapshot of the array is represented as: ,in , For the guiding matrix, For the first in the matrix The steering vector corresponding to the direction of the incoming wave. The incident direction vector, For a single snapshot signal vector, The superscript represents the impulse noise vector received by the array. This represents transposition.

[0074] Step Two: For those from A linear array consisting of 100 elements with uniform spacing between them is divided into 100 elements by a forward sliding operation. A subarray structure with overlapping regions.

[0075] like Figure 2 As shown, in order to maximize the utilization of single-shot data received by the array, for data generated by... A linear array consisting of 100 elements with uniform spacing between them is divided into 100 elements by a forward sliding operation. There are several subarray structures with overlapping regions, where the number of array elements in each subarray is set to be... One, then If the reference element is the first subarray on the left, then the... The single snapshot signal received by each subarray is represented as:

[0076]

[0077] in, For the first Sub-matrix guiding matrix For the first The first in the sub-array The steering vector corresponding to the direction of the incoming wave. Indicates the first The noise vector corresponding to each subarray .

[0078] Step 3: Utilize The received data from each subarray is used to construct the receiving matrix. Covariance processing is used to construct the matrix, and median filtering is used to process the diagonal elements to construct the Toeplitz matrix. The Toeplitz matrix is ​​then processed using the hyperbolic tangent kernel median deviation correlation entropy to obtain a low-order moment matrix based on the hyperbolic tangent kernel median deviation correlation entropy.

[0079] use The data received from each subarray is used to construct the following reception matrix:

[0080]

[0081] Based on the single-shot signal model received by the subarray, a matrix is ​​constructed using covariance processing. This addresses the issues of noise interference and insufficient feature decomposition efficiency.

[0082]

[0083] The matrix after covariance processing exhibits conjugate symmetry, but its diagonal elements exhibit mismatch. Therefore, median filtering is used to process the diagonal elements, thereby constructing the Toeplitz matrix, resulting in the matrix shown below:

[0084]

[0085] To achieve better estimation results in noisy environments, the hyperbolic tangent kernel median deviation correlation entropy is used to adjust the matrix. After processing, a low-order moment matrix based on the hyperbolic tangent kernel median deviation correlation entropy is obtained:

[0086]

[0087] The first in Line number Column elements This can be specifically expressed as follows:

[0088]

[0089] It is the kernel function of the hyperbolic tangent kernel. It is the kernel length of the kernel function. Indicates conjugate. This indicates the conjugate transpose. represent The Middle List all elements.

[0090] Step 4: Construct an orthogonal projection matrix using the guiding matrix of the uniform linear array, and finally obtain the single-shot weighted signal subspace fitting equation based on the hyperbolic tangent kernel correlation entropy by constructing a fitting relationship.

[0091] right After performing eigenvalue decomposition, we can obtain ,in and These correspond to the signal subspace constructed from eigenvectors associated with large eigenvalues ​​and the subspace constructed from eigenvectors associated with large eigenvalues, respectively. A diagonal matrix composed of large eigenvalues. and These correspond to the noise subspace constructed from feature vectors associated with small eigenvalues ​​and the diagonal matrix composed of small eigenvalues, respectively. The estimated angle of the single-shot weighted signal subspace fitting equation based on hyperbolic tangent kernel correlation entropy is... ,in It is an orthogonal projection matrix. , yes The average of the small eigenvalues, It is the identity matrix. A function for finding the trace of a matrix.

[0092] Step 5: Construct a fitness function based on the single-shot weighted signal subspace fitting equation based on hyperbolic tangent kernel correlation entropy, initialize the quantum honey bear population and set parameters.

[0093] First, set the quantum honey bear population size to [value missing]. The search space dimension of each quantum honey bear is The maximum number of iterations is The number of iterations is , No. The generation The quantum position of the quantum honey bear is The mapping rule is ,in , This represents the lower boundary of the mapping space. It represents the upper boundary of the mapping space. The probability of prey escaping. For the first The prey escape energy is defined as: , Represents a random number between (0,1).

[0094] when At time, the quantum position of each quantum honey bear is randomly initialized. The fitness value of the mapped state position of each quantum honey bear is calculated and substituted into the fitness function expression, where the _____ ... The generation The expressions for the quantum position and position fitness value of a quantum honey bear are as follows: , Record the search results for the first time. Position with the highest fitness value up to generation The corresponding optimal quantum position is .

[0095] Step Six: During the exploration phase, the Quantum Honey Bear searches for prey using a golden sine wave method, and updates the location of each Quantum Honey Bear using greedy selection.

[0096] During the exploration phase, the first The first quantum honey bear We use the golden sine wave method to search for prey, making... Its corresponding quantum rotation angle is , , According to the quantum position update formula of the simulated quantum rotating gate, In the formula, , , , , , , for Uniformly distributed random numbers in an interval and These are the weighting coefficients and multiplier coefficients. Represents a random integer in the set {1, 2}.

[0097] Once prey appears, its initial position is randomly selected within the search space, and the quantum honey bear on the ground changes its position accordingly within the search space. To search for random quantum locations within the search space, let Its corresponding quantum rotation angle is According to the quantum position update formula of the simulated quantum rotating gate, In the formula, , , , , , Represents a random integer in the set {1, 2}.

[0098] Calculate the fitness value corresponding to the new quantum position mapping state for each quantum honey bear. For the ... The quantum honey bear uses a greedy selection approach: if the calculated new quantum position optimizes the objective function, then it accepts the new quantum position. Conversely, the quantum honey bear retains its previous quantum position. The optimal quantum position to date is .

[0099] Step 7: During the development phase, the Quantum Honey Bear uses a combination of soft and hard encirclement and rapid attack strategies to dynamically adjust its position and orientation; it also employs a greedy selection approach to update the quantum position of each Quantum Honey Bear.

[0100] The second stage, where the prey escapes the predator, is also known as the development stage. In this stage, the quantum honey bear employs a combination of soft and hard encirclement strategies, along with a rapid attack strategy. Depending on the prey's energy level, it chooses between soft and hard encirclement strategies, breaking free from the constraints of local optima. For the... Only quantum honey bear, when and At that time, a soft encirclement strategy was adopted. Quantum Honey Bear The quantum rotation angle is , , , .

[0101] when and At that time, a hard encirclement strategy was adopted. Quantum Honey Bear The quantum rotation angle is .

[0102] when and During the capture process, the quantum honey bear pack employs a coordinated tactic combining gradual, rapid dives with soft encirclement. Based on the prey's deceptive movements, they dynamically adjust their position and direction to select the optimal capture location. Quantum Honey Bear The quantum rotation angle is ,in for Random numbers between It is a constant between [0,1].

[0103] when and At that time, the quantum honey bear employed a tactic combining gradual, rapid hunting with hard encirclement, attempting to close the average distance between itself and its target prey. Quantum Honey Bear The quantum rotation angle is In the formula, For the first The generation Quantum Honey Bear The average position. According to the quantum position update formula for a simulated quantum rotating gate, it is: and .

[0104] Calculate the fitness value corresponding to the quantum position mapping state position for each quantum honey bear. For the th The quantum honey bear uses a greedy selection approach: if the calculated new quantum position optimizes the objective function, then it accepts the new quantum position. Conversely, the quantum honey bear retains its previous quantum position. The optimal quantum position to date is .

[0105] Step 8: Update the population using a crossover mutation strategy.

[0106] Crossover refers to performing a crossover operation on two adjacent quantum honey bears in the quantum honey bear population. For the first... Only with the first A quantum honey bear has a crossover probability of 1. , Generate uniformly random numbers between [0,1]. , No. Only with the first Quantum Honey Bear The quantum positions of the two dimensions evolve into:

[0107]

[0108] in, yes Random numbers between .

[0109] Mutation refers to the probability of mutation in each dimension of the position of each quantum honey bear in the quantum honey bear population. Generate uniformly random numbers between [0,1]. , No. Quantum Honey Bear The quantum positions of the two dimensions evolve into:

[0110] .

[0111] Calculate the fitness value corresponding to the new quantum position mapping state for each quantum honey bear. For the ... The quantum honey bear uses a greedy selection approach: if the calculated new quantum position optimizes the objective function, then it accepts the new quantum position. Conversely, the quantum position of the quantum honey bear. Unchanged, the optimal quantum position so far is .

[0112] Step 9: Determine if the current iteration number is equal to... If they are not equal, then let If the previous step is not completed, return to step six; otherwise, output the position of the last generation of globally optimal quantum honey bears. This is output as the direction of arrival estimation result of single-shot orientation finding.

[0113] The single-shot wave direction of arrival estimation method (algorithm) for quantum honey bear foraging mechanism under impact noise proposed in this invention is the underlying technical core of this invention, and various products can be derived based on the algorithm.

[0114] Based on the method proposed in this invention, a single-shot direction-of-arrival estimation system for the quantum honey bear foraging mechanism under impact noise is developed using a programming language. This system has program modules corresponding to the steps of the above-mentioned technical solution, and executes the steps in the single-shot direction-of-arrival estimation method for the quantum honey bear foraging mechanism under impact noise during runtime.

[0115] The developed system (software) computer program is stored on a computer-readable storage medium. This computer program is configured to, when called by a processor, implement the steps of the single-shot direction-of-arrival estimation method for the quantum honey bear foraging mechanism under impact noise described above. In other words, the invention is materialized on a carrier, becoming a computer program product.

[0116] Various implementations of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, application-specific integrated circuits (ASICs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0117] The computational programs (also referred to as programs, software, software applications, or code) of this invention include machine instructions of a programmable processor and can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device PLD) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0118] The beneficial effects of the present invention will be described below with reference to specific embodiments.

[0119] Example 1

[0120] The direction finding method based on the quantum honey bear foraging mechanism and hyperbolic tangent kernel correlation entropy weighted signal subspace fitting designed in this invention is abbreviated as "GCOA-HMCE-WSF"; the direction finding method based on the quantum honey bear foraging mechanism and fractional low-order covariance weighted signal subspace fitting is abbreviated as "GCOA-FLOC-WSF".

[0121] In the simulation experiment, three information sources respectively... Three different directions incident at a distance of A uniform linear array, The simulation experiment parameters are designed as follows, using the carrier wavelength: , The number of information sources is 3, and the kernel length of the hyperbolic tangent kernel correlation entropy is set. , , , , , , , , The experiment was repeated 100 times to statistically analyze the performance. Other parameters in GCOA-FLOC-WSF were referenced from the "Subspace Fitting Direction Finding Algorithm for Reconstructing Low-Order Covariance of Fractions" published by Gao Hongyuan et al. in the Journal of Radio Science (2009, 24(04)).

[0122] exist Figure 3 Three information sources with coherent characteristics in the middle The impulse noise was incident on the receiving array in a specific direction. The characteristic index of the impulse noise was set to 0.95, and 100 Monte Carlo simulation experiments were conducted. Figure 3 The simulation results clearly demonstrate that the single-shot direction finding method based on weighted signal subspace fitting using hyperbolic tangent kernel median deviation correlation entropy, designed under different GSNR conditions, has a high probability of successfully estimating coherent signal sources.

[0123] exist Figure 4 Three information sources with coherent characteristics in the middle The impulse noise was incident on the receiving array in a specific direction. The characteristic index of the impulse noise was set to 0.95, and 100 independent experiments were conducted using the Monte Carlo simulation method. Figure 4 The simulation results clearly demonstrate that the single-shot direction finding method based on weighted signal subspace fitting using hyperbolic tangent kernel median deviation correlation entropy, designed under different GSNR conditions, has high estimation accuracy for coherent signal sources and exhibits certain robustness.

[0124] exist Figure 5 Three information sources with coherent characteristics in the middle The impulse noise was incident on the receiving array in the following directions. The characteristic index of the impulse noise was set to 0.95, the generalized signal-to-noise ratio was set to 8dB, and 100 independent experiments were conducted using the Monte Carlo simulation method. Figure 5 The simulation results clearly demonstrate that the single-shot direction finding method based on weighted signal subspace fitting of hyperbolic tangent kernel median deviation correlation entropy, designed under different feature indices, has a high probability of successfully estimating coherent signal sources.

[0125] exist Figure 6 Three information sources with coherent characteristics in the middle The light was incident on the receiving array in the specified direction. The generalized signal-to-noise ratio was set to 8dB, and 100 independent experiments were conducted using the Monte Carlo simulation method. Figure 6 The simulation results clearly demonstrate that the single-shot direction finding method based on weighted signal subspace fitting of hyperbolic tangent kernel median deviation correlation entropy, designed under different characteristic indices, has high estimation accuracy for coherent signal sources and has a certain degree of robustness.

[0126] exist Figure 7 Three information sources with coherent characteristics in the middle The light was incident on the receiving array in the specified direction. The generalized signal-to-noise ratio was set to 8dB, and 100 independent experiments were conducted using the Monte Carlo simulation method. Figure 7 The simulation results clearly demonstrate that most of the estimated values ​​obtained from the experiment agree well with the true values. In summary, the single-shot direction finding method based on weighted signal subspace fitting using the hyperbolic tangent kernel median deviation correlation entropy proposed in this invention maintains high-precision coherent signal DOA estimation performance under both low signal-to-noise ratio and strong impulse noise environments, as shown by the simulation results.

[0127] While the present invention has been disclosed above, its scope of protection is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and all such changes and modifications will fall within the scope of protection of the present invention.

Claims

1. A single-shot direction-of-arrival estimation method for the quantum honey bear foraging mechanism under impulsive noise, characterized in that, Includes the following steps: Step 1: Establish a single snapshot sampling signal model under impact noise environment: Step Two: For those from A linear array consisting of 100 elements with uniform spacing between them is divided into 100 elements by a forward sliding operation. A subarray structure with overlapping regions; Step 3: Utilize The received data from each subarray is used to construct the receiving matrix. Covariance processing is used to construct the matrix, and median filtering is used to process the diagonal elements to construct the Toeplitz matrix. The Toeplitz matrix is ​​processed using the hyperbolic tangent kernel median deviation correlation entropy to obtain a low-order moment matrix based on the hyperbolic tangent kernel median deviation correlation entropy; Step 4: Construct an orthogonal projection matrix using the guiding matrix of a uniform linear array, and finally obtain the single-shot weighted signal subspace fitting equation based on hyperbolic tangent kernel correlation entropy by constructing a fitting relationship. Step 5: Construct a fitness function based on the single-shot weighted signal subspace fitting equation based on hyperbolic tangent kernel correlation entropy, initialize the quantum honey bear population and set parameters; Step Six: During the exploration phase, the Quantum Honey Bears use a golden sine wave method to search for prey, and use greedy selection to update the location of each Quantum Honey Bear. Step 7: During the development phase, the Quantum Honey Bear uses a combination of soft and hard encirclement and rapid attack strategies to dynamically adjust its position and orientation; it also employs a greedy selection approach to update the quantum position of each Quantum Honey Bear. Step 8: Update the population using a crossover and mutation strategy; Step 9: Repeat steps 6 to 8 until the position of the globally optimal quantum honey bear is output, and output it as the direction of arrival estimation result of single snapshot direction finding.

2. The method according to claim 1, characterized in that, Step one includes the following steps: Assume an equidistant uniform linear array containing There are 1 array element, and the distance between any two adjacent array elements is 1. A far-field narrowband signal source emits an incident signal to the array, and the signal is independent of the noise. The incident angle of each signal is , The wavelength of the signal is ; The first array element is selected as the reference array element, and in the second array element... Time of the first The signals incident on the array from each signal source are denoted as follows: In the Time of the first There exists a matching element at each array element. Stable impulse noise is expressed as follows: In the Time of the first The received signal of each array element is represented as , For complex units, The received signal of a single snapshot of the array is represented as: ,in , For the guiding matrix, For the first in the matrix The steering vector corresponding to the direction of the incoming wave. The incident direction vector, For a single snapshot signal vector, The superscript represents the impulse noise vector received by the array. This represents transposition.

3. The method according to claim 2, characterized in that, Step two includes the following steps: For by A linear array consisting of 1000 elements with uniform spacing between them is divided into 1000 elements by a forward sliding operation. There are several subarray structures with overlapping regions, where the number of array elements in each subarray is set to be... One, then ; If the reference array element is the first subarray on the left, then the... The single snapshot signal received by each subarray is represented as: in, For the first Sub-matrix guiding matrix For the first The first in the sub-array The steering vector corresponding to the direction of the incoming wave. Indicates the first The noise vector corresponding to each subarray .

4. The method according to claim 3, characterized in that, Step three includes the following steps: use The data received by each subarray is used to construct the following reception matrix: Based on the single-shot signal model received by the subarray, a matrix is ​​constructed using covariance processing. Addressing the issues of noise interference and insufficient feature decomposition efficiency: Median filtering was applied to the diagonal elements to obtain the Toeplitz matrix shown below: Using hyperbolic tangent kernel median deviation correlation entropy to pair the matrix After processing, a low-order moment matrix based on the hyperbolic tangent kernel median deviation correlation entropy is obtained: The first in Line number Column elements This can be specifically expressed as follows: It is the kernel function of the hyperbolic tangent kernel. It is the kernel length of the kernel function. Indicates conjugate. This indicates the conjugate transpose. represent The Middle List all elements.

5. The method according to claim 4, characterized in that, Step four includes the following steps: right Perform feature decomposition to obtain ,in and These correspond to the signal subspace constructed from the eigenvectors associated with large eigenvalues ​​and the signal subspace constructed from the eigenvectors associated with large eigenvalues, respectively. A diagonal matrix composed of large eigenvalues and Corresponding to the noise subspace constructed from feature vectors associated with small eigenvalues ​​and the diagonal matrix composed of small eigenvalues, respectively, the estimated angle of the single-shot weighted signal subspace fitting equation based on the hyperbolic tangent kernel correlation entropy is: ,in It is an orthogonal projection matrix. , yes The average of the small eigenvalues, It is the identity matrix. A function for finding the trace of a matrix.

6. The method according to claim 5, characterized in that, Step five includes the following steps: First, set the quantum honey bear population size to [value missing]. The search space dimension of each quantum honey bear is The maximum number of iterations is The number of iterations is , No. The generation The quantum position of the quantum honey bear is The mapping rule is ,in , This represents the lower boundary of the mapping space. It represents the upper boundary of the mapping space. The probability of prey escaping. For the first The prey escape energy is defined as: , Represents a random number between (0,1). ; when At time, the quantum position of each quantum honey bear is randomly initialized, the fitness value of the mapped state position of each quantum honey bear is calculated, and the value is substituted into the fitness function expression, where the th... The generation The expressions for the quantum position and position fitness value of a quantum honey bear are as follows: Remember the search results for the first time. Position with the highest fitness value up to generation The corresponding optimal quantum position is .

7. The method according to claim 6, characterized in that, Step six includes the following steps: During the exploration phase, the first The first quantum honey bear We use the golden sine wave method to search for prey, making... Its corresponding quantum rotation angle is , , According to the quantum position update formula of the simulated quantum rotating gate, In the formula, , , , , , , for Uniformly distributed random numbers in an interval and These are the weighting coefficients and multiplier coefficients. Represents a random integer in the set {1, 2}; Once prey appears, its initial position is randomly selected within the search space, and the quantum honey bear on the ground changes its position accordingly within the search space. To search for random quantum locations within the search space, let Its corresponding quantum rotation angle is According to the quantum position update formula of the simulated quantum rotating gate, In the formula, , , , , , Represents a random integer in the set {1, 2}; Calculate the fitness value corresponding to the new quantum position mapping state position for each quantum honey bear; for the th The quantum honey bear uses a greedy selection approach: if the calculated new quantum position optimizes the objective function, then it accepts the new quantum position. ; Conversely, the quantum honey bear retains its previous quantum position. The optimal quantum position is then obtained as .

8. The method according to claim 7, characterized in that, Step seven includes the following steps: During the development phase, a choice is made between soft and hard encirclement strategies based on the energy contained in the prey, breaking through the constraints of local optima; for the first... Only quantum honey bear, when and When using a soft encirclement strategy, the first Quantum Honey Bear The quantum rotation angle is , , , ; when and At that time, a hard encirclement strategy was adopted. Quantum Honey Bear The quantum rotation angle is ; when and At that time, during the prey-hunting process, the quantum honey bear group implemented a coordinated tactic combining gradual, rapid dives and soft encirclement, dynamically adjusting its position and direction based on the prey's deceptive movements to select the optimal capture location; Quantum Honey Bear The quantum rotation angle is ,in for Random numbers between A constant between [0,1]; when and At that time, the quantum honey bear employed a tactic combining gradual, rapid hunting with hard encirclement, attempting to close the average distance between itself and its target prey. Quantum Honey Bear The quantum rotation angle is In the formula, For the first The generation Quantum Honey Bear The average position; according to the quantum position update formula of the simulated quantum rotation gate, it is: and ; Calculate the fitness value corresponding to the quantum position mapping state position of each quantum honey bear; for the th The quantum honey bear uses a greedy selection mechanism: if the calculated new quantum position optimizes the objective function, then it accepts the new quantum position. ; Conversely, the quantum honey bear retains its previous quantum position. The optimal quantum position is .

9. The method according to claim 8, characterized in that, Step eight includes the following steps: For the Only with the first The crossover probability of a quantum honey bear is... , Generate uniformly distributed random numbers between [0,1]. , No. Only with the first Quantum Honey Bear The quantum positions of the two dimensions evolve as follows: in, yes Random numbers between; The mutation probability of the quantum honey bear is Generate uniformly random numbers between [0,1]. , No. Quantum Honey Bear The quantum position evolves as follows: Calculate the fitness value corresponding to the new quantum position mapping state position for each quantum honey bear; for the th The quantum honey bear uses a greedy selection mechanism: if the calculated new quantum position optimizes the objective function, then it accepts the new quantum position. Conversely, the quantum position of the quantum honey bear. If it remains unchanged, then the optimal quantum position is obtained as follows: .

10. A single-shot direction-of-arrival estimation system for the quantum honey bear foraging mechanism under impulsive noise, characterized in that, The system has a program module corresponding to the steps of the method described in any one of claims 1 to 9, and executes the steps in the single-shot direction-of-arrival estimation method for the quantum honey bear foraging mechanism under impact noise described above when running.

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