A cognitive radar waveform library construction and online optimization method

Abstract

The application relates to a cognitive radar waveform library construction and online optimization method. The method comprises the following steps: designing a first cognitive radar waveform set according to the environmental clutter characteristics and the non-scheduled abnormal target detection task of a machine to the greatest extent to inhibit environmental interference; designing a second cognitive radar waveform set according to the characteristics of the environmental and scheduled target aliasing and the scheduled target detection task to the greatest extent to enhance the target signal; compressing the first cognitive radar waveform set and the second cognitive radar waveform set, transmitting a linear frequency modulation signal by the cognitive radar, inversing the environmental or environmental and target aliasing characteristics online according to the echo of the linear frequency modulation signal, matching the best description information in the compressed cognitive radar waveform library according to the real-time environmental characteristic understanding result, and taking the waveform corresponding to the best description information in the library as the best signal for the next transmission of the cognitive radar. The method can construct the cognitive radar waveform library and select the optimal waveform online.

Description

Technical Field

[0001] This application relates to the field of cognitive radar technology, and in particular to a method for constructing and online optimizing a cognitive radar waveform library. Background Technology

[0002] Modern radar applications are becoming increasingly widespread, and the environmental interference they face is becoming increasingly complex. Against this backdrop, cognitive radar technology has attracted widespread attention from scholars, but practical systems based on this technology have yet to be realized. Cognitive radar can optimize radar transmission waveforms based on the perception of environmental characteristics, thereby achieving optimal mission performance. Summary of the Invention

[0003] Therefore, it is necessary to provide a method for constructing and online optimizing a cognitive radar waveform library, which can build a cognitive radar waveform library and select the optimal waveform, in order to address the above-mentioned technical problems.

[0004] A method for constructing and online optimizing a cognitive radar waveform library, the method comprising:

[0005] Acquire radar prior information; radar prior information includes the thermal noise characteristics of radar equipment and the environmental clutter characteristics in its operating environment, as well as the characteristics of the predetermined target to be measured.

[0006] Based on the characteristics of environmental clutter and the mission of detecting unexpected and unpredictable abnormal targets, a first cognitive radar waveform set is designed with the criterion of maximizing the suppression of environmental interference; and a second cognitive radar waveform set is designed with the criterion of maximizing the enhancement of the target signal, taking into account the characteristics of the aliasing between the environment and the predetermined target and the mission of detecting the predetermined target.

[0007] Clustering techniques are used to compress the first and second cognitive radar waveform sets to obtain a compressed cognitive radar waveform set. The descriptive information of the environment or the aliasing characteristics of the environment and the target corresponding to each waveform in the compressed cognitive radar waveform library is extracted.

[0008] By using cognitive radar to transmit linear frequency modulated signals, and relying on the echo of the linear frequency modulated signals to perform online inversion of the environment or the aliasing characteristics of the environment and the target, real-time environmental characteristic understanding results are obtained.

[0009] Based on the cross-correlation technique and the understanding of real-time environmental characteristics, the best descriptive information is matched in the compressed cognitive radar waveform library; the waveform in the library corresponding to the best descriptive information is used as the best signal for the next transmission of the cognitive radar.

[0010] In one embodiment, a first cognitive radar waveform set is designed based on environmental clutter characteristics and the objective of detecting unexpected, unplanned anomalous targets, with the criterion of maximizing the suppression of environmental interference. This set includes:

[0011] Based on environmental clutter characteristics and the objective of detecting unexpected, unplanned anomalous targets, the first cognitive radar waveform set was designed with the principle of minimizing environmental interference as the guiding principle.

[0012]

[0013] in, Represents the power spectrum of environmental clutter, i = 1, 2, ..., I, indicating a total of I values, PSD u and PSD n These represent the power spectrum vector of the radar equipment's thermal noise and the power spectrum vector of a uniformly distributed, unplanned target, respectively. and Temp i,u (f n ) represents the indirectly defined parameters in the optimization problem solution process, n = 1, 2, ..., N represents the frequency index corresponding to the optimized waveform, and has the same dimension as the aforementioned power spectral vector. n X represents the physical frequency corresponding to the nth frequency point. i,u (f n ) represents the power spectrum condition of the i-th environmental clutter when the physical frequency is f. n When, for the detection mission of unexpected and unpredictable abnormal targets, the optimal cognitive waveform spectrum is defined, where Energy represents the energy constraint of the optimized waveform and W represents the agility bandwidth space of the optimized waveform.

[0014] In one embodiment, a second cognitive radar waveform set is designed to maximize target signal enhancement based on the characteristics of environmental and target aliasing and the target detection task, including:

[0015] The second cognitive radar waveform set is designed to maximize target signal enhancement in response to the characteristics of environmental and target aliasing and the target detection mission.

[0016]

[0017] in, Let j = 1, 2, ..., J, representing the set of energy spectrum distributions of the predetermined target, and J = 1, 2, ..., J, indicating a total of J different detection perspectives. and Temp i,j (f n X is an indirectly defined parameter in the process of solving the optimization problem. i,j (f n ) represents the power spectrum of the i-th environmental clutter and the j-th detection angle, when the physical frequency is f. n At that time, the optimal cognitive waveform spectrum for the predetermined target detection mission.

[0018] In one embodiment, clustering techniques are used to compress the first and second cognitive radar waveform sets to obtain a compressed cognitive radar waveform set, including:

[0019] For tasks involving the detection of unusual targets due to environmental characteristics and unforeseen circumstances, clustering techniques are employed to group the first radar waveform set {X}. i,u} i＝1,2,…,I Clustering with L centers, each cluster center is used as the compressed radar waveform to obtain the compressed first cognitive radar waveform set {Y}. l,u} l＝1,2,…,L Each cognitive radar waveform corresponds to an environmental description; where i represents the sequence number of the waveform in the first cognitive radar waveform set, I represents the total number of waveforms in the first cognitive radar waveform set; u represents the identifier of the impromptu, unplanned abnormal target detection task, and l represents the sequence number of the cluster center.

[0020] Clustering techniques are used to analyze the characteristics of environmental and target aliasing and to perform target detection tasks, thereby enabling the second radar waveform set {X} to be processed. i,j} i＝1,2,…,I；j＝1,2,…,J Clustering with S centers, each cluster center is used as the compressed radar waveform to obtain the compressed second cognitive radar waveform set {Y}. s,t} s＝1,2,…,S Each cognitive radar waveform corresponds to a description of the aliasing of the environment and the target; where j represents the target power spectrum vector index under the geometric detection perspective, J represents the total number of target power spectrum vectors under the geometric detection perspective, and t represents the predetermined target detection mission identifier.

[0021] Based on the compressed first cognitive radar waveform set and the compressed second cognitive radar waveform set, a compressed cognitive radar waveform set is constructed as {Y}. q} q＝1,2,…,Q ={Y l,u} l＝1,2,…,L ∪{Y s,t} s＝1,2,…,S Where q represents the cluster center number and Q represents the total number of cluster centers.

[0022] In one embodiment, the set constructed by extracting and compressing the descriptive information of the environment or the aliasing characteristics between the environment and the target corresponding to each waveform in the cognitive radar waveform library is as follows: Where Q = L + S, PSD q This represents a collection of descriptive information. This represents the mean of the environmental description information corresponding to the same type of waveform. This represents the mean of the environmental and target aliasing description information corresponding to the same type of waveform.

[0023] In one embodiment, a cognitive radar transmits a linear frequency modulated (LFM) signal, and based on the echo of the LFM signal, the environment or the aliasing characteristics of the environment and the target are retrieved online to obtain real-time environmental characteristic understanding results, including:

[0024] By utilizing cognitive radar to transmit linear frequency modulated (LFM) signals, and relying on the online inversion of the environment or the aliasing characteristics of the environment and target based on the echo of the LFM signals, real-time environmental characteristic understanding results are obtained.

[0025] PSD e =PSD(R) / {abs[[FFT(LFM)]]} 2

[0026] Wherein, PSD(R) represents calculating the power spectrum of the received signal echo vector R, FFT(LFM) represents calculating the Fourier transform of the linear frequency modulated signal, abs[·] represents calculating the modulus of the operator, and · represents dividing the two vectors point by point.

[0027] In one embodiment, based on cross-correlation techniques and the understanding of real-time environmental characteristics, the optimal descriptive information is matched in the compressed cognitive radar waveform library; the waveform in the library corresponding to the optimal descriptive information is used as the optimal signal for the next transmission of the cognitive radar, including:

[0028] Real-time environmental characteristics understanding results PSD e A collection of PSDs constructed with descriptive information q The elements in the dataset are matched one by one to obtain the power spectrum element with the highest correlation coefficient, which is then used in the compressed cognitive radar waveform set {Y}. q} q＝1,2,…,Q Select the optimal cognitive emission waveform Y corresponding to the power spectral element with the highest correlation coefficient. q This serves as the optimal signal for the next transmission of the cognitive radar.

[0029] The aforementioned method for constructing and online optimizing a cognitive radar waveform library involves designing a first cognitive radar waveform set based on environmental clutter characteristics and the detection of unexpected, unplanned abnormal targets, with the criterion of maximizing the suppression of environmental interference. A second cognitive radar waveform set is designed to maximize the enhancement of the target signal, considering the characteristics of environmental and target aliasing and the target detection task. Clustering techniques are used to compress the first and second cognitive radar waveform sets, resulting in a compressed cognitive radar waveform set. The descriptive information of the environment or environmental-target aliasing characteristics corresponding to each waveform in the compressed cognitive radar waveform library is extracted. Through waveform design and compression, a cognitive radar waveform library is constructed, where each waveform has a corresponding descriptive information of its environment or environmental-target aliasing characteristics. During online optimization, a linear frequency modulated (LFM) signal is transmitted using cognitive radar. The echo of the LFM signal is used to invert the environment or the aliasing characteristics between the environment and the target, obtaining a real-time environmental characteristic understanding. Based on cross-correlation techniques and the real-time environmental characteristic understanding, the optimal descriptive information is matched in the compressed cognitive radar waveform library. This provides high feedback and a high-quality characteristic prior for establishing an online closed-loop feedback mechanism for cognitive radar, significantly improving the accuracy of online waveform optimization. The waveform in the library corresponding to the optimal descriptive information is used as the optimal signal for the next transmission by the cognitive radar. Compared to the online waveform optimization design approach, the offline construction of the cognitive radar waveform library and the online optimization approach offer better real-time performance. Furthermore, the cognitive radar waveform library construction and compression approach proposed in this application has excellent scalability and portability for different application scenarios. Attached Figure Description

[0030] Figure 1 This is a flowchart illustrating a cognitive radar waveform library construction and online optimization method in one embodiment. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0032] In one embodiment, such as Figure 1 As shown, a method for constructing and online optimizing a cognitive radar waveform library is provided, including the following steps:

[0033] Step 102: Obtain radar prior information; radar prior information includes the thermal noise characteristics of the radar equipment and the environmental clutter characteristics in its working environment, as well as the characteristics of the predetermined target to be measured.

[0034] Assume that there is some prior information about the radar's intended mission target and operating area. For example, the radar equipment manufacturer may provide the radar equipment's thermal noise power spectral vector (PSD).n ; Relying on electromagnetic scattering characteristic calculation software or radar echo equivalent technology, the power spectrum characteristics of environmental clutter in land or land areas detected by radar are obtained, and then combined with... It means that among them This represents the i-th environmental clutter power spectrum vector obtained based on prior loading information, totaling I vectors; then, based on the prior electromagnetic scattering characteristics of the predetermined target, the set of power spectrum distributions of the predetermined target under different geometric detection perspectives is obtained. in This represents the target power spectrum vector obtained from the j-th geometric detection viewpoint based on prior loading information; there are a total of J vectors. Let N be the set of positive real numbers with N rows and 1 column.

[0035] Step 104: Design a first cognitive radar waveform set based on environmental clutter characteristics and the objective of detecting unplanned abnormal targets, with the criterion of maximizing the suppression of environmental interference; and design a second cognitive radar waveform set based on the characteristics of environmental and target aliasing and the objective of the target detection task, with the criterion of maximizing the enhancement of the target signal.

[0036] The first cognitive radar waveform set, designed based on environmental clutter characteristics and the objective of detecting unplanned, non-pre-defined anomalous targets, is primarily used for detecting unplanned, non-pre-defined anomalous targets. This can prevent the missed detection of high-value targets. The second cognitive radar waveform set, designed to maximize the enhancement of target signals for the objective target detection objective and considering the characteristics of the environment and the objective target overlap, can effectively avoid subsequent waveform design errors caused by the assumption that the target and environment overlap during environmental understanding.

[0037] Assuming there are no targets to be detected in the radar's intended operating area, for each element in the aforementioned set of environmental clutter power spectra... Configure the unplanned target power spectrum as a uniformly distributed PSD. u Based on this, for the unexpected and unpredictable abnormal target detection mission, the first cognitive radar waveform set is designed with the principle of suppressing environmental interference to the greatest extent. Assuming that the radar's predetermined working area contains the predetermined target to be detected but the geometric detection angle is unknown, the second cognitive radar waveform set is designed with the principle of maximizing the enhancement of the target signal, considering the characteristics of the environment and the predetermined target overlap and the predetermined target detection mission.

[0038] Step 106: Use clustering technology to compress the first cognitive radar waveform set and the second cognitive radar waveform set to obtain a compressed cognitive radar waveform set, and extract the descriptive information of the environment or the aliasing characteristics of the environment and the target corresponding to each waveform in the compressed cognitive radar waveform library.

[0039] The purpose of compressing the cognitive radar waveform set is to reduce the space for subsequent online waveform selection and improve the timeliness of online selection of cognitive radar waveforms.

[0040] Step 108: Use cognitive radar to transmit linear frequency modulated signals, and rely on the echo of the linear frequency modulated signals to perform online inversion of the environment or the aliasing characteristics of the environment and the target to obtain the real-time environmental characteristic understanding results.

[0041] By using a cognitive radar to transmit a set of linear frequency modulated (LFM) signals with a bandwidth of W, and relying on the echo R of the LFM signals, the real-time environmental characteristics are understood through online inversion of the environment or the aliasing characteristics between the environment and the target, and the real-time environmental characteristics understanding results are obtained.

[0042] Step 110: Based on the cross-correlation technology and the understanding results of real-time environmental characteristics, the best descriptive information is matched in the compressed cognitive radar waveform library; the waveform in the library corresponding to the best descriptive information is used as the best signal for the next transmission of the cognitive radar.

[0043] Based on the cross-correlation technology and the understanding of real-time environmental characteristics, the optimal descriptive information is matched in the compressed cognitive radar waveform library. The feedback is high, providing a high-quality characteristic prior for the establishment of the cognitive radar online closed-loop feedback mechanism. This greatly improves the accuracy of online waveform optimization. Furthermore, the waveform in the library corresponding to the optimal descriptive information is used as the optimal signal for the next transmission of the cognitive radar. Compared with the online waveform optimization design approach, the offline construction of the cognitive radar waveform library and the online optimization approach have better real-time performance and accuracy.

[0044] In the aforementioned method for constructing and online optimizing a cognitive radar waveform library, this application designs a first cognitive radar waveform set based on environmental clutter characteristics and the objective of detecting unplanned abnormal targets, with the criterion of maximizing the suppression of environmental interference. A second cognitive radar waveform set is designed based on the characteristics of environmental and target aliasing and the objective of the target detection task, with the criterion of maximizing target signal enhancement. Clustering techniques are used to compress the first and second cognitive radar waveform sets to obtain a compressed cognitive radar waveform set. The descriptive information of the environment or environmental-target aliasing characteristics corresponding to each waveform in the compressed cognitive radar waveform library is extracted. Through waveform design and compression, a cognitive radar waveform library is constructed, in which each waveform has a corresponding descriptive information of the environment or environmental-target aliasing characteristics. During online optimization, a linear frequency modulated (LFM) signal is transmitted using cognitive radar. The echo of the LFM signal is used to invert the environment or the aliasing characteristics between the environment and the target, obtaining a real-time environmental characteristic understanding. Based on cross-correlation techniques and the real-time environmental characteristic understanding, the optimal descriptive information is matched in the compressed cognitive radar waveform library. This provides high feedback and a high-quality characteristic prior for establishing an online closed-loop feedback mechanism for cognitive radar, significantly improving the accuracy of online waveform optimization. The waveform in the library corresponding to the optimal descriptive information is used as the optimal signal for the next transmission by the cognitive radar. Compared to the online waveform optimization design approach, the offline construction of the cognitive radar waveform library and the online optimization approach offer better real-time performance. Furthermore, the cognitive radar waveform library construction and compression approach proposed in this application has excellent scalability and portability for different application scenarios.

[0045] In one embodiment, a first cognitive radar waveform set is designed based on environmental clutter characteristics and the objective of detecting unexpected, unplanned anomalous targets, with the criterion of maximizing the suppression of environmental interference. This set includes:

[0046] Based on environmental clutter characteristics and the objective of detecting unexpected, unplanned anomalous targets, the first cognitive radar waveform set was designed with the principle of minimizing environmental interference as the guiding principle.

[0047]

[0048] in, Represents the power spectrum of environmental clutter, i = 1, 2, ..., I, indicating a total of I values, PSD u and PSD n These represent the power spectrum vector of the radar equipment's thermal noise and the power spectrum vector of a uniformly distributed, unplanned target, respectively. and Temp i,u (f n ) represents the indirectly defined parameters in the optimization problem solution process, n = 1, 2, ..., N represents the frequency index corresponding to the optimized waveform, and has the same dimension as the aforementioned power spectral vector. nX represents the physical frequency corresponding to the nth frequency point. i,u (f n ) represents the power spectrum condition of the i-th environmental clutter when the physical frequency is f. n When, for the detection mission of unexpected and unpredictable abnormal targets, the optimal cognitive waveform spectrum is defined, where Energy represents the energy constraint of the optimized waveform and W represents the agility bandwidth space of the optimized waveform.

[0049] In one embodiment, a second cognitive radar waveform set is designed to maximize target signal enhancement based on the characteristics of environmental and target aliasing and the target detection task, including:

[0050] The second cognitive radar waveform set is designed to maximize target signal enhancement in response to the characteristics of environmental and target aliasing and the target detection mission.

[0051]

[0052] in, Let j = 1, 2, ..., J, representing the set of energy spectrum distributions of the predetermined target, and J = 1, 2, ..., J, indicating a total of J different detection perspectives. and Temp i,j (f n X is an indirectly defined parameter in the process of solving the optimization problem. i,j (f n ) represents the power spectrum of the i-th environmental clutter and the j-th detection angle, when the physical frequency is f. n At that time, the optimal cognitive waveform spectrum for the predetermined target detection mission.

[0053] In one embodiment, clustering techniques are used to compress the first and second cognitive radar waveform sets to obtain a compressed cognitive radar waveform set, including:

[0054] For tasks involving the detection of unusual targets due to environmental characteristics and unforeseen circumstances, clustering techniques are employed to group the first radar waveform set {X}. i,u} i＝1,2,…,I Clustering with L centers, each cluster center is used as the compressed radar waveform to obtain the compressed first cognitive radar waveform set {Y}. l,u} l＝1,2,…,L Each cognitive radar waveform corresponds to an environmental description; where i represents the sequence number of the waveform in the first cognitive radar waveform set, I represents the total number of waveforms in the first cognitive radar waveform set; u represents the identifier of the impromptu, unplanned abnormal target detection task, and l represents the sequence number of the cluster center.

[0055] Clustering techniques are used to analyze the characteristics of environmental and target aliasing and to perform target detection tasks, thereby enabling the second radar waveform set {X} to be processed.i,j} i＝1,2,…,I；j＝1,2,…,J Clustering with S centers, each cluster center is used as the compressed radar waveform to obtain the compressed second cognitive radar waveform set {Y}. s,t} s＝1,2,…,S Each cognitive radar waveform corresponds to a description of the aliasing of the environment and the target; where j represents the target power spectrum vector index under the geometric detection perspective, J represents the total number of target power spectrum vectors under the geometric detection perspective, and t represents the predetermined target detection mission identifier.

[0056] Based on the compressed first cognitive radar waveform set and the compressed second cognitive radar waveform set, a compressed cognitive radar waveform set is constructed as {Y}. q} q＝1,2,...,Q ={Y l,u} l＝1,2,...,L ∪{Y s,t} s＝1,2,...,S Where q represents the cluster center number and Q represents the total number of cluster centers.

[0057] In one embodiment, the set constructed by extracting and compressing the descriptive information of the environment or the aliasing characteristics between the environment and the target corresponding to each waveform in the cognitive radar waveform library is as follows: Where Q = L + S, PSD q This represents a collection of descriptive information. This represents the mean of the environmental description information corresponding to the same type of waveform. This represents the mean of the environmental and target aliasing description information corresponding to the same type of waveform.

[0058] In one embodiment, a cognitive radar transmits a linear frequency modulated (LFM) signal, and based on the echo of the LFM signal, the environment or the aliasing characteristics of the environment and the target are retrieved online to obtain real-time environmental characteristic understanding results, including:

[0059] By utilizing cognitive radar to transmit linear frequency modulated (LFM) signals, and relying on the online inversion of the environment or the aliasing characteristics of the environment and target based on the echo of the LFM signals, real-time environmental characteristic understanding results are obtained.

[0060] PSD e =PSD(R) / {abs[[FFT(LFM)]} 2

[0061] Wherein, PSD(R) represents calculating the power spectrum of the received signal echo vector R, FFT(LFM) represents calculating the Fourier transform of the linear frequency modulated signal, abs[·] represents calculating the modulus of the operator, and · / represents dividing the two vectors point by point.

[0062] In one embodiment, based on cross-correlation techniques and the understanding of real-time environmental characteristics, the optimal descriptive information is matched in the compressed cognitive radar waveform library; the waveform in the library corresponding to the optimal descriptive information is used as the optimal signal for the next transmission of the cognitive radar, including:

[0063] Real-time environmental characteristics understanding results PSD e A collection of PSDs constructed with descriptive information q The elements in the dataset are matched one by one to obtain the power spectrum element with the highest correlation coefficient, which is then used in the compressed cognitive radar waveform set {Y}. q} q＝1,2,…,Q Select the optimal cognitive emission waveform Y corresponding to the power spectral element with the highest correlation coefficient. q This serves as the optimal signal for the next transmission of the cognitive radar.

[0064] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0065] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0066] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for constructing and online optimizing a cognitive radar waveform library, characterized in that, The method includes: Acquire radar prior information; the radar prior information includes the thermal noise characteristics of the radar equipment and the environmental clutter characteristics in its working environment, as well as the characteristics of the predetermined target to be measured. Based on the environmental clutter characteristics and the unexpected, unplanned abnormal target detection task, a first cognitive radar waveform set is designed with the criterion of maximizing the suppression of environmental interference; and a second cognitive radar waveform set is designed with the criterion of maximizing the enhancement of the target signal, taking into account the characteristics of the environment and the predetermined target aliasing task. Clustering techniques are used to compress the first and second cognitive radar waveform sets to obtain a compressed cognitive radar waveform set. The descriptive information of the environment or the aliasing characteristics of the environment and the target corresponding to each waveform in the compressed cognitive radar waveform library is extracted. By using cognitive radar to transmit linear frequency modulated signals, and relying on the echo of the linear frequency modulated signals to perform online inversion of the environment or the aliasing characteristics of the environment and the target, real-time environmental characteristic understanding results are obtained. Based on the cross-correlation technique and the understanding results of the real-time environmental characteristics, the best descriptive information is matched in the compressed cognitive radar waveform library; the waveform in the library corresponding to the best descriptive information is used as the best signal for the next transmission of the cognitive radar. Based on the environmental clutter characteristics and the unexpected, unplanned target detection mission, a first cognitive radar waveform set is designed with the criterion of maximizing the suppression of environmental interference, including: Based on the environmental clutter characteristics and the objective of detecting unexpected, unplanned anomalous targets, the first cognitive radar waveform set is designed with the principle of maximizing the suppression of environmental interference. in, Represents the power spectrum of environmental clutter. , indicating total indivual, and These represent the power spectrum vector of the radar equipment's thermal noise and the power spectrum vector of a uniformly distributed, unplanned target, respectively. , and Indirectly defined parameters in the process of solving optimization problems. This indicates the frequency index corresponding to the optimized waveform, and its dimension is equal to that of the aforementioned power spectral vector. Indicates the first The physical frequency corresponding to each frequency point Indicates the first Under the condition of environmental clutter power spectrum, when the physical frequency is At that time, the optimal cognitive waveform spectrum for detecting unexpected and unpredictable abnormal targets is... This represents the energy constraint for optimizing the waveform. This represents the agile bandwidth space of the optimized waveform; A second cognitive radar waveform set is designed to maximize target signal enhancement for the characteristics of environmental and target aliasing and the target detection mission, including: The second cognitive radar waveform set is designed to maximize target signal enhancement in response to the characteristics of environmental and target aliasing and the target detection mission. in, This represents the set of energy spectral distributions of the intended detection target. , indicating total A different detection perspective , and Indirectly defined parameters in the process of solving optimization problems. Indicates the first The environmental clutter power spectrum and the first Under the condition of one detection angle, when the physical frequency is At that time, the optimal cognitive waveform spectrum for the predetermined target detection mission.

2. The method according to claim 1, characterized in that, Clustering techniques are used to compress the first and second cognitive radar waveform sets to obtain a compressed cognitive radar waveform set, including: Clustering techniques are used to analyze first-cognitive radar waveforms for detection tasks involving environmental characteristics and unexpected, unpredictable anomalies. Clustering The number of cluster centers is used to obtain the compressed first cognitive radar waveform set by taking each cluster center as the compressed radar waveform. Each cognitive radar waveform corresponds to an environmental description; among them, This indicates the sequence number of the waveform in the first cognitive radar waveform set. This represents the total number of waveforms in the first cognitive radar waveform set; This indicates an identifier for an unexpected, unplanned target detection mission. Indicates the index of the cluster center; Clustering techniques are used to analyze the characteristics of environmental and target aliasing and to perform target detection tasks, thereby enabling the second cognitive radar waveform set to be processed. Clustering The number of cluster centers is used to obtain the compressed second cognitive radar waveform set by taking each cluster center as the compressed radar waveform. Each cognitive radar waveform corresponds to a descriptive information about the aliasing of the environment and the target; among them, This represents the target power spectrum vector index from the geometric detection perspective. This represents the total number of target power spectral vectors from the geometric detection perspective. Indicates the identifier for the predetermined target detection mission; Based on the compressed first cognitive radar waveform set and the compressed second cognitive radar waveform set, a compressed cognitive radar waveform set is constructed as follows: ,in, Indicates the cluster center number. This represents the total number of cluster centers.

3. The method according to claim 2, characterized in that, The set of descriptive information about the environment or the aliasing characteristics between the environment and the target corresponding to each waveform in the refined and compressed cognitive radar waveform library is as follows: ,in, , This represents a collection of descriptive information. This represents the mean of the environmental description information corresponding to the same type of waveform. This represents the mean of the environmental and target aliasing description information corresponding to the same type of waveform.

4. The method according to claim 1, characterized in that, By utilizing cognitive radar to transmit linear frequency modulated (LFM) signals and relying on the echoes of these LFM signals to perform online inversion of the environment or the aliasing characteristics of the environment and target, real-time environmental characteristic understanding results are obtained, including: By utilizing cognitive radar to transmit linear frequency modulated (LFM) signals, and relying on the online inversion of the environment or the aliasing characteristics of the environment and target based on the echo of the LFM signals, real-time environmental characteristic understanding results are obtained. in, Represents the echo vector of the received signal. Find the power spectrum. This represents the Fourier transform of a linear frequency modulated signal. Modulo operation means taking the modulus of the operator. This indicates that two vectors are divided point by point.

5. The method according to claim 1, characterized in that, Based on the cross-correlation technique and the understanding results of the real-time environmental characteristics, the best descriptive information is matched in the compressed cognitive radar waveform library; Using the in-library waveform corresponding to the optimal description information as the optimal signal for the next transmission by the cognitive radar, including: Understanding the characteristics of the real-time environment A collection constructed with descriptive information Each element in the dataset is matched one by one to obtain the power spectral element with the highest correlation coefficient, which is then used in the compressed cognitive radar waveform set. Select the optimal cognitive transmission waveform corresponding to the power spectral element with the highest correlation coefficient. This serves as the optimal signal for the next transmission of the cognitive radar.

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