Active and passive hybrid radar antenna selection and power distribution method based on graph theory
By using graph theory-based methods, combined with log-likelihood ratio and Gale-Shapley algorithm, the antenna selection and power allocation of a hybrid active-passive radar system are optimized, solving the performance optimization problem under system resource constraints and achieving improved system performance and reduced computational complexity.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SOUTHWEST PETROLEUM UNIV
- Filing Date
- 2026-03-23
- Publication Date
- 2026-05-05
AI Technical Summary
In hybrid active and passive radar systems, due to system resource and cost constraints, traditional antenna selection and power allocation methods are difficult to effectively cope with the discrete power adjustment levels and limited processing capabilities in real-world environments, leading to difficulties in performance optimization.
A graph theory-based approach is adopted to establish a hybrid active-passive radar system model, determine the optimal detector using the log-likelihood ratio and NP criterion, and combine the Gale-Shapley algorithm (AGS algorithm) for antenna selection and power allocation to optimize the detection probability.
With limited total transmission power and system processing capacity, the system performance was optimized, computational complexity was reduced, and the feasibility of practical applications was improved.
Smart Images
Figure CN121978634A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of radar technology, and in particular to a method for selecting and allocating power to a hybrid active-passive radar antenna, specifically a graph theory-based method for selecting and allocating power to a hybrid active-passive radar antenna. Background Technology
[0002] In hybrid active-passive radar systems, the actual number of active antennas that can operate and the number of passive antennas that can be processed are limited due to system resource and cost constraints, while the number of passive antennas that can be processed in the environment far exceeds this limit. Therefore, how to optimize overall performance by selecting appropriate active antennas for operation and passive antennas for processing within the constraints of system processing capabilities becomes a critical issue. Simultaneously, the transmit power allocation of active antennas in the system directly affects performance. However, traditional power allocation and antenna selection methods are usually based on idealized assumptions and are difficult to effectively address the discrete power adjustment levels and limited processing capabilities in real-world environments. Summary of the Invention
[0003] The purpose of this invention is to provide a graph theory-based method for selecting and allocating active and passive radar antennas, in order to solve the problem of poor adaptability of traditional methods in real-world environments.
[0004] To achieve the above objectives, the present invention provides the following technical solution: A graph theory-based method for selecting and allocating active and passive radar antennas includes the following steps: S1. Establish a model of a hybrid active and passive radar system; S2. Establish the target detection problem based on the received signal vector in the aforementioned active-passive hybrid radar system model; S3. Substitute the target detection problem into the log-likelihood ratio to transform the log-likelihood ratio expression; S4. The log-likelihood ratio expression is based on the NP criterion to determine the optimal detector and derive the detection statistic. S5. Calculate the output signal-to-noise ratio of the active-passive hybrid radar system based on the detection statistics, as a substitute indicator for the detection probability; S6. Establish a graph theory model of the active-passive hybrid radar system based on the detection probability; S7. Based on the graph theory model, an active antenna selection, power allocation, and passive antenna selection algorithm, the AGS algorithm, is proposed. Under the limited total transmit power and system processing capability, the detection probability is maximized, reasonable active and passive antennas are selected to work, and reasonable power is allocated to the active antennas.
[0005] As a specific embodiment of the present invention, step S1 includes: S11. Define binary selection variables and determine their constraints. The binary selection variables include active antenna selection variables, power allocation variables, and passive antenna selection processing variables. In the formula, The variable is used to select the active antenna; a value of 1 indicates that the first antenna is selected. The candidate active antenna is used as the first If it is an active antenna that is working, the value is 0 otherwise. For power allocation variables, a value of 1 indicates the first... The active antenna selected for operation has a transmit power of Otherwise, the value is 0; This is a variable for the selection and processing of passive antennas; a value of 1 indicates the first... The selected antenna processing position is the [number]. One passive antenna; This indicates the total number of active antennas that need to be operational; Indicates the number of candidate active antennas; The transmit power of an active antenna is derived from a finite power set. Selected from ,in It is a set The number of power levels indicates the total number of power levels. Selectable discrete power, power of each active antenna They are all finite sets One of the elements; This indicates the total number of passive antennas processed; Indicates the number of passive antennas to be processed; Setting up a hybrid active and passive radar system includes Number of candidate active radar transmitters (number of candidate active antennas) N One receiver and Select from 10 candidate passive antennas (passive antennas to be processed). Each radar transmitting antenna operates and its power is allocated, selecting... Passive antenna processing; S12, Assuming the target is located at , obtain the Each receiving antenna (receiver) in The received signal at time t is represented as: The first item originates from active radar antennas; the second item originates from passive antennas. In the formula, surface Each receiving antenna is in The received signal at any time, of which Indicates the serial number of the receiving antenna. Number the sampling points. The sampling period; Indicates in Time of the first Noise at each receiving antenna; and Let represent the reflection coefficients of the active antenna path and the passive antenna path, respectively. Assume they are independent, zero-mean complex Gaussian random variables with variances of . and ; and These represent the corresponding time delays; Indicates the first The distance from each receiving antenna to the target location; Indicates the selected number The distance from each active antenna to the target location; Indicates the first The transmitted signal of a single active antenna; Indicates the number to be processed The distance from the passive antenna to the target location; Indicates the first The transmitted signal of the passive antenna being processed; Indicates the first The transmit power of the passive antenna being processed; S13. Repeat step S12 to obtain the... The received signals from the receiving antenna at different times are obtained to obtain the first... The vector of all received signals from each receiving antenna is represented as: in, In the formula, Indicates the first The vector of all received signals from each receiving antenna; , , Indicates the first Each receiving antenna is in , , The received signal at a given time; the symbol "†" indicates transpose; Indicates the first Noise vector of each receiving antenna; , , They represent the first Each receiving antenna is in , , Momentary noise; Indicates the first Channel vectors of active antennas at each receiving antenna; , , They represent the first The receiving antennas and the 1st, 2nd... Channel gain of each active antenna; Indicates the first Channel vector of the passive antenna at each receiving antenna; , , They represent the first The receiving antennas and the 1st, 2nd... Channel gain of a passive antenna; S14. Repeat step S13 to obtain the vector of all received signals from each receiving antenna. When a target appears, it is represented as: in, , dimension , dimension , In the formula, , , They represent the 1st, 2nd, and 3rd respectively. The vector of all received signals from each receiving antenna; This represents the matrix used to collect signals from all active antennas. , , They represent the 1st, 2nd, and 3rd respectively. The receiving antenna contains a matrix of all signals transmitted by the active antennas; This represents the matrix used to collect all passive antenna signals; , , They represent the 1st, 2nd, and 3rd respectively. The receiving antenna contains a matrix of passive antenna signals being processed; Represents the noise vector; , , They represent the 1st, 2nd, and 3rd respectively. The noise vector at the receiving antenna; This represents the active antenna signal vector in operation; , , They represent the 1st, 2nd, and 3rd respectively. Each receiving antenna receives the signal vector from the active antenna. This represents the passive antenna channel vector being processed; , , They represent the 1st, 2nd, and 3rd respectively. The signal vector received by each receiving antenna from the processed passive antenna; express The conjugate transpose of; express The conjugate transpose of; Represents the mathematical expectation; express The covariance matrix; Indicate The covariance matrix; , , As an intermediate variable, its general formula for calculation is: , , As an intermediate variable, its general formula for calculation is: , They all follow a zero-mean complex Gaussian distribution and are independent of each other.
[0006] As a specific embodiment of the present invention, the target detection problem is: in, This indicates that the target does not exist; This indicates that the target exists.
[0007] In one specific embodiment of the present invention, in step S3, the detection problem is... and Substituting the log-likelihood ratio into the expression, the log-likelihood ratio is obtained as follows: in, In the formula, This represents the covariance matrix of the received vector when a target is present. Represents the noise vector covariance matrix; , Indicates intermediate variables; Representing vectors The conjugate transpose of; Representing vectors The conjugate transpose of; Represents the mathematical expectation; express The reverse; Represents a determinant.
[0008] In one specific embodiment of the present invention, step S4 includes the following operations: Under the NP criterion, the optimal detector is expressed as follows: This represents the decision threshold obtained based on the false alarm probability constraint; The detection statistic is defined as follows: The expression for the detection statistic is derived as follows: In the formula, This represents the variance of the noise. Indicates the first At the receiving antenna, corresponding to the first... The candidate antenna is the first one. The output signal of the matched filter of each working antenna; Indicates the first At the receiving antenna, corresponding to the first... The passive antenna to be processed is the first The output signal of a matched filter that processes passive antennas; Represents the modulus of a complex number.
[0009] In one specific embodiment of the present invention, in step S5, the expression for the output signal-to-noise ratio of the detector is as follows: In the formula, This represents the mathematical expectation of the detection statistic given the existence of the target. denoted as the mathematical expectation of the detection statistic when the target does not exist.
[0010] As a specific embodiment of the present invention, step S6 includes: S61. The active radar section models the candidate radar transmitting antennas and selectable power together as a set of vertices in a bipartite graph, and models the active radar transmitting antennas as the other set of vertices in the bipartite graph, thus constructing a complete bipartite graph. ,in Let represent the joint vertex set of candidate active radar antennas and their power, with a set size of . , , , and Both are vertex sets The vertices in the diagram represent the power levels of the first candidate active radar antenna as 1, 2, and 3 respectively. , No. The power levels of the candidate active radar antennas are ; , represents the set of vertices of the working radar transmitting antennas, with a set size of . , , , They represent the 1st, 2nd, and 3rd respectively. A working radar transmitting antenna; the edge set of the active radar part is represented as The elements of each edge in the edge set , They come from the set ,gather This maps the relationship between the selection of radar transmitting antennas and power allocation, with the weight of each edge defined as follows: ; S62. The passive radar section treats the passive antenna and the passive antenna processing seat as two vertices of a bipartite graph, constructing a complete bipartite graph of the passive antenna. ; , represents the set of passive antenna processing positions, with a set size of . , Indicates the first to the second One passive antenna processing seat; , represents the set of passive antenna vertices, with a set size of . , Indicates the first to the second One passive antenna to be processed; edge set This ensures that each edge in the edge set has two vertices that come from a set of passive antennas. A collection of seats capable of handling passive antennas This maps the relationship between the total passive antenna and the passive antenna processing seats, with the weight of each edge defined as follows: .
[0011] As a specific embodiment of the present invention, step S7 is specifically as follows: S71. Set initial parameters: Number of receive antennas and distance Number of passive antennas Number of radar transmitting antennas Passive antenna position Location of active radar transmitting antenna Passive antenna transmit power Selectable power of radar transmitting antenna The variance of the target reflection coefficient corresponding to the passive antenna The variance of the target reflection coefficient corresponding to the active radar transmitting antenna ; S72. Calculate the weight matrix based on system parameters. and And establish a preference list for passive antennas. (The preference list is sorted from largest to smallest weight), preference list for radar transmitting antennas. The preferred list of passive antenna seats to be processed A list of preferred locations and power combinations for radar transmitting antennas. ; S73. In the passive section, handle the seat. In its preference list Select the highest-ranked passive antenna that has not been matched. If already matched And in Ranking in Then the updated matching result is Otherwise, maintain the original match; in the active part, workstations In its list Select the highest-ranked and unoccupied antenna-power combination, if that combination is already occupied. Occupied, and Mid-ranking If the power after replacement is less than the predetermined power, then the active radar antenna selection set is updated to... Otherwise, maintain the original match and iterate until the match is complete.
[0012] The beneficial effects of this invention are as follows: The graph theory-based antenna selection and power method proposed in this invention can optimize system performance while considering the processing capability limitations of hybrid active and passive radar systems.
[0013] This invention utilizes the AGS algorithm to efficiently match active and passive antennas and allocate appropriate power to the active antenna, reducing computational complexity and improving feasibility in practical applications. Simulation experiments have verified the effectiveness and advantages of this invention, providing new ideas and methods for the design and optimization of hybrid active and passive radar systems. Attached Figure Description
[0014] Figure 1 In the graph theory-based active-passive hybrid radar antenna selection and power allocation provided as an exemplary embodiment of the present invention, when... ROC plots for different methods; Figure 2 In the graph theory-based active-passive hybrid radar antenna selection and power allocation provided as an exemplary embodiment of the present invention, when... ROC plots for different methods. Detailed Implementation
[0015] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of systems and methods consistent with some aspects of the invention as detailed in the appended claims.
[0016] This invention utilizes graph theory to solve discrete optimization problems, proposing an AGS algorithm based on the Gale-Shapley algorithm. The method includes constructing a received signal model, solving the detection problem, calculating the log-likelihood ratio, determining the optimal detector, and calculating the output signal-to-noise ratio. By matching antenna selection and power allocation using the AGS algorithm, the detection probability can be maximized within limited total transmit power and system processing capabilities.
[0017] This invention provides a graph theory-based method for selecting and allocating active and passive radar antennas, aiming to solve the above-mentioned technical problems in the prior art.
[0018] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of the present invention will now be described with reference to the accompanying drawings.
[0019] A graph theory-based method for selecting and allocating active and passive radar antennas includes the following steps: S1. Establish a hybrid active / passive radar system model. This step includes the following operations: S11. Define binary selection variables and determine their constraints. The binary selection variables include active antenna selection variables, power allocation variables, and passive antenna selection processing variables. In the formula, The variable is used to select the active antenna; a value of 1 indicates that the first antenna is selected. The candidate active antenna is used as the first If it is an active antenna that is working, the value is 0 otherwise. For power allocation variables, a value of 1 indicates the first... The active antenna selected for operation has a transmit power of Otherwise, the value is 0; This is a variable for the selection and processing of passive antennas; a value of 1 indicates the first... The selected antenna processing position is the [number]. One passive antenna; This indicates the total number of active antennas that need to be operational; Indicates the number of candidate active antennas; The transmit power of an active antenna is derived from a finite power set. Selected from ,in It is a set The number of power levels indicates the total number of power levels. Selectable discrete power, power of each active antenna They are all finite sets One of the elements; This indicates the total number of passive antennas processed; This indicates the number of passive antennas to be processed.
[0020] Of the constraints mentioned above, the first three indicate that each candidate radar transmitting antenna will operate at most once, and the required operation time is... Each radar transmitting antenna must be selected from candidate radar transmitting antennas, and the power allocated to the operating radar antennas must be selected from a specified power set, and the sum of the powers of all operating radars cannot exceed a predetermined power value; the latter two constraints indicate that each passive antenna is selected at most once, and the... Each passive antenna being processed originates from at least one passive signal source at the transmitting end.
[0021] Setting up a hybrid active and passive radar system includes Number of candidate active radar transmitters (number of candidate active antennas) N One receiver and Select from 10 candidate passive antennas (passive antennas to be processed). Each radar transmitting antenna operates and its power is allocated, selecting... Passive antenna processing.
[0022] Assuming the target is located , No. Each receiving antenna is in The received signal at time t can be represented as: The first item comes from the active radar antenna, while the second item comes from the passive antenna; In the formula, surface Each receiving antenna is in The received signal at any time, of which Indicates the serial number of the receiving antenna. Number the sampling points. The sampling period; Indicates in Time of the first Noise at each receiving antenna; and Let represent the reflection coefficients of the active antenna path and the passive antenna path, respectively. Assume they are independent, zero-mean complex Gaussian random variables with variances of . and ; and These represent the corresponding time delays; Indicates the first The distance from each receiving antenna to the target location; Indicates the selected number The distance from each active antenna to the target location; Indicates the first The transmitted signal of a single active antenna; Indicates the number to be processed The distance from the passive antenna to the target location; Indicates the first The transmitted signal of the passive antenna being processed; Indicates the first The transmit power of the passive antenna being processed.
[0023] Get the The received signals from the receiving antennas at different times are obtained, and the first... The vector of all received signals from each receiving antenna is represented as: in, In the formula, Indicates the first The vector of all received signals from each receiving antenna; , , Indicates the first Each receiving antenna is in , , The received signal at a given time; the symbol "†" indicates transpose; Indicates the first Noise vector of each receiving antenna; , , They represent the first Each receiving antenna is in , , Momentary noise; Indicates the first Channel vectors of active antennas at each receiving antenna; , , They represent the first The receiving antennas and the 1st, 2nd... Channel gain of each active antenna; Indicates the first Channel vector of the passive antenna at each receiving antenna; , , They represent the first The receiving antennas and the 1st, 2nd... Channel gain of a passive antenna.
[0024] Obtain the vector of all received signals from each receiving antenna. When a target appears, the total received signal vector of the receiving antennas is: in, , dimension ; , dimension ; , All of them follow a zero-mean complex Gaussian distribution and are independent, with covariance matrices of respectively. , .
[0025] The noise vector is represented as ; In the formula, , , They represent the 1st, 2nd, and 3rd respectively. The vector of all received signals from each receiving antenna; This represents the matrix used to collect signals from all active antennas. , , They represent the 1st, 2nd, and 3rd respectively. The receiving antenna contains a matrix of all signals transmitted by the active antennas; This represents the matrix used to collect all passive antenna signals; , , They represent the 1st, 2nd, and 3rd respectively. The receiving antenna contains a matrix of passive antenna signals being processed; Represents the noise vector; , , They represent the 1st, 2nd, and 3rd respectively. The noise vector at the receiving antenna; This represents the active antenna signal vector in operation; , , They represent the 1st, 2nd, and 3rd respectively. Each receiving antenna receives the signal vector from the active antenna. This represents the passive antenna channel vector being processed; , , They represent the 1st, 2nd, and 3rd respectively. The signal vector received by each receiving antenna from the processed passive antenna; express The conjugate transpose of; express The conjugate transpose of; Represents the mathematical expectation; express The covariance matrix; Indicate The covariance matrix.
[0026] S2. Construct the detection problem based on the received signal vector from step one, as follows: in, This indicates that the target does not exist (only noise exists). This indicates that the target exists.
[0027] S3. Substitute the target detection problem into the log-likelihood ratio and transform the log-likelihood ratio expression; in, In the formula, This represents the covariance matrix of the received vector when a target is present. Represents the noise vector covariance matrix; , Indicates intermediate variables; Representing vectors The conjugate transpose of; Representing vectors The conjugate transpose of; Represents the mathematical expectation; express The reverse; Represents a determinant.
[0028] S4. The log-likelihood ratio expression determines the optimal detector based on the NP criterion and derives the detection statistic. Under the NP criterion, the optimal detector is represented as: It is a decision threshold obtained based on the false alarm probability constraint.
[0029] The detection statistic can be defined as Through derivation, the detection statistic can be obtained as follows: In the formula, This represents the variance of the noise. Indicates the first At the receiving antenna, corresponding to the first... The candidate antenna is the first one. The output signal of the matched filter of each working antenna; Indicates the first At the receiving antenna, corresponding to the first... The passive antenna to be processed is the first The output signal of a matched filter that processes passive antennas; Represents the modulus of a complex number.
[0030] S5. Calculate the output signal-to-noise ratio of the active-passive hybrid radar system based on the detection statistics, as a substitute indicator for the detection probability; The output signal-to-noise ratio of the detector is expressed as: In the formula, This represents the mathematical expectation of the detection statistic given the existence of the target. denoted as the mathematical expectation of the detection statistic when the target does not exist.
[0031] S6. Establish a graph theory model of the active-passive hybrid radar system based on the detection probability; The active radar component models the candidate radar transmitting antennas and selectable power together as a set of vertices in a bipartite graph, and the active radar transmitting antennas as the other set of vertices in the bipartite graph, thus constructing a complete bipartite graph. ,in , represents the joint set of candidate active radar antennas and their power, with a set size of . , , represents the set of vertices of the working radar transmitting antennas, with a set size of . The edge set representation of the active radar part is as follows: The elements of each edge in the edge set come from the set. ,gather This maps the relationship between the selection of radar transmitting antennas and power allocation, with the weight of each edge defined as follows: .
[0032] The passive radar section treats the passive antenna and the passive antenna processing seat as two vertices of a bipartite graph, constructing a complete bipartite graph of the passive antenna. . , represents the set of passive antenna processing positions, with a set size of . ,in , represents the set of passive antenna vertices, with a set size of . Edge set This ensures that for each set in the edge set, two vertices come from a set of passive antennas. A collection of seats capable of handling passive antennas This maps the relationship between the total passive antenna and the passive antenna processing seats, with the weight of each edge defined as follows: .
[0033] S7. Based on the graph theory model, an active antenna selection, power allocation, and passive antenna selection algorithm is proposed, namely, the Gale-Shapley (GS) joint optimization algorithm (AGS algorithm). Under the limited total transmit power and system processing capability, the detection probability is maximized, reasonable active and passive antennas are selected to work, and reasonable power is allocated to the active antennas.
[0034] First, set the initial parameters: number of receive antennas. and distance Number of passive antennas Number of radar transmitting antennas Passive antenna position Location of active radar transmitting antenna Passive antenna transmit power Selectable power of radar transmitting antenna The variance of the target reflection coefficient corresponding to the passive antenna The variance of the target reflection coefficient corresponding to the active radar transmitting antenna Calculate the weight matrix based on system parameters. and And establish a preference list for passive antennas. Radar transmitting antenna preference list The preferred list of passive antenna seats to be processed A list of preferred locations and power combinations for radar transmitting antennas. In the passive part, the handling seat. In its preference list Select the highest-ranked passive antenna that has not been matched. If already matched And in Ranking in Then the updated matching result is Otherwise, maintain the original match. In the active part, workstations In its list Select the highest-ranked and unoccupied antenna-power combination, if that combination is already occupied. Occupied, and in Mid-ranking And the power after replacement is less than the predetermined power, update the active radar antenna selection set to... Otherwise, maintain the original match and iterate until the match is complete.
[0035] To verify the accuracy of the method of the present invention, this embodiment employs multiple methods to select the active-passive hybrid radar antenna and allocate power, and the results are shown in [the table below]. Figure 1 , Figure 2 See Table 1.
[0036] like Figure 1 As shown when ROC plots for different methods are shown. The results show that the proposed algorithm almost matches the performance of the exhaustive search method and outperforms the greedy algorithm and the random algorithm, thus confirming the effectiveness of the proposed algorithm.
[0037] like Figure 2 As shown when ROC plots for different methods are shown. The results show that the exhaustive method has too high computational complexity and cannot run effectively. The proposed algorithm still outperforms the greedy algorithm and the random algorithm.
[0038] Table 1 shows a comparison of the complexity differences between the exhaustive search method, the AGS algorithm, and the PGS algorithm. The results indicate that the computational complexity of the exhaustive search method increases dramatically with the number of antennas, resulting in longer computer runtimes, while the proposed algorithm significantly reduces runtime. Therefore, it can be concluded that the algorithm's complexity is significantly lower than that of the exhaustive search method.
[0039] Table 1. Differences in complexity between exhaustive search, AGS algorithm, and PGS algorithm. In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed.
[0040] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.
[0041] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or in the form of hardware plus software functional modules.
[0042] Those skilled in the art will understand that embodiments of the present invention can be provided as methods or apparatus. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects.
[0043] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.
[0044] The above are merely embodiments of the present invention and are not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.
[0045] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the foregoing claims.
[0046] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.
Claims
1. A graph theory-based method for selecting and allocating active and passive hybrid radar antennas, characterized in that, include: S1. Establish a model of a hybrid active and passive radar system; S2. Establish the target detection problem based on the received signal vector in the aforementioned active-passive hybrid radar system model; S3. Substitute the target detection problem into the log-likelihood ratio to transform the log-likelihood ratio expression; S4. The log-likelihood ratio expression is based on the NP criterion to determine the optimal detector and derive the detection statistic. S5. Calculate the output signal-to-noise ratio of the active-passive hybrid radar system based on the detection statistics, as a substitute indicator for the detection probability; S6. Establish a graph theory model of the active-passive hybrid radar system based on the detection probability; S7. Based on the graph theory model, an active antenna selection, power allocation, and passive antenna selection algorithm are proposed. Under the limited total transmit power and system processing capability, the detection probability is maximized, reasonable active and passive antennas are selected to work, and reasonable power is allocated to the active antennas.
2. The power allocation and antenna selection method for hybrid active / passive radar target detection according to claim 1, characterized in that, Step S1 includes: S11, Setting up a hybrid active / passive radar system includes One candidate active antenna, N One receiver and Select from 10 candidate passive antennas. Each radar transmitting antenna operates and its power is allocated, selecting... Passive antenna processing; define binary selection variables and determine their constraints, the binary selection variables including active antenna selection variables, power allocation variables and passive antenna selection processing variables; In the formula, The variable is used to select the active antenna; a value of 1 indicates that the first antenna is selected. The candidate active antenna is used as the first If it is an active antenna that is working, the value is 0 otherwise. For power allocation variables, a value of 1 indicates the first... The active antenna selected for operation has a transmit power of Otherwise, the value is 0; This is a variable for the selection and processing of passive antennas; a value of 1 indicates the first... The selected antenna processing position is the [number]. One passive antenna; This indicates the total number of active antennas that need to be operational; Indicates the number of candidate active antennas; The transmit power of an active antenna is derived from a finite power set. Selected from ,in It is a set The number of power levels indicates the total number of power levels. Selectable discrete power, power of each active antenna They are all finite sets One of the elements; This indicates the total number of passive antennas processed; Indicates the number of passive antennas to be processed; S12, Assuming the target is located at , obtain the Each receiving antenna (receiver) in The received signal at time t is represented as: In the formula, surface Each receiving antenna is in The received signal at any time, of which Indicates the serial number of the receiving antenna. Number the sampling points. The sampling period; Indicates in Time of the first Noise at each receiving antenna; and Let represent the reflection coefficients of the active antenna path and the passive antenna path, respectively. Assume they are independent, zero-mean complex Gaussian random variables with variances of . and ; and These represent the corresponding time delays; Indicates the first The distance from each receiving antenna to the target location; Indicates the selected number The distance from each active antenna to the target location; Indicates the first The transmitted signal of a single active antenna; Indicates the number to be processed The distance from the passive antenna to the target location; Indicates the first The transmitted signal of the passive antenna being processed; Indicates the first The transmit power of the passive antenna being processed; S13. Repeat step S12 to obtain the... The received signals from the receiving antenna at different times are obtained to obtain the first... The vector of all received signals from each receiving antenna is represented as: in, In the formula, Indicates the first The vector of all received signals from each receiving antenna; , , Indicates the first Each receiving antenna is in , , The received signal at a given time; the symbol "†" indicates transpose; Indicates the first Noise vector of each receiving antenna; , , They represent the first Each receiving antenna is in , , Momentary noise; Indicates the first Channel vectors of active antennas at each receiving antenna; , , They represent the first The receiving antennas and the 1st, 2nd... Channel gain of each active antenna; Indicates the first Channel vector of the passive antenna at each receiving antenna; , , They represent the first The receiving antennas and the 1st, 2nd... Channel gain of a passive antenna; S14. Repeat step S13 to obtain the vector of all received signals from each receiving antenna, represented as: in, , dimension , dimension ， In the formula, , , They represent the 1st, 2nd, and 3rd respectively. The vector of all received signals from each receiving antenna; This represents the matrix used to collect signals from all active antennas. , , They represent the 1st, 2nd, and 3rd respectively. The receiving antenna contains a matrix of all signals transmitted by the active antennas; This represents the matrix used to collect all passive antenna signals; , , They represent the 1st, 2nd, and 3rd respectively. The receiving antenna contains a matrix of passive antenna signals being processed; Represents the noise vector; , , They represent the 1st, 2nd, and 3rd respectively. The noise vector at the receiving antenna; This represents the active antenna signal vector in operation; , , They represent the 1st, 2nd, and 3rd respectively. Each receiving antenna receives the signal vector from the active antenna. This represents the passive antenna channel vector being processed; , , They represent the 1st, 2nd, and 3rd respectively. The signal vector received by each receiving antenna from the processed passive antenna; express The conjugate transpose of; express The conjugate transpose of; Represents the mathematical expectation; express The covariance matrix; Indicate The covariance matrix; , They all follow a zero-mean complex Gaussian distribution and are independent of each other; , , As an intermediate variable, its general formula for calculation is: , , As an intermediate variable, its general formula for calculation is: 。 3. The graph theory-based active / passive hybrid radar antenna selection and power allocation method according to claim 2, characterized in that, In step S2, the target detection problem is: in, This indicates that the target does not exist; This indicates that the target exists.
4. The graph theory-based active / passive hybrid radar antenna selection and power allocation method according to claim 3, characterized in that, In step S3, the log-likelihood ratio is expressed as follows: in, In the formula, This represents the covariance matrix of the received vector when a target is present. Represents the noise vector covariance matrix; , Indicates intermediate variables; Representing vectors The conjugate transpose of; Representing vectors The conjugate transpose of; Represents the mathematical expectation; express The reverse; Represents a determinant.
5. The graph theory-based active / passive hybrid radar antenna selection and power allocation method according to claim 4, characterized in that, Step S4 includes the following operations: Under the NP criterion, the optimal detector is expressed as follows: This represents the decision threshold obtained based on the false alarm probability constraint; The detection statistic is defined as follows: The expression for the detection statistic is derived as follows: In the formula, This represents the variance of the noise. Indicates the first At the receiving antenna, corresponding to the first... The candidate antenna is the first one. The output signal of the matched filter of each working antenna; Indicates the first At the receiving antenna, corresponding to the first... The passive antenna to be processed is the first The output signal of a matched filter that processes passive antennas; Represents the modulus of a complex number.
6. The graph theory-based active / passive hybrid radar antenna selection and power allocation method according to claim 5, characterized in that, In step S5, the expression for the output signal-to-noise ratio of the detector is as follows: In the formula, This represents the mathematical expectation of the detection statistic given the existence of the target. This represents the mathematical expectation of the detection statistic when the target does not exist. , All are intermediate variables.
7. The graph theory-based active / passive hybrid radar antenna selection and power allocation method according to claim 6, characterized in that, Step S6 includes: S61. The active radar section models the candidate radar transmitting antennas and selectable power together as a set of vertices in a bipartite graph, and models the active radar transmitting antennas as the other set of vertices in the bipartite graph, thus constructing a complete bipartite graph. ,in Let represent the joint vertex set of candidate active radar antennas and their power, with a set size of . , , , and Both are vertex sets The vertices in the diagram represent the power levels of the first candidate active radar antenna as 1, 2, and 3 respectively. , No. The power levels of the candidate active radar antennas are ; , represents the set of vertices of the working radar transmitting antennas, with a set size of . , , , They represent the 1st, 2nd, and 3rd respectively. A working radar transmitting antenna; the edge set of the active radar part is represented as The elements of each edge in the edge set , They come from the set ,gather This maps the relationship between the selection of radar transmitting antennas and power allocation, with the weight of each edge defined as follows: ; S62. The passive radar section treats the passive antenna and the passive antenna processing seat as two vertices of a bipartite graph, constructing a complete bipartite graph of the passive antenna. ; , represents the set of passive antenna processing positions, with a set size of . , Indicates the first to the second One passive antenna processing seat; , represents the set of passive antenna vertices, with a set size of . , Indicates the first to the second One passive antenna to be processed; edge set This ensures that each edge in the edge set has two vertices that come from a set of passive antennas. A collection of seats capable of handling passive antennas This maps the relationship between the total passive antenna and the passive antenna processing seats, with the weight of each edge defined as follows: .
8. The power allocation and antenna selection method for hybrid active / passive radar target detection according to claim 7, characterized in that, Step S7 is as follows: S71. Set initial parameters: Number of receive antennas and distance Number of passive antennas Number of radar transmitting antennas Passive antenna position Location of active radar transmitting antenna Passive antenna transmit power Selectable power of radar transmitting antenna The variance of the target reflection coefficient corresponding to the passive antenna The variance of the target reflection coefficient corresponding to the active radar transmitting antenna ; S72. Calculate the weight matrix based on system parameters. and And establish a preference list for passive antennas. Radar transmitting antenna preference list The preferred list of passive antenna seats to be processed A list of preferred locations and power combinations for radar transmitting antennas. ; S73. In the passive section, handle the seat. In its preference list Select the highest-ranked passive antenna that has not been matched. If already matched And in Ranking in Then the updated matching result is Otherwise, maintain the original match; in the active part, workstations In its list Select the highest-ranked and unoccupied antenna-power combination, if that combination is already occupied. Occupied, and Mid-ranking If the power after replacement is less than the predetermined power, then the active radar antenna selection set is updated to... Otherwise, maintain the original match and iterate until the match is complete.