Power distribution and antenna selection method for active and passive hybrid radar target detection
The game theory design algorithm optimizes the power distribution and antenna selection of active and passive hybrid radar systems, which solves the problem of poor adaptability of traditional methods in the actual environment, and achieves optimization of system performance and reduction of computational complexity.
Patent Information
- Application Number
- CN202510151062.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-11
- Publication Date
- 2025-07-04
AI Technical Summary
Traditional active passive hybrid radar systems have difficulty in adapting to discrete power levels and limited processing capabilities in actual environments, resulting in difficulty in optimizing system performance.
Using game theory design algorithm, by constructing a received signal model, detecting problems, log-likelihood ratio and optimal detector, defining the utility functions of power distribution and antenna selection, and propose an iterative power distribution and antenna selection algorithm to optimize the detection probability.
With the limited total transmit power and system processing capacity, the optimization of system performance is achieved, reducing the computational complexity and improving the feasibility of practical applications.
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Figure CN120254794A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of radar technology, and particularly to a power allocation and antenna selection method for active and passive hybrid radar target detection. This technology is funded by the National Natural Science Foundation of China (NO. 62301465). Background Art
[0002] In a hybrid active and passive (HAP) radar system, the maximum number of IOs that the system can handle is usually limited, while there are a large number of available IOs in the environment. Therefore, in order to optimize the system performance while considering the system processing capacity limitations, it is crucial to reasonably select the IOs. At the same time, in the active part, the power allocation of the transmitter is also a key issue. However, traditional power allocation and antenna selection methods often rely on ideal scenarios and are difficult to adapt to the discrete power levels and limited processing capabilities in the actual environment.
[0003] Gao Yi et al. (Gao Yi, Li Honghua, "Joint transmit and receive beamforming for hybrid active cpassive radar", Journal of Signal Processing, Vol. 24, No. 1.6, pp. 779-783, 2017.) considered joint transmit and receive beamforming to maximize the signal-to-noise ratio of HAP radar. Wang Feng et al. (Wang Feng, Li Hui, "Joint waveform and receiver design for co-channel hybrid active-passive sensing with timing uncertainty", Journal of Signal Processing, vol. 68, pp. 466-477, 2020.) considered the joint optimization design of the HAP radar receive filter and the radar waveform, and considered timing uncertainty. Zhang Wei et al. (Zhang Wei, Shi Zhenhua, Zhou Jianjun, Yan Jianjun, "Convex optimization-based power allocation strategies for target localization in distributed hybrid non-coherent active-passive radar networks", Journal of Signal Processing, vol. 70, pp. 2476-2488, 2022.) proposed a power allocation technique for target localization in a distributed HAP radar network to optimize the power allocation of each active radar under power constraints. Dai Jianjun et al. (Dai Jianjun, Lv Jianjun, Dai Jianjun, Pu Wei, Liu Honghua, "Composed resource optimization for multitarget tracking in active and passive radar network", IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022.) considered the resource optimization strategy of the HAP radar network for multitarget tracking, which can optimize the receive beams of the primary and passive radars and the transmit power of the active radar. In practice, the power level is usually preferably a series of discrete values. Game theory is a suitable tool for many studies to solve discrete optimization problems. Therefore, the present invention uses game theory to design an algorithm to obtain the optimal solutions for power allocation and antenna selection. Summary of the Invention
[0004] The present invention provides a method for power allocation and antenna selection for target detection of a hybrid active-passive radar, which solves the problem of poor adaptability of traditional methods in actual environments.
[0005] In a first aspect, the present invention provides a method for power allocation and antenna selection in active and passive hybrid radar target detection, comprising the following steps:
[0006] Step 1: Assume that there is a target at times (x, y). The received signal of the nth radar receiver at times k, T, and s can be expressed as
[0007]
[0008] The first term is from the active radar transmitter, while the second term is from the input / output (IOs). and denotes the target reflection coefficient associated with the nm R th active and the nm R th passive propagation paths, and are the corresponding time delays, and represent the corresponding Doppler frequencies, w n [k] represents clutter plus noise, assumed to follow a white complex Gaussian distribution with zero mean and where denotes the mathematical expectation. is the selection variable for the I / O port.
[0009] The received signal vector is
[0010]
[0011] where the superscript + denotes the transpose,
[0012]
[0013] where
[0014] The overall received signal in the presence of a target is written as
[0015]
[0016] where
[0017] Step 2: Based on the received signal vector in Step 1, a detection problem can be constructed
[0018]
[0019] where H0 represents the absence of a target, and H1 represents the current target. And and CN(μ, C) represents a complex Gaussian distribution characterized by the mean vector μ and the covariance matrix C. Step 3: Substitute H0 and H1 from the detection problem in Step 2 into the log-likelihood ratio
[0020]
[0021] Among them, f(r|H1) and f(r|H0) respectively represent the probability density functions of the observation vector r under the two hypotheses.
[0022] Step 4: Calculate the optimal detector using the NP criterion
[0023]
[0024] η represents the detection threshold determined by the expected false alarm probability.
[0025] Step 5: Calculate the detection probability of the radar target using the optimal detector in Step 4.
[0026]
[0027] Among them The false alarm probability can also be obtained from Step 4 as Among them, η is the detection threshold, η = σQ -1 (P FA ). Among them is the complementary distribution function of the standard Gaussian distribution, expressed as
[0028] Step 6: Define
[0029]
[0030] Since S can be expressed as And So
[0031] Step 7: Since P D is determined by S in Step 6, the problem of maximizing the detection probability of joint discrete power allocation and antenna selection in the HAP radar network can be constructed. Under the condition that the total transmission power of the active radar and the number of IOs that the system can handle are limited, the problem of maximizing the detection probability of joint discrete power allocation and antenna selection in the HAP radar network can be described as:
[0032]
[0033] Among them, C1 represents the total power constraint of the active radar, P total represents the total available power, C2 represents the maximum number of IOs that the system can handle, and M total represents the total available number of IOs.
[0034] Step 8: Since all active and passive radar transmitters need to be considered and have the same utility, and transmitters are not allowed to use transmission strategy profiles that violate C1 or C2. Since it is difficult to determine in advance which strategy profiles are infeasible and which are feasible for all transmitters, S in Step 6 cannot be used as the utility function, and a common utility function for transmitters needs to be defined.
[0035]
[0036] where β P and β M are non - negative scalars, and the penalty function The second term of the common utility function represents the total power constraint for active transmission, while the third term represents the constraint on the maximum number of IOs for passive transmission.
[0037] Step 9: The joint power allocation and antenna selection of the HAP radar are defined as a discrete game.
[0038]
[0039] Step 10: A joint power allocation and antenna selection algorithm is proposed: the iterative power allocation and antenna selection algorithm.
[0040] First, set the initial parameters. Let the iteration number t = 0. In the repeated iteration, i ranges from 1 to M R +M O , and after each iteration, t = t + 1. First, substitute the first to calculate the power u0, and update to Substitute to calculate the power u1, and compare it with u0. If u1 is greater than u0, it means that u0 is not the maximum power, and the iteration continues until the i - th one is substituted, and there is which means the maximum power u i is obtained, and the iteration is exited, and the value of is returned.
[0041] The beneficial effects of the present invention are as follows:
[0042] The proposed game - theory - based joint power allocation and antenna selection method can optimize the system performance considering the complexity and processing - capacity limitations of the HAP MIMO radar system.
[0043] Through the IPAASA algorithm, the pure - strategy Nash equilibrium of the game can be efficiently found, reducing the computational complexity and improving the feasibility in practical applications. The simulation experiments verify the effectiveness and advantages of the present invention, providing new ideas and methods for the design and optimization of the HAP MIMO radar system. Brief Description of the Drawings
[0044] The accompanying drawings described herein are used to provide a further understanding of the embodiments of the present invention, and constitute a part of the present invention, but do not limit the embodiments of the present invention. In the drawings:
[0045] Figure 1 For the power allocation and antenna selection method of active-passive hybrid radar target detection provided by an exemplary embodiment of the present invention, P D Compare the SCNR, Uniform, IPAASA of RSA and the ESA of the HAP MIMO radar using C1 and P total = 4 Watts, where M R = 6.
[0046] Figure 2 For the power allocation and antenna selection method of active-passive hybrid radar target detection provided by an exemplary embodiment of the present invention, P D Compare the SCNR, ESA of RSA and the IPAASA of the HAP MIMO radar using C1, C2, M R = 4, M O = 6, P total = 3 Watts, M total = 5.
[0047] Figure 3 For the power allocation and antenna selection method of active-passive hybrid radar target detection provided by an exemplary embodiment of the present invention, at M R and M total The convergence speed graph of IPAASA with different numbers of C1, C2, and SCNR = -3dB. Detailed implementation mode
[0048] Here, the exemplary embodiments will be described in detail, and the examples are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. On the contrary, they are merely examples of systems and methods consistent with some aspects of the present invention as detailed in the appended claims.
[0049] The present invention utilizes game theory, especially potential games, to optimize discrete optimization problems, and proposes an iterative algorithm to obtain a pure strategy Nash equilibrium. The method includes constructing a received signal model, a detection problem, a log-likelihood ratio, an optimal detector, a detection probability calculation, and defines a utility function for power allocation and antenna selection. By iteratively power allocation and antenna selection algorithms, the maximization of the detection probability can be achieved under limited total transmit power and system processing capabilities.
[0050] A method for power allocation and antenna selection in active-passive hybrid radar target detection provided by the present invention aims to solve the above technical problems in the prior art.
[0051] The following uses specific embodiments to elaborate in detail on the technical solution of the present invention and how the technical solution of the present invention solves the above technical problems. These several specific embodiments can be combined with each other, and for the same or similar concepts or processes, they may not be repeated in some embodiments. The embodiments of the present invention will be described below with reference to the accompanying drawings.
[0052] The embodiments provided by the present invention are as follows:
[0053] In the method for power allocation and antenna selection in active-passive hybrid radar target detection, Step 1: Assume that there is a target at time (x, y). The received signal of the nth radar receiver at times k, T, and s can be expressed as
[0054]
[0055] The first term is from the active radar transmitter, and the second term is from the input / output (IOs). and represent the target reflection coefficients related to the nmth R active and the nmth R passive propagation paths, and are the corresponding time delays, and represent the corresponding Doppler frequencies, w n [k] represents clutter plus noise, which is assumed to follow a white complex Gaussian distribution with zero mean and , where represents the mathematical expectation. is the selection variable for the I / O port.
[0056] The received signal vector is
[0057]
[0058] where the superscript + represents the transpose, and
[0059] where
[0060] The overall received signal when the target is present is written as
[0061]
[0062] where
[0063] Step 2: Based on the received signal vector in Step 1, a detection problem can be constructed.
[0064]
[0065] Where H0 represents the target absence, and H1 represents the current target. And And CN(μ, C) represents a complex Gaussian distribution characterized by the mean vector μ and the covariance matrix C.
[0066] Step 3: Substitute H0 and H1 in the detection problem of Step 2 into the log-likelihood ratio
[0067]
[0068] Where f(r|H1) and f(r|H0) respectively represent the probability density functions of the observation vector r under the two hypotheses.
[0069] Step 4: Use the NP criterion to calculate the optimal detector
[0070]
[0071] η represents the detection threshold determined by the expected false alarm probability.
[0072] Step 5: Use the optimal detector in Step 4 to calculate the detection probability of the radar target.
[0073]
[0074] Where The false alarm probability can also be obtained from Step 4 as
[0075] Where η is the detection threshold, η = σQ -1 (P FA ). Where Is the complementary distribution function of the standard Gaussian distribution, expressed as
[0076] Step 6: Define
[0077]
[0078] Since S can be expressed as
[0079] And So
[0080] Step 7: Since P DDetermined by S in step 6, the detection probability maximization problem of joint discrete power allocation and antenna selection in the HAP radar network can be constructed. In the case of limited total transmission power of active radars and the number of IOs that the system can handle, the detection probability maximization problem of joint discrete power allocation and antenna selection in the HAP radar network can be described as:
[0081]
[0082] where C1 represents the total power constraint of active radars, P total represents the total available power, C2 represents the maximum number of IOs that the system can handle, and M total represents the total available number of IOs.
[0083] Step 8: Define the common utility function of the transmitter
[0084]
[0085] where β P and β M are non - negative scalars, and the penalty function The second term of the common utility function represents the total power constraint of active transmission, while the third term represents the constraint on the maximum number of IOs for passive transmission.
[0086] Step 10: Joint power allocation and antenna selection algorithm: Iterative power allocation and antenna selection algorithm
[0087] First, set the initial parameters Let the iteration number t = 0. In the repeated iteration, i ranges from 1 to M R +M O , and after each iteration, t = t + 1. First, substitute the first to calculate the power u0, and update to Substitute to calculate the power u1, and compare it with u0. If it is greater than u0, it means that u0 is not the maximum power, and the iteration continues until the i - th one is substituted, and there is indicating that the maximum power u i is obtained, exit the iteration, and return the value of.
[0088] As Figure 1 shown, P D compares the SCNR, Uniform, IPAASA of RSA and the ESA of the HAP MIMO radar using C1 and P total = 4 Watts; it can be seen from the figure that the proposed IPAASA algorithm is significantly better than the RSA and Uniform algorithms. The target detection performance of the IPAASA algorithm is comparable to that of the ESA.
[0089] AsFigure 2 As shown, P D Comparing the SCNR of RSA, ESA, and using C1, C2, M R = 4, M O = 6, P total = 3 Watts, M total = 5 for the IPAASA of the HAP MIMO radar; The results show that the proposed algorithm almost matches the performance of ESA, thus verifying the effectiveness of condition (ii) in Theorem 3
[0090] As Figure 3 shown, in M R and M total Convergence speed graphs of IPAASA with different numbers of C1, C2, and SCNR = -3 dB. The results show that IPAASA shows a very fast convergence speed in the simulation, requiring less than 4 iterations. In addition, the convergence speed of IPAASA remains unaffected by the number of active radar transmitters and IOs. It can be concluded that compared with the exhaustive search method, the complexity of the algorithm is significantly reduced
[0091] In several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the module is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed.
[0092] The module described as a separate component may or may not be physically separated. The component shown as a module may or may not be a physical module, that is, it may be located in one place, or it may be distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0093] In addition, in each embodiment of the present invention, the functional modules can be integrated in one processing module, or each module can exist physically alone, or two or more modules can be integrated in one module. The above integrated module can be implemented in the form of hardware, or in the form of a hardware plus software functional module.
[0094] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method or a device. Therefore, the present invention can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects.
[0095] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent in such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, commodity or device comprising the element.
[0096] The above are only embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, various modifications and changes can be made to the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the scope of the claims of the present invention.
[0097] Those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common general knowledge or conventional technical means in the technical field not disclosed by the present invention. The specification and examples are only illustrative, and the true scope and spirit of the present invention are pointed out by the claims above.
[0098] It should be understood that the present invention is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited by the appended claims.
Claims
1. A method for power allocation and antenna selection in active and passive hybrid radar target detection, characterized in that Including: Step 1: Establish an HAP MIMO radar system model; Step 2: Establish a target detection problem based on the received signal vector in the radar system model; Step 3: Substitute the detection problem in Step 2 into the log-likelihood ratio; Step 4: Then use the NP criterion to determine the optimal detector; Step 5: Calculate the detection probability of the radar target according to the optimal detector; Step 6: Define the utility function S in the detection probability of joint discrete power allocation and antenna selection in the HAP radar network; Step 7: Construct a game theory framework, formulate the joint optimization problem of power allocation and antenna selection as a game problem, define each transmitter as a participant in the game, and describe the problem of maximizing the detection probability of joint discrete power allocation and antenna selection in the HAP radar network; Step 8: Define the common utility function of the transmitter according to S in Step 6; Step 9: Define the joint power allocation and antenna selection of the HAP radar as a discrete game; Step 10: Propose a joint power allocation and antenna selection algorithm, that is, design an iterative power allocation and antenna selection algorithm IPAASA, and maximize the detection probability under the limited total transmission power and system processing capacity, and solve the iterative power allocation and antenna selection algorithm.
2. The power allocation and antenna selection method for active and passive hybrid radar target detection according to claim 1, characterized in that The specific content of Step 1 includes: When there is a target at (x, y), the received signal of the nth radar receiver at times k, T, and s can be expressed as The first term originates from the active radar transmitter, while the second term originates from the input / output, including M R active radar transmitters, N receivers, and M O opportunistic illumination sources; and represent the target reflection coefficients associated with the nm R th active and the nm R th passive propagation paths, and are the corresponding time delays, and represent the corresponding Doppler frequencies, w n [k] represents the clutter plus noise, assumed to be a white complex Gaussian distribution with zero mean and where denotes the mathematical expectation, is the selection variable for the I / O port; The received signal vector is where the superscript + represents the transpose, Among them The overall received signal when the target exists is written as Among them 3. The power allocation and antenna selection method for active and passive hybrid radar target detection according to claim 2, characterized in that, In Step 2, the specific content of establishing a target detection problem based on the received signal vector in the radar system model includes: The received signal vector in Step 1 can construct a detection problem; H0: r = w, H1: r = μ R + μ O + w, where H0 represents the target absence, H1 represents for the current target, and and CN(μ,C) represents a complex Gaussian distribution characterized by the mean vector μ and the covariance matrix C.
4. The power allocation and antenna selection method for active and passive hybrid radar target detection according to claim 3, characterized in that, The specific content of Step 3: Substitute the detection problem in Step 2 into the log-likelihood ratio, includes: Substitute H0 and H1 in the detection problem in Step 2 into the log-likelihood ratio; where f(r|H1) and f(r|H0) respectively represent the probability density functions of the observation vector r under the two hypotheses.
5. The power allocation and antenna selection method for active and passive hybrid radar target detection according to claim 4, characterized in that The specific content of Step 4: Then use the NP criterion to determine the optimal detector, includes: Calculate the optimal detector using the NP criterion: η represents the detection threshold determined by the expected false alarm probability.
6. The power allocation and antenna selection method for active and passive hybrid radar target detection according to claim 5, characterized in that, The specific content of Step 5: Calculate the detection probability of the radar target according to the optimal detector, includes: The formula for calculating the detection probability of the radar target by the optimal detector is as follows: Among them The false alarm probability is where η is the detection threshold, η = σQ -1 (P FA ) Denoted as is the complementary distribution function of the standard Gaussian distribution.
7. The power allocation and antenna selection method for active and passive hybrid radar target detection according to claim 6, characterized in that, The specific content of Step 6 and Step 7 is: Define Since S can be expressed as and therefore P D Determined by S, in the case of limited total transmitted power of the active radar and the number of io that the system can handle, a detection probability maximization problem of joint discrete power allocation and antenna selection in the HAP radar network is constructed. The detection probability maximization problem of joint discrete power allocation and antenna selection in the HAP radar network can be described as follows: Among them, C1 represents the total power constraint of the active radar, and P total represents the total available power, C2 represents the maximum number of I / Os that the system can handle, and M total represents the total available number of I / Os.
8. The power allocation and antenna selection method for active and passive hybrid radar target detection according to claim 7, characterized in that The specific content of Step 8 and Step 9 is: Define the common utility function of the transmitter: where β P and β M are non - negative scalars, and the penalty function The second term of the common utility function represents the total power constraint of active transmission, and the third term represents the constraint on the maximum number of IOs of passive transmission; Definition of joint power allocation and antenna selection of the HAP radar: The above formula is a discrete game.
9. The power allocation and antenna selection method for active and passive hybrid radar target detection according to claim 8, characterized in that The specific content of Step 10 is: First, set the initial parameters Let the number of iterations \(t = 0\). In the repeated iteration, \(i\) ranges from 1 to \(M\) R +M O , and after each iteration, \(t=t + 1\). First, substitute the first Calculate the power \(u_0\) and update to Substitute to calculate the power \(u_1\) and compare it with \(u_0\). If \(u_1>u_0\), it means that \(u_0\) is not the maximum power, then continue the iteration until substituting the \(i\)-th one, and there is indicating that the maximum power \(u\) is obtained i , exit the iteration, and return the value of.