A Distributed MIMO Radar Resource Allocation Method for Integrated Reconnaissance, Interference and Communication
By constructing an integrated reconnaissance, interception, and communication countermeasure scenario in a distributed MIMO radar system, and using SPEB, detection probability, and communication capacity as evaluation indicators, combined with mixed integer second-order cone programming, the problem of unreasonable resource allocation in existing technologies is solved, multi-task collaborative adaptation and efficient utilization are achieved, and the adaptability of the radar system to complex battlefields is improved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HUAIBEI NORMAL UNIVERSITY
- Filing Date
- 2026-01-27
- Publication Date
- 2026-06-02
AI Technical Summary
In distributed MIMO radar systems, existing technologies lack resource allocation strategies that integrate reconnaissance, jamming, and communication, resulting in incomplete functional coverage, a single optimization dimension, insufficient multi-task coupling and cracking capabilities, and difficulty in meeting performance requirements in complex battlefield environments.
A distributed MIMO radar countermeasure scenario integrating reconnaissance, interception, and communication is constructed. The lower bound of position estimation error (SPEB), the missile's detection probability of the target, and the inter-node communication capacity are used as evaluation indicators. By minimizing the total system transmit power, the node selection and power allocation are jointly optimized through a mixed integer second-order cone programming problem. The McCormick envelope method is used for convex transformation and the solution is obtained in the CVX solver of MATLAB.
It enables multi-task collaborative adaptation in complex battlefield environments, reduces computational complexity, ensures that the performance of each function meets the standards, improves the flexibility and adaptability of the radar system, avoids repeated iterative calculations of nodes and tasks, and improves resource utilization.
Smart Images
Figure CN122131236A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the technical field of radar resource allocation, and specifically to a distributed MIMO resource allocation method for integrated reconnaissance, radar, and communications. Background Technology
[0002] As modern warfare enters the information age, radar systems face more complex battlefield environments and higher performance requirements. Traditional monostatic radars, due to their limited observation angles and detectable space, struggle to meet battlefield demands. Distributed multiple-input multiple-output (MIMO) radars, through the collaborative work of multiple nodes, can achieve stronger diversity gain and wider coverage in the spatial dimension, thus exhibiting significant advantages in parameter estimation accuracy and spatial resolution.
[0003] On the other hand, single detection or jamming functions are no longer sufficient to meet the demands of modern complex battlefield environments. Coupled with the system's own communication requirements, there is an urgent need to integrate reconnaissance, jamming, and communication into a unified design to enhance the radar system's flexibility and adaptability. In practical applications, resource competition often exists among reconnaissance, jamming, and communication tasks, involving node allocation, beam pointing, and power distribution. Typically, higher power is allocated to nodes with a greater impact on operational effectiveness, while lower power is allocated to nodes with less impact, or they are excluded from allocation, thereby improving resource utilization and extending the operational cycle. Furthermore, directly activating all nodes increases the risk of system exposure, while a reasonable node allocation strategy can maintain good concealment while reducing resource consumption. Therefore, how to effectively allocate resources is a key issue for integrated combat systems.
[0004] In response, researchers have proposed various resource allocation strategies. Some existing strategies only target radar perception performance, such as target detection, target identification, and target localization; others only target jamming performance; and still others combine perception and communication performance. There is a lack of research on the integration of radar reconnaissance, jamming, and communication performance—that is, integrated reconnaissance-jamming-communication operations. Furthermore, most studies only consider the optimization of a single resource, lacking research on the joint optimization of node selection and power allocation.
[0005] In summary, under the background of distributed MIMO radar system-based countermeasures, there is an urgent need to propose a joint optimization method for node selection and power allocation oriented towards the integration of reconnaissance, communication, and interoperability. This method aims to address the shortcomings of existing technologies, such as incomplete functional coverage, single optimization dimensions, and insufficient multi-task coupling and cracking capabilities. It will achieve a synergistic unity of multi-task performance assurance and efficient resource utilization, thereby improving the adaptability of radar systems to complex battlefields and their combat effectiveness. Summary of the Invention
[0006] The technical problem to be solved by this invention is how to solve the performance balance problem under multi-task collaboration in distributed MIMO radar air combat scenarios.
[0007] This invention solves the above-mentioned technical problems through the following technical means: a distributed MIMO radar resource allocation method for integrated reconnaissance, interception, and communication, comprising:
[0008] S1. Construct an integrated distributed MIMO radar countermeasure scenario that combines reconnaissance, communication, and surveillance. S2. The lower bound of position estimation error (SPEB), the probability of missile detection of the target, and the inter-node communication capacity are used as evaluation indicators for reconnaissance, jamming, and communication performance, respectively. S3. To minimize the total system transmit power, a model is constructed by combining reconnaissance and communication performance constraints with node task rules. S4. Transform the model into a mixed-integer second-order cone programming problem, and use the McCormick envelope method to transform convex terms into non-convex terms. Then, obtain the numerical solution based on the CVX solver in MATLAB to achieve joint optimization.
[0009] Furthermore, the construction process of the integrated distributed MIMO radar countermeasure scenario described in step S1 is as follows: Party A's early warning system detects J Party B's combat aircraft in the airspace; Party A dispatches L of its own combat aircraft to perform countermeasure missions against Party B; during the countermeasures, Party B launches K missiles; simultaneously, Party A deploys a distributed MIMO radar system on the ground consisting of M transmitters and N receivers; the distributed MIMO radar system, based on the position information of Party B's combat aircraft and prior information on radar cross section (RCS), simultaneously performs the tasks of locating Party B's combat aircraft, jamming Party B's missile launches, and ensuring communication between Party A's various nodes; where J, L, K, M, and N are all positive integers.
[0010] Furthermore, in step S2, the lower bound of the position estimation error (SPEB) is used as an evaluation index for reconnaissance performance, specifically as follows: In the adversarial scenario constructed in step S1, the position information of the j-th fighter jet of the opposing side. The expression for the Fischer matrix is: (1) In the formula, The positioning vector for Party B's j-th fighter jet, whose m-th element is... , This indicates that the m-th radar node is selected to locate the fighter jet j. Assign vectors to node power. This represents the transmit power value of radar node m. ,in, Where c is the bandwidth of the transmitted signal, and c is the speed of light. The noise power spectral density; The expression is: (2) In the formula, Let be the observation angle from the m-th launch node to the j-th fighter jet of Party B. Let be the angle of incidence from the j-th fighter jet of Party B to the n-th receiving node; Let be the power attenuation coefficient of the signal after it is reflected by fighter jet j from the m-th transmitting node to the n-th receiving node, expressed as: (3) In the formula, and Let represent the transmit gain of the m-th node and the receive gain of the n-th node in a distributed MIMO radar, respectively. This represents the radar cross-section (RCS) of fighter jet j (the aircraft of Party B). and Let represent the distances from the m-th transmitting node and the n-th receiving node to fighter jet j, respectively. Indicates the frequency of the radar signal; make , The Fischer matrix is rearranged and expressed as follows: (4) Under high signal-to-noise ratio conditions, the SPEB expression for fighter jet j is: (5) Substituting the rearranged Fischer matrix, we get: (6) In the formula, It is a column vector consisting entirely of 1s. ,and ; ,in, , ,in, , for and The Hadamaji, that is .
[0011] Furthermore, in step S2, the probability of the missile detecting the target is used as an evaluation index for interference performance, specifically as follows: Define an interference selection matrix The element in its m-th row and k-th column is ,like This indicates that the m-th launching node was used to jam the k-th missile, redirecting missile k towards the target. l The detection problem is constructed using a binary hypothesis:
[0012] The detection signals under the two hypotheses are denoted as follows: and Under the Gaussian model, it satisfies , ,in, For missile k to receive data from the target The equivalent signal amplitude is expressed as: ,in, For the target received by missile k The effective echo power is expressed as: (7) In the formula, The transmit power of the radar on missile k. and These represent the transmit and receive gains of the radar on missile k, respectively. Let be the wavelength of the radar signal transmitted on missile k. For the goal Radar cross section (RCS) For missile k and target The distance between them This represents the total noise power received by missile k, which includes its own noise. Suppression and jamming of Party A's radar ,Right now ; Interference from Party A's ground radar The expression is: (8) In the formula, Let m be the transmit gain of the transmitting node. Let be the wavelength of the radar signal transmitted by the m-th transmitting node. Let m be the distance between the m-th launch node and the k-th missile; According to the Neyman-Pearson optimal detection criterion, the thresholds for the likelihood ratios of the two hypotheses are... With significance level That is, the probability of a false alarm. related: (9) In the formula, The complementary cumulative distribution function; In a given In this case, missile k is aimed at the target. l The detection probability is: (10) In the formula, This indicates the detection signal under the H1 hypothesis. The standard deviation.
[0013] Furthermore, in step S2, the inter-node communication capacity is used as a communication performance evaluation index, specifically as follows: The communication quality between nodes is characterized by the communication capacity C, and a minimum communication quality threshold is set as follows: To meet this minimum communication quality requirement, according to the communication capacity formula... Where B is the effective receiving bandwidth. This represents the noise power at the node receiver. This refers to the communication signal power at the node's receiving end; In a distributed MIMO radar, the power transmitted from the communication transmitter m to the communication receiver i satisfy: (11) In the formula, Let i be the receiving gain of the communication receiver. The wavelength of the communication signal. Let m be the distance between the communication transmitter and the communication receiver.
[0014] Furthermore, S3 includes: Define the link from the central communication node to other communication nodes as Link 1, and the link from other communication nodes to non-communication nodes as Link 2. Set the lower limit of the communication capacity for normal communication between Link 1 and Link 2 as follows: and The maximum tolerable positioning error is The maximum allowed detection probability threshold is ; Using the above parameters as constraints on reconnaissance and communication performance, and taking into account the physical properties of distributed MIMO radar, the optimization model is constructed as follows, with minimizing the total transmit power as the objective function: (12) In the formula, This represents the communication node selection vector. This indicates that the transmitting node m has been selected as the communication node. This represents the vector for selecting the central communication node. This indicates that the transmitting node m has been selected as the communication center node. Represents the node communication matching matrix. This indicates communication from transmitting node m to transmitting node i. and These represent the communication capacity requirements of communication link 1 and communication link 2, respectively.
[0015] Furthermore, the transformation process of the reconnaissance constraint in S4 is as follows: Constraints on reconnaissance performance , and Substitute the expression and let ,get Add to both sides at the same time ,get: (14) It can be written in the form of a second-order cone matrix: (15) In the formula, , , It is the reciprocal of the maximum tolerable positioning error.
[0016] Furthermore, the transformation process of the interference constraint in S4 is as follows: For interference performance constraints As defined by the Q function, it is a monotonically decreasing function, which can be rearranged as follows: Substitute , and The expression yields: (16) After rearranging the above formula, we get: (17).
[0017] Furthermore, the transformation process of communication constraints in S4 is as follows: For constraints Expanding equation (11) gives: (18) Similarly, constraints Transform into: (19) Substituting into equation (12), we get: (20) In the formula, Com, Center, and NoCom are the set of communication nodes, the set of communication center nodes, and the set of non-communication nodes, respectively.
[0018] Furthermore, the convex-to-nonconvex transformation is performed using the McCormick envelope method, specifically as follows: make , According to McCormick's envelope method, a convex set is constructed by using four linear boundary constraints to enclose the original non-convex constraint set. The constructed boundary constraints are abbreviated as... and ,Right now: (twenty one) (twenty two) For non-convex terms in communication link constraints Using MATLAB Function definition: Mask, Mask1, Mask2 mask matrix transformation constraint form: (twenty three) (twenty four) (25) The two constraints of the communication link are transformed into: (26) McCormick's envelope method is used to transform the nonconvex terms. Perform a convex transformation, let The envelope constraint is obtained and denoted as : (27) By leveraging the switching properties of binary variables, the constructed linear polyhedron becomes the convex hull of the original non-convex set, and the optimization on this convex hull is equivalent to the optimization on the original discrete set. The final optimized model is as follows: (28) In the formula, For vectors The m-th element, Let b be the input array to be copied, b be the number of copies in the row direction, and d be the number of copies in the column direction.
[0019] The advantages of this invention are: (1) By designing a node task allocation mechanism, this invention clarifies that each node undertakes only one task (reconnaissance, jamming or communication) and is only responsible for one target (enemy aircraft or missile), making the task boundaries of each node clear and non-conflicting, effectively avoiding repeated iterative calculations of nodes and tasks, and significantly reducing the computational complexity of global optimization. On the other hand, this invention uses equivalent substitution and McCormick envelope method to accurately convexize the non-convex terms in the original model, transforming the complex non-convex problem into a convex problem that can be solved efficiently. At the same time, under the CVX framework, it uniformly represents multi-node, multi-target, and multi-task data in a vectorized manner, and relies on MATLAB's CVX solver to achieve fast convergence and solution, completely breaking through the technical barrier that traditional solutions cannot be implemented in engineering due to computational bottlenecks.
[0020] (2) This invention uses the lower bound of position estimation error (SPEB), the missile's detection probability of the target, and the inter-node communication capacity as core indicators to quantify the performance requirements of the three major functions of reconnaissance, jamming, and communication. Under the performance constraints and node task rules, an optimization model that minimizes the total transmission power is established. This model is transformed into a mixed integer second-order cone programming problem to achieve joint optimization, thereby achieving multi-task collaborative adaptation in adversarial scenarios and ensuring that the performance of each function meets the standards. Attached Figure Description
[0021] Figure 1 This is the distributed MIMO radar resource allocation method for integrated reconnaissance, interception, and communication in Embodiment 1 of the present invention; Figure 2 This is a comparison chart of power optimization results under different positioning accuracies in Embodiment 1 of the present invention. Detailed Implementation
[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0023] Example 1 like Figure 1 As shown, the distributed MIMO radar resource allocation method for integrated reconnaissance, communication, and interoperability includes: S1. Construct a distributed MIMO radar countermeasure scenario integrating reconnaissance, communication, and surveillance.
[0024] Specifically, assume that Party A's early warning system detects J Party B's combat aircraft in the airspace; Party A dispatches L of its own combat aircraft to carry out combat missions against Party B; during the combat, Party B launches K missiles; simultaneously, Party A deploys a distributed MIMO radar system on the ground, consisting of M transmitters and N receivers; the distributed MIMO radar system, based on the position information of Party B's combat aircraft and prior information on radar cross-section (RCS), simultaneously performs tasks such as locating Party B's combat aircraft, jamming Party B's missile launches, and ensuring communication between Party A's various nodes; where J, L, K, M, and N are all positive integers.
[0025] S2. The lower bound of position estimation error, the probability of missile detection of the target, and the inter-node communication capacity are used as evaluation indicators for reconnaissance, jamming, and communication performance, respectively.
[0026] Specifically, the lower bound of position estimation error (SPEB), the probability of missile detection of the target, and the communication capacity between nodes are used as evaluation indicators for reconnaissance, jamming, and communication performance, respectively. Based on the aforementioned adversarial scenarios, an evaluation system is established for the quality of performance of these three tasks by a distributed MIMO radar integrating reconnaissance, jamming, and communication. The specific implementation method is as follows: In the scenario assumed in step S1, the position information of Party B's j-th fighter jet. The expression for the Fischer matrix is: (1) In the formula, The positioning vector for Party B's j-th fighter jet, whose m-th element is... , This indicates that the m-th radar node is selected to locate the fighter jet j. Assign vectors to node power. This represents the transmit power value of node m. ,in, Where c is the bandwidth of the transmitted signal, and c is the speed of light. This represents the noise power spectral density.
[0027] The expression is: (2) In the formula, Let be the observation angle from the m-th launch node to the j-th fighter jet of Party B. Let be the angle of incidence from the j-th fighter jet of Party B to the n-th receiving node; Let be the power attenuation coefficient of the signal after it is reflected by fighter jet j from the m-th transmitting node to the n-th receiving node, expressed as: (3) In the formula, and Let represent the transmit gain of the m-th node and the receive gain of the n-th node in a distributed MIMO radar, respectively. This represents the radar cross-section (RCS) of fighter jet j (the aircraft of Party B). and Let represent the distances from the m-th transmitting node and the n-th receiving node to fighter jet j, respectively. Indicates the frequency of the radar signal.
[0028] make , Then, equation (1) can be rearranged to form the following expression: (4) Under high signal-to-noise ratio conditions, the SPEB expression for fighter jet j is: (5) Substituting the rearranged Fischer matrix, we get: (6) In the formula, It is a column vector consisting entirely of 1s. ,and ; ,in, , ,in, , for and The Hadamaji, that is .
[0029] To characterize the interference performance, an interference selection matrix is defined. The element in its m-th row and k-th column is ,like This indicates that the m-th launch node was used to interfere with the k-th missile.
[0030] Orient the missile at the target. l The detection problem is constructed using a binary hypothesis:
[0031] The detection signals under the two hypotheses are denoted as follows: and Under the Gaussian model, we have: , .in, For missile k to receive data from the target The equivalent signal amplitude is expressed as: ,in, For the target received by missile k The effective echo power is expressed as follows: (7) In the formula, The transmit power of the radar on missile k. and These represent the transmit and receive gains of the radar on missile k, respectively. Let be the wavelength of the radar signal transmitted on missile k. For the goal RCS, For missile k and target The distance between them This represents the total noise power received by missile k, which includes its own noise. Suppression and jamming of Party A's radar ,Right now .
[0032] Interference from Party A's ground radar The expression is: (8) In the formula, Let m be the transmit gain of the transmitting node. Let be the wavelength of the radar signal transmitted by the m-th transmitting node. Let be the distance between the m-th launch node and the k-th missile.
[0033] According to the Neyman-Pearson optimal detection criterion, the thresholds for the likelihood ratios of the two hypotheses are... And significance level α, which is the probability of a false alarm related: (9) In the formula, It is the complementary cumulative distribution function (CCDF).
[0034] In a given In the given case, the probability of missile k detecting target l is: (10) This serves as a characterization of jamming performance; specifically, the lower the probability of a missile detecting a target, the better the jamming effect of the distributed MIMO radar.
[0035] The communication quality between nodes is characterized by the communication capacity C. Let the minimum communication quality threshold be... To meet the minimum communication quality requirements, according to the communication capacity formula... It can be seen that, during communication information transmission, if the transmitting node m acts as the communication sender and the transmitting node i acts as the communication receiver, to meet the minimum communication quality requirements, the power transmitted from the communication sender m to the communication receiver i in a distributed MIMO radar must meet certain conditions. satisfy: (11) In the formula, Let i be the receiving gain of the communication receiver. The wavelength of the communication signal. Let m be the distance between the communication transmitter and the communication receiver.
[0036] S3. With the goal of minimizing the total system transmit power, a model is constructed by combining reconnaissance and communication performance constraints with node task rules.
[0037] Specifically, each transmitting node of a MIMO radar must perform one of three tasks: reconnaissance, jamming, or communication, depending on the requirements. Communication nodes are the transmitting nodes selected to perform communication tasks; the central communication node acts as the center (starter) of communication, sending information to other communication nodes; non-communication nodes are those that do not perform communication tasks, i.e., those performing reconnaissance or jamming tasks. These nodes are all transmitting nodes of the radar and are independent of the receiving nodes.
[0038] Link 1 is designated as the path from the central communication node to other communication nodes, and link 2 is designated as the path from communication nodes other than the central communication node to non-communication nodes. and These are the lower limits of communication capacity to ensure normal communication between Link 1 and Link 2. The maximum tolerable positioning error is... The maximum allowed detection probability threshold is .
[0039] Using the above parameters as constraints on reconnaissance and communication performance, and taking into account the physical properties of distributed MIMO radar, the optimization model is constructed as follows, with minimizing the total transmit power as the objective function: (12) In the formula, This represents the communication node selection vector. This indicates that the transmitting node m has been selected as the communication node. This represents the vector for selecting the central communication node. This indicates that the transmitting node m has been selected as the communication center node. Represents the node communication matching matrix. This indicates communication from transmitting node m to transmitting node i. and These represent the communication capacity requirements of communication link 1 and communication link 2, respectively. The upper and lower limits of the last power ensure that the transmit power of the radar node will not be negative and limit the maximum transmit power of each node. This avoids extreme power allocation to a few radars, which could lead to single-unit overload. At the same time, it can also avoid over-reliance on a single radar to some extent. If a radar fails, the system can still operate normally, increasing robustness.
[0040] and To simplify the constraints on all nodes in this system and improve the readability of the integrated surveillance and communication model, the complete constraints are as follows: (13) In the formula, Com, Center, and NoCom represent the set of communication nodes, the set of communication center nodes, and the set of non-communication nodes, respectively. The first constraint ensures that each radar node of Party A can locate at most one aircraft of Party B; the second constraint ensures that each radar node of Party A can jam at most one missile; the third constraint guarantees that each radar node completes only one of the three tasks: reconnaissance, jamming, or communication; the fourth and fifth constraints state that there is only one communication center node, which is selected from the communication nodes. This non-fixed allocation method of the communication center node avoids the failure of a certain node from affecting the normal operation of the entire system communication, and has a certain degree of robustness; constraints six, seven, and eight state that, except for the communication center node, all other nodes are served by a single node, and that communication between nodes only exists in two cases: link 1 and link 2.
[0041] S4. Transform the model into a mixed-integer second-order cone programming problem, and use the McCormick envelope method to transform convex terms into non-convex terms. Then, obtain the numerical solution based on the CVX solver in MATLAB to achieve joint optimization.
[0042] Specifically, regarding reconnaissance performance constraints , and Substitute the expression and let ,get Add to both sides at the same time ,get: (14) Finally, it can be written in the form of a second-order cone matrix: (15) In the formula, , , It is the reciprocal of the maximum tolerable positioning error.
[0043] For interference performance constraints As defined by the Q function, it is a monotonically decreasing function, which can be rearranged as follows: Substitute , and The expression yields:
[0044] (16) After rearranging the above formula, we get: (17) For communication constraints Expanding equation (11) gives: (18) Similarly, constraints It can also be transformed into: (19) Substituting into equation (12), we get: (20) Due to Since all external variables are discrete, this model is a Mixed-Integer Second Order Cone Programming (MISOCP) problem. Such combinatorial optimization problems are inherently NP-hard and can only be solved through numerical simulation. Furthermore, the CVX framework requires both the objective function and constraints to be convex, while the above model... , as well as The terms are all non-convex, and can be transformed using the following method: make , According to McCormick's envelope method, a convex set (envelope) is constructed by using four linear boundary constraints to enclose the original non-convex constraint set, thus obtaining a convex optimization problem that is easier to solve. The constructed boundary constraints are abbreviated as... and ,Right now: (twenty one) (twenty two) For nonconvex terms Using deformation techniques in MATLAB A function that can convert vectors Copy in the row direction b Next, copy in the column direction. dThis yields a new matrix. Define three new matrices: Mask, Mask1, and Mask2: (twenty three) (twenty four) (25) The two constraints related to the communication link are transformed into: (26) Using McCormick's envelope method to further examine non-convex terms Perform a convex transformation, let The envelope constraint is obtained, which is simply referred to as : (27) here The two replaced terms are both discrete variables, similar to the transformation of the previous two non-convex terms. Due to the switching properties of binary variables, the constructed linear polyhedron is precisely the convex hull of the non-convex set. Optimization on this convex hull is equivalent to optimization on the discrete set of the original problem, because the optimal solution must appear at the extreme points of the convex hull, and these extreme points are the originally feasible discrete points. The final optimization model is as follows: (28) The model already satisfies linearity and convexity, and a numerical solution can be obtained directly by calling the CVX solver in MATLAB. For example... Figure 2 As shown, prior art 1 is a distance-heuristic node selection strategy. When allocating nodes, it prioritizes assigning the nearest node to the target, following the task order of reconnaissance, then jamming, and finally communication. Power allocation is then optimized after node allocation. Prior art 2 uses a random node allocation strategy, where node allocation for all tasks is randomized. Similar to prior art 1, power is optimized after node allocation. This patented method has a significant advantage over prior art in minimizing the total system transmit power.
[0045] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A distributed MIMO radar resource allocation method for integrated reconnaissance, interception, and communication, characterized in that, include: S1. Construct an integrated distributed MIMO radar countermeasure scenario that combines reconnaissance, communication, and surveillance. S2. The lower bound of position estimation error (SPEB), the probability of missile detection of the target, and the inter-node communication capacity are used as evaluation indicators for reconnaissance, jamming, and communication performance, respectively. S3. To minimize the total system transmit power, a model is constructed by combining reconnaissance and communication performance constraints with node task rules. S4. Transform the model into a mixed-integer second-order cone programming problem, and use the McCormick envelope method to transform convex terms into non-convex terms. Then, obtain the numerical solution based on the CVX solver in MATLAB to achieve joint optimization.
2. The distributed MIMO radar resource allocation method for integrated reconnaissance, interception, and communication as described in claim 1, characterized in that, The construction process of the integrated distributed MIMO radar countermeasure scenario described in step S1 is as follows: Party A's early warning system detects J Party B's combat aircraft in the airspace; Party A dispatches L of its own combat aircraft to perform countermeasure missions against Party B; during the countermeasures, Party B launches K missiles; simultaneously, Party A deploys a distributed MIMO radar system on the ground consisting of M transmitters and N receivers; the distributed MIMO radar system, based on the position information and prior information of Party B's combat aircraft's radar cross-section (RCS), simultaneously performs tasks such as locating Party B's combat aircraft, jamming Party B's missile launches, and ensuring communication between Party A's nodes; where J, L, K, M, and N are all positive integers.
3. The distributed MIMO radar resource allocation method for integrated reconnaissance, interception, and communication as described in claim 1, characterized in that, In step S2, the lower bound of the position estimation error (SPEB) is used as the reconnaissance performance evaluation index, specifically as follows: In the adversarial scenario constructed in step S1, the position information of the j-th fighter jet of the opposing side. The expression for the Fischer matrix is: (1) In the formula, The positioning vector for Party B's j-th fighter jet, whose m-th element is... , This indicates that the m-th radar node is selected to locate the fighter jet j. Assign vectors to node power. This represents the transmit power value of radar node m. ,in, Where c is the bandwidth of the transmitted signal, and c is the speed of light. The noise power spectral density; The expression is: (2) In the formula, Let be the observation angle from the m-th launch node to the j-th fighter jet of Party B. Let be the angle of incidence from the j-th fighter jet of Party B to the n-th receiving node; Let be the power attenuation coefficient of the signal after it is reflected by fighter jet j from the m-th transmitting node to the n-th receiving node, expressed as: (3) In the formula, and Let represent the transmit gain of the m-th node and the receive gain of the n-th node in a distributed MIMO radar, respectively. This represents the radar cross-section (RCS) of fighter jet j (the aircraft of Party B). and Let represent the distances from the m-th transmitting node and the n-th receiving node to fighter jet j, respectively. Indicates the frequency of the radar signal; make , The Fischer matrix is rearranged and expressed as follows: (4) Under high signal-to-noise ratio conditions, the SPEB expression for fighter jet j is: (5) Substituting the rearranged Fischer matrix, we get: (6) In the formula, It is a column vector consisting entirely of 1s. ,and ; ,in, , ,in, , for and The Hadamaji, that is .
4. The distributed MIMO radar resource allocation method for integrated reconnaissance, interception, and communication as described in claim 1, characterized in that, In step S2, the detection probability of the missile against the target is used as an evaluation index for interference performance, specifically as follows: Define an interference selection matrix The element in its m-th row and k-th column is ,like This indicates that the m-th launching node was used to jam the k-th missile, redirecting missile k towards the target. l The detection problem is constructed using a binary hypothesis: The detection signals under the two hypotheses are denoted as follows: and Under the Gaussian model, it satisfies , ,in, For missile k to receive data from the target The equivalent signal amplitude is expressed as: ,in, For the target received by missile k The effective echo power is expressed as: (7) In the formula, The transmit power of the radar on missile k. and These represent the transmit and receive gains of the radar on missile k, respectively. Let be the wavelength of the radar signal transmitted on missile k. For the goal Radar cross section (RCS) For missile k and target The distance between them This represents the total noise power received by missile k, which includes its own noise. Suppression and jamming of Party A's radar ,Right now ; Interference from Party A's ground radar The expression is: (8) In the formula, Let m be the transmit gain of the transmitting node. Let be the wavelength of the radar signal transmitted by the m-th transmitting node. Let m be the distance between the m-th launch node and the k-th missile; According to the Neyman-Pearson optimal detection criterion, the thresholds for the likelihood ratios of the two hypotheses are... With significance level That is, the probability of a false alarm. related: (9) In the formula, The complementary cumulative distribution function; In a given In this case, missile k is aimed at the target. l The detection probability is: (10) In the formula, This indicates the detection signal under the H1 hypothesis. The standard deviation.
5. The distributed MIMO radar resource allocation method for integrated reconnaissance, interception, and communication as described in claim 1, characterized in that, In step S2, the inter-node communication capacity is used as the communication performance evaluation index, specifically as follows: The communication quality between nodes is characterized by the communication capacity C, and a minimum communication quality threshold is set as follows: To meet this minimum communication quality requirement, according to the communication capacity formula... Where B is the effective receiving bandwidth. This represents the noise power at the node receiver. This refers to the communication signal power at the node's receiving end; In a distributed MIMO radar, the power transmitted from the communication transmitter m to the communication receiver i satisfy: (11) In the formula, Let i be the receiving gain of the communication receiver. The wavelength of the communication signal. Let m be the distance between the communication transmitter and the communication receiver.
6. The distributed MIMO radar resource allocation method for integrated reconnaissance, interception, and communication as described in claim 1, characterized in that, S3 includes: Define the link from the central communication node to other communication nodes as Link 1, and the link from other communication nodes to non-communication nodes as Link 2. Set the lower limit of the communication capacity for normal communication between Link 1 and Link 2 as follows: and The maximum tolerable positioning error is The maximum allowed detection probability threshold is ; Using the above parameters as constraints on reconnaissance and communication performance, and taking into account the physical properties of distributed MIMO radar, the optimization model is constructed as follows, with minimizing the total transmit power as the objective function: (12) In the formula, This represents the communication node selection vector. This indicates that the transmitting node m has been selected as the communication node. This represents the vector for selecting the central communication node. This indicates that the transmitting node m has been selected as the communication center node. Represents the node communication matching matrix. This indicates communication from transmitting node m to transmitting node i. and These represent the communication capacity requirements of communication link 1 and communication link 2, respectively.
7. The distributed MIMO radar resource allocation method for integrated reconnaissance, interception, and communication as described in claim 1, characterized in that, The transformation process of the reconnaissance constraints in S4 is as follows: Constraints on reconnaissance performance , and Substitute the expression and let ,get Add to both sides at the same time ,get: (14) It can be written in the form of a second-order cone matrix: (15) In the formula, , , It is the reciprocal of the maximum tolerable positioning error.
8. The distributed MIMO radar resource allocation method for integrated reconnaissance, interception, and communication as described in claim 1, characterized in that, The transformation process of the interference constraint in S4 is as follows: For interference performance constraints As defined by the Q function, it is a monotonically decreasing function, which can be rearranged as follows: Substitute , and The expression yields: (16) After rearranging the above formula, we get: (17)。 9. The distributed MIMO radar resource allocation method for integrated reconnaissance, interception, and communication as described in claim 1, characterized in that, The transformation process of communication constraints in S4 is as follows: For constraints Expanding equation (11) gives: (18) Similarly, constraints Transform into: (19) Substituting into equation (12), we get: (20) In the formula, Com, Center, and NoCom are the set of communication nodes, the set of communication center nodes, and the set of non-communication nodes, respectively.
10. The distributed MIMO radar resource allocation method for integrated reconnaissance, interception, and communication as described in claim 1, characterized in that, The convex-to-nonconvex transformation of terms using the McCormick envelope method is specifically as follows: make , According to McCormick's envelope method, a convex set is constructed by using four linear boundary constraints to enclose the original non-convex constraint set. The constructed boundary constraints are abbreviated as... and ,Right now: (21) (22) For non-convex terms in communication link constraints Using MATLAB Function definition: Mask, Mask1, Mask2 mask matrix transformation constraint form: (23) (24) (25) The two constraints of the communication link are transformed into: (26) McCormick's envelope method is used to transform the nonconvex terms. Perform a convex transformation, let The envelope constraint is obtained and denoted as : (27) By leveraging the switching properties of binary variables, the constructed linear polyhedron becomes the convex hull of the original non-convex set, and the optimization on this convex hull is equivalent to the optimization on the original discrete set. The final optimized model is as follows: (28) In the formula, For vectors The m-th element, Let b be the input array to be copied, b be the number of copies in the row direction, and d be the number of copies in the column direction.