A method for verifying and evaluating performance of a networked radar system

Abstract

The application discloses a kind of verification and evaluation method of networked radar system performance, comprising: according to opening operation display networked radar multiple operating modes;Different working modes corresponding to the type of virtual radar is different;Each operating mode corresponds to the simulation of networked radar one kind of virtual radar;Display the radar performance of multiple in selected operating mode;When the radar performance selected by user is first radar performance, according to the score of the index class contained in the radar performance input, the score of evaluation index in each index class and index data determine the verification and evaluation result of input index class influence degree, index data and evaluation index influence degree in the aspect of this radar performance;When the radar performance selected by user is second radar performance, according to the input data of the to-be-evaluated item contained in the radar performance input and the weight of the performance influence factor contained, determine the verification and evaluation result of the radar performance under this input data and weight.

Description

Technical Field

[0001] This invention belongs to the field of networked radar technology, specifically relating to a method for verifying and evaluating the performance of a networked radar system. Background Technology

[0002] As combat environments become increasingly complex, the capabilities of a single radar node are insufficient to meet new combat demands. Against this backdrop, a new radar system has emerged, utilizing the concept of the internet: networked radar. In recent years, more and more functions have been implemented in networked radar systems, thus increasing the need for performance verification and evaluation of these systems.

[0003] Currently, most research on radar performance evaluation focuses on individual radars. As a crucial component of a networked radar system, the detection capabilities, anti-jamming capabilities, and anti-stealth capabilities of a single radar significantly impact the overall performance of the networked radar system. Current evaluations of single radar performance primarily focus on signal processing, with research on evaluating the performance of networked radar systems largely lacking. Furthermore, networked radar systems are far more complex; conducting repeated experiments in real-world scenarios is difficult and extremely resource-intensive. Summary of the Invention

[0004] To address the aforementioned problems in related technologies, this invention provides a method for verifying and evaluating the performance of a networked radar system. The technical problem to be solved by this invention is achieved through the following technical solution:

[0005] This invention provides a method for verifying and evaluating the performance of a networked radar system, comprising:

[0006] The operation of the networked radar displays various working modes; each working mode corresponds to a virtual radar simulated by the networked radar; different working modes correspond to different types of virtual radars.

[0007] Based on the mode selection operation, it displays various radar performance characteristics under the selected operating mode;

[0008] When the selected radar performance is determined to be of the first category based on the performance selection operation, the following are displayed: the index classes included in the selected radar performance, the evaluation indexes in each index class, the index class influence degree input box for each index class, and the index data and evaluation index influence degree input boxes for the evaluation indexes. The evaluation indexes for each first category of radar performance are the factors that affect that first category of radar performance. The index class influence degree for each index class represents the degree of influence of that index class on the first category of radar performance. The evaluation index influence degree for each evaluation index represents the degree of influence of that evaluation index on the first category of radar performance.

[0009] Based on the indicator class influence, evaluation indicator influence, and indicator data entered in the input box, as well as the indicator class to which each evaluation indicator belongs, determine the verification and evaluation results of the input indicator class influence, indicator data, and evaluation indicator influence in terms of the selected radar performance.

[0010] When the selected radar performance is determined to be of the second category based on the performance selection operation, the evaluation items and performance influencing factors included in the selected radar performance are displayed, as well as the input boxes for the evaluation items and the weight input boxes for the performance influencing factors. The performance influencing factors are the factors that affect the selected radar performance, and the weight of each performance influencing factor represents the degree of influence of the performance influencing factor on the second category of radar performance.

[0011] Based on the input data to be evaluated and the input weights, the verification and evaluation results of the data to be evaluated and the weights in terms of the selected radar performance are determined.

[0012] The present invention has the following beneficial technical effects:

[0013] The method for verifying and evaluating the performance of a networked radar system provided by this invention selects different radar performances under different operating modes of the networked radar system and inputs the data to be verified and evaluated under the radar performance. The input data is then verified and evaluated, thereby enabling the verification and evaluation of different performances of the networked radar system according to user needs. Furthermore, it consumes fewer resources and is easier to repeat the evaluation and verification.

[0014] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0015] Figure 1 An optional flowchart for the verification and evaluation method of the networked radar system performance provided in the embodiments of the present invention;

[0016] Figure 2 A schematic diagram of an exemplary GUI interface for evaluating radar detection performance provided in an embodiment of the present invention;

[0017] Figure 3 The exemplary radar detection performance indicators provided for embodiments of the present invention include a class of indicators, and a schematic diagram showing the relationship between the evaluation indicators included in each class of indicators.

[0018] Figure 4 A schematic diagram of an exemplary GUI interface for displaying the influence of input indicator types and evaluating the influence of indicator types, provided for embodiments of the present invention;

[0019] Figure 5 A schematic diagram of an exemplary GUI interface for displaying score values ​​provided in an embodiment of the present invention;

[0020] Figure 6 A schematic diagram of an exemplary GUI interface for evaluating radar ranging performance provided in an embodiment of the present invention;

[0021] Figure 7 A schematic diagram of an exemplary GUI interface for displaying the number of ranging radar units and the target distance, provided for an embodiment of the present invention;

[0022] Figure 8 A schematic diagram of an exemplary GUI interface for displaying input weights provided in an embodiment of the present invention;

[0023] Figure 9 This is a schematic diagram of an exemplary GUI interface for displaying score ranking results and the optimal solution, provided for an embodiment of the present invention. Detailed Implementation

[0024] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0025] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0026] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. In addition, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0027] Although the invention has been described herein in conjunction with various embodiments, those skilled in the art will understand and implement other variations of the disclosed embodiments by reviewing the accompanying drawings, disclosure, and appended claims in carrying out the claimed invention. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0028] Figure 1 This is a flowchart of a method for verifying and evaluating the performance of a networked radar system provided in an embodiment of the present invention, as shown below. Figure 1 As shown, the method includes the following steps:

[0029] S101. Based on the opening operation, multiple working modes of the networked radar are displayed; each working mode corresponds to a virtual radar simulated by the networked radar; different working modes correspond to different types of virtual radars.

[0030] In this embodiment of the invention, the method for verifying and evaluating the performance of a networked radar system can be executed by a device running software for verifying and evaluating the performance of a networked radar system; and the device can receive user operations on the software's interface and execute the method based on the received user operations.

[0031] In this embodiment of the invention, the opening operation can be either the user opening the networked radar system performance verification and evaluation software, or the user opening a running interface of the networked radar system performance verification and evaluation software.

[0032] For example, four operating modes can be included. Operating mode 1 corresponds to a virtual active monostatic radar, which is a cluster composed of some antenna transceiver nodes in a networked radar, operating in a self-transmitting and self-receiving state. Operating mode 2 corresponds to a virtual transceiver-distributed dual / multistatic radar, which is multiple clusters composed of some antenna transceiver nodes in a networked radar, and these multiple clusters operate in a transceiver-distributed state; the multiple clusters are used for collaborative work or each cluster operates independently. Operating mode 3 corresponds to a virtual MIMO radar, which is multiple clusters composed of some antenna transceiver nodes in a networked radar, and these multiple clusters operate simultaneously in a transceiver-distributed state, and also collaborate. Operating mode 4 corresponds to a virtual sparse / ultra-sparse distributed coherent radar, which is multiple clusters composed of some antenna transceiver nodes in a networked radar, wherein different antenna nodes are sparsely arranged, and within each antenna node, a dense half-wavelength arrangement is used; the multiple clusters are obtained by grouping different antenna nodes according to a preset aperture size; the multiple clusters are used for collaborative work or each cluster operates independently.

[0033] Here, the multiple clusters constituting the virtual transceiver dual / multi-base radar can operate in one-transmit-multiple-receive and multiple-transmit-multiple-receive modes. Furthermore, each receiving cluster in these multiple clusters can operate independently without cooperating. Alternatively, the array position constraints of the multiple receiving clusters in these multiple clusters can be limited through optimized configuration, so that the multiple receiving clusters meet the conditions for cooperative operation. Thus, in search and tracking modes, the multiple receiving clusters can perform signal-level fusion cooperative processing to achieve target detection and tracking.

[0034] Here, the multiple clusters that constitute the virtual MIMO radar all operate in a separate transmit and receive state. By optimizing the configuration, the array position constraints of multiple transmit and receive clusters in these multiple clusters can be limited, so that multiple transmitters and receivers can meet the conditions for cooperative operation. Thus, in search and tracking mode, multiple receive clusters can perform signal-level fusion cooperative processing to achieve target detection and tracking.

[0035] Here, in the multiple clusters constituting the virtual sparse / ultra-sparse distributed coherent radar, the different antenna nodes within each cluster are sparsely arrayed, while the antenna nodes themselves are densely arranged at half wavelengths. These multiple clusters are obtained by grouping different antenna nodes according to a certain aperture size. These multiple clusters can independently perform search or tracking operations, or they can collaboratively observe the same target simultaneously. The node composition of each cluster has a certain degree of dynamic adjustment capability. Furthermore, the radar processing within each cluster is coherent at the signal level, while the processing between different clusters can be coherent or non-coherent.

[0036] S102. Select the mode and display the radar performance under the selected operating mode.

[0037] In this embodiment of the invention, when a user's mode selection operation is received, the user's selected working mode can be obtained, and the radar performance supported by the working mode can be displayed.

[0038] Here, operating mode 1 supports the following radar performance characteristics: radar detection performance, radar power performance, radar ranging performance, radar velocity measurement performance, radar angle measurement performance, radar range resolution performance, radar angle resolution performance, radar imaging and recognition performance, passive radar (external radiation source radar) parameter estimation performance, radar intelligence performance, and performance related to the electromagnetic environment and complexity of the combat scenario. Operating modes 2, 3, and 4 support the following radar performance characteristics: array optimization performance, waveform optimization design performance, signal-level fusion performance, data-level fusion performance, intelligence-level fusion performance, system reconfigurability performance, radar intelligence performance, anti-electronic stealth performance, anti-electronic jamming performance, anti-electronic reconnaissance performance, radiation-resistant destruction performance, and performance related to the electromagnetic environment complexity of the combat scenario.

[0039] S103. When the selected radar performance is determined to be a first-class radar performance based on the performance selection operation, the following are displayed: the index class included in the selected radar performance, the evaluation index in each index class, the index class influence degree input box for each index class, and the index data and evaluation index influence degree input boxes for the evaluation index. The evaluation index for each first-class radar performance is the factor that affects the first-class radar performance. The index class influence degree for each index class represents the degree of influence of the index class on the first-class radar performance to which it belongs. The evaluation index influence degree for each evaluation index represents the degree of influence of the evaluation index on the first-class radar performance to which it belongs.

[0040] Here, when a user's performance selection operation is received, the radar performance selected by the user can be determined, and it can be determined whether the radar performance selected by the user belongs to the first type of radar performance or the second type of radar performance. When it is determined that the radar performance selected by the user belongs to the first type of radar performance, the index class included in the selected radar performance, the evaluation index in each index class, the index class influence of each index class, and the index data and the evaluation index influence input box are displayed.

[0041] Here, the first category of radar performance includes: radar detection performance, radar power performance, radar resolution performance, radar imaging and recognition performance, passive radar (external radiation source radar) parameter estimation performance, radar intelligence performance, array optimization performance, signal-level fusion performance, data-level fusion performance, intelligence-level fusion performance, system reconfigurability performance, radar intelligence performance, anti-electronic stealth performance, and electromagnetic environment complexity performance in combat scenarios. The second category of radar performance includes: radar ranging performance, radar velocity measurement performance, radar angle measurement performance, anti-electronic jamming performance, anti-electronic reconnaissance performance, and radiation-resistant destruction performance.

[0042] Here, each first-class radar performance may include at least one index class, and each index class may include at least one evaluation index.

[0043] Radar detection performance includes the following categories of indicators: environmental and target characteristic evaluation indicators, detection performance technical indicators, and detection performance tactical indicators. The environmental and target characteristic evaluation indicators include environmental interference and target radar cross-section. The detection performance technical indicators include transmit power, operating frequency, and antenna gain. The detection performance tactical indicators include range accuracy, velocity accuracy, and azimuth accuracy.

[0044] The evaluation indicators directly included in radar power performance include: maximum effective range, minimum detectable radar cross section, received signal power, and receiver aperture area (that is, the indicator category included in radar power performance is radar power performance, and the evaluation indicators included in this indicator category include: maximum effective range, minimum detectable radar cross section, received signal power, and receiver aperture area; the same applies to other radar performance).

[0045] The evaluation indicators directly included in radar resolution performance are: range resolution and angular resolution.

[0046] The evaluation indicators directly included in radar imaging recognition performance include: imaging resolution and target scene scattering intensity.

[0047] The passive radar parameter estimation performance includes the following indices: radiation source threat level, target RCS scattering characteristics, frequency difference compensation, and multi-beam accumulation. The evaluation indices for radiation source threat level include: radiation source repetition rate, pulse width, azimuth angle, frequency, and target range. The evaluation indices for target RCS scattering characteristics include: frequency, target velocity, azimuth angle, and elevation angle. The evaluation indices for frequency difference compensation include: accumulation time and frequency difference. The evaluation indices for multi-beam accumulation include: accumulation time and accumulation peak value.

[0048] Radar intelligence performance includes the following metrics: tracking accuracy, stable tracking range, and automatic flight establishment time. Tracking accuracy includes the following evaluation metrics: target detection probability, target range, track duration, target identification time, and successful identification rate. Stable tracking range includes the following evaluation metrics: target range, azimuth, and target speed. Automatic flight establishment time includes the following evaluation metrics: target range, azimuth, target speed, and detection period.

[0049] The performance metrics for array optimization include: array complexity, array high-resolution capability, and array gain. The evaluation metrics for array complexity include: target cross-section, radar range resolution, and pulse repetition frequency. The evaluation metrics for array high-resolution capability include: main lobe width and peak-to-side lobe ratio. The evaluation metrics for array gain include: number of array elements.

[0050] The evaluation metrics directly included in signal-level fusion performance include: peak sidelobe ratio, integral sidelobe ratio, probability density function, noise variance, accumulation loss, and signal-to-noise ratio.

[0051] Evaluation metrics directly encompassed by data-level fusion performance include: data-level fusion performance. At the lowest level: peak sidelobe ratio, integral sidelobe ratio, probability density function, noise variance, cumulative loss, and signal-to-noise ratio.

[0052] The evaluation metrics directly included in intelligence-level fusion performance include: target speed, target range, azimuth, pitch angle, fusion error, false alarm rate, automatic flight establishment time, processing capacity, track fragmentation, and situational consistency.

[0053] Evaluation metrics directly included in system reconfigurability performance include robustness, evolution, and survivability.

[0054] The indicators for radar intelligence performance include: AI adoption rate, informatization, interactive integration, and radar performance. AI adoption rate includes the following evaluation indicators: AI adoption rate. Informatization includes the following evaluation indicators: information sharing rate, information update frequency, communication success rate, node failure probability, throughput, and spatiotemporal reference. Interactive integration includes the following evaluation indicators: environment fit, environment adaptability, degree of integration, and manual workload. Radar performance includes the following evaluation indicators: scheduling success rate, deadline miss rate, time utilization rate, and execution threat level.

[0055] The anti-electron stealth performance includes the following metrics: Doppler frequency shift decoherence, ionospheric decoherence, target scattering decoherence, phase error decoherence, and array aperture decoherence. Doppler frequency shift decoherence includes the following evaluation metrics: target radial velocity, radar velocity resolution, target maneuverability, and parameter estimation algorithm accuracy. Ionospheric decoherence includes the following evaluation metrics: operating frequency, path TEC (integral value of electron concentration along the propagation path), and signal bandwidth. Target scattering decoherence includes the following evaluation metrics: target scattering cross-section, radar range resolution, and pulse repetition period. Phase error decoherence includes the following evaluation metrics: environmental noise interference, signal channel error, and oscillator error. Array aperture decoherence includes the following evaluation metrics: target range, array aperture size, and target size.

[0056] The evaluation indicators directly included in the electromagnetic environment complexity performance of the battle scenario include: electromagnetic signal pattern correlation coefficient, frequency occupancy coefficient, frequency overlap coefficient, electromagnetic signal density coefficient, and background signal intensity coefficient.

[0057] In some embodiments, the influence of each indicator class or each evaluation indicator can be a score between 1 and 9.

[0058] S104. Based on the indicator class influence, evaluation indicator influence, and indicator data entered in the input box, as well as the indicator class to which each evaluation indicator belongs, determine the verification and evaluation results of the input indicator class influence, indicator data, and evaluation indicator influence in terms of the selected radar performance.

[0059] Here, based on the influence of the indicator class and the influence of the evaluation indicator, as well as the indicator class to which the evaluation indicator belongs, the importance of each evaluation indicator to its respective indicator class and the importance of each indicator class to the selected radar performance can be determined. Then, based on the importance of each evaluation indicator to its respective indicator class and the importance of each indicator class to the selected radar performance, the verification and evaluation results of the influence of the indicator class, the indicator data, and the influence of the evaluation indicator on the selected radar performance can be determined.

[0060] Here, you can enter the corresponding indicator category impact, evaluation indicator impact, and indicator data through the input boxes.

[0061] S105. When the selected radar performance is determined to be the second type of radar performance based on the performance selection operation, the evaluation items and performance influencing factors included in the selected radar performance are displayed, as well as the input boxes for the evaluation items and the weight input boxes for the performance influencing factors. The performance influencing factors are the factors that affect the selected radar performance, and the weight of each performance influencing factor represents the degree of influence of the performance influencing factor on the second type of radar performance to which it belongs.

[0062] Here, each category II radar performance evaluation item includes multiple items and may contain at least two performance influencing factors.

[0063] The evaluation items for radar angle measurement performance include: the number of angle measurement methods, the number of angles measured, the true angle value, and the measured angle value. The performance influencing factors for radar angle measurement performance include: the number of angle measurement measures, the angle measurement error, and the complexity of the angle measurement measures.

[0064] The evaluation items for radar ranging performance include: the number of virtual radars, the number of target distances, and the true and measured distance values. The performance influencing factors for radar ranging performance include: the number of ranging radars, range resolution, and the complexity of the ranging methods.

[0065] The evaluation items for radar speed measurement performance include: the number of speed-measuring radars, the number of target speeds, and the true and measured speed values. The performance influencing factors include: the number of speed-measuring radars, speed resolution, and the complexity of the speed measurement measures.

[0066] The evaluation items for anti-electronic jamming performance include: the number of anti-jamming measures, the number of jamming types, the signal-to-interference-plus-noise ratio (SNR) of the signal source, and the SNR after anti-jamming. The performance influencing factors include: the number of anti-jamming measures, target position error, and the complexity of the anti-jamming measures.

[0067] The evaluation criteria for counter-electronic reconnaissance performance include: the number of counter-reconnaissance methods, the radar operating wavelength, and data on various parameters before and after counter-reconnaissance. These parameters include: radar transmitter power, reconnaissance receiver antenna gain, radar antenna gain in the direction of the reconnaissance receiver, the loss factor of the enemy reconnaissance aircraft at the radar transmitter power, and the sensitivity of the enemy reconnaissance aircraft receiver. The performance influencing factors for counter-electronic reconnaissance performance include: the number of counter-reconnaissance methods, the counter-reconnaissance interception range, and the complexity of counter-reconnaissance measures.

[0068] The evaluation items for radiation resistance performance include: the number of anti-radiation measures, the operating wavelength of the seeker radar, the radar coordinates, the radar power distribution, and the coordinates of the multi-static radar transmitter elements. The performance influencing factors include: the number of anti-radiation measures, the missile kill radius, and the complexity of the anti-radiation measures.

[0069] Here, you can enter the data to be evaluated and the weights of each performance influencing factor through the input boxes.

[0070] In some embodiments, the weight of each performance influencing factor can be a decimal between 0 and 1, and the sum of the weights of the performance influencing factors included in each second type of radar performance is 1.

[0071] S106. Based on the input data to be evaluated and the input weights, determine the verification and evaluation results of the data to be evaluated and the weights in terms of the selected radar performance.

[0072] Here, a decision matrix can be constructed based on the input data to be evaluated. Based on the decision matrix and the input weights, the verification and evaluation results of the data to be evaluated and the input weights in terms of the selected radar performance can be determined.

[0073] Here, the verification and evaluation results can be scores, and higher scores indicate better results, while lower scores indicate worse results.

[0074] In this embodiment of the invention, by selecting different radar performances under different operating modes of a networked radar system and inputting the data that needs to be verified and evaluated under that radar performance, the different performances of the networked radar system can be verified and evaluated according to user needs. Furthermore, it consumes less resources and is easier to repeat the evaluation and verification.

[0075] In some embodiments, the performance selection operation is further used to select the number of virtual radars corresponding to the selected radar performance, thereby determining how many virtual radars are needed to verify and evaluate the selected radar performance based on the user's performance selection operation. Based on this, S103 can be implemented as follows: when the selected radar performance is determined to be a first type of radar performance based on the performance selection operation, and the number of virtual radars is preset, the following input boxes are displayed: the index class included in the selected radar performance, the evaluation index in each index class, the index class influence of each index class, and the index data and evaluation index influence of the evaluation index; wherein, the index class influence of each index class input box is used to input the index class influence of each virtual radar corresponding to that index class in the preset number of virtual radars; the index data input box is used to input the index data of each evaluation index corresponding to each virtual radar in the preset number of virtual radars; and the evaluation index influence input box is used to input the evaluation index influence of each evaluation index corresponding to each virtual radar in the preset number of virtual radars.

[0076] Here, when the performance selection operation is only used to select radar performance, it can be determined that one virtual radar is needed to verify and evaluate the selected radar performance. Furthermore, this virtual radar is used to verify and evaluate a set of input data (input indicator class influence, evaluation indicator influence, and indicator data). However, when the performance selection operation is used not only to select radar performance but also to select the number of virtual radars corresponding to the selected radar performance, it can be determined that N virtual radars selected by the user are needed to verify and evaluate the selected radar performance. In this case, these N virtual radars need to be used simultaneously to verify and evaluate N different sets of input data. This improves the efficiency of verification and evaluation.

[0077] In some embodiments, when the performance selection operation is also used to select the number of virtual radars corresponding to the selected radar performance, the above-mentioned S104 can be implemented by S1041 to S1043:

[0078] S1041. For any virtual radar among the preset number of virtual radars, based on the influence degree of the index class and the influence degree of the evaluation index corresponding to any virtual radar, as well as the index class to which the evaluation index belongs, determine the importance of each evaluation index to its index class and the importance of each index class to the performance of the selected radar.

[0079] Here, when there are N virtual radars, each virtual radar corresponds to a set of input data, which includes: the influence degree of each index category of the selected radar performance, the influence degree of the evaluation indexes included in each index category, and the index data of each evaluation index. For each virtual radar, the verification and evaluation results of the set of input data can be calculated based on the input data corresponding to that virtual radar.

[0080] Here, for each virtual radar, the importance of each evaluation indicator in each evaluation indicator class relative to itself and relative to every other evaluation indicator can be determined based on the influence degree of the evaluation indicators contained in each indicator class, thus obtaining the judgment matrix of the indicator class. Based on the eigenvalues ​​and eigenvectors of the judgment matrix of the indicator class, the importance of each evaluation indicator in the indicator class to the indicator class can be determined. At the same time, based on the influence degree of each indicator class contained in the selected radar performance, the importance of each indicator class contained in the selected radar performance relative to itself and relative to every other indicator class can be determined, thus obtaining the judgment matrix of the selected radar performance. Based on the eigenvalues ​​and eigenvectors of the judgment matrix of the selected radar performance, the importance of each indicator class contained in the selected radar performance to the selected radar performance can be determined.

[0081] The principle behind obtaining the judgment matrix is ​​as follows:

[0082] For example, when an indicator class includes 3 evaluation indicators, or when the selected radar performance includes 3 indicator classes, the importance of each evaluation indicator relative to itself and relative to other evaluation indicators can be calculated using the following formula (1). ij Alternatively, the importance of each indicator class relative to itself and relative to other indicator classes can be calculated using the following formula (1). ij ;a ij Then it is each element in the judgment matrix of the index class or the selected radar performance; where, formula (1) is as follows:

[0083]

[0084] Where i is 1, 2, 3, and j is also 1, 2, 3, representing 3 evaluation indicators or 3 indicator classes; P(i) represents the influence of the i-th evaluation indicator or the influence of the i-th indicator class; abs(.) represents the absolute value function; a ij This represents the importance of the i-th evaluation indicator relative to the j-th evaluation indicator, or the importance of the i-th indicator class relative to the j-th indicator class; where the importance of the j-th evaluation indicator relative to the i-th evaluation indicator, or the importance of the j-th indicator class relative to the i-th indicator class, is:

[0085] Here, after obtaining the judgment matrix (e.g., judgment matrix A) for each indicator class, the eigenvalues ​​and eigenvectors of judgment matrix A for that indicator class can be calculated. These eigenvectors are then used as column vectors of the degree matrix (e.g., degree matrix V). Furthermore, the eigenvector corresponding to the maximum value (e.g., eigenvalue n) of the eigenvalues ​​of judgment matrix A is used as the column vector corresponding to the eigenvalue n of degree matrix V, thus obtaining an eigenvector W. In other words, this obtained eigenvector is the eigenvector of judgment matrix A with eigenvalue n, and each element in this eigenvector represents the importance of an evaluation indicator included in that indicator class to that indicator class. Similarly, the importance of each indicator class to the selected radar performance is also determined using this principle.

[0086] Here, when a selected radar performance directly includes evaluation indicators, the radar performance itself is the index class it contains. Thus, the judgment matrix of the index class can be directly obtained according to the above method. Based on the obtained judgment matrix of the index class, the importance of each evaluation indicator to the radar performance can be directly calculated using the above method.

[0087] S1042. Based on the importance of each evaluation indicator to its respective indicator class, and the importance of each indicator class to the selected radar performance, determine the importance of each evaluation indicator to the selected radar performance.

[0088] Here, for each evaluation metric, the product of the importance of the evaluation metric to its class of metrics and the importance of the class of metrics to the selected radar performance can be used as the importance of the evaluation metric to the selected radar performance.

[0089] S1043. Based on the importance of each evaluation indicator to the selected radar performance and the indicator data corresponding to any virtual radar, determine the verification and evaluation results of the indicator class influence degree, indicator data, and evaluation indicator influence degree of any virtual radar in terms of the selected radar performance.

[0090] Here, for each evaluation index of each virtual radar, the index data of the corresponding evaluation index of the virtual radar can be dedimensionalized to obtain the quantitative value of the evaluation index. The product between the quantitative value of the evaluation index and the importance of the evaluation index to the selected radar performance is determined to obtain the product value of the evaluation index. Based on the product values ​​of each evaluation index included in the selected radar performance, the score of the virtual radar in the selected radar performance is determined. The score is used as the verification and evaluation result of the influence of the index class, index data and evaluation index of the virtual radar in the selected radar performance.

[0091] For example, when the selected radar performance includes 8 evaluation metrics, the score of each virtual radar in terms of the selected radar performance can be expressed by formula (2):

[0092] Score = 100 × [W] CA (1)×Data(1)+W CA (2)×Data(2)+…W CA (8)×Data(8)] (2);

[0093] Among them, W CA (1) indicates the importance of the first evaluation index to the selected radar performance, and Data(1) indicates the quantification value of the first evaluation index.

[0094] Here, dedimensionalization refers to mapping data to the range [0,1] to obtain a number belonging to 0 to 1.

[0095] In some embodiments, the item to be evaluated may include: data items, the number of virtual radars, and task items; based on this, the above S105 can be implemented as follows: when the selected radar performance is determined to be a second type of radar performance according to the performance selection operation, the data items, the number of virtual radars, the task items, and the performance influencing factors included in the selected radar performance are displayed, as well as input boxes for data items, input boxes for the number of virtual radars, input boxes for the task items, and input boxes for the weights of the performance influencing factors; wherein, the input box for data items is used to input at least one set of different data values; the input box for the number of virtual radars used to generate at least one set of different data values ​​is used to input the number of virtual radars; the input box for the task items is used to input the number of tasks to be performed; the at least one set of different data values ​​includes: measurement values ​​obtained by a preset number of virtual radars performing tasks and parameter values ​​for each task; the input box for the weights of the performance influencing factors is used to input the weight of each performance influencing factor.

[0096] Here, each set of data values ​​includes: a measured value and a parameter value for the task; the number of virtual radars can be a content item representing the number of virtual radars. For example, when the selected radar performance is radar ranging performance, the data items are the true range value and the measured range value, and each set of data values ​​consists of one measured range value and one true range value. The task item is the number of targets to be measured (the true range value is the actual range of each target), and the number of virtual radars is the number of virtual radars (indicating how many virtual radars there are). As another example, when the selected radar performance is radar angle measurement performance, the data items are the true angle value and the measured angle value, and each set of data values ​​consists of one measured angle value and one true angle value. The task item is the number of angles to be measured (the true angle value is the actual angle value), and the number of virtual radars is the angle measurement method (the number of methods indicates the number of virtual radars).

[0097] In some embodiments, when the items to be evaluated include: data items, the number of virtual radars, and task items, the above-mentioned S106 can be implemented by S1061 to S1064:

[0098] S1061. Based on at least one set of different input data values, the number of virtual radars, and the number of tasks, construct an initial matrix with the number of virtual radars as the number of rows, the number of tasks as the number of columns, and each set of data values ​​as an element; wherein the row coordinate of each element represents the virtual radar used to generate that element.

[0099] For example, when six different sets of data values, three virtual radars, and two tasks are input, the initial matrix constructed is a 3*2 matrix. The element in the first row and first column represents a set of data values ​​generated when performing the first task using the first virtual radar, and the element in the first row and second column represents a set of data values ​​generated when performing the second task using the first virtual radar. The other elements are similarly represented and will not be repeated here. For instance, when the task is a ranging task, the element in the first row and first column represents a distance measurement value obtained when measuring the first target using the first virtual radar, and the actual distance of the first target. The obtained distance measurement value and the actual distance constitute a set of data values.

[0100] S1062. For each element in the initial matrix, determine the first benefit value corresponding to that element, and obtain a benefit matrix with the first benefit value as the element, the number of virtual radars as the number of rows, and the number of tasks as the number of columns.

[0101] Here, each element in the initial matrix is ​​a set of data values, and each data value contains a true value and a measured value. The reciprocal of the error between the true value and the measured value can be used as the first benefit value corresponding to the data value, that is, the first benefit value of the element corresponding to the data value is obtained.

[0102] For example, the first benefit value corresponding to each element can be calculated using formula (3):

[0103]

[0104] Among them, a ij d_real represents the first benefit value corresponding to the element in the i-th row and j-th column (i.e., the element in the i-th row and j-th column of the obtained benefit matrix), d_real represents the true value, and d_test represents the measured value.

[0105] S1063. Construct a decision matrix based on the number of virtual radars, the number of tasks, the benefit matrix, and the preset values ​​of preset factors in the performance response factors included in the selected radar performance; the number of rows in the decision matrix is ​​the number of at least one set of different data values.

[0106] Here, the benefit matrix can be normalized first to obtain a normalized benefit matrix. Then, a decision matrix can be constructed based on the number of virtual radars, the number of tasks, the normalized benefit matrix, and the preset values ​​of preset factors in the performance response factors included in the selected radar performance.

[0107] Here, the preset factor among the performance response factors included in the selected radar performance can be any one of the performance influencing factors among all the performance response factors included in the selected radar performance, or it can be a specific performance influencing factor among the performance response factors included in the selected radar performance. For example, when the selected radar performance is radar ranging performance, and the performance response factors included in radar ranging performance are the number of ranging radars, ranging error, and the complexity of ranging measures, the preset factor can be the complexity of ranging measures.

[0108] Here, the preset values ​​of the preset factors can be set according to actual needs; for example, they can be 1.

[0109] For example, the principle of normalization can be expressed by formula (4):

[0110]

[0111] Among them, a' ij For the element in the i-th row and j-th column of the normalized benefit matrix, a ij Let be the element in the i-th row and j-th column of the benefit matrix, min represents the minimum value among the minimum element values ​​in each column of the benefit matrix, and max represents the maximum value among the maximum element values ​​in each column of the benefit matrix.

[0112] Here, the following principle can be used to construct the decision matrix:

[0113] S1. Based on the number of virtual radars and the number of tasks, determine all execution schemes for virtual radars to perform tasks. The number of execution schemes is the product of the number of virtual radars and the number of tasks. The number of execution schemes is the same as the number of groups of the at least one set of different data groups. Each execution scheme contains the sequence number R of the task and the sequence number X of the virtual radar, indicating that the Rth task is executed with the Xth sequence number.

[0114] S2. For each implementation plan, find the element in the R-th row and X-th column of the benefit matrix, and use the sum of the found elements as the column element of the first column of the decision matrix;

[0115] S3. Take the preset values ​​of the preset factors in the performance response factors included in the selected radar performance as the column elements of the second column of the decision matrix to obtain the decision matrix.

[0116] For example, when the selected radar performance is radar ranging performance, the number of virtual radars is the number of virtual radar units, and the number of tasks is the number of targets to be measured; for example, when the number of virtual radar units is 3 and there are 2 targets to be measured, there are 6 execution schemes: simultaneously using radar 1 to measure the range of targets 1 and 2, simultaneously using radar 2 to measure the range of targets 1 and 2, simultaneously using radar 3 to measure the range of targets 1 and 2, using radar 1 and radar 2 to measure the range of targets 1 and 2, using radar 1 and radar 3 to measure the range of targets 1 and 2, and using radar 2 and radar 3 to measure the range of targets 1 and 2; the corresponding decision matrix Y is as shown in formula (5):

[0117]

[0118] Among them, y 11 ~y 61 y represents the second benefit value corresponding to each of the six implementation schemes. 12 ~y 62 This is a preset value corresponding to the complexity of the ranging measure; where, when y 11 When representing the second benefit value corresponding to the first execution plan (i.e., simultaneously using radar 1 to range targets 1 and 2 respectively), this second benefit value is the sum of the elements in the first row of the benefit matrix. 41 When representing the second benefit value corresponding to the fourth execution scheme (i.e., using radar 1 and radar 2 to measure the distance to targets 1 and 2), the second benefit value is the sum of the first element of the first row and the second element of the second row in the benefit matrix, and the other elements are similarly represented.

[0119] S1064. Based on the decision matrix and the input weights, determine the verification and evaluation results corresponding to each set of data values ​​under the selected radar performance; the verification and evaluation results characterize the verification and evaluation results of the virtual radar used to generate the set of data values ​​in terms of the selected radar performance.

[0120] Here, the decision matrix can be normalized to obtain a normalized decision matrix. Based on the input weights and the normalized decision matrix, a weighted normalization matrix is ​​determined. Each element in the weighted normalization matrix corresponds to a set of data values. The minimum element in each column of the weighted normalization matrix is ​​taken as the ideal solution, and the maximum element is taken as the negative ideal solution. For each element in the weighted normalization matrix, a first distance value between the element and each ideal solution, and a second distance value between the element and each negative ideal solution are calculated. Based on the first and second distance values, the score value corresponding to the element is calculated. This score value is used as the verification and evaluation result of the set of data values ​​corresponding to the element under the selected radar performance.

[0121] For example, the principle of determining the weighted normalization matrix based on the input weights and the normalized decision matrix can be expressed by formula (6):

[0122] q ij =w x ·z ij ,i=1,…,T,j=1,…,M (6);

[0123] Where, q ij Let w be the element in the i-th row and j-th column of the weighted normalization matrix Q, where T represents the number of rows in the weighted normalization matrix Q, M represents the number of input weights, and w x z represents the weight of each input. ij This represents the element in the i-th row and j-th column of the normalized decision matrix.

[0124] For example, for each element in the weighted normalization matrix Q, the principle of calculating the score corresponding to that element based on the first distance value and the second distance value is as shown in formula (7):

[0125]

[0126] Where, q ij Let i be the element in the i-th row and j-th column of the weighted normalization matrix Q. For q ij The score value, where 'a' is the number of ideal solutions. This represents each ideal solution, where b is the number of negative ideal solutions. This represents each negative ideal solution. q ij The first distance value between each ideal solution, qij The second distance value between each negative ideal solution.

[0127] The following section uses the selected first type of radar performance as the radar detection performance evaluation and the selected second type of radar performance as the radar ranging performance evaluation. Furthermore, the method of the present invention is implemented through GUI software as an example to further illustrate the present invention.

[0128] Figure 2 This is a GUI interface for evaluating radar detection performance. Figure 3 This includes the categories and evaluation metrics for radar detection performance. For example... Figure 2 or Figure 3 As shown, the indicators for evaluating radar detection performance include: environmental and target characteristic evaluation indicators, detection performance technical indicators, and detection performance tactical indicators. The environmental and target characteristic evaluation indicators include: environmental interference and target radar cross-section. The detection performance technical indicators include: transmit power, operating frequency, and antenna gain. The detection performance tactical indicators include: range accuracy, velocity accuracy, and azimuth accuracy. Figure 2 As shown, the input box for the influence of an indicator category can be a blank input box located after the indicator category. The input box for the influence of each evaluation indicator can be a blank input box located after that evaluation indicator. The input box for the indicator data of each evaluation indicator can be a LODA control located after that evaluation indicator. Indicator data is selected and entered by manipulating this control. Furthermore, the entered data can be displayed in the corresponding box on the interface. Continuing as... Figure 2 As shown, a judgment matrix generation control can be displayed below each indicator category and below the radar detection performance evaluation indicator. Users can use this control to generate judgment matrices for each indicator category and the radar detection performance evaluation indicator. When users access other options on this interface ( Figure 2 (Not shown in the image) Five virtual radars were selected to evaluate radar detection performance, and, as... Figure 4 As shown, when the influence scores of each evaluation indicator and the influence scores of each indicator class for each virtual radar are input, (at least one score of the influence scores of each evaluation indicator and the influence scores of each indicator class for the five virtual radars is different). Figure 4 This only displays the impact of the evaluation index and the impact of the index category for one virtual radar. Based on these input scores, all judgment matrices corresponding to each of the five virtual radars can be generated. For example... Figure 2 or Figure 4 As shown, each indicator category and radar detection performance evaluation indicator has a judgment matrix generation control displayed below it. When the user interacts with these controls, judgment matrices for each indicator category and radar detection performance are generated, and can then be viewed... Figure 2 The generated judgment matrix is ​​displayed in the corresponding display box. Then, when the user operates the start control, the software system can determine the five scores corresponding to the five virtual radars based on all the judgment matrices corresponding to each of the five virtual radars, and can display these five scores in the display box. Figure 2 The corresponding box in the text, for example, Figure 5 The results for these 5 scores are displayed.

[0129] Figure 6 A GUI interface for evaluating radar ranging performance, such as Figure 6 As shown, the evaluation items in radar ranging performance include: true / measured value items (i.e., data items), the number of ranging radar units (i.e., virtual radar quantity items), and the target distance quantity items (i.e., task items). The performance influencing factors of radar ranging performance include the number of ranging radars, ranging resolution, and the complexity of the ranging measures. For example... Figure 6 As shown, the input boxes for the True / Measured Value items are two LODA controls: LODA True Range Value and LODA Measured Range Value. Users can select and input the true and measured range values ​​by manipulating these controls. The input boxes for the Range Radar Quantity and Target Distance Quantity items can be blank input boxes, where users can enter data, for example... Figure 7 As shown, the number of ranging radar units can be 3, and the number of target distances can be 2; also, the weight input box can be a blank input box following each performance influencing factor. When the user inputs the weights and confirms the operation of the control, the weight data can be input into the software system. For example, the input weights can be as follows: Figure 8 As shown, and continuing as Figure 6 As shown, all input data can be displayed in the corresponding boxes on the GUI interface. Additionally, as... Figure 6 As shown, a benefit matrix generation control is also displayed below the LODA control. When the user inputs the true range value, measured range value, number of ranging radars, and number of target distances, and operates the benefit matrix generation control, the software system can generate the corresponding benefit matrix and display it in the corresponding box on the GUI interface. Then, when the user operates the start control, the system generates a score for each execution plan (i.e., using which radar to measure which target) based on the input weights and the generated benefit matrix, and displays the scores of all execution plans from high to low. Simultaneously, it selects and displays the optimal plan for each task based on the score, for example, as... Figure 9As shown, when the number of ranging radars input is 3 and the number of target distances is 2, there are 6 execution schemes. The ranking results of the scores of these 6 schemes can be displayed, and the optimal scheme corresponding to measuring each target can be displayed. For example, for target 1, the optimal scheme is to use the 3rd virtual radar for measurement, and for target 2, the optimal scheme is to use the 3rd virtual radar for measurement.

[0130] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A method for verifying and evaluating the performance of a networked radar system, characterized in that, include: The operation of the networked radar displays multiple operating modes; each operating mode corresponds to a virtual radar simulated by the networked radar. Different types of virtual radars correspond to different operating modes; Based on the mode selection operation, it displays various radar performance characteristics under the selected operating mode; When the selected radar performance is determined to be of the first category based on the performance selection operation, the following are displayed: the index classes included in the selected radar performance, the evaluation indexes in each index class, the index class influence degree input box for each index class, and the index data and evaluation index influence degree input boxes for the evaluation indexes. The evaluation indexes for each first category of radar performance are the factors that affect that first category of radar performance. The index class influence degree for each index class represents the degree of influence of that index class on the first category of radar performance. The evaluation index influence degree for each evaluation index represents the degree of influence of that evaluation index on the first category of radar performance. Based on the indicator class influence, evaluation indicator influence, and indicator data entered in the input box, as well as the indicator class to which each evaluation indicator belongs, the verification and evaluation results of the input indicator class influence, indicator data, and evaluation indicator influence in the selected radar performance are determined. When the selected radar performance is determined to be of the second category based on the performance selection operation, the evaluation items and performance influencing factors included in the selected radar performance are displayed, as well as the input boxes for the evaluation items and the weight input boxes for the performance influencing factors; the performance influencing factors are the factors that affect the selected radar performance, and the weight of each performance influencing factor represents the degree of influence of the performance influencing factor on the second category of radar performance. Based on the input data to be evaluated and the input weights, the verification and evaluation results of the data to be evaluated and the weights in terms of the selected radar performance are determined; The performance selection operation is also used to select the number of virtual radars corresponding to the selected radar performance; when the selected radar performance is determined to be a first-class radar performance according to the performance selection operation, the following are displayed: the index classes included in the selected radar performance, the evaluation indexes in each index class, the input box for the index class influence of each index class, and the index data and the input box for the evaluation index influence, including: When the selected radar performance is determined to be of the first type based on the performance selection operation, and the number of virtual radars is preset, the following are displayed: the index class included in the selected radar performance, the evaluation index in each index class, the input box for the index class influence degree of each index class, and the index data and evaluation index influence degree input boxes for the evaluation indexes. The input box for the index class influence degree of each index class is used to input the index class influence degree corresponding to each virtual radar in the preset number of virtual radars. The input box for the index data of the evaluation indexes is used to input the index data corresponding to each virtual radar in the preset number of virtual radars. The input box for the evaluation index influence degree of the evaluation indexes is used to input the evaluation index influence degree of each evaluation index corresponding to each virtual radar in the preset number of virtual radars. The process of determining the verification and evaluation results of the input indicator class influence, indicator influence, and indicator data in the selected radar performance based on the indicator class influence, indicator data, and indicator influence input in the input box, as well as the indicator class to which each evaluation indicator belongs, includes: For any virtual radar in the preset number of virtual radars, based on the influence of the evaluation indicators contained in each indicator class, the importance of each evaluation indicator contained in that indicator class relative to itself and relative to each evaluation indicator other than itself is determined, and the judgment matrix of that indicator class is obtained. Based on the eigenvalues ​​and eigenvectors of the judgment matrix of the indicator class, determine the importance of each evaluation indicator contained in the indicator class to the indicator class; Based on the influence of each index class included in the selected radar performance, the importance of each index class included in the selected radar performance relative to itself and relative to each index class other than itself is determined, and the judgment matrix of the selected radar performance is obtained. Based on the eigenvalues ​​and eigenvectors of the selected radar performance judgment matrix, determine the importance of each index class included in the selected radar performance to the selected radar performance. The importance of each evaluation indicator to the selected radar performance is determined based on the importance of each evaluation indicator to its respective indicator class, and the importance of each indicator class to the selected radar performance. For each evaluation index, the index data corresponding to any virtual radar is dedimensionalized to obtain the quantitative value of the evaluation index. The product of the quantitative value of the evaluation index and the importance of the evaluation index to the selected radar performance is determined to obtain the product value of the evaluation index. The score of any virtual radar in relation to the selected radar performance is determined based on the product of the various evaluation metrics included in the selected radar performance. The score is used as the verification and evaluation result of the indicator class influence degree, indicator data and evaluation indicator influence degree of any virtual radar in terms of the selected radar performance. The virtual radars corresponding to the various operating modes include: Virtual active monostatic radar is a cluster of antenna transceiver nodes in a networked radar that operates in a self-transmitting and self-receiving state. A virtual transceiver split dual / multi-base radar is composed of multiple clusters of antenna transceiver nodes in a networked radar, and these multiple clusters operate in a transceiver split state; the multiple clusters are used to work collaboratively or each of the multiple clusters can work independently. A virtual MIMO radar is composed of multiple clusters of antenna transceiver nodes in a networked radar. These multiple clusters operate simultaneously in a state of separate transmission and reception, and they work together. Virtual sparse / ultra-sparse distributed coherent radar is composed of multiple clusters of antenna transceiver nodes in a networked radar. Different antenna nodes are sparsely arranged, while antenna nodes are densely arranged at half wavelengths. The multiple clusters are obtained by grouping different antenna nodes according to a preset aperture size. The multiple clusters are used for collaborative work or each cluster can work independently.

2. The method for verifying and evaluating the performance of a networked radar system according to claim 1, characterized in that, The items to be evaluated include: data items, the number of virtual radars, and task items; When the selected radar performance is determined to be of the second type based on the performance selection operation, the system displays the evaluation items and performance influencing factors included in the selected radar performance, as well as input boxes for the evaluation items and weight input boxes for the performance influencing factors, including: When the selected radar performance is determined to be the second type of radar performance based on the performance selection operation, the data items, quantity items, task items and performance influencing factors included in the selected radar performance are displayed, as well as the input boxes for the data items, the quantity items, the task items and the weights of the performance influencing factors. The input box for the data item is used to input at least one set of different data values; the input box for the quantity item is used to input the number of virtual radars used to generate the at least one set of different data values; the input box for the task item is used to input the number of tasks to be performed; the at least one set of different data values ​​includes: the measurement values ​​obtained by the preset number of virtual radars performing the tasks and the parameter values ​​of each task; the input box for the weight of the performance influencing factors is used to input the weight of each performance influencing factor.

3. The method for verifying and evaluating the performance of a networked radar system according to claim 2, characterized in that, The step of determining the verification and evaluation results of the input data to be evaluated and the input weights in terms of the selected radar performance, based on the input data to be evaluated and the input weights, includes: Based on at least one set of different input data values, the number of virtual radars, and the number of tasks, an initial matrix is ​​constructed with the number of virtual radars as the number of rows, the number of tasks as the number of columns, and each set of data values ​​as an element; wherein the row coordinate of each element represents the virtual radar used to generate that element. For each element in the initial matrix, determine the first benefit value corresponding to that element to obtain a benefit matrix with the first benefit value as the element, the number of virtual radars as the number of rows, and the number of tasks as the number of columns; A decision matrix is ​​constructed based on the number of virtual radars, the number of tasks, the benefit matrix, and the preset values ​​of preset factors in the performance response factors included in the selected radar performance; the number of rows in the decision matrix is ​​the number of groups of at least one set of different data values. Based on the decision matrix and the input weights, the verification and evaluation results corresponding to each set of data values ​​are determined under the selected radar performance; the verification and evaluation results characterize the verification and evaluation results of the virtual radar used to generate the set of data values ​​in terms of the selected radar performance.

4. The method for verifying and evaluating the performance of a networked radar system according to claim 3, characterized in that, The step of determining the verification and evaluation results corresponding to each set of data values ​​under the selected radar performance, based on the decision matrix and the input weights, includes: The decision matrix is ​​normalized to obtain a normalized decision matrix; Based on the input weights and the normalized decision matrix, a weighted normalization matrix is ​​determined; each element in the weighted normalization matrix corresponds to a set of data values. The minimum element in each column of the weighted normalization matrix is ​​taken as the ideal solution, and the maximum element is taken as the negative ideal solution. For each element in the weighted normalization matrix, calculate the first distance value between the element and each ideal solution, and the second distance value between the element and each negative ideal solution; Calculate the score corresponding to the element based on the first distance value and the second distance value; The score is used as the verification and evaluation result of a set of data values ​​corresponding to the element under the selected radar performance.

5. The method for verifying and evaluating the performance of a networked radar system according to claim 1, characterized in that, The first category of radar performance includes: radar detection performance, radar power performance, radar resolution performance, radar imaging and recognition performance, passive radar parameter estimation performance, radar intelligence performance, array optimization performance, signal-level fusion performance, data-level fusion performance, intelligence-level fusion performance, system reconfigurability performance, radar intelligence performance, anti-electronic stealth performance, and electromagnetic environment complexity performance in combat scenarios; the second category of radar performance includes: radar ranging performance, radar velocity measurement performance, radar angle measurement performance, anti-electronic jamming performance, anti-electronic reconnaissance performance, and radiation-resistant destruction performance.

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