A radar countermeasure effect evaluation method based on MBSE model building

Abstract

This invention discloses a radar countermeasure effectiveness evaluation method based on MBSE model construction. This method uses a model-based systems engineering approach, starting from the detection principle of radar countermeasures, to characterize and design the model construction. The model design studies radar equations, interference signal modulation methods, interference effects, and other aspects, analyzes user and functional requirements of the model, and uses these requirements to drive the design. The overall composition and performance of the coordinated model are optimized to ensure consistency among requirements, functions, and models. This invention utilizes an MBSE-based model construction method to unify and collaboratively design countermeasure models. Under existing circumstances, it can effectively implement the design and simulation of radar countermeasure models and the evaluation of countermeasure effectiveness.

Description

Technical Field

[0001] The present invention belongs to information processing technology, and in particular relates to a radar countermeasure effect evaluation method based on MBSE model construction. Background Art

[0002] Currently, electronic warfare (EW) has gradually become the dominant force in information warfare, and radar countermeasures, as a key component, are being studied in depth. Radars are widely used in combat due to their long range and high range resolution. Therefore, proficiency in radar system operation and research in radar data processing are becoming increasingly important. With the continuous advancement of modern radar technology, radar systems are becoming increasingly complex, both in terms of composition and performance. This results in high operational costs and the difficulty of analyzing radar countermeasure data. Therefore, using model construction to evaluate radar countermeasure effectiveness has become an essential component of radar technology development. Radar countermeasure model construction involves building different types of radar countermeasure simulation models based on the principles of radar countermeasures, flexibly configuring radar countermeasure environments, and conducting comparative analysis and evaluation of countermeasure effectiveness based on simulation data, providing simulation data support and tactical strategies for actual combat.

[0003] During the construction of the radar countermeasure model, the system engineering approach is mainly used for design. In this way, on the one hand, the composition and performance of the model are optimized as a whole. At the same time, comprehensive consideration, analysis and research are carried out on various factors related to the radar countermeasure model and the coordination relationship between the various components to ensure the effective construction of the model and achieve optimal performance.

[0004] Traditional radar countermeasure models are primarily constructed using a file-based systems engineering approach. This approach, known as file-based, means that during the model development process, all system engineering designs and specifications are documented using natural language. Each engineering activity produces a document, which can be in paper or electronic format. Documents are then used to communicate and interact with each engineering activity. The documents describe the requirements and design information required for each activity, and all activities are carried out in a unified and orderly manner according to the documents, ensuring a rigorous development process. However, this file-based approach, when utilizing natural language, can lead to inaccurate, incomplete, and ambiguous descriptions. This approach also compromises traceability and change impact assessment, and fails to fully and accurately describe system requirements and design specifications. Summary of the Invention

[0005] This paper proposes a radar countermeasure effectiveness assessment method based on MBSE model construction. Starting from the detection principle of radar countermeasures and building on the MBSE visual data model, the model construction process is characterized from three aspects: requirements analysis, functional decomposition, and model generation, ensuring the consistency of information transmission between requirements, functions, and models. Requirements analysis in the early stages of design drives functional decomposition, while model generation incorporates system functions and implements model design through iterative optimization. The model design examines radar equations, interference signal modulation methods, and interference effects, constructing a radar countermeasure model and evaluating countermeasure effectiveness.

[0006] The technical solution to achieve this invention is: a radar countermeasure effect evaluation method based on MBSE model construction, the steps are as follows:

[0007] Step 1: Conduct demand analysis on the radar countermeasure model.

[0008] Step 2: Based on the functional requirements of the radar adversary model obtained from the requirements analysis of the radar adversary model, design the functional architecture of the radar adversary model.

[0009] Step 3: Design the radar model based on the designed adversarial model functional architecture:

[0010] According to various requirements for radar model functions, the radar model includes a data input submodule, an antenna simulation submodule, a radar signal processing submodule, a data generation submodule, a power generation submodule and a data output submodule.

[0011] Step 4: Design the target model based on the input parameters.

[0012] Step 5: Design an interference model based on whether there is interference in the input parameters. If there is interference, proceed to step 6; otherwise, proceed directly to step 7.

[0013] Step 6: Design the interference model based on the input parameters.

[0014] Step 7: Obtain a radar countermeasure model based on the radar model, target model, and interference model to evaluate the radar countermeasure effect.

[0015] Compared with the prior art, the present invention has the following significant advantages:

[0016] To evaluate the effectiveness of radar countermeasures, a model-based systems engineering approach was employed to design the model. This approach involved researching and analyzing user and functional requirements, using these requirements as a driving factor. This led to a unified and collaborative design of the countermeasure model, ensuring that the design met both requirements and design requirements. Under current circumstances, this approach effectively enabled the design and simulation of the radar countermeasure model, as well as the evaluation of its effectiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a diagram of the functional modules of the radar countermeasure model of the present invention.

[0018] Figure 2 This is a workflow diagram of the radar countermeasure model functional module of the present invention.

[0019] Figure 3 The radar model design component block diagram of the present invention.

[0020] Figure 4 This is a block diagram of the interference model design of the present invention.

[0021] Figure 5 This is a block diagram of the antenna simulation submodule implementation of the present invention.

[0022] Figure 6 This is a diagram of the interference countermeasures of a single radar of the present invention.

[0023] Figure 7 This is a diagram of the multi-radar interference countermeasures of the present invention. DETAILED DESCRIPTION

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] The following will further introduce the specific implementation methods, as well as the technical difficulties and inventive points of this invention in combination with this design example.

[0026] Combine Figures 1 to 5 The MBSE-based model construction radar countermeasure effect evaluation method of the present invention comprises the following steps:

[0027] Step 1: Conduct a requirements analysis for the radar countermeasure model. The key to a model-based system engineering design approach is that it is driven by requirements. To fully implement the functionality of the countermeasure model, the first step is to conduct a requirements analysis for the radar countermeasure model, using the functional requirements of the radar countermeasure model as the guiding basis for model design. First, starting from the user's needs, analyze and study the user's expectations for the countermeasure model and the functions that can be completed using the model. Organize and complete the user's activities and their interaction behaviors and data with the model, and describe the usage scenarios and participants of the countermeasure model. For the radar countermeasure model, its external participants mainly include radar operators, decision makers, jammer operators, and targets. The user requirements of the model are the needs of these participants, mainly including the radar's need to detect targets, the jammer's need to interfere with the radar's need to detect targets, the radar's own need to achieve detection, and the jammer's own need to achieve interference.

[0028] After obtaining user requirements for the radar countermeasure model, the functional requirements of the model were determined by analyzing the implementation requirements and implementation process. Based on the application purpose and characteristics of the radar countermeasure model, it can be clearly stated that the model's main functions are to enable radar detection of targets, various jammer interference functions on radars, and target motion generation.

[0029] Based on the functional requirements of the radar countermeasure model, the model's functional design is further refined. In line with these requirements, the functional modules must possess both signal-level and functional-level simulation capabilities during the design process, allowing for the adoption of appropriate functional modules for different application scenarios and requirements. Furthermore, the functional modules must be configurable, enabling dynamic combination and configuration of modules based on different simulation requirements to achieve configurable countermeasure simulation. Furthermore, each functional module must simulate real-world conditions as accurately as possible to ensure the effectiveness of the countermeasure simulation.

[0030] Step 2: Based on the functional requirements of the confrontation model obtained from the demand analysis of the radar confrontation model, the functional architecture of the radar confrontation model is designed. The radar confrontation model is roughly divided into three functional modules: parameter setting functional module, model simulation functional module, and model output functional module to construct the confrontation model. Among them, the model simulation functional module establishes four models including radar confrontation model, interference model, target model, and environment model. The radar confrontation model functional module is composed of the following: Figure 1 shown.

[0031] Combine Figure 2 The radar countermeasure model is decomposed into three functional modules: parameter setting module, model simulation module, and model output module. These three modules provide a detailed and clear functional description of the countermeasure model, fully designing the module composition and functional interaction between modules, laying the foundation for subsequent modeling implementation.

[0032] The parameter setting function module, model simulation function module, and model output function module have their own division of labor, and the functions they implement are:

[0033] The parameter setting function module mainly receives external model setting parameters through the external interface, judges the validity of the setting parameters according to the required model type and the valid range of the model parameters, and at the same time retrieves the relevant parameters and data information of the model calculation and transmits them to the model simulation function module for simulation calculation.

[0034] The model simulation function module mainly selects the corresponding simulation model based on the simulation data information given by the parameter setting function module, calls related simulation calculations, obtains the real-time status and performance parameters of the model, and transmits them to the model output function module.

[0035] The model output function module receives the data parameters transmitted by the model simulation function module, classifies and organizes them according to the output requirements, and can transmit them to other subsystems for use through external interfaces, store them in the database for use in other model calculations, and organize them into visual data for output display.

[0036] Step 3: Design the radar model based on the designed adversarial model functional architecture. Based on the various functional requirements of the radar model, the radar model design mainly includes six submodules: data input submodule, antenna simulation submodule, radar signal processing submodule, data generation submodule, power generation submodule, and data output submodule.

[0037] Combine Figure 3 , each module is designed as follows:

[0038] Data input submodule: mainly receives external input model setting parameters and data, system parameters required for model simulation, control parameters for radar operation, and parameters and data required for calculations involved in the model simulation process.

[0039] Antenna simulation submodule: Based on the received antenna parameters, such as speed, steering mode, and rotation direction, the radar antenna system is simulated. By simulating the antenna pattern and antenna motion, the antenna transmission and reception gains for targets within the detection range are calculated. The implementation block diagram of the antenna simulation submodule is as follows: Figure 5 shown.

[0040] Radar signal processing submodule: This primarily includes pulse compression, signal detection, and trace estimation. Pulse compression technology typically uses matched filtering to process wide-bandwidth signals like linear frequency modulation. A matched filter is an optimal filter designed to maximize the signal-to-noise ratio. Pulse compression further improves the system's signal-to-noise ratio.

[0041] Assuming a passive filter is used, the pulse width after the filter is τ = 1 / B, where B is the input signal bandwidth. The gain of the signal is:

[0042] Gain=B·τ (1)

[0043] Where P is the signal power and Gain is the gain obtained by the signal.

[0044] After the target echo signal is processed by pulse compression, the range spectrum of the baseband signal can be obtained. Then, the signal detection processing is used to detect the target according to a certain threshold value and estimate the target's point trace parameters.

[0045] Data generation submodule: It mainly processes the target track, including track generation, track association and track tracking, so as to accurately establish the track and keep tracking the target when the target enters the detection range, while avoiding false tracks caused by clutter, interference and other factors as much as possible. Since the multiple estimates generated in the signal processing process of the radar signal processing submodule will be used as multiple observation values ​​of the current batch of radar, which include both real targets and false targets, this method eliminates multiple values ​​by associating with existing tracks. During the track association processing, the state estimate of the target track at the most recent moment n is used as the center of the circle, and the association threshold is set. Based on this threshold, a truncated fan-shaped association area Q is established. n If a value among the multiple estimates obtained falls within the associated region, then the estimate forms an association hypothesis with the target's existing track.

[0046] Q n =Span(r n max ,r n min ,α n max ,α n min ) (2)

[0047] In the formula, Span(·) is a sector, r nmax and r nmin They correspond to the outer radius and inner radius of the fan-shaped associated area at time n, respectively. nmax and α nmin They correspond to the maximum azimuth and minimum azimuth of the fan, respectively, where:

[0048]

[0049] Where r n and α n Corresponding to the predicted estimates of the distance and direction of the target track at the next moment, r i ' and r i They correspond to the distance measurement and estimation of the target track at the existing time, α i ' and α iare the azimuth measurement and estimate of the target track at the existing time, κ and ζ are the weighted coefficients of the mean square error and deviation between the distance measurement and the estimate, ν and χ are the weighted coefficients of the mean square error and deviation between the azimuth measurement and the estimate, and i is the serial number of the sector-related area.

[0050] Power generation submodule: It can calculate the radar range in real time based on radar working parameters, environmental parameters, meteorological parameters and geographic information parameters, and generate radar power coverage area. The fundamental function of radar is to detect targets and locate them, so the maximum detection range R max It is one of the important performance indicators of radar, which can be expressed as:

[0051]

[0052] Where, P R is the radar’s transmitting power, G R is the radar antenna gain, λ is the radar wavelength, σ is the target scattering cross-sectional area, SNR min is the minimum detectable power.

[0053] Data output submodule: The radar data output submodule can output signal-level and function-level simulation data according to the model simulation requirements.

[0054] Step 4: Design the target model based on the input parameters. If the radar's transmitted signal is LFMCW, the basic form of the transmitted signal S(t) is:

[0055]

[0056] Where f0 is the starting frequency of the signal, T is the FM period, α is the FM slope, θ0 is the initial phase, A is the transmitted signal amplitude, t is time, k is the number of FM periods, and j is the imaginary unit.

[0057] Then the target echo signal S r (t) can be expressed as:

[0058]

[0059] Where τ is the signal delay when the echo reaches the radar receiver, τ = 2R / c, R is the distance between the target and the radar, c is the speed of light, and A r is the target echo signal amplitude.

[0060] Step 5: Design an interference model based on whether there is interference in the input parameters. If there is interference, proceed to step 6; otherwise, proceed directly to step 7.

[0061] Step 6: Design the interference model based on the input parameters. The interference model design includes data input module, interference generation module, and data output module. Figure 4 shown.

[0062] The interference generation module generates suppression interference and deceptive interference. Suppression interference mainly transmits high-power interference signals into the radar receiver, thereby greatly reducing the signal-to-noise ratio of the target echo signal, making radar target detection difficult. In the case of suppression interference, the interference signal power P received by the radar is rj It can be expressed as:

[0063]

[0064] Where, P j is the jammer transmission power, G j is the antenna gain of the jammer in the direction of the radar; G r is the radar antenna gain in the jammer direction; λ is the radar wavelength; R j is the distance from the jammer to the radar; L is the jamming signal loss during the transmission process.

[0065] The maximum detection range of the radar under interference R max It can be expressed as:

[0066]

[0067] Where, P R is the radar’s transmitting power, G R is the radar antenna gain, λ is the radar wavelength, σ is the target scattering cross-sectional area, SNR min is the minimum detectable power, P rj is the interference signal power received by the radar.

[0068] Deceptive jamming mainly acts on the radar by generating jamming signals containing false target information, preventing the radar from detecting the real target or correctly obtaining the target's true parameters, thereby confusing and disrupting the radar's target detection and tracking. Assume that the echo signal X(t) of the real target can be expressed as:

[0069]

[0070] Among them, A T is the amplitude of the echo signal of the real target, and f is the frequency of the echo signal of the real target.

[0071] The interference signal containing false distance information can be described as a signal that is delayed relative to the target echo signal:

[0072]

[0073] Where, J s (t) is the interference signal containing false distance information, A j is the interference signal amplitude, and Δt is the signal delay corresponding to the false distance information.

[0074] The interference signal containing false velocity information can be regarded as a signal with a Doppler frequency offset Δf relative to the target echo signal, that is,

[0075]

[0076] Among them, J v (t) is the interference signal containing false speed information.

[0077] Step 7: Design the radar model, target model, and interference model based on steps 2 to 6 to design a radar countermeasure model for evaluation.

[0078] The experiment uses the MBSE-based model proposed in the article to build a radar countermeasure effect evaluation method, and conducts experimental verification and testing by using the mutual confrontation between the model-simulated radar system and the jamming system. Through the algorithm in this article, the authenticity of the radar countermeasure effect is close to the actual situation, the model design efficiency is high, and it can meet user needs. The results are as follows Figure 6 、 Figure 7 shown.

Claims

1. A radar countermeasure effect evaluation method based on MBSE model construction, characterized by: Here are the steps: Step 1: Conduct demand analysis on radar countermeasure model; Step 2: Based on the functional requirements of the radar adversary model obtained from the requirements analysis of the radar adversary model, design the functional architecture of the radar adversary model; Step 3: Design the radar model based on the designed adversarial model functional architecture: According to various requirements for radar model functions, the radar model includes a data input submodule, an antenna simulation submodule, a radar signal processing submodule, a data generation submodule, a power generation submodule, and a data output submodule; The details are as follows: Data input submodule: used to receive external input model setting parameters and data, system parameters required for model simulation, radar operation control parameters, and parameters and data required for calculation involved in the model simulation process; Antenna simulation submodule: simulates the radar antenna system based on the received antenna parameters; calculates the antenna transmission and reception gains for targets within the detection range by simulating the antenna pattern and antenna motion; Radar signal processing submodule: including pulse compression, signal detection, and trace estimation; Data generation submodule: processes target tracks, including track generation, track association, and track tracking, to accurately establish and track the target when it enters the detection range, while minimizing false tracks caused by clutter and interference. In the track association process, the state estimate of the target track at the latest time n is used as the center of the circle, and the association threshold is set. Based on this threshold, the truncated fan-shaped association area Q is established. n If a value among the multiple estimates obtained falls within the associated region, then the estimate forms an association hypothesis with the target's existing track: Q n =Span(r nmax ,r nmin ,a nmax ,a nmin ) (2) In the formula, Span(·) is a sector, r nmax and r nmin They correspond to the outer radius and inner radius of the fan-shaped associated area at time n, respectively. nmax and α nmin They correspond to the maximum azimuth and minimum azimuth of the fan, respectively, where: Where r n and α n Corresponding to the predicted estimates of the distance and direction of the target track at the next moment, r i ' and r i They correspond to the distance measurement and estimation of the target track at the existing time, α i ' and α i They correspond to the azimuth measurement and estimated value of the target track at the existing time, κ and ζ correspond to the weighted coefficients of the mean square error and deviation between the distance measurement and the estimated value, ν and χ correspond to the weighted coefficients of the mean square error and deviation between the azimuth measurement and the estimated value, and i is the serial number of the sector-related area. Power generation submodule: It can calculate the radar range in real time based on radar operating parameters, environmental parameters, meteorological parameters and geographic information parameters, and generate the radar power coverage area; Data output submodule: outputs signal-level and function-level simulation data according to model simulation requirements; Step 4: Design the target model based on the input parameters; Step 5: Design an interference model based on whether there is interference in the input parameters. If there is interference, proceed to step 6; otherwise, proceed directly to step 7. Step 6: Design an interference model based on the input parameters; Step 7: Obtain a radar countermeasure model based on the radar model, target model, and interference model to evaluate the radar countermeasure effect.

2. The MBSE-based model building radar countermeasure effect evaluation method according to claim 1 is characterized in that: In step 1, the requirements analysis of the radar countermeasure model is carried out as follows: The functional requirements of the radar countermeasure model are used as the guiding basis for model design. First, starting from the user's needs, we analyze and study the user's expectations of the countermeasure model and the functions that can be completed using the model. We organize and complete the user's activities and their interaction behaviors and data with the model, and describe the usage scenarios and participants of the countermeasure model. For the radar countermeasure model, its external participants mainly include radar operators, decision makers, jammer operators, and targets. The user requirements of the model are the needs of these participants, including the radar's need to detect targets, the jammer's need to interfere with the radar's target detection, the radar's own need to achieve detection, and the jammer's own need to achieve interference. After obtaining the user requirements of the radar countermeasure model, the functional requirements of the model are determined by analyzing the implementation requirements and implementation process of these requirements. Based on the application purpose and characteristics of the radar countermeasure model, the model's functions are clearly defined as being able to realize the radar's detection function of the target, the jammer's various interference functions on the radar, and the target's motion generation function. According to the functional requirements of the radar countermeasure model, the model is further functionally designed. In combination with the functional requirements, the functional modules must have both signal-level and function-level simulation functions during the design process, so that functional modules suitable for different application scenarios and requirements can be adopted. At the same time, the functional modules must also be configurable and can be dynamically combined and configured according to different types of simulation requirements to achieve configurable countermeasure simulation. In addition, each functional module must simulate the real situation as accurately as possible to ensure the effectiveness of the countermeasure simulation.

3. The MBSE-based model building radar countermeasure effect evaluation method according to claim 2 is characterized in that: Based on the functional requirements of the radar adversarial model obtained from the requirements analysis results in step 2, the functional architecture of the radar adversarial model is designed as follows: The radar countermeasure model is decomposed into three functional modules according to its functions: parameter setting module, model simulation module, and model output module; The parameter setting function module receives external model setting parameters through the external interface, judges the validity of the setting parameters according to the required model type and the valid range of the model parameters, and retrieves the relevant parameters and data information of the model calculation and transmits them to the model simulation function module for simulation calculation; The model simulation function module selects the corresponding simulation model according to the simulation data information provided by the parameter setting function module, calls the relevant simulation calculation, obtains the real-time status and performance parameters of the model, and transmits them to the model output function module; The model output function module receives the data parameters transmitted by the model simulation function module, classifies and organizes them according to the output requirements, and can transmit them to other subsystems for use through external interfaces, store them in the database for use in other model calculations, and organize them into visual data for output display.

4. The MBSE-based model building radar countermeasure effect evaluation method according to claim 3 is characterized in that: Step 4: Design the target model based on the input parameters, as follows: If the radar's transmitted signal is LFMCW, the basic form of the transmitted signal S(t) is: Where f0 is the starting frequency of the signal, T is the FM period, α is the FM slope, θ0 is the initial phase, A is the transmitted signal amplitude, t is the time, k is the number of FM periods, and j is the imaginary unit; Then the target echo signal S r (t) is expressed as: Where τ is the signal delay when the echo reaches the radar receiver, τ = 2R / c, R is the distance between the target and the radar, c is the speed of light, and A r is the target echo signal amplitude.

5. The MBSE-based model building radar countermeasure effect evaluation method according to claim 4 is characterized in that: In step 6, the interference model design includes a data input module, an interference generation module, and a data output module. The interference generation module is used to generate suppressive interference and deceptive interference.

6. The MBSE-based model-building radar countermeasure effect evaluation method according to claim 5 is characterized by: In the case of suppressive jamming, the jamming signal power P received by the radar is rj Expressed as: Where, P j is the jammer transmission power, G j is the antenna gain of the jammer in the direction of the radar; G r is the radar antenna gain in the jammer direction; λ is the radar wavelength; R j is the distance from the jammer to the radar; L is the jamming signal loss during transmission; The maximum detection range of the radar under interference R max Expressed as: Where, P R is the radar’s transmitting power, G R is the radar antenna gain, λ is the radar wavelength, σ is the target scattering cross-sectional area, SNR min is the minimum detectable power, P rj is the interference signal power received by the radar; Assume that the echo signal X(t) of the real target is expressed as: Among them, A T is the amplitude of the echo signal of the real target, and f is the frequency of the echo signal of the real target; The interference signal containing false distance information is described by a signal that is delayed relative to the target echo signal: Where, J s (t) is the interference signal containing false distance information, A j is the interference signal amplitude, Δt is the signal delay corresponding to the false distance information; The interference signal containing false velocity information is regarded as a signal with a Doppler frequency offset Δf relative to the target echo signal, that is, Among them, J v (t) is the interference signal containing false speed information.

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