Cooperative interference effect online evaluation method based on attention mechanism enhancement

Abstract

The invention discloses a cooperative interference effect online evaluation method based on attention mechanism enhancement. The method comprises the following steps: acquiring radar parameters and interference parameters in a radar cooperative interference scene; inputting the radar parameters and the interference parameters into a pre-trained first-stage evaluation model and a pre-trained second-stage evaluation model, and respectively outputting a first-stage interference effect evaluation result and a second-stage interference effect evaluation result; wherein the first-level evaluation model is obtained by performing supervised training on a static interference evaluation score sample; the second-level evaluation model is obtained by performing supervised training on a dynamic interference evaluation score sample calculated based on distance change; and determining a target interference evaluation result according to the first-stage interference effect evaluation result and the second-stage interference effect evaluation result. According to the invention, a two-stage evaluation mechanism is adopted, static interference effect evaluation and dynamic interference effect evaluation are realized at the same time, and the accuracy of the evaluation result is improved.

Description

Technical Field

[0001] This invention belongs to the field of radar technology, specifically relating to an online evaluation method for cooperative jamming effects based on attention mechanism enhancement. Background Technology

[0002] With the development of radar anti-jamming technology, traditional jamming methods are unable to cope with complex battlefield environments. Coordinated jamming technology, through the coordinated operation of various jammers, can effectively improve combat effectiveness. Evaluation of the effectiveness of coordinated jamming is one of the core issues of this technology. Current research on coordinated jamming effectiveness evaluation focuses on two main directions: the selection of jamming effect indicators and the design of evaluation methods. In actual combat, the jamming side needs to evaluate the effectiveness of coordinated jamming in real time based on subjective information. However, there are relatively few relevant studies and conclusive results both domestically and internationally.

[0003] Modern radars possess multiple operating modes. Taking airborne fire control radar as an example, its operating modes include Search-while-ranging (RWS), Search-and-track (TAS), Search-while-tracking (TWS), and Single-target tracking (STT). In coordinated jamming operations, each jammer is constrained by its deployment, jamming resources, and operational intent. Without knowing the specific performance of the radars, it is necessary to make a reliable assessment of the coordinated jamming effect based on its own jamming resources, with the ultimate goal of achieving tactical objectives, to guide subsequent coordinated jamming decisions.

[0004] Currently, existing methods for evaluating the effectiveness of coordinated jamming need to address two main issues. First, current radar countermeasures are increasingly moving towards multi-platform, multi-jamming pattern coordination, often incorporating intelligent methods. However, there's a lack of reasonable, quantifiable online evaluation indicators to support an effective closed-loop coordinated jamming system. Second, modern electronic warfare aircraft are equipped with various jamming methods. A key challenge is effectively evaluating the effectiveness of coordinated jamming in complex scenarios with numerous jamming units, patterns, and deployment methods. Current methods primarily focus on static gains under a given situation, lacking consideration for dynamic gains based on operational progression. Summary of the Invention

[0005] To address the aforementioned problems in the prior art, this invention provides an online evaluation method for the collaborative interference effect based on attention mechanism enhancement.

[0006] The technical problem to be solved by this invention is achieved through the following technical solution: This invention provides an online evaluation method for the collaborative interference effect based on attention mechanism enhancement, comprising: Acquire radar parameters and jamming parameters in radar cooperative jamming scenarios; Radar parameters and interference parameters are input into a pre-trained first-level evaluation model and a second-level evaluation model, which output the first-level interference effect evaluation results and the second-level interference effect evaluation results, respectively. The first-level evaluation model is obtained through supervised training using first radar parameter samples, first interference parameter samples, and pre-calculated static interference evaluation score samples. The second-level evaluation model is obtained through supervised training using second radar parameter samples, second interference parameter samples, and pre-calculated dynamic interference evaluation score samples based on range changes. Based on the results of the first-level interference effect assessment and the second-level interference effect assessment, the target interference assessment results are determined.

[0007] This invention provides an online evaluation method for collaborative interference effects based on attention mechanism enhancement. It adopts a two-level evaluation mechanism. The first level is a static interference effect evaluation under fixed distance and fixed parameter combination. The second level is a dynamic interference effect evaluation considering the dynamic change of distance under fixed parameter combination. Thus, while describing the interference effect at the current moment, it also quantifies the dynamic interference effect, improving the accuracy of the evaluation results. Furthermore, it combines a deep learning network model to fit the interference effect, making it portable.

[0008] The present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0009] Figure 1 This is a flowchart illustrating an online evaluation method for collaborative interference effects based on attention mechanism enhancement, provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of radar operating state transition according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a radar cooperative interference sample scenario according to an embodiment of the present invention. Detailed Implementation

[0010] 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.

[0011] This invention provides an online evaluation method for the effect of collaborative interference based on an attention mechanism enhancement. See also... Figure 1 The method includes the following steps: S10. Obtain radar parameters and interference parameters under radar cooperative jamming scenarios.

[0012] For example, the radar in this embodiment can be an airborne fire control radar, and the radar parameters and interference parameters are shown in Table 1 and Table 2 below, respectively.

[0013] Table 1 Radar Parameters

[0014] Table 2 Interference Parameters

[0015] S20. Input the radar parameters and jamming parameters into the pre-trained first-level evaluation model and second-level evaluation model, and output the first-level jamming effect evaluation results and the second-level jamming effect evaluation results respectively.

[0016] The first-level evaluation model is obtained through supervised training using the first radar parameter sample, the first interference parameter sample, and pre-calculated static interference evaluation score samples; the second-level evaluation model is obtained through supervised training using the second radar parameter sample, the second interference parameter sample, and pre-calculated dynamic interference evaluation score samples based on distance changes.

[0017] Optionally, the first-level evaluation model is the attention-interpretable table learning network TabNet, and the second-level evaluation model is the temporal convolutional network TCN.

[0018] Specifically, the first radar parameter sample, the first interference parameter sample, the second radar parameter sample, and the second interference parameter sample used for model training can be parameters corresponding to the same radar cooperative interference sample scenario, or parameters corresponding to different radar cooperative interference sample scenarios. This embodiment does not impose any restrictions on this, and the specific parameters included can be referred to Table 1 and Table 2, which will not be elaborated here.

[0019] Optionally, the process of pre-calculating static interference assessment score samples includes: A1. Based on the first radar parameter sample and the first interference parameter sample, calculate the carrier frequency dispersion, bandwidth change, radar signal amplitude change intercepted by the jammer, and interference gain corresponding to deception interference before and after the interference is applied.

[0020] For example, the degree of carrier frequency dispersion, the degree of bandwidth variation, and the amount of amplitude variation of the radar signal intercepted by the jammer can be used as quantitative indicators to evaluate the jamming effect.

[0021] Optionally, the carrier frequency dispersion is expressed as:

[0022] In the formula, This indicates the degree of change in the radar's operating frequency before and after the application of interference. Indicates the number of samples. Indicates the first The radar operating frequency of the second sampling express The average operating frequency of the radar in the sub-sample.

[0023] Specifically, radar frequency agility is an anti-jamming technique that allows the radar operating frequency to rapidly change within a certain range. Generally, the stronger the interference received by the radar receiver, the more drastic the frequency hopping will be, in order to escape the interference signal's frequency band. Therefore, the degree of dispersion essentially indicates the drastic change in the radar's operating frequency.

[0024] Optionally, the degree of bandwidth variation is expressed as:

[0025] In the formula, Indicates the degree of bandwidth variation. This indicates the bandwidth of the radar signal after interference is applied. This indicates the bandwidth of the radar signal before the interference was applied.

[0026] Specifically, variations in radar signal bandwidth are primarily used to counter suppressive jamming. If the jamming signal has a narrow bandwidth, the radar can increase its bandwidth to prevent the jamming signal from achieving full coverage. Conversely, if the jammer forcibly increases its bandwidth, the jamming intensity will decrease accordingly. Therefore, if a radar attempts to increase its bandwidth, it indicates that the jamming is affecting its normal operation, necessitating this attempt.

[0027] Optionally, the change in radar signal amplitude intercepted by the jammer is expressed as:

[0028] In the formula, This represents the change in the amplitude of the radar signal intercepted by the jammer. This indicates the amplitude of the radar signal intercepted after the interference was applied. This indicates the amplitude of the radar signal intercepted before the interference was applied.

[0029] Specifically, radar has some special operating modes, such as burn-through mode, which can essentially be regarded as an anti-jamming measure. It can forcibly increase the transmission power in an attempt to re-detect and track the target after it has been lost or interfered with. Therefore, if the jammer intercepts a radar signal with a sudden increase in strength, it means that the jamming has forced the radar to use high-power radiation, achieving a certain jamming effect.

[0030] In one alternative implementation, the number of decoys and the deception interference power can also be adjusted. The number of false targets is used as a quantitative indicator to evaluate the effectiveness of jamming. Specifically, in deception jamming, the number of false targets is a parameter controlled by the jammer. If the number of false targets is set to 3, 3 different deceptive jamming signals will be emitted, causing the radar to detect the range and velocity information of 3 non-existent false targets. Since the jammer's transmission power is limited, all false targets share the jammer's transmission power. If the jamming power is too high, the radar system's protection mechanism will detect that it is a false target; if the jamming power is too low, the radar will detect a real target, and the deception effect will not be achieved.

[0031] Optionally, the interference benefit corresponding to deceptive interference is expressed as:

[0032] In the formula, This represents the interference benefit corresponding to deceptive interference. Indicates the number of false targets. This represents the preset optimal number of false targets. Indicates quantity tolerance. This indicates the transmission power of the decoy target. This indicates the power of the radar signal entering the jammer. Indicates power tolerance. and These represent the weights for the number of false targets and the power of the false targets, respectively.

[0033] A2. Determine the jamming benefit corresponding to suppression jamming based on the carrier frequency dispersion, bandwidth variation, and radar signal amplitude variation.

[0034] Optionally, the interference gain corresponding to suppression interference is expressed as:

[0035] In the formula, This represents the interference gain corresponding to suppressive interference. , and These represent the preset reference frequency agility range, reference bandwidth range, and reference power adjustment range, respectively. , and These represent the preset weight coefficients for the corresponding items.

[0036] A3. Determine the first interference benefit based on the interference benefit corresponding to deceptive interference and the interference benefit corresponding to suppressive interference.

[0037] Optionally, the benefit of the first disturbance is expressed as:

[0038] In the formula, This represents the benefit of the first interference.

[0039] A4. Based on the predetermined changes in pulse repetition frequency, pulse width, and pulse number before and after the application of interference, as well as the preset thresholds corresponding to each change, determine the switching parameters of the radar operating state.

[0040] Optionally, the change in pulse repetition frequency is expressed as:

[0041] In the formula, This indicates the amount of change in the pulse repetition frequency. This indicates the frequency of radar signal pulse repetition intercepted after the interference is applied. This indicates the frequency of radar signal pulse repetition intercepted before the interference is applied.

[0042] Optionally, the pulse width variation is expressed as:

[0043] In the formula, This indicates the amount of pulse width variation. This indicates the pulse width of the radar signal intercepted after the jamming was applied. Indicates the pulse width of the radar signal intercepted before the interference was applied; Optionally, the change in pulse number is expressed as:

[0044] In the formula, This indicates the change in the number of pulses. This indicates the number of radar signal pulses intercepted after the interference was applied. This indicates the number of radar signal pulses intercepted before the interference was applied.

[0045] Specifically, by judging whether the three parameters of pulse repetition frequency change, pulse width change, and pulse number change all exceed their respective preset thresholds, it can be determined whether the radar operating state has changed. Then, by combining the pulse repetition frequency, pulse width, and pulse number before and after the change, the transition parameters of the radar operating state can be determined. Table 3 reflects the parameter ranges of pulse repetition frequency, pulse width, and pulse number corresponding to each radar operating state.

[0046] Table 3 Waveform parameters under different radar operating conditions

[0047] A5. Determine the second interference benefit based on the radar operating state transition parameters and the preset operating state transition reward and punishment rules.

[0048] Optionally, the second interference gain is expressed as:

[0049] In the formula, Indicates that the radar is in working state Switch to work mode The conversion parameters; radar operating states include Search-while-ranging (RWS), Search-while-tracking (TAS), Search-while-tracking (TWS), and Single-target tracking (STT).

[0050] For example, This represents the transition of radar operating states, and the preset reward and punishment rules for operating state transitions are designed as follows: Figure 2 As shown, it is necessary to... The quantitative explanation is as follows: Table 4. Basic Reward and Punishment Parameter Settings

[0051] Table 5 Radar Status Degradation Reward Table

[0052] Range While Scan (RWS) refers to radar simultaneously scanning the airspace and measuring the distance to targets. It involves the radar emitting a beam to cover a wide scan area while simultaneously measuring the distance to targets within that area, but its target tracking accuracy is limited. Its characteristics include: a wide scan area, capable of detecting multiple targets simultaneously; inability to accurately track changes in target angle or velocity; and its common use for initial target detection, identifying and initially locating enemy aircraft, providing a foundation for subsequent TAS, TWS, and STT operations.

[0053] Track-and-Scan (TAS) refers to radar simultaneously scanning the airspace and tracking multiple targets. The radar continuously scans the airspace while using tracking algorithms (such as Kalman filtering) to record the target's position, velocity, and heading. Its characteristics include: the ability to track multiple targets in a simple manner; a certain level of tracking accuracy, but lower than TWS and STT; and the ability to provide guidance information to weapon systems because it tracks targets.

[0054] Track-While-Scan (TWS) refers to the simultaneous and stable tracking of multiple targets to establish high-quality tracks. By employing intelligent scanning with radar, it intelligently allocates "illumination time" based on the positions of tracked targets, rapidly switching between multiple targets. Simultaneously, filtering algorithms (commonly α-β or Kalman filtering) predict the next target's position, resulting in a smooth and accurate track. Its characteristics include: higher quality target tracks and a higher data rate compared to Track-While-Scan (TAS).

[0055] Single Target Track (STT) refers to locking onto and tracking a single target with high precision. It involves focusing the radar on the target and using continuous wave or pulse signals to accurately measure the target's distance, velocity, and angle. Its characteristics include: highest accuracy, suitable for direct missile guidance; inability to track other targets simultaneously; fast data update rate, suitable for highly maneuverable targets; and cessation of all scanning and search activities.

[0056] Finally, based on the radar's operational status transition parameters and the preset operational status transition reward and penalty rules, the following is determined: The corresponding value will yield the second interference benefit.

[0057] A6. Determine the static interference evaluation score sample based on the first interference gain and the second interference gain.

[0058] Specifically, static interference assessment score samples Represented as: .

[0059] Optionally, the process of pre-calculating dynamic interference assessment score samples includes: B1. Divide the distance between the cover target and the jammer from the radar in the radar cooperative jamming sample scenario into multiple range segments; and determine multiple distance points in each range segment.

[0060] For example, refer to Figure 3 A radar cooperative jamming sample scenario is illustrated. The distances between the cover target and the jammer and the radar in the scenario are segmented according to a first preset distance interval, resulting in multiple distance segments. Within each distance segment, multiple distance points are determined according to a second preset distance interval. Assuming the initial distances between the cover target and the jammer and the radar in the scenario are [60km, 55km, 52km] (representing the distances of the three aircraft relative to the radar), each distance segment is divided into 10km segments, and six distance slices (distance points) are set in each segment, namely [60km, 55km, 52km], [58km, 53km, 50km], [56km, 51km, 48km], [54km, 49km, 46km], [52km, 47km, 44km], and [50km, 45km, 42km]. Step B2 is executed on these six distance slices.

[0061] B2. Based on multiple range segments, multiple range points in each range segment, second radar parameter samples, second interference parameter samples, and the pre-constructed state value function corresponding to the radar operating state, determine the dynamic interference assessment score sample.

[0062] Optionally, the dynamic interference evaluation score sample is represented as:

[0063] In the formula, This represents a sample of dynamic interference assessment scores. This indicates the number of distance points in a single distance segment. The state value function represents the radar's operating state. Indicates the radar's operating status. Indicates the object currently being tracked by the radar. This represents the cooperative gain correction term. This represents the distance corresponding to each distance point.

[0064] For example, the radar operating state corresponding to each range point is determined based on the second radar parameter sample and the second interference parameter sample, and a corresponding dynamic interference assessment score sample can be obtained for each range segment. The state value function corresponding to the radar operating state is expressed as:

[0065] in, This represents the basic reward weight based on the radar's operating status. This indicates the radar's operational status at a distance of m. Indicates the object currently being tracked by the radar; where:

[0066]

[0067] The cooperative gain correction term (due to the possibility of simultaneous suppression and deception) is represented as:

[0068]

[0069] The distance weighting coefficient is represented as:

[0070] Specifically, R represents the distance to each distance point. The closer the distance, the more difficult it is to interfere, and the higher the value for the interfering party.

[0071] S30. Based on the results of the first-level interference effect assessment and the second-level interference effect assessment, determine the target interference assessment result.

[0072] For example, the results of the first-level interference effect assessment and the second-level interference effect assessment are combined to obtain the target interference assessment result.

[0073] This embodiment of the online evaluation method for collaborative interference effects based on attention mechanism enhancement has the following advantages compared to existing technologies: 1. Existing interference assessment scenarios are usually based on a single jammer against a single radar, while the method in this embodiment can reliably assess the cooperative jamming effect in a multi-jammer cooperative scenario.

[0074] 2. Existing interference effect assessment methods do not consider dynamic assessment and only describe the interference effect under the current situation. This embodiment proposes a two-level assessment method. The first level is a static interference effect assessment under a fixed distance and a fixed parameter combination. The second level considers the dynamic scenario simulation and performs a dynamic interference effect assessment under a fixed parameter combination over a distance segment (multiple distance slices). Thus, while describing the interference effect at the current moment, it also quantifies the dynamic interference effect.

[0075] 3. This embodiment combines a deep learning network model to fit the interference effect, and is portable.

[0076] It should be noted that the terms "first," "second," etc., are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention.

[0077] 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 or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0078] 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 and the disclosure in carrying out the claimed invention. In the description of the invention, the word "comprising" does not exclude other components or steps, "a" or "an" does not exclude a plurality, and "a plurality" means two or more, unless otherwise explicitly specified. Furthermore, while different embodiments may describe certain measures, this does not mean that these measures cannot be combined to produce good results.

[0079] 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. An online evaluation method for the collaborative interference effect based on attention mechanism enhancement, characterized in that, include: Acquire radar parameters and jamming parameters in radar cooperative jamming scenarios; The radar parameters and the interference parameters are input into a pre-trained first-level evaluation model and a second-level evaluation model, which respectively output the first-level interference effect evaluation result and the second-level interference effect evaluation result. The first-level evaluation model is obtained through supervised training using a first radar parameter sample, a first interference parameter sample, and a pre-calculated static interference evaluation score sample. The second-level evaluation model is obtained through supervised training using a second radar parameter sample, a second interference parameter sample, and a pre-calculated dynamic interference evaluation score sample based on distance changes. Based on the first-level interference effect evaluation results and the second-level interference effect evaluation results, the target interference evaluation results are determined.

2. The online evaluation method for collaborative interference effects based on attention mechanism enhancement according to claim 1, characterized in that, The process of pre-calculating the static interference evaluation score samples includes: Based on the first radar parameter sample and the first interference parameter sample, the carrier frequency dispersion, bandwidth change, radar signal amplitude change intercepted by the jammer, and interference benefit corresponding to deceptive interference are calculated before and after the interference is applied. The jamming benefit corresponding to the suppression jamming is determined based on the carrier frequency dispersion, the bandwidth variation, and the radar signal amplitude variation. The first interference gain is determined based on the interference gain corresponding to the deceptive interference and the interference gain corresponding to the suppressive interference. Based on the predetermined changes in pulse repetition frequency, pulse width, and pulse number before and after the application of interference, as well as the preset thresholds corresponding to each change, the transition parameters of the radar operating state are determined. The second interference benefit is determined based on the radar operating state transition parameters and the preset operating state transition reward and punishment rules; The static interference evaluation score sample is determined based on the first interference gain and the second interference gain.

3. The online evaluation method for collaborative interference effects based on attention mechanism enhancement according to claim 2, characterized in that, The process of pre-calculating the dynamic interference evaluation score samples includes: In the radar-cooperative jamming sample scenario, the distance between the cover target and the jammer and the radar is segmented to obtain multiple distance segments; and multiple distance points are determined in each distance segment. The dynamic interference assessment score sample is determined based on the multiple range segments, multiple range points in each range segment, the second radar parameter sample, the second interference parameter sample, and the pre-constructed state value function corresponding to the radar operating state.

4. The online evaluation method for collaborative interference effects based on attention mechanism enhancement according to claim 2, characterized in that, The degree of carrier frequency dispersion is expressed as: In the formula, This indicates the degree of carrier frequency dispersion, that is, the degree of change in the radar's operating frequency before and after the interference is applied. Indicates the number of samples. Indicates the first The radar operating frequency of the second sampling express The average operating frequency of the radar in the next sampling; The degree of bandwidth variation is expressed as follows: In the formula, This indicates the degree of bandwidth variation. This indicates the bandwidth of the radar signal after interference is applied. Indicates the bandwidth of the radar signal before the interference is applied; The change in radar signal amplitude intercepted by the jammer is expressed as: In the formula, This represents the change in the amplitude of the radar signal intercepted by the jammer. This indicates the amplitude of the radar signal intercepted after the interference was applied. This indicates the amplitude of the radar signal intercepted before the interference was applied.

5. The online evaluation method for collaborative interference effects based on attention mechanism enhancement according to claim 4, characterized in that, The interference benefit corresponding to the suppression interference is expressed as: In the formula, This indicates the interference benefit corresponding to the suppression interference. , and These represent the preset reference frequency agility range, reference bandwidth range, and reference power adjustment range, respectively. , and These represent the preset weight coefficients for the corresponding items; The interference benefit corresponding to the deceptive interference is expressed as: In the formula, This indicates the interference benefit corresponding to the deceptive interference. Indicates the number of false targets. This represents the preset optimal number of false targets. Indicates quantity tolerance. This indicates the transmission power of the decoy target. This indicates the power of the radar signal entering the jammer. Indicates power tolerance. and These represent the weights for the number of false targets and the power of the false targets, respectively. The first interference benefit is expressed as: In the formula, This represents the benefit of the first interference.

6. The online evaluation method for collaborative interference effects based on attention mechanism enhancement according to claim 2, characterized in that, The change in pulse repetition frequency is expressed as: In the formula, This represents the amount of change in the pulse repetition frequency. This indicates the frequency of radar signal pulse repetition intercepted after the interference is applied. Indicates the frequency of radar signal pulse repetition intercepted before the interference is applied; The change in pulse width is expressed as: In the formula, This represents the amount of change in pulse width. This indicates the pulse width of the radar signal intercepted after the jamming was applied. Indicates the pulse width of the radar signal intercepted before the interference was applied; The change in the number of pulses is expressed as: In the formula, This represents the change in the number of pulses. This indicates the number of radar signal pulses intercepted after the interference was applied. This indicates the number of radar signal pulses intercepted before the interference was applied.

7. The online evaluation method for cooperative interference effects based on attention mechanism enhancement according to claim 6, characterized in that, The second interference benefit is expressed as: In the formula, Indicates that the radar is in working state Switch to work mode The conversion parameters; radar operating states include Search-while-ranging (RWS), Search-while-tracking (TAS), Search-while-tracking (TWS), and Single-target tracking (STT).

8. The online evaluation method for collaborative interference effects based on attention mechanism enhancement according to claim 3, characterized in that, The dynamic interference evaluation score sample is represented as follows: In the formula, This represents the dynamic interference evaluation score sample. This indicates the number of distance points in a single distance segment. The state value function represents the radar's operating state. Indicates the radar's operating status. Indicates the object currently being tracked by the radar. This represents the cooperative gain correction term. This represents the distance corresponding to each distance point.

9. The online evaluation method for collaborative interference effects based on attention mechanism enhancement according to claim 1, characterized in that, The first-level evaluation model is the attention-interpretable table learning network TabNet, and the second-level evaluation model is the temporal convolutional network TCN.

Images