A shipboard radio equipment failure mechanism analysis system and method

Abstract

The application provides a ship frequency equipment failure mechanism analysis system and method, comprising: an electromagnetic environment simulation module, used for simulating and generating a complex electromagnetic environment signal containing a working signal and an interference signal; an electromagnetic sensitive module model library, containing an amplifier model, a filter model and a mixer model at a signal level or a behavior level; an electromagnetic stress simulation module, configured to input the complex electromagnetic environment signal as an electromagnetic stress into a target electromagnetic sensitive module model, drive the model to run and obtain an output response signal of the target electromagnetic sensitive module; and a failure mechanism analysis module, configured to determine an action mechanism of the electromagnetic stress on the target electromagnetic sensitive module by analyzing distortion characteristics of the output signal, and associate the action mechanism to a system-level failure mode of the frequency equipment. The application can effectively reduce test cost, shorten analysis period, reveal the electromagnetic failure mechanism of the equipment, and provide technical support for reliability evaluation and electromagnetic interference resistance design of the ship frequency equipment.

Description

Technical Field

[0001] This invention belongs to the field of marine electronic information technology, specifically relating to a failure mechanism analysis system and method for marine frequency equipment. Background Technology

[0002] As a core component of ship communication, navigation, and detection systems, the reliability of marine frequency equipment directly impacts the safety of ship navigation and operations. In complex marine electromagnetic environments, various electromagnetic radiations and coupling effects can easily create high-intensity electromagnetic stress on electromagnetically sensitive modules within the equipment, leading to performance degradation, functional abnormalities, and even permanent failure. Existing equipment failure analyses largely rely on physical testing and on-site investigation, which suffers from high experimental costs, difficulty in reproducing operating conditions, and inaccurate location of failure causes. Furthermore, traditional analysis methods often focus on the surface manifestations of electromagnetic interference, failing to establish a quantitative correlation between electromagnetic stress, module response, and equipment failure at the mechanistic level. This hinders accurate prediction and in-depth analysis of the failure patterns of marine frequency equipment, making it difficult to meet the high reliability and high anti-interference design and evaluation requirements of modern marine electronic equipment. Therefore, there is an urgent need to establish a failure mechanism analysis method based on electromagnetic sensitivity characteristics and multiphysics simulation to accurately locate the weak points of marine frequency equipment in strong electromagnetic environments, providing theoretical and methodological support for equipment reliability assessment and optimized design. Summary of the Invention

[0003] The primary objective of this invention is to provide a failure mechanism analysis system and method for shipboard frequency equipment to overcome the aforementioned shortcomings of the prior art.

[0004] To achieve this objective, in a basic implementation scheme, the present invention provides a failure mechanism analysis system for shipboard frequency equipment, characterized in that it includes: The electromagnetic environment simulation module is used to simulate and generate complex electromagnetic environment signals containing operating signals and interference signals based on a predetermined test profile.

[0005] The electromagnetic sensitive module model library contains signal-level or behavioral-level amplifier models, filter models, and mixer models, wherein the amplifier models adopt a polynomial transmission model based on nonlinear characteristics.

[0006] The electromagnetic stress simulation module connects the electromagnetic environment simulation module and the electromagnetic sensitive module model library. It is configured to input the complex electromagnetic environment signal as electromagnetic stress into the target electromagnetic sensitive module model, drive the model to run, and obtain the output response signal of the target electromagnetic sensitive module.

[0007] The failure mechanism analysis module, connected to the electromagnetic stress simulation module, is configured to determine the mechanism of electromagnetic stress on the target electromagnetic sensitive module by analyzing the distortion characteristics of the output signal based on the output response signal, and to associate it with the system-level failure mode of the frequency-using equipment.

[0008] Furthermore, it also includes: The frequency-sensitive equipment channel model includes multiple electromagnetically sensitive module models integrated in the order of the signal link.

[0009] The failure mechanism analysis module is further configured as follows: Based on the simulation results of the frequency-using equipment channel model by the electromagnetic stress simulation module, the response and distortion of the signal in the channel cascade transmission are analyzed.

[0010] Identify the reliability weaknesses in the frequency-using equipment channels and generate a mapping matrix containing a list of weaknesses and corresponding fault information.

[0011] Furthermore, it also includes: The failure knowledge base pre-stores historical failure data and failure modes of typical radar, communication, and navigation frequency equipment. The failure modes include at least one or more of the following: target ranging error, increased communication bit error rate, and positioning error.

[0012] The failure mechanism analysis module is connected to the failure knowledge base and is configured to compare and correlate the module action mechanism and system-level failure performance obtained from simulation analysis with historical data in the failure knowledge base in order to verify and correct the failure mechanism analysis conclusions.

[0013] Another objective of this invention is to provide a method for analyzing the failure mechanism of shipboard frequency equipment, characterized by comprising: Step S1: Construct a digital simulation model of the electromagnetic sensitive module. The digital simulation model includes at least an amplifier model using a nonlinear polynomial transmission model, a filter model based on frequency characteristics, and a mixer model at the signal level.

[0014] Step S2: Construct and simulate the complex electromagnetic environment of a typical test profile, and generate interference signals and working signals as electromagnetic stress inputs.

[0015] Step S3: Input the electromagnetic stress into the digital simulation model of the target electromagnetic sensitive module, conduct an electromagnetic stress simulation test, and obtain the output response signal of the module.

[0016] Step S4: Analyze the distortion characteristics of the output response signal relative to the normal operating signal, and infer the electromagnetic stress attribute that caused the distortion based on the distortion characteristics, thereby establishing the correspondence between "electromagnetic stress and module response distortion" and completing the module-level failure mechanism analysis.

[0017] Step S5: Based on the module-level failure mechanism analyzed in step S4, and combined with the signal processing flow of the frequency-using equipment, infer and verify the equipment system-level failure mode caused by the failure of the module.

[0018] Furthermore, the construction of the amplifier model in step S1 specifically includes: Obtain the linear gain, 1dB compression point, and third-order intermodulation cutoff point characteristic parameters of the target amplifier.

[0019] Based on the aforementioned characteristic parameters, the coefficients of the linear term, the third-order nonlinear term, and the fifth-order nonlinear term in the polynomial transmission model describing the amplifier's nonlinear response are calculated and determined, thereby establishing an amplifier model that can be used to simulate the nonlinear effects introduced by high-power interference signals.

[0020] Furthermore, the construction of the mixer model in step S1 specifically includes: The input signal is modeled from both the RF port and the local oscillator port.

[0021] The output signal is modeled from the intermediate frequency port, and the image frequency interference effect is characterized in the model.

[0022] The simulation of the mixer in step S3 includes studying the effect of image frequency interference caused by the downconversion process on the output intermediate frequency signal after a specific frequency interference signal is coupled into the mixer.

[0023] Furthermore, the step S1 of constructing the filter model specifically includes: selecting the Butterworth or Chebyshev filter type according to the frequency characteristic parameters of the target filter, and setting the corresponding filter order to construct the model.

[0024] The simulation of the filter in step S3 includes using a signal containing multi-frequency components and Gaussian white noise as input to verify the filtering response characteristics of the filter model to input signals of different frequencies.

[0025] Further, step S5 includes: The digital simulation models of multiple electromagnetically sensitive modules are integrated according to the actual link sequence in the receiving channel of the frequency-using equipment to construct a channel-level simulation model of the frequency-using equipment.

[0026] The complex electromagnetic environment stress generated in step S2 is applied to the integrated channel-level simulation model.

[0027] The effects of response accumulation and distortion propagation caused by electromagnetic stress during the cascade transmission of electromagnetic stress in the channel are analyzed.

[0028] Based on the final output signal quality of the channel, determine whether the system function of the frequency-using equipment has failed, and trace the failure result back to the sensitive module in the channel that first experienced severe distortion or contributed the most to the overall failure, and identify that module as the weak link in reliability.

[0029] Furthermore, the method also includes: Step S0: Establish a failure knowledge base, summarize and store historical failure data and corresponding failure modes of typical radar, communication and navigation frequency equipment in complex electromagnetic environments.

[0030] In step S5, the system-level failure modes inferred from the simulation analysis are matched and associated with the historical failure modes in the failure knowledge base. The rationality of the simulation analysis results is verified by using historical data, and the mechanism chain of "specific electromagnetic stress → specific module response → specific equipment failure" is improved.

[0031] Furthermore, the method ultimately outputs a "matrix of electromagnetic weak points and failure modes for typical frequency-using equipment." The rows of this matrix represent different electromagnetically sensitive modules, and the columns represent different key electromagnetic stress parameters or equipment failure modes. The matrix elements describe the risk level of a specific module failing under a specific electromagnetic stress or the causal relationship leading to the failure of a specific piece of equipment. In a preferred embodiment, The beneficial effects of this invention are as follows: First, it enables non-destructive, visual, and repeatable analysis of the electromagnetic failure mechanism of frequency-using equipment. By constructing a signal-level simulation system that includes nonlinear amplifier, mixer, and filter models, the entire process of electromagnetic stress acting on the RF receiving channel is fully reproduced in the digital domain. This overcomes the inherent shortcomings of field testing, such as high cost, long cycle, destructive nature to equipment, and difficulty in observing internal signal details, allowing researchers to arbitrarily set and observe fault conditions as if in a "digital laboratory."

[0032] Secondly, this invention reveals the coupling failure mechanism under multi-module cascading, enabling precise tracing from symptoms to root causes. Traditional methods often only observe the final failure phenomenon of the system (such as communication interruption). By integrating the models of each sensitive module according to the actual link, this invention can simulate the transmission and distortion process of electromagnetic stress in the "limiter-high frequency amplifier-mixer-intermediate frequency amplifier-filter" channel. In particular, the amplifier model of this invention can accurately simulate the gain compression and intermodulation products caused by large signals. The mixer model can specifically analyze the image frequency interference problem caused by frequency conversion under interference. This allows the analysis not only to confirm the failure, but also to clearly determine the key stress parameters and the culprit module that caused the failure. For example, is it interference at a specific frequency that causes intermodulation in the mixer, or broadband noise that saturates the amplifier, thus providing a direct basis for targeted hardening.

[0033] Third, a simulation-based failure knowledge system was constructed, significantly improving assessment efficiency and design level. Through extensive simulation experiments and comparison with historical data, this invention can systematically summarize the electromagnetic susceptibility patterns of different equipment and modules, ultimately forming an "electromagnetic weak point and failure mode mapping matrix." This matrix transforms discrete failure cases into structured knowledge, reducing the time for predicting the electromagnetic vulnerability of newly developed equipment by more than 70%, and enabling the identification of potential reliability shortcomings during the design phase, guiding design optimization, and fundamentally improving the electromagnetic compatibility and operational robustness of shipboard frequency-based equipment. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a schematic diagram of the failure mechanism analysis system for ship frequency equipment of the present invention; Figure 2 This is a graph showing the input-output response of the limiter of the present invention. Figure 3 This is a typical high-amplitude output response curve of the present invention; Figure 4 This is a typical high-amplitude amplifier output amplitude-frequency response curve of the present invention; Figure 5 This is a block diagram of the radar equipment signal processing and data processing of the present invention; Figure 6 This is a block diagram of the signal processing and data processing of the communication equipment of the present invention; Figure 7 This is a block diagram of the signal processing and data processing of the navigation equipment of the present invention; Figure 8 This is a schematic diagram illustrating the effect of the interference signal pulse width of the present invention on the sensitivity threshold of a certain type of radar; Figure 9 This is a schematic diagram illustrating the impact of the interference signal bandwidth of the present invention on the sensitivity threshold of a certain type of communication equipment; Figure 10 This is a schematic diagram illustrating the effect of the center frequency of the interference signal of the present invention on the sensitivity threshold of a certain type of communication equipment; Figure 11 This is a schematic diagram of the noise figure of different receiver circuit structures according to the present invention; Figure 12 This is a simulation diagram of the amplifier output response when the input signal power is -20dBm. Figure 13 This is a schematic diagram of the input signal waveform and spectrum of the present invention, which is doped with interference frequencies; Figure 14 This is a schematic diagram of the waveform and spectrum of the mixer output signal doped with the mirror frequency according to the present invention; Figure 15 This is a schematic diagram of the waveform and spectrum of the image-free mixing output signal of the present invention. Figure 16 This is a schematic diagram of the multi-frequency signal filtering process of the present invention; Figure 17 This is a schematic diagram of the Gaussian white noise filtering process of the present invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] Please refer to Figures 1 to 17 This invention provides a failure mechanism analysis system for shipboard frequency equipment, comprising: The electromagnetic environment simulation module is used to simulate and generate complex electromagnetic environment signals containing operating signals and interference signals based on a predetermined test profile.

[0038] The electromagnetic sensitive module model library contains signal-level or behavioral-level amplifier models, filter models, and mixer models, wherein the amplifier models adopt a polynomial transmission model based on nonlinear characteristics.

[0039] The electromagnetic stress simulation module connects the electromagnetic environment simulation module and the electromagnetic sensitive module model library. It is configured to input the complex electromagnetic environment signal as electromagnetic stress into the target electromagnetic sensitive module model, drive the model to run, and obtain the output response signal of the target electromagnetic sensitive module.

[0040] The failure mechanism analysis module, connected to the electromagnetic stress simulation module, is configured to determine the mechanism of electromagnetic stress on the target electromagnetic sensitive module by analyzing the distortion characteristics of the output signal based on the output response signal, and to associate it with the system-level failure mode of the frequency-using equipment.

[0041] The technical solution of the present invention will be further described below with reference to specific embodiments.

[0042] Step 1: Analyze the electromagnetic response mechanism of electromagnetic sensitive modules in typical frequency-using equipment Step 1.1 Analyze the electromagnetic susceptibility response mechanism of the RF receiving channel Through historical data analysis and module principle analysis, the electromagnetic susceptibility response mechanism of modules such as limiters, high-frequency amplifiers, mixers, and filters is studied.

[0043] Step 1.1.1 Analysis of the electromagnetic susceptibility response mechanism of a typical limiter The limiter utilizes the nonlinear limiting mechanism of PIN diodes. Its equivalent microwave impedance is controlled by the microwave power. For low-power signals, the attenuation of the PIN diode is minimal, with only a small insertion loss, allowing the microwave signal to pass through almost unobstructed. However, high-power microwave signals will cause conductivity modulation in the PIN diode, attenuating the input microwave signal and thus protecting power-sensitive devices in the microwave signal receiving system from being burned out by the high-power microwave signal. A typical limiter input-output response curve is shown below. Figure 1 As shown.

[0044] Step 1.1.2 Analysis of Typical High-Voltage Electromagnetic Sensitive Response Mechanism High-frequency amplifiers in the radio frequency channel are nonlinear devices, and in practical applications they often exhibit significant memory effects. These memory nonlinear effects can cause distortion of the input signal. In addition to describing the amplification characteristics of the power amplifier in the linear region, high-frequency amplifier modeling also needs to consider its nonlinear effects and memory effects.

[0045] In the time domain, the output amplitude of the high-frequency amplifier has a non-linear relationship with the input signal amplitude; the system gain varies with the input signal amplitude, such as... Figure 2 As shown. In the frequency domain, due to the influence of nonlinear effects, the output signal of the high-frequency amplifier will generate harmonic frequency components and intermodulation frequency components, thus causing spectral broadening, such as... Figure 3 As shown.

[0046] Step 1.1.3 Analysis of the Electromagnetic Sensitivity Response Mechanism of Typical Mixers Mixers can be categorized into active and passive mixers based on the presence or absence of bias current. Passive mixers generally lack transconductance and have no static bias current, while active mixers do the opposite. Generally, passive mixers have lower power consumption and flicker noise, and higher linearity, but they suffer from higher conversion losses, and their high local oscillator power requirements limit their application in high-frequency bands. In contrast, active mixers offer advantages such as smaller size and better conversion gain and spurious suppression performance, making them widely used in receiver components. However, active mixers also require larger DC voltages due to their stacked structure, and their linearity is relatively poor. Therefore, in RF transceiver systems, mixer nonlinearity is a crucial performance indicator, and the impact of mixer nonlinear effects and port signal leakage on the performance of frequency-using equipment in complex electromagnetic environments must be carefully considered.

[0047] (1) Nonlinear effects of mixer The linearity of a mixer is measured by the 1dB compression point and the third-order intermodulation point. The nonlinear characteristics of a mixer are similar to those of a high-frequency amplifier, and the same analytical approach can be used.

[0048] (2) Port signal leakage effect A mixer includes signal input ports, a local oscillator signal input port, and signal output ports. Theoretically, the ports of a mixer are isolated from each other, and the power of any port will not leak to other ports. However, in reality, signal leakage between ports is difficult to avoid.

[0049] Step 1.1.4 Analysis of the Electromagnetic Sensitivity Response Mechanism of Typical Filters Filters commonly used in frequency-controlled equipment can be categorized by passband type into low-pass filters, band-pass filters, high-pass filters, and band-stop filters. Radio frequency (RF) filters are typically band-pass filters, used to filter out out-of-band signals from the received RF and IF frequencies. Based on their frequency response characteristics, common band-pass filters can be classified into Butterworth filters, Chebyshev filters, and elliptic filters.

[0050] When the power of out-of-band interference signals is high enough, the filter output signal will still have residual components, which will further affect the frequency-using equipment in the receiving channel and signal processing channel.

[0051] Step 1.2 Analysis of the electromagnetic susceptibility response mechanism of the signal processing channel The signal processing flow of frequency-using equipment such as radar, communication, and navigation is analyzed, and the sensitive response mechanism of the signal processing channel to electromagnetic stress is studied.

[0052] Step 1.2.1 Signal and data processing of typical frequency-using equipment The radar signal processing channel takes the digital intermediate frequency (IF) signal converted from digital to analog signal from the radio frequency (RF) channel as input. After sidelobe masking and anti-asynchronous processing, sidelobe interference and asynchronous interference signals are eliminated. Further, clutter suppression and signal-to-noise ratio improvement are achieved through pulse compression, target processing, and video accumulation. Finally, target range, azimuth, velocity, and trajectory information are extracted through constant false alarm rate (CFAR) detection and track processing. Radar equipment signal processing and data processing methods are as follows: Figure 4 .

[0053] The signal processing and data processing channels of communication equipment are shown in the diagram below. The transmitting link includes voice and data encoding, spreading, and quadrature modulation to generate a communication transmission signal input to the radio frequency channel. After DA conversion, filtering, amplification, and up-conversion, the signal is radiated outward through the transmitting antenna. The receiving link converts the received signal into a digital signal via the receiving antenna and radio frequency channel. After quadrature demodulation, despreading, and decoding, voice, message, image, and video data results are obtained. The signal processing and data processing methods of communication equipment are as follows: Figure 5 .

[0054] Based on the digital intermediate frequency (IF) signal output from the RF front-end, signal processing is performed, including signal acquisition and tracking, to obtain measurement parameters such as Doppler frequency shift, pseudorange, and carrier phase. The signal is then demodulated to obtain the navigation message. After signal processing, relevant calculations are performed on the obtained measurement parameters and navigation message. Data processing is then performed to obtain the positioning result, and various navigation information is output. The signal processing and data processing methods for navigation equipment are as follows: Figure 6 .

[0055] Step 1.2.2 Sensitive Response Mechanism Analysis of Signal Processing and Data Processing Channels in Typical Frequency-Using Equipment Radar signal processing is complex, and the marine electromagnetic environment affects various modules of the radar signal processing channel. Furthermore, the mechanisms by which different electromagnetic environment parameters affect each module vary. By summarizing and analyzing historical failure data and digital simulation data of typical radar equipment under complex electromagnetic environments, the impact of the marine electromagnetic environment on the radar signal processing channel mainly falls into two categories: First, suppression interference blocks small-amplitude echo signals, affecting the radar's detection range, positioning accuracy, velocity measurement accuracy, and navigation stability for small-amplitude echo targets. Second, deceptive interference generates false targets after passing through the radar signal processing channel, thereby affecting the detection and positioning of real targets.

[0056] For typical shipboard data link signal processing channels, because the communication signals of the data link are usually processed through encoding, encryption, and spread spectrum, it is difficult for enemy electronic countermeasures equipment to modulate incorrect information into the jamming signal to achieve deceptive interference. Other unintentional interference will also not have a deceptive interference effect. Jamming signals usually interfere with the data link by affecting the establishment of the link or the acquisition of information by the receiving equipment. In complex electromagnetic environments, when the amplitude of the jamming signal is large enough, the signal-to-noise ratio or signal-to-interference ratio in the data link receiving channel decreases, and the bit error rate in the signal processing channel increases, thereby affecting the transmission of voice, message, and image data in communication, causing some data errors, and in severe cases, leading to functional unavailability.

[0057] Taking the BeiDou Navigation Satellite System receiver as an example, this paper summarizes and concludes the impact of the maritime battlefield electromagnetic environment on the signal processing channel of the BeiDou Navigation Satellite System by analyzing historical failure data and digital simulation test data of typical BeiDou Navigation Satellite System receivers under complex electromagnetic environments. The main impacts are twofold: First, suppression interference. When the interference signal amplitude is sufficiently large, the signal-to-noise ratio (SNR) or signal-to-interference ratio (SINR) within the BeiDou Navigation Satellite System receiver channel decreases, causing errors in positioning information calculation and potentially rendering the positioning and timing functions unusable. Second, deceptive interference. Based on the modulation and message format of the BeiDou signal, incorrect navigation information is modulated onto the interference signal and coupled into the navigation system receiver through the receiving antenna, causing it to generate incorrect positioning results.

[0058] Step 2: Electromagnetic susceptibility stress characterization of typical frequency-using equipment electromagnetic susceptibility modules Step 2.1 Multi-domain characterization method for electromagnetically sensitive stress In maritime collaborative operations, the main sources of electromagnetic susceptibility stress faced by our frequency-using equipment include: the complex electromagnetic environment within the formation generated by radiation from friendly radar, communication, and navigation equipment; and the natural background electromagnetic environment such as land and sea clutter and meteorological clutter.

[0059] The complex electromagnetic environment within the formation and the complex counter-environment electromagnetic environment are characterized by modeling the electromagnetic radiation characteristics of the radiation source, and described from the aspects of time domain, frequency domain, spatial domain, energy domain, and polarization domain. The clutter background electromagnetic environment is characterized by the radiation characteristic model of the radiation source and the clutter model.

[0060] Step 2.2 Analysis of Electromagnetic Induction Force Elements in Frequency-Using Equipment Analysis and research were conducted on past simulation data and actual measured data to study the impact of various elements of interference signals on the sensitive response of typical radar and communication equipment, and the main interference signal elements affecting the sensitive response of the equipment were identified. As shown in Table 1, the interference signal elements can be divided into the following categories according to time, frequency, space, energy, and polarization domain: repetition period, pulse width, modulation method, center frequency, signal bandwidth, beam deviation angle, peak power, and polarization method.

[0061] Table 1: Electromagnetic Stress Interference Factors of Frequency-Using Equipment (1) Analysis of electromagnetic stress factors affecting radar equipment To address interference factors such as repetition period, pulse width, modulation scheme, center frequency, signal bandwidth, beam deviation angle, peak power, and polarization, a controlled variable method is employed. A specific interference factor is selected, and different parameters are set to generate an interfering electromagnetic environment. The sensitivity threshold of typical radar equipment under different parameters is tested, comparing whether changes in the interference factor's parameters affect the equipment's sensitivity threshold. Taking the interference signal pulse width as an example, while keeping other interference factors constant, the interference signal pulse width is varied, and the radar equipment's sensitivity threshold is tested. The test results for a certain type of radar are as follows: Figure 7 As shown in the figure, the sensitivity threshold of interference signals with different pulse widths varies significantly. Therefore, pulse width is a key interference factor for this radar.

[0062] (2) Analysis of electromagnetic stress factors affecting communication equipment Taking the interference signal bandwidth as an example, keeping other interference factors constant, the interference signal bandwidth is varied, and the sensitivity threshold of the communication equipment is tested. For a certain type of communication equipment, the test results are as follows: Figure 8 As shown in the figure, the sensitivity threshold of interference signals with different bandwidths does not change significantly. Therefore, the bandwidth of the interference signal is not a key interference factor for this communication equipment. With other interference factors kept constant, the sensitivity threshold of the communication equipment was tested by varying the center frequency of the interference signal. The test results are shown below. Figure 9 As shown in the figure, the sensitivity threshold of interference signals varies significantly with different frequencies. Therefore, the center frequency of the interference signal is not a key interference factor for this communication equipment.

[0063] Step 3: Establishment of electromagnetic sensitive module model for typical frequency-using equipment Step 3.1 (High Frequency / Intermediate Frequency) Amplifier Modeling Step 3.1.1 Amplifier Nonlinear Effect Model To accurately describe the nonlinear effect of the amplifier, a polynomial model of the input signal is used to represent the output signal. (1) Where x(t) is the input signal, y(t) is the output signal, and an are the coefficients of each order. When N=1, the polynomial model degenerates into a linear model, where a1 is the linear amplification factor, and its relationship with the amplifier gain G can be expressed as: (2) Where G is the amplifier gain expressed in dB.

[0064] Modeling the nonlinear effects of an amplifier essentially involves solving for the nonlinear coefficients a2, a3...aN in a polynomial model. The solution method uses test data from the amplifier's 1dB compression point and third-order intermodulation point to calculate these nonlinear coefficients. The following section will examine in detail how to solve for the higher-order nonlinear coefficients of the amplifier using measured data.

[0065] Step 3.1.2 Method for solving third-order intermodulation points When an amplifier's input signal contains multiple frequency components, its output signal will generate new frequency components, called intermodulation frequencies. When the input signal is small, the intermodulation frequency signal is also small, having little impact on the signal detection at the output. As the input signal increases, the intermodulation frequency signal increases rapidly. The output signal level corresponding to the third-order intermodulation signal amplitude being equal to the linear gain output amplitude is called the third-order intermodulation point.

[0066] Using a dual-frequency component signal as input, derive the third-order intermodulation point of the amplifier. The input signal x(t) can be expressed as... (3) The amplifier's third-order intermodulation output is reflected in the cube term of the input signal. (4) in , , and All are third-order intermodulation terms. Because they differ significantly from the signal frequency, and It will be filtered out by the filter. and If the frequency is close to the signal frequency, it will enter the subsequent circuit.

[0067] Amplitude of the third-order intermodulation term output signal of the amplifier The amplitude of the output signal with the linear gain term When they are equal, the corresponding output signal power is the third-order intermodulation point. (5) Where VI3 is the peak voltage corresponding to the third-order intermodulation point, the relationship between the third-order term coefficients and the peak voltage of the third-order intermodulation point can be obtained from the above equation, as shown below. (6) The relationship between the peak voltage of the third-order intermodulation point and the third-order intermodulation point is as follows: (7) Where PI3 is the third-order intermodulation point, and RL is the load resistance, typically taken as 50Ω. From this, the relationship between the third-order term coefficients and the third-order intermodulation point can be obtained. (8) In the above formula, PI3 is the third-order intermodulation point expressed in dBm form.

[0068] Therefore, the coefficients of the third-order terms in the amplifier polynomial model can be represented by the technical parameters or test data of the third-order intermodulation points.

[0069] Step 3.1.3 1dB Compression Point Calculation Method As the input power increases, the nonlinear effect of the amplifier intensifies, and the gain gradually decreases. When the amplifier gain drops to 1dB lower than the ideal amplifier gain, the corresponding output signal level is called the 1dB compression point.

[0070] Using a single-frequency component signal as input, derive the 1dB compression point of the amplifier. The input signal x(t) can be expressed as... (9) Taking the first five orders of the amplifier output as an approximation, it can be expressed as: (10) The frequency components of the even-order terms output differ significantly from the signal frequency and will be filtered out by subsequent filter circuits; therefore, they can be disregarded. The expansion of the cubic term is as follows: (11) The expansion of the fifth term is as follows: (12) The third and fifth harmonics in the expansion result will be filtered out by the subsequent filter circuit and are therefore disregarded. The final fundamental term coefficients are: (13) According to the definition of 1dB compression point (14) The linear term output and the 1dB compression point have the following relationship: (15) P1dB is the 1dB compression point expressed in dBm form. Therefore, the coefficient A can be obtained as follows: (16) Based on the definition of the 1dB compression point, the relationship between the coefficients of the fifth-order term, the coefficients of the linear term, and the coefficients of the third-order term can be determined. (17) The coefficients A, linear term coefficient a1, and third-order term coefficient a3 in the above formula have been obtained. Therefore, the fifth-order term coefficients can also be obtained from the test data of the 1dB compression point.

[0071] Based on the above derivation process, the coefficients of the linear term, the third-order term, and the fifth-order term in the amplifier polynomial model have been completely solved, thus obtaining the polynomial nonlinear model and completing the amplifier signal level modeling process.

[0072] Step 3.1.4 Amplifier Saturated Output Power Calculation A polynomial model is used to approximate the amplifier's output response. When the input power reaches a certain value, its output power will approach saturation; this output power is called the amplifier's saturation output power. Using the first five orders of a polynomial for approximation, neglecting even-order terms, the relationship between the output signal and the input signal can be obtained as follows: (18) Taking the derivative of the output signal y(t), y(t) reaches its maximum value when the derivative is zero, which is the saturated output power. (19) Where xmax is the input level corresponding to the saturated output power point, it can be solved according to the above equation. (20) The amplifier's saturation output level is (twenty one) Step 3.1.5 Amplifier noise modeling Noise is a major factor limiting receiver sensitivity. Receiver noise originates from multiple sources: internally, active devices or modules such as amplifiers and mixers in the circuitry generate noise. Externally, noise is introduced through the antenna and includes antenna thermal noise, industrial interference, atmospheric interference, and cosmic interference. Typically, the internal noise level of the receiver is much higher than the external noise level. This invention focuses on modeling methods for internal receiver noise, specifically the noise generated by amplifiers and mixers.

[0073] Step 3.1.5.1 Noise Modeling The amplifier generates internal Gaussian white noise, and the focus of modeling this noise is determining its power. The noise power can be expressed as: (twenty two) Where Pn is the noise power, and k is the Boltzmann constant. B is the receiver bandwidth, and Tn is the noise temperature. The noise temperature can be expressed as... (twenty three) Where T0 is taken as a room temperature value of 290K. F is the noise figure, defined as the difference between the signal-to-noise ratio at the input and output of a device or module, which can be expressed as: (twenty four) In practice, the noise figure can be obtained through testing and is usually expressed in dB.

[0074] The following relationship exists between noise power and noise mean square amplitude: (25) Where Vn is the noise mean square amplitude, and RL is the load resistance, typically 50Ω. The internal noise generated by the amplifier can be written as... (26) Where N(t) is the amplifier noise sequence and Gauss(t) is the normalized Gaussian white noise sequence that follows a standard normal distribution.

[0075] Based on the above derivation process, the amplifier noise sequence was obtained from the measured value of the amplifier noise figure. The amplifier noise is additive noise, thus forming a modeling method for amplifier noise.

[0076] Step 3.1.5.2 Noise figure of cascaded circuit Generally, a receiver is typically composed of multiple stages of amplifiers, mixers, etc., and the noise figure of the cascaded circuit is expressed as... (27) As can be seen from the above formula, as long as the gain of the first stage is large enough, the total noise figure is determined by the first stage.

[0077] Figure 10 The noise figures of three radar receivers with different topologies are presented, where the order of the amplification, filtering, and mixing modules differs. The first receiver passes through a high-frequency amplifier (HFA), then a filter, and finally is mixed. The second receiver passes through a filter, then an HFA, and finally is mixed. The third receiver passes through an HFA and then is mixed directly. The parameters of the main components are as follows: HFA gain 30dB, HFA noise figure 0.5dB, and mixer noise figure 6dB.

[0078] For the first circuit, since the first stage is a high-gain, low-noise-figure amplifier, the overall noise figure of the circuit is determined by the first stage, and is approximately 0.5 dB. For the second circuit, the first stage has no amplification gain, and the overall noise figure increases significantly, to approximately 5.5 dB. For the third circuit, the high-gain amplifier is directly connected to a mixer with a very high noise figure, thus the overall noise figure also increases, to approximately 3.5 dB.

[0079] Step 3.2 Filter Modeling Step 3.2.1 Define the filter modeling parameters, including the filter type (low-pass, high-pass, band-pass, or band-stop) and frequency response parameters (passband frequency, passband loss, stopband frequency, stopband attenuation). Based on the actual filter modeling parameters, solve for the frequency response parameters of the corresponding low-pass prototype filter.

[0080] Step 3.2.2: Obtain and construct the low-pass prototype filter model. Commonly used filter models include the Butterworth filter and the Chebyshev filter. Based on the insertion loss model and frequency response parameters of the low-pass prototype filter, solve for the minimum filter order. Taking the Butterworth filter model as an example: Step 3.2.2.1 Based on the Butterworth filter insertion loss model and frequency response parameters, determine the minimum filter order. The Butterworth filter insertion loss L has the following relationship with the frequency f: (28) Where n is the filter order and A is a constant coefficient. When f = fp, L = Lp, and thus the constant coefficient A can be solved. (29) Solving for the minimum filter order Based on the Butterworth filter insertion loss model, when f = fs, L = Ls, and thus the filter order n can be determined. (30) The filter order obtained from the above formula is usually not an integer. The actual minimum filter order is a smaller integer than the smallest integer in the above formula.

[0081] Step 3.2.2.2 Based on the filter order, obtain the coefficients of the denominator polynomial of the Butterworth low-pass filter transfer function by looking up a table, thus obtaining the filter transfer function. The transfer function of a low-pass filter is in all-pole form, as shown below. (31) H(s) represents the transfer function in the Laplace domain, and the transfer function h(t) in the time domain can be obtained through the inverse Laplace transform. Table 2 shows the correspondence between the coefficients of the denominator polynomial of the Butterworth low-pass filter and the filter order. The transfer function of the Butterworth low-pass filter can then be calculated.

[0082] Table 2: Denominator polynomial coefficients of Butterworth low-pass filters Step 3.2.3 Based on the filter order, the coefficients of the denominator polynomials of the Butterworth low-pass filter and the Chebyshev low-pass filter can be obtained by looking up the table, and thus the transfer function of the low-pass prototype filter can be obtained.

[0083] Step 3.2.4 Based on the frequency transformation relationship between the high-pass, band-pass, and band-stop prototype filters and the low-pass prototype filters, the transfer function of the actual filter can be obtained, thus completing the filter signal level modeling process.

[0084] Step 3.2.4.1 Based on the frequency response parameters of the actual filter, solve for the frequency response parameters of the corresponding low-pass prototype filter.

[0085] Based on the above modeling process for the low-pass filter's transmission response, solving for the transfer function of the prototype low-pass filter requires obtaining its normalized stopband frequency. (32) Where Ωs is the normalized stopband frequency of the low-pass prototype filter. and These are the stopband frequency and the passband frequency, respectively. The following describes the relationship between the frequency response parameters of the high-pass, band-pass, and band-stop filters and the normalized stopband frequency of the low-pass prototype filter.

[0086] 1) Low-pass prototype conversion of a high-pass filter The frequency response parameters of a high-pass filter are: passband frequency fp, stopband frequency fs, passband loss Lp, and stopband attenuation Ls.

[0087] The normalized stopband frequency of the low-pass prototype filter is (33) This is the reciprocal of the normalized stopband frequency of the high-pass filter. The passband loss and stopband attenuation of the low-pass prototype filter remain unchanged.

[0088] 2) Low-pass prototype conversion of bandpass filter The frequency response parameters of the bandpass filter are: passband frequencies fp1 and fp2, stopband frequencies fs1 and fs2, passband loss Lp, and stopband attenuation Ls.

[0089] Define the variables as follows: (34) Redefining variables and (35) Normalized stopband frequency of low-pass prototype filter Pick and The smaller of the two. The passband loss and stopband attenuation of the low-pass prototype filter remain unchanged.

[0090] 3) Low-pass prototype conversion of band-stop filter The frequency response parameters of the band-stop filter are: passband frequencies fp1 and fp2, stopband frequencies fs1 and fs2, passband loss Lp, and stopband attenuation Ls.

[0091] Define the variables as follows: (36) Redefining variables and (37) Normalized stopband frequency of low-pass prototype filter Pick and The smaller of the two. The passband loss and stopband attenuation of the low-pass prototype filter remain unchanged.

[0092] Step 3.2.4.2 Using the low-pass filter signal level modeling method, the normalized stopband frequency of the low-pass prototype filter is obtained based on the frequency characteristic parameters of the actual filter, and then the transfer function of the low-pass prototype filter can be obtained.

[0093] Step 3.2.4.3 Use frequency transformation to solve for the transfer function of the actual filter.

[0094] The transfer function of the low-pass prototype filter will be converted into the transfer function of the corresponding high-pass, band-pass, or band-stop filter based on the principle of frequency conversion.

[0095] 1) Frequency conversion of high-pass filter The frequency transformation relationship between the high-pass filter and the low-pass prototype filter is as follows: (38) in s is the complex frequency variable of the low-pass prototype filter, s is the complex frequency variable of the high-pass filter, and fp is the passband frequency of the high-pass filter.

[0096] 2) Frequency conversion of bandpass filter The frequency transformation relationship between the bandpass filter and the low-pass prototype filter is as follows: (39) in s is the complex frequency variable of the low-pass prototype filter, s is the complex frequency variable of the band-pass filter, and fp1 and fp2 are the passband frequencies.

[0097] 3) Frequency conversion of band-stop filter The frequency transformation relationship between the band-stop filter and the low-pass prototype filter is as follows: (40) in s is the complex frequency variable of the low-pass prototype filter, s is the complex frequency variable of the band-stop filter, and fp1 and fp2 are the passband frequencies.

[0098] Step 3.3 Establishing the mixer signal level model Step 3.3.1 Modeling of RF Input Signal Step 3.3.1.1 Modeling the nonlinear effect of negative gain at the RF end The effect of the RF signal passing through the mixer is similar to that passing through a negative gain amplifier, and its modeling method is similar to the polynomial modeling method for amplifiers. Since most mixers typically only provide third-order intermodulation parameters, this section presents the process of nonlinear modeling of the mixer using the third-order intermodulation point. The RF signal of the mixer can be written as: (41) The coefficient a1 of the linear term can be expressed as: (42) Where G is the negative gain of the mixer, expressed in dB, with a typical value of -6 dB. The third-order term coefficient a3 is related to the third-order intermodulation point power of the mixer and can be expressed as follows: (43) Taking the derivative of the output signal y(t), y(t) reaches its maximum value when the derivative is zero, which is the saturated output power. (44) in Given the input level corresponding to the saturated output power point, it can be solved using the above equation: (45) The amplifier's saturation output level is: (46) in This represents the saturation output level of the amplifier.

[0099] Step 3.3.1.2 Modeling the nonlinear effects of RF terminal leakage term Because mixer circuits cannot achieve perfect matching and balance, power coupling or leakage exists between the ports, especially at the local oscillator terminal where the signal amplitude is very large, affecting the signals at the RF and IF terminals. The signal coupled from the local oscillator terminal to the RF terminal can be expressed as: (47) in The coupling degree from the local oscillator terminal to the radio frequency terminal is expressed as... The form indicates a positive value.

[0100] The complete RF input signal is the sum of the negative gain output signal from the RF terminal and the signal leaking from the local oscillator terminal to the RF terminal, which can be expressed as: (48) Step 3.3.2 Modeling of the local oscillator input signal The local oscillator input of a mixer is typically driven by a large-amplitude signal, and the transistor exhibits switching characteristics. The nonlinear effect at the local oscillator input mainly manifests as higher-order odd harmonics generated by the switching characteristics. The signal at the local oscillator input can be written as: (49) in For fundamental voltage amplitude, This is the local oscillator frequency. The signal power at the local oscillator is obtained through... In formal terms, it is related to the fundamental voltage amplitude. The relationship between them can be represented as: (50) Step 3.3.3 Modeling of the intermediate frequency output signal The intermediate frequency (IF) output signal includes the product of the RF (radio frequency) and local oscillator (LO) signals, as well as the signals leaked from the LO and RF terminals to the IF terminal. The product of the RF and LO signals can be expressed as: (51) The signal leaking from the local oscillator terminal to the intermediate frequency terminal can be expressed as: (52) in The coupling degree from the local oscillator terminal to the intermediate frequency terminal is expressed as... The form indicates a positive value.

[0101] The signal leaking from the RF end to the IF end can be represented as: (53) in The coupling degree from the RF end to the IF end is expressed as... The form indicates a positive value.

[0102] Step 4: Construction of Electromagnetic Environment Simulation System Step 4.1 Construction of a Complex Electromagnetic Environment Step 4.1.1 Establishment of the Electromagnetic Ship Module for Natural Environment Step 4.1.1.1 Electromagnetic wave propagation module in sea and air domains Step 4.1.1.1.1 Based on the current platform flight status and the beam pointing azimuth and elevation angle corresponding to the current radiation source event, complete the real-time beam coverage area / sea area calculation.

[0103] Step 4.1.1.1.2 Based on the beam coverage area / sea area, read the corresponding digital elevation map of the combat area, surface feature data, wave level of the monitored sea area, and other data.

[0104] Step 4.1.1.1.3 Calculate the amplitude and power spectrum distribution characteristics of the sea clutter based on topographic and marine information.

[0105] Step 4.1.1.1.4 Receive the motion status of each entity object, such as position, velocity, acceleration, and heading, sent by the platform.

[0106] Step 4.1.1.2 Establishment of Background Electromagnetic Radiation Characteristics Module Step 4.1.1.2.1 Calculate the transmission loss caused by the attenuation of radio electromagnetic waves due to their diffusion into space during propagation.

[0107] Step 4.1.1.2.2 Calculate the refraction effect of electromagnetic waves during propagation.

[0108] Step 4.1.1.2.3 Calculate the energy loss of the radar due to absorption by gas (mainly oxygen and water vapor) along the radar wave propagation path.

[0109] Step 4.1.1.2.4 Receive the motion status of each entity object, such as position, velocity, acceleration, and heading, sent by the platform.

[0110] Step 4.1.2 Establishment of the electromagnetic radiation characteristics module for the radiation source Step 4.1.2.1 Summarize the operating frequency bands, modulation patterns, and frequency usage methods of various typical radiation sources.

[0111] Step 4.1.2.2 Establish mathematical descriptions of various radiation sources based on their characteristics and rationality analysis. Step 4.1.2.3 Finally, select appropriate operating and performance parameters for description.

[0112] Step 4.2 Frequency-Using Equipment Channel Model The frequency-use equipment channel model mainly completes the model construction of typical frequency-use equipment such as radar, communication, and navigation, including modules such as antenna, high-frequency amplifier, mixer, intermediate frequency amplifier, and signal processing.

[0113] (1) The radar model consists of six parts: resource scheduling module, antenna simulation module, signal generation module, receiver processing module, target distance detection module, and disturbance state analysis.

[0114] (2) The communication model consists of five parts: antenna simulation module, transmitter, channel module, communication signal processing and receiver.

[0115] (3) The navigation model includes ground equipment and airborne equipment.

[0116] Step 4.3 Establishment of Electromagnetic Environment Database Module The electromagnetic environment database includes an equipment database and an electromagnetic environment database. The electromagnetic environment database stores various electromagnetic environment data used in combat simulation experiments of frequency-using equipment, including target characteristics, radiation source parameters, antenna characteristics, and environmental noise. The equipment database stores the functional performance parameters and mission-related parameters of various weapons and equipment used by both the red and blue forces in weapon and equipment functional performance tests and combat simulation experiments.

[0117] Step 5: Establishment of electromagnetic stress simulation model for typical frequency-using equipment electromagnetic sensitive module Step 5.1 Simulation of complex electromagnetic environment in typical test profile: Based on the constructed test profile, simulate and generate the working signals and interference signals of the receiving end of communication equipment and radar equipment in the reconnaissance mission scenario.

[0118] Step 5.2 (High Frequency / Intermediate Frequency) Amplifier Output Response Simulation The simulation object is a microwave-band radio frequency amplifier with the following characteristics: linear gain G = 30dB, third-order intermodulation point PI3 = 31.38dBm, and 1dB compression point P1dB = 17.3dBm. Based on these frequency response parameters, the saturated output power of the amplifier can be calculated to be 20dBm.

[0119] Based on the above characteristic parameters, and using the derivation process in the previous section, the coefficients of the linear term, the third-order term, and the fifth-order term in the polynomial model of the amplifier can be obtained, and the polynomial transmission model of the amplifier can be established.

[0120] Using a single-frequency signal as input, the input signal Represented as: (54) Where A is the amplitude of the input signal, and its relationship with the power of the input signal is as follows: (55) The load resistance RL is taken as 50Ω, and P is the input signal power in dBm form. The input signal can be written as: (56) When the signal frequency f is 3.3 GHz, the output response of the amplifier is simulated under different input power conditions, and the amplifier gain is calculated.

[0121] Figure 11The time-domain waveform and spectrum of the input signal are given when the input signal power is -20dBm, as well as the time-domain waveform and spectrum of the output signal after amplification. This output power is far below the amplifier's 1dB compression point, and the output signal is essentially a linear amplification of the input signal with no waveform distortion. The amplifier gain is 29.4dB.

[0122] Step 5.3 Simulation of Mixer Output Response The receiver's useful signal carrier frequency remains 3.1 GHz, and the signal is a linear frequency modulated (LFM) signal with a bandwidth of 5 MHz and a pulse width of 10 μs. The intermediate frequency (IF) signal after mixing is 50 MHz, and the local oscillator (LO) signal frequency is 3.15 GHz. In addition to the useful signal, the signal entering the mixer also contains a LFM signal with a frequency of 3.2 GHz, a bandwidth of 3 MHz, and a pulse width of 10 μs. 3.2 GHz is exactly the image frequency. Because the frequencies of 3.1 GHz and 3.2 GHz are very close, the pre-stage RF filter cannot filter out the 3.2 GHz frequency. Therefore, the 3.2 GHz signal is directly mixed into the mixer's output after mixing.

[0123] The time-domain waveform and spectrum of the signal entering the mixer front end are as follows: Figure 12 As shown, the useful signal at 3.1 GHz and the image interference signal at 3.2 GHz are clearly visible.

[0124] The time-domain waveform and spectrum of a signal mixed with image frequencies, after mixing and filtering, are as follows: Figure 13 and Figure 14 As shown, the image interference component can still be clearly distinguished, showing a significant difference from the processing results without image frequencies.

[0125] Step 5.4 Filter Output Response Simulation The simulation object is a microwave bandpass filter with the following frequency response parameters: passband frequencies fp1 = 4.5 GHz, fp2 = 5.5 GHz, passband loss 0.5 dB, stopband frequencies fs1 = 4.5 GHz, fs2 = 5.5 GHz, and stopband attenuation 30 dB. A Butterworth filter with an order of 7 is used.

[0126] Step 5.4.1 Simulation of transmission response of multi-frequency signals The input multi-frequency signals are shown below: (57) The input signal contains three frequency components: f1 = 3 GHz, f2 = 5 GHz, and f3 = 8 GHz. f2 is within the filter's passband, while f1 and f3 fall within the stopband. The time-domain waveform and spectrum of the input signal are plotted, clearly showing the three frequency components. After passing through the filter, the frequency components f1 and f3, which fall within the stopband, are removed, leaving only the frequency component f2 within the lower passband.

[0127] Step 5.4.2 Simulation of the transmission response of Gaussian white noise Gaussian white noise is a wide-spectrum input signal, and its time-domain waveform and spectrum are as follows: Figure 16 As shown, after passing through the filter, the frequency components falling outside the band are significantly reduced, and the spectral attenuation of the transition band can be clearly seen.

[0128] Step 6: Analysis of the failure mechanism of the electromagnetic sensing module Step 6.1 Failure Mechanism Analysis of Typical Frequency-Using Equipment (1) Typical radar equipment Failure history data and digital simulation test data of typical radar equipment under complex electromagnetic environments are summarized and analyzed. The main failure modes caused by complex electromagnetic environments are as follows: The target ranging error is too large.

[0129] The target direction finding error is too large.

[0130] The target speed measurement error is too large.

[0131] The target cannot be detected.

[0132] The target cannot be tracked.

[0133] The target's trajectory is unstable.

[0134] False targets have appeared.

[0135] False flight paths have appeared.

[0136] Excessive target ranging, direction finding, and velocity measurement errors are typically caused by suppressive jamming or pulse jamming coinciding with the target's range threshold. This jamming reduces the signal-to-noise ratio (SNR) and signal-to-interference ratio (SIR) near the target, thus affecting the accuracy of ranging, direction finding, and velocity measurement. Target undetectability and untrackability are also usually caused by suppressive jamming. This jamming reduces the SNR and SIR, preventing the target echo signal from exceeding the decision threshold, thus preventing target detection or tracking. Unstable target tracks can be caused by two factors: unstable target detection leading to track interruption, and false targets or tracks. When false targets or tracks appear around the target track, they interfere with the fusion of target points, causing track interruption. The appearance of false targets or tracks is usually caused by deception jamming or unintentional pulse jamming.

[0137] (2) Typical communication equipment Failure history data and digital simulation test data of typical data link equipment under complex electromagnetic environments are summarized and analyzed. The main failure modes caused by complex electromagnetic environments are as follows: Data transmission is inconsistent.

[0138] Data transmission error (unclear voice, data error, etc.).

[0139] Increased communication latency.

[0140] The link connection is unstable.

[0141] The link cannot be connected.

[0142] (3) Typical navigation equipment Failure history data and digital simulation test data of typical BeiDou navigation systems under complex electromagnetic environments are summarized and analyzed. The main failure modes caused by the influence of complex electromagnetic environments are as follows: The positioning error is too large.

[0143] Location information acquisition is unstable.

[0144] Location information could not be obtained.

[0145] Excessive positioning errors can lead to track deviations. Unstable positioning information can cause track interruptions or jumps. Failure to obtain positioning information can result in track interruptions and inability to update position.

[0146] Step 6.2 Failure Mechanism Analysis of Sensitive Modules Step 6.2.1 Sensitive Factor Analysis of Typical Sensitive Modules Analyzing the electromagnetic stress simulation results of sensitive modules reveals the sensitivity factors of limiters, (high-frequency / intermediate-frequency) amplifiers, and mixers. Taking the limiter as an example, it typically operates in a wide bandwidth and is relatively insensitive to the frequency of interference signals. Its sensitivity activation mainly depends on the peak power of the interference signal coupled into the limiter. When the interference power exceeds the sensitivity threshold, the limiter saturates, resulting in distortion of the useful signal.

[0147] Step 6.2.2 List of Weak Links in Reliability Reliability simulation models are used to conduct reliability simulations of frequency-using equipment under complex electromagnetic environments. Combined with historical data, the disturbance patterns of typical radar, communication, and navigation frequency-using equipment are analyzed, and weak links and failure modes in reliability are summarized.

[0148] Table 3 below shows a list of weak points in the reliability of typical radar equipment and a fault information matrix: Table 3: List of typical weak links in the reliability of radar equipment Step 6.2.3 Fault Information Matrix Reliability simulation models are used to conduct reliability simulations of frequency-using equipment under complex electromagnetic environments. Combined with historical data, the disturbance patterns, disturbance levels, and failure probabilities of typical radar, communication, and navigation frequency-using equipment are analyzed, and their failure information matrix is ​​summarized.

[0149] For typical communication equipment, the fault information matrix is ​​shown in Table 4 below: Table 4: Typical Communication Equipment Fault Information Matrix The above embodiments or implementation methods are merely illustrative of the present invention. The present invention may also be implemented in other specific ways or forms without departing from the spirit or essential characteristics of the invention. Therefore, the described embodiments should be considered illustrative rather than limiting in any respect. The scope of protection of the present invention should be determined by the contents of the claims, and any changes equivalent to the intent and scope of the claims should also be included within the scope of the present invention.

Claims

1. A failure mechanism analysis system for shipboard frequency equipment, characterized in that, include: The electromagnetic environment simulation module is used to simulate and generate complex electromagnetic environment signals containing working signals and interference signals based on a predetermined test profile. The electromagnetic sensitive module model library contains signal-level or behavioral-level amplifier models, filter models, and mixer models, wherein the amplifier models adopt a polynomial transmission model based on nonlinear characteristics. An electromagnetic stress simulation module is connected to the electromagnetic environment simulation module and the electromagnetic sensitive module model library. It is configured to input the complex electromagnetic environment signal as electromagnetic stress into the target electromagnetic sensitive module model, drive the model to run, and obtain the output response signal of the target electromagnetic sensitive module. The failure mechanism analysis module, connected to the electromagnetic stress simulation module, is configured to determine the mechanism of electromagnetic stress on the target electromagnetic sensitive module by analyzing the distortion characteristics of the output signal based on the output response signal, and to associate it with the system-level failure mode of the frequency-using equipment.

2. The system according to claim 1, characterized in that, Also includes: The frequency-use equipment channel model includes multiple electromagnetically sensitive module models integrated in the order of the signal link; The failure mechanism analysis module is further configured as follows: Based on the simulation results of the electromagnetic stress simulation module on the channel model of the frequency-using equipment, the response and distortion of the signal in the channel cascade transmission are analyzed. Identify the reliability weaknesses in the frequency-using equipment channels and generate a mapping matrix containing a list of weaknesses and corresponding fault information.

3. The system according to claim 2, characterized in that, Also includes: The failure knowledge base pre-stores historical failure data and failure modes of typical radar, communication and navigation frequency equipment. The failure modes include at least one or more of the following: target ranging error, increased communication bit error rate, and positioning error. The failure mechanism analysis module is connected to the failure knowledge base and is configured to compare and correlate the module action mechanism and system-level failure performance obtained from simulation analysis with historical data in the failure knowledge base in order to verify and correct the failure mechanism analysis conclusions.

4. A method for analyzing the failure mechanism of shipboard frequency-controlled equipment, characterized in that, include: Step S1: Construct a digital simulation model of the electromagnetic sensitive module. The digital simulation model includes at least an amplifier model using a nonlinear polynomial transmission model, a filter model based on frequency characteristics, and a mixer model at the signal level. Step S2: Construct and simulate the complex electromagnetic environment of a typical test profile, and generate interference signals and working signals as electromagnetic stress inputs; Step S3: Input the electromagnetic stress into the digital simulation model of the target electromagnetic sensitive module, conduct an electromagnetic stress simulation test, and obtain the output response signal of the module; Step S4: Analyze the distortion characteristics of the output response signal relative to the normal operating signal, and infer the electromagnetic stress properties that cause the distortion based on the distortion characteristics, thereby establishing the correspondence between "electromagnetic stress and module response distortion" and completing the module-level failure mechanism analysis. Step S5: Based on the module-level failure mechanism analyzed in step S4, and combined with the signal processing flow of the frequency-using equipment, infer and verify the equipment system-level failure mode caused by the failure of the module.

5. The method according to claim 4, characterized in that, The construction of the amplifier model in step S1 specifically includes: Obtain the linear gain, 1dB compression point, and third-order intermodulation cutoff point characteristic parameters of the target amplifier; Based on the aforementioned characteristic parameters, the coefficients of the linear term, the third-order nonlinear term, and the fifth-order nonlinear term in the polynomial transmission model describing the amplifier's nonlinear response are calculated and determined, thereby establishing an amplifier model that can be used to simulate the nonlinear effects introduced by high-power interference signals.

6. The method according to claim 4, characterized in that, The construction of the mixer model in step S1 specifically includes: The input signal is modeled from both the RF port and the local oscillator port. The output signal is modeled from the intermediate frequency port, and the image frequency interference effect is characterized in the model; The simulation of the mixer in step S3 includes studying the effect of image frequency interference caused by the downconversion process on the output intermediate frequency signal after a specific frequency interference signal is coupled into the mixer.

7. The method according to claim 4, characterized in that, The step S1 of constructing the filter model specifically includes: selecting the Butterworth or Chebyshev filter type according to the frequency characteristic parameters of the target filter, and setting the corresponding filter order to construct the model. The simulation of the filter in step S3 includes using a signal containing multi-frequency components and Gaussian white noise as input to verify the filtering response characteristics of the filter model to input signals of different frequencies.

8. The method according to claim 4, characterized in that, Step S5 includes: The digital simulation models of multiple electromagnetic sensitive modules are integrated according to the actual link sequence in the receiving channel of the frequency-using equipment to construct a channel-level simulation model of the frequency-using equipment. The complex electromagnetic environment stress generated in step S2 is applied to the integrated channel-level simulation model. The effects of response accumulation and distortion propagation caused by electromagnetic stress during the cascade transmission of electromagnetic stress in the channel are analyzed. Based on the final output signal quality of the channel, determine whether the system function of the frequency-using equipment has failed, and trace the failure result back to the sensitive module in the channel that first experienced severe distortion or contributed the most to the overall failure, and identify that module as the weak link in reliability.

9. The method according to claim 4, characterized in that, The method further includes: Step S0: Establish a failure knowledge base, summarize and store historical failure data and corresponding failure modes of typical radar, communication and navigation frequency equipment in complex electromagnetic environments; In step S5, the system-level failure modes inferred from the simulation analysis are matched and associated with the historical failure modes in the failure knowledge base. The rationality of the simulation analysis results is verified by using historical data, and the mechanism chain of "specific electromagnetic stress → specific module response → specific equipment failure" is improved.

10. The method according to claim 8 or 9, characterized in that, The method ultimately outputs a "matrix of electromagnetic weak points and failure modes of typical frequency-using equipment". The rows of the matrix represent different electromagnetically sensitive modules, the columns represent different key electromagnetic stress parameters or equipment failure modes, and the matrix elements describe the risk level of failure of a specific module under a specific electromagnetic stress or the causal relationship that leads to the failure of a specific piece of equipment.

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