Outdoor field electromagnetic, infrared and optical multiband background simulation method and system

Abstract

The invention relates to the technical field of background simulation, and discloses an outdoor field electromagnetic, infrared and optical multiband background simulation method and system. An intelligent sensor array of the system collects initial environment parameters of a target test area, wherein the initial environment parameters comprise electromagnetic radiation intensity, infrared thermal distribution and optical reflectivity. The background feature extraction module performs multiband feature decomposition on the initial environment parameters according to standard feature databases of grassland, desert and snowfield to generate a background feature vector set containing electromagnetic, infrared and optical band features. The dynamic simulation control module calculates a dynamic adjustment sequence of electromagnetic modulation parameters, infrared emission parameters and optical projection parameters through a multi-objective optimization algorithm based on the feature vector set. And the wave band coupling actuator synchronously drives the electromagnetic wave generator, the infrared radiator and the optical filtering device according to the dynamic adjustment sequence to generate a composite simulation background field matched with the target background type.

Description

Technical Field

[0001] This invention relates to the field of background simulation technology, specifically to a method and system for simulating multi-band electromagnetic, infrared, and optical backgrounds in outdoor fields. Background Technology

[0002] In fields such as military reconnaissance, environmental monitoring, and remote sensing equipment testing, it is often necessary to simulate various natural background environments under controlled conditions to evaluate the performance of sensors or detection equipment. Natural background environments are complex and varied, encompassing multi-band characteristics such as electromagnetic waves, infrared radiation, and visible light. For example, typical backgrounds such as grasslands, deserts, and snowfields exhibit significant differences in electromagnetic reflection characteristics, infrared radiation characteristics, and optical reflection characteristics. Traditional background simulation methods often employ single-band simulation techniques, such as simulating only infrared characteristics or only electromagnetic characteristics, making it difficult to realistically reproduce the combined effects of multi-band coupling in natural environments.

[0003] In existing technologies, electromagnetic background simulation typically employs the emission of electromagnetic waves at specific frequencies, infrared background simulation relies on blackbody radiation sources or infrared emitting materials, and optical background simulation is accomplished through projection or screen display. These methods often operate independently, lacking a coordinated control mechanism, leading to inconsistencies in the simulated background field across different wavebands. For example, a simulated desert scene may exhibit high-temperature characteristics in the infrared band, but its electromagnetic reflection properties may not match those of a real desert, and this distortion can affect the reliability of test results. Furthermore, traditional simulation systems often use static parameter settings, failing to dynamically respond to environmental changes or the temporal evolution of the simulated background, thus limiting their application in long-term, dynamic testing scenarios.

[0004] Another technical challenge lies in the coupled optimization of multi-band parameters. In natural backgrounds, electromagnetic, infrared, and optical properties do not exist independently but are interconnected and mutually influential. For example, surface temperature (affecting infrared radiation) varies with sunlight (optical conditions), while surface material (affecting electromagnetic reflection) determines its heat capacity and infrared emissivity. Existing systems lack the ability to model and optimize such coupling relationships, and parameter adjustments often rely on manual experience, making it difficult to quickly and accurately generate composite simulation fields that highly match specific background types.

[0005] There is an urgent need for a background simulation system that can integrate multi-band sensing, intelligent feature extraction, collaborative parameter optimization, and synchronous execution to achieve high-fidelity, dynamic simulation of complex natural backgrounds. Summary of the Invention

[0006] The purpose of this invention is to provide an outdoor electromagnetic, infrared, and optical multi-band background simulation method and system to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides an outdoor electromagnetic, infrared, and optical multi-band background simulation system, the system comprising: A smart sensor array is used to collect initial environmental parameters of the target test area, including electromagnetic radiation intensity, infrared thermal distribution, and optical reflectivity. The background feature extraction module is used to perform multi-band feature decomposition on the initial environmental parameters based on a preset standard feature database of grassland, desert and snow, and generate a background feature vector set containing electromagnetic band features, infrared band features and optical band features. The dynamic simulation control module is used to calculate the dynamic adjustment sequence of electromagnetic modulation parameters, infrared emission parameters and optical projection parameters based on the background feature vector set through a multi-objective optimization algorithm; A band-coupled actuator is used to synchronously drive an electromagnetic wave generator, an infrared radiator, and an optical filter according to the dynamic adjustment sequence to generate a composite simulated background field that matches the target background type.

[0008] Preferably, when the background feature extraction module performs multi-band feature decomposition: Electromagnetic radiation intensity is divided into low-frequency geomagnetic disturbance characteristics, mid-frequency atmospheric scattering characteristics, and high-frequency artificial interference characteristics according to frequency bands. Spatial gradient analysis was used to extract the temperature field uniformity and thermal radiation attenuation characteristics from the infrared thermal distribution. The absorption characteristics in the visible light band and the reflection characteristics in the near-infrared band were separated by the spectral response curve of optical reflectance.

[0009] Preferably, the process by which the dynamic simulation control module generates the dynamic adjustment sequence includes: Establish a time-domain coupling model between electromagnetic band characteristics and infrared band characteristics, and calculate the compensation coefficient of electromagnetic modulation parameters on infrared emission parameters. Based on the frequency domain distribution characteristics of the optical band, determine the phase synchronization constraint conditions between the optical projection parameters and the electromagnetic modulation parameters. A nonlinear programming algorithm is used to solve for the optimal combination of parameters that satisfies the constraints of compensation coefficient and phase synchronization, generating a dynamic adjustment sequence sorted by timestamp.

[0010] Preferably, when the band-coupled actuator drives the electromagnetic wave generator: Based on the characteristics of low-frequency geomagnetic disturbances in the dynamic adjustment sequence, the excitation current waveform of the ring coil array is configured; Adjust the resonant frequency of the Helmholtz coil according to the mid-frequency atmospheric scattering characteristics; To address the characteristics of high-frequency artificial interference, a square wave signal with adjustable pulse width is applied to the radiating antenna element.

[0011] Preferably, when the band-coupled actuator controls the infrared radiator: The temperature field uniformity characteristics are converted into zoned heating commands for the blackbody radiation surface. The power output curve of the semiconductor cooler is dynamically adjusted based on the thermal radiation attenuation characteristics. A PID control algorithm is used to maintain the steady-state output of the target's infrared band characteristics.

[0012] Preferably, when the band-coupled actuator adjusts the optical filter device: The tilt angle of the multilayer interference filter is rotated according to the absorption characteristics in the visible light band. The operating mode of the liquid crystal tunable filter is switched based on the near-infrared band reflection characteristics. Intensity fluctuations in optical projection parameters are compensated by using a stepper motor to drive a neutral density filter wheel.

[0013] Preferably, the system further includes: The background verification module is used to monitor the actual band parameters of the composite simulated background field in real time and calculate the feature matching degree between the actual band parameters and the target background type. The feedback calibration module is used to reinitialize the multi-band feature decomposition strategy of the background feature extraction module and trigger the iterative optimization process of the dynamic simulation control module when the feature matching degree is lower than the threshold.

[0014] Preferably, when the background verification module calculates the feature matching degree: Wavelet packet energy analysis was used to extract the frequency band energy ratio error for electromagnetic band characteristics; Apply a thermal imaging centroid offset detection algorithm to infrared band features; Introducing spectral correlation coefficients into optical band characteristics quantifies similarity deviations.

[0015] Preferably, when the feedback calibration module performs the iterative optimization process: The impedance matching network parameters of the electromagnetic wave generator are corrected based on the frequency band energy ratio error. Adjusting the focal plane array drive voltage of the infrared radiator based on the thermal imaging centroid offset; The color temperature calibration coefficient of the optical filter is updated based on the deviation of the spectral correlation coefficient.

[0016] Preferably, the present invention also includes an outdoor electromagnetic, infrared, and optical multi-band background simulation method, which includes all the modules and method flow of the above-mentioned outdoor electromagnetic, infrared, and optical multi-band background simulation system.

[0017] Compared with the prior art, the beneficial effects of the present invention are: This invention utilizes an intelligent sensor array to synchronously acquire multi-band environmental parameters, providing a comprehensive and consistent initial data foundation for background simulation. Traditional methods often collect data in different bands at different times and with different devices, which can easily lead to data asynchrony due to environmental changes. This system achieves synchronous acquisition of electromagnetic, infrared, and optical parameters, ensuring the accuracy of the initial scene representation and creating conditions for high-fidelity simulation.

[0018] The background feature extraction module performs multi-band feature decomposition based on a standard feature database, enabling quantitative analysis and feature abstraction of complex background environments. This module transforms the collected raw environmental parameters into a structured set of feature vectors. These feature vectors can accurately characterize the typical attributes of specific background types (such as grassland, desert, and snowfield) across multiple bands, providing a clear target basis for subsequent parameter optimization.

[0019] The dynamic simulation control module employs a multi-objective optimization algorithm to calculate the dynamic adjustment sequence of equipment in each band, effectively solving the problem of multi-parameter coupled optimization. The algorithm simultaneously considers the target matching requirements of multiple bands, including electromagnetic, infrared, and optical bands, and automatically optimizes and generates a consistent sequence of equipment control parameters. This avoids the blindness and subjectivity of manual adjustments, greatly improving simulation accuracy and efficiency.

[0020] The band-coupled actuator enables synchronous driving and coordinated operation of multi-band simulation equipment, ensuring the spatiotemporal consistency of the generated composite simulated background field. The actuator strictly follows the optimized parameter sequence to synchronously control the electromagnetic wave generator, infrared radiator, and optical filter, enabling the simulated background to exhibit highly coordinated electromagnetic, infrared, and optical properties, realistically reproducing the combined effect of the target's natural background.

[0021] This system constructs a closed-loop simulation process from environmental perception, feature extraction, intelligent decision-making to precise execution, realizing the automation and intelligence of multi-band background simulation. The system can quickly respond to different simulation needs, dynamically adjust simulation parameters, and adapt to various test scenarios, providing a reliable and efficient testing platform for sensor performance evaluation and equipment environmental adaptability testing. Attached Figure Description

[0022] Figure 1 This is a multi-band composite background field intensity distribution map; Figure 2 The flowchart shows the multi-band feature decomposition of the background feature extraction module. Figure 3 A flowchart for dynamically adjusting the sequence generation for the dynamic simulation control module; Figure 4 This is a timing diagram for parameter control of a band-coupled actuator. Detailed Implementation

[0023] 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.

[0024] Please see Figure 1 This invention provides a method and system for simulating multi-band electromagnetic, infrared, and optical backgrounds in outdoor environments. The system comprises a smart sensor array, a background feature extraction module, a dynamic simulation control module, and a band-coupled actuator working in concert. Specific implementation details are as follows: A smart sensor array is deployed in the target test area, employing a multi-band electromagnetic probe, an infrared thermal imager, and a spectroradiometer to simultaneously acquire initial environmental parameters, including electromagnetic radiation intensity, infrared thermal distribution, and optical reflectivity. These parameters are transmitted to the background feature extraction module via a high-speed data bus. The background feature extraction module has a built-in standard feature database for grassland, desert, and snowfields, storing typical feature templates for various backgrounds in the electromagnetic, infrared, and optical bands. The module performs real-time analysis of the initial environmental parameters, extracting key feature terms through a multi-band feature decomposition algorithm to generate a structured background feature vector set, where vector elements correspond to electromagnetic, infrared, and optical band features, respectively. After receiving the background feature vector set, the dynamic simulation control module invokes a multi-objective optimization algorithm, using minimizing the feature differences between the simulated background and the target background type as the optimization objective, to calculate the dynamic adjustment sequence of electromagnetic modulation parameters, infrared emission parameters, and optical projection parameters. This sequence includes timestamped parameter combinations to ensure the temporal synchronization of multi-band parameters. The band-coupled actuator analyzes the dynamic adjustment sequence and synchronously drives the electromagnetic wave generator, infrared radiator, and optical filter device through a digital signal processor. The electromagnetic wave generator uses a programmable signal source combined with a power amplifier to generate a customized electromagnetic field; the infrared radiator outputs controllable thermal radiation based on the blackbody principle and semiconductor temperature control technology; and the optical filtering device modulates the optical band through tunable filters and a projection system. These components are superimposed in physical space to generate a composite simulated background field that matches the target background type.

[0025] Example 1: See Figure 2During the multi-band feature decomposition process in the background feature extraction module, the electromagnetic radiation intensity parameters are finely divided based on their frequency domain characteristics. The extraction of low-frequency geomagnetic disturbance features relies on the raw signal acquired by a high-precision fluxgate sensor. This signal is passed through a Butterworth low-pass filter with a cutoff frequency strictly set below 1kHz to filter out high-frequency noise and retain the essential features reflecting the slow changes in the Earth's magnetic field. Its amplitude fluctuation period and baseline drift are quantized as key dimensions in the feature vector. The processing of mid-frequency atmospheric scattering features employs superheterodyne receiver technology to downconvert signals in the 1kHz to 10MHz frequency band to the intermediate frequency. Subsequently, a fast Fourier transform is performed to obtain the power spectral density distribution. The scattering and absorption effects of electromagnetic energy by the atmosphere are characterized by analyzing the envelope shape of the continuous spectrum and specific resonance peaks. The capture of high-frequency artificial interference features requires the cooperation of a wideband receiving antenna and a high-speed sampling circuit. Real-time monitoring is performed on the spectrum above 10MHz. A digital signal processor is used to run an adaptive filtering algorithm to separate specific spectral structures generated by artificial sources such as narrowband communication signals and broadband power line noise from complex background noise. The center frequency, bandwidth and occurrence probability of these structures are recorded as characteristic parameters.

[0026] The analysis of infrared thermal distribution employs spatial gradient analysis, which processes two-dimensional temperature field data acquired from a long-wave infrared focal plane array. A sliding window is defined on the thermal image, and the absolute value of the temperature difference between each pixel and its eight neighboring pixels is calculated. The statistical distribution of all differences, such as variance and range, is used to quantify the spatial non-uniformity of the temperature field, thereby extracting the temperature field uniformity characteristics. Obtaining the thermal radiation attenuation characteristics requires combining distance measurement data. Multiple calibration points are set along the radiation propagation path within the test area, and the radiation intensity at each point is measured using a high-precision infrared radiometer. The measured values ​​are then fitted with a nonlinear curve to the distance values, typically using an exponential or polynomial attenuation model. The attenuation coefficient obtained from the fitting, together with the initial radiation intensity, constitutes the numerical description of the attenuation characteristics. The entire analysis process emphasizes capturing the dynamic characteristics of the thermal field, rather than merely the static temperature distribution.

[0027] The core of optical reflectance processing lies in analyzing the spectral response curve of the material. Measurements are performed using a spectrophotometer equipped with an integrating sphere, acquiring bidirectional reflectance distribution function data for the target region within the wavelength range of 400 nm to 2500 nm under standard illuminance. Analysis of visible light absorption characteristics focuses on the 400 nm to 700 nm range. Algorithms identify the locations of absorption valleys on the spectral curve, calculating the depth, half-width at half-maximum (FWHM), and area of ​​each valley, while considering the sensitivity of absorption characteristics to changes in the incident angle. These parameters collectively describe the energy absorption characteristics of the material in the visible light band. Analysis of near-infrared reflectance characteristics covers the 700 nm to 2500 nm band. Spectral curves in this band typically exhibit characteristic reflection peaks due to molecular overtone combination absorption. Reflectivity is quantified by calculating the average reflectance, slope, and reflectance ratio of specific bands (such as the water absorption band or the red edge of vegetation) within a continuous wavelength range.

[0028] In the entire feature extraction process, data preprocessing and postprocessing form the foundation for ensuring data quality. Raw electromagnetic, infrared, and optical data enter the preprocessing pipeline after acquisition. Sensor data calibration relies on laboratory-calibrated physical reference sources. For example, for infrared thermal imagers, calibration is performed in a controlled environment using a blackbody radiation source, stabilized at multiple known temperature points (e.g., 50℃, 100℃, 150℃). The thermal imager collects a large amount of sample data at each temperature point, and the linear conversion coefficient between the digital output value and the absolute temperature is fitted using the least squares method. Electromagnetic sensor calibration uses a standard signal generator as the input source, injecting sine waves of different frequencies and amplitudes, recording the sensor's response curve, and establishing an inverse model to compensate for nonlinear errors. Optical data calibration utilizes a standard color chart and photometer, capturing images under uniform illumination to establish a mapping relationship between pixel values ​​and actual brightness and color coordinates.

[0029] The calibrated data enters the normalization stage, aiming to eliminate the influence of differences in the dimensions and magnitudes of different sensors. The min-max normalization method is systematically applied. This process requires calculating the global minimum and maximum values ​​for each feature dimension from historical datasets. For example, the normalization of infrared temperature uses the lowest and highest temperatures collected throughout the year as boundary values, and optical brightness uses the theoretical range of image pixel values ​​(0-255) as a benchmark. For real-time input data streams, the system dynamically maintains extreme value statistics within a sliding window. When the value of a new data point exceeds the historical range, the extreme value database is smoothly updated instead of being immediately replaced, avoiding drastic jumps in the normalization results. All feature parameters are linearly mapped to a closed interval [0,1]. For example, if the original reading range of an electromagnetic sensor is [-10V, +10V], a reading of -5V will be converted to 0.25.

[0030] The post-processing stage focuses on the quality control and format standardization of feature vectors. Normalized data undergoes outlier filtering and smoothing. Statistical process control methods are used to identify outliers. The system calculates the mean and standard deviation of each feature dimension within a sliding window, marking data points deviating more than three times the standard deviation from the mean as outliers. These points are replaced by median filtering instead of being directly deleted. Smoothing employs a moving average algorithm, with the window size adaptively adjusted based on the data sampling frequency. For example, for high-frequency electromagnetic data at 1000 points per second, the window is set to 100 sampling points; for infrared images at 1 frame per second, the window is set to 30 frames. The final processed feature vectors are organized into a standardized data structure in chronological order, with fixed vector dimensions. Missing values ​​are filled in using linear interpolation between adjacent time points, forming a well-structured dataset that can be directly used by machine learning models.

[0031] The final output of multi-band feature decomposition is a structured set of background feature vectors. This set concatenates sub-features extracted from the three independent bands (electromagnetic, infrared, and optical) into a high-dimensional vector in a predetermined order. Each dimension of the vector corresponds to a specific physical feature. For example, the initial dimensions of the vector might represent, in order, the mean intensity of low-frequency geomagnetic disturbances, the specific resonant peak frequency of mid-frequency atmospheric scattering, the duty cycle of high-frequency artificial interference, the spatial gradient variance of the temperature field, the attenuation coefficient of thermal radiation, the center wavelength of the main absorption peak of visible light, and the average reflectivity of the near-infrared region. This feature vector set constitutes a numerical profile of the environmental parameters of the target test area. Its structural design is completely consistent with the template vectors in the standard feature database, providing direct and standardized input for the subsequent dynamic simulation control module to perform similarity calculations and parameter optimization. The entire decomposition process emphasizes the interpretability and physical meaning of the features, ensuring that each vector dimension can be clearly associated with physical phenomena or processes in the actual environment.

[0032] The generation of the feature vector set is not a one-time process, but a dynamic loop that accompanies continuous data acquisition. The intelligent sensor array continuously acquires environmental parameters at a fixed sampling rate, while the background feature extraction module performs corresponding real-time or near-real-time feature decomposition and vector updates. The system maintains a first-in, first-out (FIFO) data buffer to ensure that the data segments input to the feature extraction module are temporally continuous, thus capturing the slow changing trends of the environmental background. This design allows the system to adapt to natural background feature drift caused by changes from dawn to dusk or slight variations in weather conditions. The module's internal logic also includes a simple outlier detection and removal mechanism to prevent instantaneous strong interference or sensor malfunction data from contaminating the feature vector set, ensuring the robustness and reliability of the system output.

[0033] Example 2: See Figure 3The dynamic simulation control module generates a dynamic adjustment sequence, a complex computational task involving multi-physics coupling and numerical optimization. This module receives a structured background feature vector set from the background feature extraction module. Its core function is to transform static feature descriptions into a set of control commands that evolve precisely over time. Internally, the module initiates the construction of a time-domain coupling model. This model aims to quantify the dynamic interaction between electromagnetic band features and infrared band features. The model is built based on extensive prior experimental data, establishing a set of difference equations or state-space equations through system identification methods to describe how specific electromagnetic modulation parameters affect the temperature field of the infrared radiator's sensitive element, thus causing a hysteresis response in the infrared emission parameters. The compensation coefficient is calculated by analyzing the frequency response characteristics of this coupled model. For the amplitude attenuation and phase delay of the model's transfer function in the main frequency band, a complex-form compensation operator is calculated. This operator is fed forward to the calculation path of the infrared emission parameters during the parameter optimization stage to pre-compensate for thermal radiation distortion caused by electromagnetic interference.

[0034] The determination of phase synchronization constraints relies on a thorough analysis of the characteristic frequency domain distribution of optical bands. Optical projection parameters typically involve the displacement or rotational speed of mechanical moving parts (such as filter wheels and steering mirrors), whose operating period is expressed in the frequency domain as the fundamental frequency and its harmonic components. At the same time, electromagnetic modulation parameters (especially periodic signals) also have significant line spectrum characteristics. The constraint generation algorithm performs convolution operations on the expected frequency domain distribution of these two types of parameters to predict the possible difference frequency and sum frequency components. Its goal is to avoid energy accumulation in sensitive frequency bands, thereby determining a set of inequality constraints that require the fundamental period of the optical parameters to maintain a rational proportional relationship with the dominant frequency period of the electromagnetic parameters, and their phase difference to be locked within a narrow allowable range. This ensures that the coherence of simulated energy in different bands in the time dimension is controlled within the system's allowable limits.

[0035] Solving the nonlinear programming algorithm is the core step in the entire sequence generation process. The algorithm constructs the multi-band simulation problem as a constrained multi-objective optimization problem. The decision variables include the amplitude, frequency, and phase of the electromagnetic modulation parameters; the temperature setpoint and heating rate of the infrared emitter; and the filter position, transmittance, and light source intensity of the optical projection device. The objective function is designed to minimize the weighted Euclidean distance or cosine similarity between the eigenvectors of the simulated background field and the standard eigenvectors of the target background type. The compensation coefficients calculated above are used as linear constraints on the infrared decision variables, and the phase synchronization condition is incorporated into the optimization model as a nonlinear equality or inequality constraint between the decision variables. The solver typically employs algorithms suitable for large-scale constrained problems, such as the interior-point method or sequential quadratic programming. It iteratively searches from a set of reasonable initial guesses, calculating the gradient of the objective function and solving an approximate quadratic programming subproblem in each iteration, gradually approximating the optimal parameter combination that satisfies all physical constraints and performance indicators.

[0036] The final formatted output of the dynamically adjusted sequence reflects the system's stringent requirements for timing accuracy. The optimal parameter combination is not output as a static snapshot, but rather mapped onto a continuous time axis. The length and resolution of this time axis are jointly determined by the duration requirements of the simulation task and the response speed of the actuator. Each timestamp (e.g., a millisecond interval) corresponds to a complete set of optimized parameter values. The sequence contains not only the instantaneous values ​​of the parameters but may also include their first derivatives (rate of change) to guide the actuator in smooth motion and avoid instantaneous distortion caused by step responses. The sequence data is indexed by timestamps and stored in a circular buffer in matrix form, ensuring that drive commands can be read and decoded by the band-coupled actuator with low latency and high fidelity.

[0037] The entire dynamic adjustment sequence generation process is not an open-loop operation. The module integrates a lightweight physical effect predictor. Based on simplified physical laws and actuator models, this predictor performs forward simulation on the generated parameter sequence, predicting key features of the potential composite background field and quickly comparing the prediction results with the target features. If the prediction deviation exceeds a certain threshold, an internal fine-tuning loop is triggered to adjust the weight coefficients or constraint boundaries in the optimization problem and re-solve the problem. This design adds a layer of robustness to the generated dynamic adjustment sequence, enabling it to compensate for uncertainties caused by model mismatch and actuator nonlinearity to a certain extent. The module's operation heavily relies on its internally stored calibration database, which contains actual actuator response data under different operating conditions, correction coefficients for coupling relationships, and typical patterns of environmental disturbances. This data is continuously updated during routine system maintenance and calibration cycles, ensuring that the model and algorithm can adapt to equipment aging and environmental changes, maintaining simulation accuracy over long periods.

[0038] Example 3: The band-coupled actuator, as a key mechanism for converting control sequences into physical fields, drives an electromagnetic wave generator. This process involves the coordinated operation of multiple electromagnetic field generation devices. The control unit inside the actuator analyzes and dynamically adjusts the parameter segments corresponding to the electromagnetic bands in the sequence. These parameter segments contain differentiated instructions for low-frequency, mid-frequency, and high-frequency characteristics. For simulating low-frequency geomagnetic disturbances, the instructions are converted into excitation current waveform settings for the ring coil array. Waveform generation relies on a high-precision current amplifier receiving waveform data from a direct digital synthesizer. This data pre-stores typical patterns for simulating natural geomagnetic disturbances, such as sine waves, triangular waves, or composite waveforms containing specific harmonic distortions. The amplitude of the current precisely corresponds to the intensity of the disturbance in the eigenvector, while the frequency reflects its rate of change. The geometric layout of the coil array is optimized to generate a relatively uniform low-frequency magnetic field at the center of the target region.

[0039] The mid-frequency atmospheric scattering characteristics are achieved by adjusting the resonant frequency of the Helmholtz coil pair. The actuator changes the natural frequency of the resonant circuit in real time through a digital potentiometer or variable capacitor array according to the sequence parameters, so as to accurately track the mid-frequency point of the target. At the same time, the injected current intensity is proportional to the equivalent intensity of atmospheric scattering. The structure of the Helmholtz coil ensures that a magnetic field environment with a very small gradient can be generated in its central region, which can be used to simulate free-space propagation conditions. The loading of high-frequency artificial interference characteristics is applied to a dedicated radiating antenna unit. The radio frequency synthesis module in the actuator generates a square wave or complex modulation signal with dynamically programmable pulse width and repetition frequency. This signal is fed to the antenna after being amplified by a broadband power amplifier. The rise time, duty cycle and peak power of the pulse are set according to the artificial interference characteristics described in the feature vector to reproduce the electromagnetic noise characteristics generated by various industrial equipment or communication facilities.

[0040] In terms of controlling the infrared radiator, the actuator converts the received infrared emission parameters into precise control of the heat source unit. The realization of the temperature field uniformity characteristics depends on the partitioned heating control of the blackbody radiating surface. The blackbody panel is divided into a matrix composed of multiple independent heating units. Each unit embeds a miniature heating resistor and is driven by an independent current source. The actuator converts the two-dimensional temperature distribution map described in the feature vector into the target power setting value of each heating unit and controls the current flowing into each resistor through pulse width modulation, thereby constructing a spatial temperature distribution on the radiating surface that is consistent with the target scene. The simulation of thermal radiation attenuation characteristics is achieved by dynamically adjusting the power output curve of the semiconductor cooler. A high-bandwidth controller inside the actuator compares the actual radiation intensity fed back by the infrared temperature probe with the target radiation intensity in the dynamic adjustment sequence in real time. The difference is processed by a proportional-integral-derivative control algorithm, and the output control signal dynamically adjusts the magnitude and direction of the current flowing to the cooler, thereby achieving rapid fine-tuning of the temperature of the blackbody radiation surface to accurately simulate the attenuation effect of thermal radiation on the propagation path. The parameters of the PID controller are pre-tuned to achieve a balance between response speed and stability, avoiding thermal oscillations caused by overshoot.

[0041] The adjustment of an optical filter is a precise electromechanical-optical integrated process. The actuator drives different optical elements to work in tandem according to optical projection parameters. Simulation of visible light absorption characteristics is achieved by rotating the tilt angle of a multi-layer interference filter. The filter is mounted on a high-precision rotary table. The actuator converts the absorption spectrum described in the eigenvector into a corresponding incident angle command. A stepper motor receives the command and drives the rotary table to change the angle of the filter relative to the incident optical axis, thereby continuously adjusting its central transmission wavelength to match the spectral characteristics of the transmitted light with the absorption characteristics of the target background material. Simulation of near-infrared reflection characteristics requires switching the operating mode of a liquid crystal tunable filter. The actuator applies a voltage sequence calculated based on the eigenvector to the filter's control electrodes. This voltage changes the alignment of the liquid crystal molecules, thereby adjusting its birefringence characteristics and achieving electrically controlled adjustment of the center wavelength and bandwidth of its transmission spectrum, thus simulating the specific reflection peaks and absorption valleys of the target in the near-infrared band. The compensation for intensity fluctuations in optical projection parameters is handled by a neutral density filter wheel, which is equipped with filters of different optical densities. Based on the feedback readings from the light intensity sensor, the actuator uses another stepper motor to rotate a suitable neutral density filter into the optical path to maintain the stability of the output light intensity. Its control logic can be expressed as follows: in: This represents the number of steps the stepper motor needs to drive (with a sign indicating the direction). It is a proportionality coefficient related to the system's transmission mechanism and light intensity-density characteristics. It is the real-time measurement value from the light intensity sensor. It is a dynamic adjustment of the target light intensity value set in the sequence.

[0042] The entire band-coupled actuator emphasizes strict timing synchronization. It has an internal high-precision clock source that provides a unified time reference for processing drive commands from the electromagnetic, infrared, and optical channels. This ensures that various actuators driven by different physical principles are precisely aligned in time, maintaining the preset inter-band correlation characteristics of the generated composite simulated background field at every moment. Communication between the actuator and the upper-level control module uses a high-reliability industrial bus with short data transmission cycles, ensuring timely execution of dynamically adjusted sequence commands. The actuator also features a self-monitoring function, continuously monitoring the operating status of each actuator. Upon detecting an anomaly, it immediately reports an error and safely shuts down, preventing equipment damage and ensuring the safety of the simulation experiment. The rigidity, thermal stability, and electromagnetic compatibility of the actuator's mechanical structure are specially designed to minimize interference introduced into the simulated field, ensuring the realism of the simulated background.

[0043] See Figure 4 The diagram illustrates the detailed working principle of the band-coupled actuator. The top figure shows the time-varying characteristics of three key parameters of the electromagnetic wave generator: the coil excitation current simulates low-frequency geomagnetic disturbances, exhibiting a smooth sinusoidal waveform; the resonant frequency adjustment is used to realize mid-frequency atmospheric scattering characteristics, displayed as a frequency modulation signal; and the antenna pulse signal simulates high-frequency artificial interference characteristics, exhibiting modulated square wave characteristics. The bottom figure demonstrates the coordinated control of the infrared radiator and optical filter. The blackbody temperature curve reflects the precise temperature regulation of the zoned heating control, the semiconductor power curve shows the dynamic compensation of thermal radiation attenuation characteristics, the filter angle change corresponds to the simulation of visible light band absorption characteristics, and the light intensity output curve reflects the compensation effect of the neutral density filter wheel on intensity fluctuations. The temporal synchronization of these parameters ensures the accurate generation of the multi-band background field.

[0044] Example 4: The background verification module and the feedback calibration module together form a closed-loop control system. Its continuous operation ensures that the composite simulated background field maintains a high degree of consistency with the target background type over a long period. The monitoring link of the background verification module is independent of the main simulation link of the system. It includes a calibrated monitoring sensor array, spatially optimized to cover a representative area of ​​the simulated field, avoiding obstruction or interference to the main simulation signal. The monitoring sensor array includes a high-sensitivity electromagnetic spectrum analyzer with a wide-bandwidth receiving probe that continuously scans the electromagnetic environment from low to high frequencies; a high-accuracy mid-wave infrared thermal imager that captures the temperature distribution image of the entire simulated area at a fixed frame rate; and an fiber-coupled spectrometer whose incident probe is aligned with a standard reflector in the simulated field to measure the spectral reflectance characteristics under simulated illumination conditions in real time. These monitoring data are synchronously acquired at a high sampling rate, with timestamps strictly aligned with the dynamic adjustment sequence of the main simulation system.

[0045] The acquired actual band parameters are sent to the data processing core of the background verification module, which runs a feature matching degree calculation algorithm. The matching degree calculation is not a simple point-to-point comparison, but rather, based on feature extraction principles similar to those of the background feature extraction module, a real-time "actual background feature vector set" is recalculated from the monitoring data. This actual vector set is then compared dimension-by-dimensionally and with weighted similarity to a pre-stored "standard feature vector set" of the target background type. The entire comparison process generates a comprehensive matching degree score, a scalar value between 0% and 100%, quantifying the realism of the simulated field. The system presets a configurable threshold, such as 90%. When the average matching degree of a single calculation or multiple consecutive calculations falls below this threshold, the current simulated background field is deemed to have a significant deviation.

[0046] The feedback calibration module is activated upon receiving a signal with a matching degree below a threshold. Its primary task is to reinitialize the strategy of the background feature extraction module. This initialization is not a simple reset, but an adjustment with a correction purpose. The module analyzes the magnitude and direction of the deviation between the current actual feature vector set and the target feature vector set in each dimension. For example, if the deviation is mainly concentrated in the high-frequency part of the electromagnetic band, the high-frequency band division boundary or noise basis subtraction algorithm during feature extraction may be adjusted; if the temperature field uniformity deviation of the infrared features is large, the size of the sliding window or the algorithm parameters for gradient calculation in spatial gradient analysis may be modified. This strategy adjustment aims to make the feature extraction module more sensitive to the difference between the current simulated field and the target field, thereby extracting more discriminative features for subsequent control.

[0047] While adjusting the feature extraction strategy, the feedback calibration module sends a trigger signal to the dynamic simulation control module, initiating its iterative optimization process. This process differs from the initial parameter sequence generation; it's an optimization process with memory functionality. The optimizer compares the current parameters causing a decrease in matching accuracy with successful parameter settings from historical data. It may introduce new constraints, such as limiting the variation of certain actuator parameters from the previous setting to prevent system oscillations. The optimization objective function is also weighted, focusing more on correcting the identified major bias dimensions. Based on the new feature extraction strategy and optimization objective, the dynamic simulation control module recalculates a new set of dynamically adjusted sequences. This new set of sequences aims to systematically correct the observed biases.

[0048] This closed-loop process continues until the matching degree calculated by the background verification module stably recovers to above the threshold. Referring to Table 1, the system records key data throughout the calibration process, including deviation characteristics, adjusted strategy parameters, and optimized control sequences. This data is stored in the log for subsequent analysis of the system's performance and common deviation patterns under different simulation scenarios.

[0049] Table 1: Feature matching degree calculation when simulating grassland background Feature Dimension Target value Actual monitoring value absolute deviation Weighting coefficient Dimensional matching degree Low-frequency geomagnetic disturbance intensity (nT) 15.2 16.8 1.6 0.15 89.5% Mid-frequency atmospheric scattering intensity (dBμV / m) -45.3 -42.1 3.2 0.20 85.0% High-frequency artificial interference percentage (%) 8.5 12.3 3.8 0.25 70.6% Temperature field uniformity (°C) 0.5 0.9 0.4 0.10 80.0% Thermal radiation attenuation coefficient (m⁻¹) 0.02 0.019 0.001 0.15 95.0% Visible light absorption peak wavelength (nm) 550 548 2 0.05 96.0% Near-infrared average reflectance (%) 35.0 32.5 2.5 0.10 85.7% Taking a scenario simulating a typical grassland background as an example, the background verification module periodically calculates the matching degree. Assume its calculation process is based on the data shown in the table above. The table displays the target values, actual monitored values, calculated absolute deviations, pre-set weight coefficients based on feature importance, and the matching degree for each of the seven key feature dimensions extracted from the monitoring data. The overall matching degree is obtained by summing the matching degrees of each dimension multiplied by their weight coefficients. In this example, the high proportion of high-frequency artificial interference and the large deviation in temperature field uniformity result in low dimensional matching degrees. Due to the high weight of high-frequency features, they significantly lower the overall matching degree. Assume the calculated overall matching degree is 82%, which is below the 90% threshold.

[0050] The feedback calibration module analyzes the data and identifies high-frequency electromagnetic interference and infrared temperature uniformity as the main issues. It may instruct the background feature extraction module to further refine the high-frequency signal in subsequent feature decomposition, for example, further decomposing "high-frequency artificial interference features" into "narrowband communication interference" and "broadband noise interference" to more accurately pinpoint the source of the deviation. It may also suggest that the dynamic simulation control module prioritize optimizing the square wave signal parameters of the radiating antenna element (to reduce the excessive proportion of artificial interference) and adjust the zoned heating commands of the blackbody radiating surface (to improve temperature uniformity) when optimizing the next round of dynamic adjustment sequences. The dynamic simulation control module accepts these inputs and focuses on addressing these identified weaknesses in its iterative optimization process, generating new control commands. After the band-coupled actuator executes the new commands, the background verification module monitors again, and the matching degree is expected to improve, thus completing a full feedback calibration cycle. This targeted calibration based on specific deviation data makes the system correction process more intelligent and efficient.

[0051] Example 5: The background verification module's calculation of feature matching degree relies on a series of dedicated algorithms for different waveband physical characteristics. For electromagnetic band feature analysis, wavelet packet energy analysis is employed. This method decomposes high dynamic range electromagnetic time-domain signals into multi-level wavelet packets. The decomposition tree structure continuously bisects the original signal frequency band until a preset number of decomposition levels is reached, forming a series of sub-bands covering the entire frequency band to be analyzed, with equal bandwidth and non-overlapping center frequencies. Each sub-band corresponds to a wavelet packet node, and the mean of the sum of squares of the node's coefficients is used as the energy representation of that sub-band. In the example of simulating a grassland background, the target features may require a higher energy proportion for a sub-band representing the natural environment (such as the very low frequency band corresponding to lightning activity), while a lower energy proportion should be used for a sub-band representing urban noise. The frequency band energy proportion error is obtained by comparing the relative differences in energy proportions between the actual simulated field and the target background template in each sub-band. This error value comprehensively reflects the degree of deviation of the simulated electromagnetic environment from the ideal state in terms of spectral shape, rather than just the difference in overall intensity.

[0052] A thermal imaging centroid offset detection algorithm is applied to infrared band features. This algorithm processes the two-dimensional temperature matrix acquired by the infrared thermal imager. The thermal image is preprocessed, including non-uniformity correction and defective pixel replacement. A temperature threshold is set based on the target background type, and the image is binarized into a region of interest (ROI) and background. The weighted average of the coordinates of all pixels within the ROI is calculated, with the weight being the temperature value of each pixel, thus obtaining the centroid coordinates of the thermal image. When simulating a desert area with uneven sunlight, the target thermal centroid may be located at a specific position in the image, while the centroid position of the thermal image generated by the actual simulated field may shift due to uneven heating by the infrared radiator. The centroid offset is obtained by calculating the Euclidean distance between the actual centroid and the target centroid. This value directly reflects whether the overall spatial balance of the simulated heat distribution meets the preset requirements.

[0053] In optical band characteristics, a spectral correlation coefficient is introduced to quantify similarity deviation. Calculating the spectral correlation coefficient requires two spectral curves sampled at the same wavelength: the standard reflectance curve of the target background and the spectral reflectance curve obtained from actual monitoring. The covariance between the reflectance values ​​of the two curves at each sampling point is calculated, and then divided by the product of their respective standard deviations. The resulting spectral correlation coefficient is a scalar between -1 and 1. When simulating a grassland with specific vegetation spectral characteristics, its standard curve has a reflectance peak in the green band, an absorption valley in the red band, and a high reflectance plateau in the near-infrared band. The actual simulated spectral curve may have slight differences in shape from the standard curve, such as insufficient depth of the absorption valley or different slopes of the reflection plateau. The spectral correlation coefficient can sensitively capture these overall shape similarities; the closer its value is to 1, the more similar the two curves are. The similarity deviation is represented by 1 minus the correlation coefficient.

[0054] When the feedback calibration module executes the iterative optimization process, its correction actions are closely coupled with the aforementioned deviation calculation results. Based on the frequency band energy ratio error obtained from wavelet packet energy analysis, the module generates correction commands that apply to the impedance matching network of the electromagnetic wave generator. For example, if the analog energy in a certain high-frequency sub-band is found to be consistently low, the calibration logic will determine that the antenna system's radiation efficiency at that frequency may be poor. Therefore, it will fine-tune the capacitance value of the adjustable capacitor or the inductance value of the adjustable inductor in the impedance matching network through digital control signals, so that the output impedance of the transmitting circuit is rematched with the input impedance of the antenna at that specific frequency, thereby increasing the radiated energy output of that frequency band and reducing the energy ratio error.

[0055] Based on the detection results of the thermal imaging centroid offset, the feedback calibration module adjusts the driving voltage of the focal plane array of the infrared radiator. The infrared radiator typically contains a focal plane array composed of multiple independent heating units, each with its own driving voltage controlling its heating power. When the centroid offset indicates an overall tilt in the thermal distribution in a certain direction, the calibration algorithm calculates a voltage adjustment matrix containing fine-tuning adjustments to the driving voltage of each unit in the array. This requires increasing the voltage of the heating units on the opposite side of the centroid offset direction while appropriately decreasing the voltage on the side with the offset direction. This fine voltage redistribution pulls the centroid back to the target position, thereby improving the spatial uniformity of the temperature field. Based on the similarity deviation calculated using the spectral correlation coefficient, the feedback calibration module updates the color temperature calibration coefficient of the optical filter device. Optical simulation systems typically include adjustable color temperature light sources or filter combinations. The color temperature calibration coefficient is a mapping parameter that converts the target spectral characteristics into a control variable for the filter device. When the monitored spectrum deviates from the target spectrum in terms of overall shape, the feedback calibration module does not directly adjust the mechanical position of the filter or the current of the light source. Instead, it corrects the internal color temperature calibration coefficient lookup table or fitting parameters. For example, if the simulated spectrum is generally weak in the short-wavelength direction, the system may update the coefficients so that in subsequent control, when light with a specific color temperature is needed, the system will automatically assign higher weights or intensities to the short-wavelength components, thereby compensating for the deviation in spectral shape and making the overall shape of the output spectrum closer to the target curve.

[0056] The entire feedback calibration process operates iteratively, with each iteration consisting of a measurement-calculation-judgment-correction loop. The module has a maximum number of iterations and a convergence threshold to prevent it from entering an infinite loop when corrections are impossible. The parameter corrections generated in each iteration are typically recorded, forming a personalized calibration history for the specific simulation scenario. This data helps the system converge to its optimal state more quickly when encountering similar scenarios in the future. Through this refined, targeted feedback mechanism based on specific physical deviations, the system can gradually improve and maintain the comprehensive feature matching degree of the composite simulated background field above the set threshold level.

[0057] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0058] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An outdoor electromagnetic, infrared, and optical multi-band background simulation system, characterized in that, include: A smart sensor array is used to collect initial environmental parameters of the target test area, including electromagnetic radiation intensity, infrared thermal distribution, and optical reflectivity. The background feature extraction module is used to perform multi-band feature decomposition on the initial environmental parameters based on a preset standard feature database of grassland, desert and snow, and generate a background feature vector set containing electromagnetic band features, infrared band features and optical band features. The dynamic simulation control module is used to calculate the dynamic adjustment sequence of electromagnetic modulation parameters, infrared emission parameters and optical projection parameters based on the background feature vector set through a multi-objective optimization algorithm; A band-coupled actuator is used to synchronously drive an electromagnetic wave generator, an infrared radiator, and an optical filter according to the dynamic adjustment sequence to generate a composite simulated background field that matches the target background type.

2. The outdoor electromagnetic, infrared, and optical multi-band background simulation system according to claim 1, characterized in that, When the background feature extraction module performs multi-band feature decomposition: Electromagnetic radiation intensity is divided into low-frequency geomagnetic disturbance characteristics, mid-frequency atmospheric scattering characteristics, and high-frequency artificial interference characteristics according to frequency bands. Spatial gradient analysis was used to extract the temperature field uniformity and thermal radiation attenuation characteristics from the infrared thermal distribution. The absorption characteristics in the visible light band and the reflection characteristics in the near-infrared band were separated by the spectral response curve of optical reflectance.

3. The outdoor electromagnetic, infrared, and optical multi-band background simulation system according to claim 2, characterized in that, The process by which the dynamic simulation control module generates the dynamic adjustment sequence includes: Establish a time-domain coupling model between electromagnetic band characteristics and infrared band characteristics, and calculate the compensation coefficient of electromagnetic modulation parameters on infrared emission parameters. Based on the frequency domain distribution characteristics of the optical band, determine the phase synchronization constraint conditions between the optical projection parameters and the electromagnetic modulation parameters. A nonlinear programming algorithm is used to solve for the optimal combination of parameters that satisfies the constraints of compensation coefficient and phase synchronization, generating a dynamic adjustment sequence sorted by timestamp.

4. The outdoor electromagnetic, infrared, and optical multi-band background simulation system according to claim 3, characterized in that, When the band-coupled actuator drives the electromagnetic wave generator: Based on the characteristics of low-frequency geomagnetic disturbances in the dynamic adjustment sequence, the excitation current waveform of the ring coil array is configured; Adjust the resonant frequency of the Helmholtz coil according to the mid-frequency atmospheric scattering characteristics; To address the characteristics of high-frequency artificial interference, a square wave signal with adjustable pulse width is applied to the radiating antenna element.

5. The outdoor electromagnetic, infrared, and optical multi-band background simulation system according to claim 4, characterized in that, When the band-coupled actuator controls the infrared radiator: The temperature field uniformity characteristics are converted into zoned heating commands for the blackbody radiation surface. The power output curve of the semiconductor cooler is dynamically adjusted based on the thermal radiation attenuation characteristics. A PID control algorithm is used to maintain the steady-state output of the target's infrared band characteristics.

6. The outdoor electromagnetic, infrared, and optical multi-band background simulation system according to claim 5, characterized in that, When the band-coupled actuator adjusts the optical filter device: The tilt angle of the multilayer interference filter is rotated according to the absorption characteristics in the visible light band. The operating mode of the liquid crystal tunable filter is switched based on the near-infrared band reflection characteristics. Intensity fluctuations in optical projection parameters are compensated by using a stepper motor to drive a neutral density filter wheel.

7. The outdoor electromagnetic, infrared, and optical multi-band background simulation system according to claim 6, characterized in that, Also includes: The background verification module is used to monitor the actual band parameters of the composite simulated background field in real time and calculate the feature matching degree between the actual band parameters and the target background type. The feedback calibration module is used to reinitialize the multi-band feature decomposition strategy of the background feature extraction module and trigger the iterative optimization process of the dynamic simulation control module when the feature matching degree is lower than the threshold.

8. The outdoor electromagnetic, infrared, and optical multi-band background simulation system according to claim 7, characterized in that, When the background verification module calculates the feature matching degree: Wavelet packet energy analysis was used to extract the frequency band energy ratio error for electromagnetic band characteristics; Apply a thermal imaging centroid offset detection algorithm to infrared band features; Introducing spectral correlation coefficients into optical band characteristics quantifies similarity deviations.

9. An outdoor electromagnetic, infrared, and optical multi-band background simulation system according to claim 8, characterized in that, When the feedback calibration module executes the iterative optimization process: The impedance matching network parameters of the electromagnetic wave generator are corrected based on the frequency band energy ratio error. Adjusting the focal plane array drive voltage of the infrared radiator based on the thermal imaging centroid offset; The color temperature calibration coefficient of the optical filter is updated based on the deviation of the spectral correlation coefficient.

10. A method for simulating multi-band electromagnetic, infrared, and optical background in outdoor fields, characterized in that, It includes all modules and method flows of the outdoor electromagnetic, infrared, and optical multi-band background simulation system as described in any one of claims 1 to 9.

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