A seeker test method, device, equipment and medium based on target signal

Abstract

This application provides a target signal-based seeker testing method, apparatus, equipment, and medium for radar seeker testing technology. It addresses the problem of low testing accuracy caused by inaccurate analog signals used in existing tests. The method includes: determining a corresponding feature model based on target feature parameters corresponding to a simulated target; modulating the detection signal corresponding to the seeker under test according to the feature model to obtain an active echo signal; determining corresponding radar features from a radar feature library and dynamically modeling the radar features to obtain a corresponding signal feature parameter set; directly digitally synthesizing the radar features and signal feature parameter set to obtain a corresponding passive radar signal; and superimposing interference and clutter signals to obtain a target environment signal; performing signal fusion to obtain the target signal and testing the seeker under test. This improves the accuracy of the simulated target signal and the seeker testing precision.

Description

Technical Field

[0001] This application relates to the field of radar seeker testing technology, and in particular to a seeker testing method, apparatus, equipment and medium based on target signals. Background Technology

[0002] As the core sensing unit of precision-guided weapons, the technological level of radar seekers directly determines the combat effectiveness of weapon systems. In the context of the widespread use of stealth targets, complex electromagnetic environments, and diverse combat scenarios on modern battlefields, radar seekers are making leaps and bounds towards broadband, multimodal, networked, and intelligent development. However, in practical applications, a single physical combat test and verification of a radar seeker may involve huge consumption of manpower and material resources. Therefore, by generating target echoes and background clutter in the laboratory that more closely resemble actual combat requirements, the effect of hardware-in-the-loop simulation can be improved, thereby enhancing the weapon verification completion rate and tracking and strike capabilities, and reducing the overall development cycle and cost.

[0003] Existing seeker testing methods are mainly implemented through hardware-in-the-loop simulation, combining real seekers and simulators for closed-loop testing. This includes acquiring the seeker's transmitted signal, performing delay and Doppler effects, and then returning the corresponding signal. Alternatively, it can be based on a multi-probe array, where the corresponding analog signal is determined by switching the analog angle. Or, the transmitted signal can be acquired, stored, digitally modulated, and then played back.

[0004] However, existing seeker testing methods suffer from low testing accuracy due to inaccurate analog signals. Summary of the Invention

[0005] This application provides a method, apparatus, device, and medium for testing seekers based on target signals, in order to solve the problem of low testing accuracy caused by inaccurate analog signals in existing seeker testing methods.

[0006] In a first aspect, this application provides a seeker testing method based on a target signal, the method comprising: Based on the target characteristic parameters corresponding to the simulated target, the corresponding characteristic model is determined, and according to the characteristic model, the detection signal corresponding to the seeker to be tested is modulated to obtain the active echo signal. Based on the radar type corresponding to the simulated target, the corresponding radar features are determined from the radar feature library, and the radar features are dynamically modeled to obtain the corresponding signal feature parameter set. The radar feature and signal feature parameter sets are directly digitally synthesized to obtain the corresponding passive radar signal, and the interference signal and clutter signal are superimposed to obtain the target environment signal; Based on a multi-source signal fusion algorithm, active echo signals, passive radar signals, and target environment signals are fused to obtain the target signal. Based on the target signal, the seeker to be tested is then tested.

[0007] In some embodiments of this application, a corresponding feature model is determined based on the target feature parameters corresponding to the simulated target, including: The attitude change trajectory of the simulated target is analyzed to obtain multiple target attitudes. Based on the dynamic RCS modeling method, the reflection characteristics corresponding to each target attitude are modeled to obtain the radar cross-section value corresponding to the simulated target. Based on the distance, velocity, and radar cross-section values ​​of the simulated target, the target characteristic parameters are determined and the corresponding characteristic model is constructed.

[0008] In some embodiments of this application, the detection signal corresponding to the seeker to be tested is modulated according to the feature model to obtain an active echo signal, including: The initial probe signal emitted by the seeker under test is acquired, and the initial probe signal is denoised according to the adaptive wavelet threshold denoising algorithm to obtain the probe signal. Based on the characteristic model, the detection signal is modulated to obtain the active echo signal.

[0009] In some embodiments of this application, radar features are dynamically modeled to obtain a corresponding set of signal feature parameters, including: The signal amplitude and signal pulse corresponding to the radar characteristics are extracted in time sequence to obtain their respective time sequence characteristics, and the corresponding signal characteristic parameter set is determined based on the time sequence characteristics.

[0010] In some embodiments of this application, before fusing active echo signals, passive radar signals, and target environment signals based on a multi-source signal fusion algorithm to obtain the target signal, the method further includes: Based on the clock synchronization module, the active echo signal, passive radar signal and target environment signal are clock synchronized, and the amplitude of the active echo signal, passive radar signal and target environment signal after clock synchronization is calibrated according to the current test scenario.

[0011] In some embodiments of this application, amplitude calibration is performed on the active echo signal, passive radar signal, and target environment signal after clock synchronization, according to the current test scenario, including: Based on the test category corresponding to the current test scenario, determine the preset amplitude ratios of the active echo signal, passive radar signal, and target environment signal, and perform amplitude calibration based on the preset amplitude ratios to obtain the amplitude-calibrated active echo signal, passive radar signal, and target environment signal.

[0012] Secondly, this application provides a seeker test system based on target signals. The system includes a radio frequency processing channel device, a system control and monitoring device, and a seeker test device based on target signals. Each device is connected to the other through a VPX backplane. The radio frequency processing channel device is used to provide independent radio frequency link support for the seeker test device based on the target signal; The system control and monitoring device is used to monitor for anomalies in signals from other devices based on the VPX backplane; The target signal-based seeker testing device is used to determine the corresponding feature model based on the target feature parameters corresponding to the simulated target, and modulate the detection signal corresponding to the seeker under test according to the feature model to obtain the active echo signal; determine the corresponding radar features from the radar feature library according to the radar type corresponding to the simulated target, and dynamically model the radar features to obtain the corresponding signal feature parameter set; directly digitally synthesize the radar features and signal feature parameter set to obtain the corresponding passive radar signal, and superimpose the interference signal and clutter signal to obtain the target environment signal; based on the multi-source signal fusion algorithm, fuse the active echo signal, passive radar signal and target environment signal to obtain the target signal, and perform signal testing on the seeker under test according to the target signal.

[0013] Thirdly, this application provides a seeker head testing device based on a target signal, the device comprising: The modulation module is used to determine the corresponding feature model based on the target feature parameters corresponding to the simulated target, and modulate the detection signal corresponding to the seeker to be tested according to the feature model to obtain the active echo signal. The modeling module is used to determine the corresponding radar features from the radar feature library based on the radar type of the simulated target, and to dynamically model the radar features to obtain the corresponding signal feature parameter set. The synthesis module is used to directly digitally synthesize radar features and signal feature parameter sets to obtain the corresponding passive radar signal, and to superimpose interference signals and clutter signals to obtain the target environment signal; The fusion module is used to fuse active echo signals, passive radar signals, and target environment signals based on a multi-source signal fusion algorithm to obtain the target signal, and to perform signal testing on the seeker to be tested based on the target signal.

[0014] Fourthly, this application provides a computer device, including: a processor, and a memory communicatively connected to the processor; The memory stores the instructions that the computer executes; The processor executes computer execution instructions stored in memory to implement the method of this application.

[0015] Fifthly, this application provides a computer-readable storage medium storing program code, which, when executed by a processor, is used to implement the method of this application.

[0016] Compared with existing technologies, the method in this application modulates the seeker's detection signal based on a target feature model, thereby accurately simulating an active echo signal containing real characteristics such as target range, velocity, and RCS, providing accurate active target detection excitation for the seeker. Furthermore, by dynamically modeling radar features and combining them with DDS technology to generate passive radar signals, it can reproduce the real radiation characteristics of radar antenna scanning, amplitude, and temporal dynamic changes, achieving high-fidelity simulation of passive target signals. By superimposing interference and clutter to form a target environment signal, it constructs a complex electromagnetic background close to actual combat, simulating battlefield clutter and various interference environments. Through a multi-source signal fusion algorithm, the three types of signals are simultaneously calibrated and fused, thereby generating a comprehensive target signal that simultaneously possesses active echo, passive radiation, and complex electromagnetic environment characteristics, improving the accuracy of the target simulation signal. This allows for accurate testing and verification of the radar seeker's detection performance, tracking capability, passive interception and identification capability, and anti-jamming performance, improving testing precision. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] Figure 1 A flowchart illustrating a seeker testing method based on a target signal, provided in an embodiment of this application; Figure 2 A schematic diagram of a target signal-based seeker testing device provided in an embodiment of this application; Figure 3 This is a structural block diagram of an apparatus for performing a target signal-based seeker testing method according to an embodiment of this application. Detailed Implementation

[0019] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0020] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0021] Figure 1 This is a flowchart illustrating a seeker testing method based on a target signal, provided as an embodiment of this application. Figure 1 As shown, this target signal-based seeker testing method may include the following steps: S110. Based on the target characteristic parameters corresponding to the simulated target, determine the corresponding characteristic model, and according to the characteristic model, modulate the detection signal corresponding to the seeker to be tested to obtain the active echo signal.

[0022] Among them, simulated targets refer to artificially constructed virtual targets used to simulate real targets that the seeker needs to detect and track in actual combat. They can cover various radar detection targets such as fighter jets, missiles, ships, and vehicles. Different attributes such as stealth / non-stealth and stationary / moving can also be set according to test requirements, so that the seeker can perceive the existence and movement of real targets in the test.

[0023] Target characteristic parameters are the core quantitative parameters describing the radar and motion characteristics of the simulated target. They include radar characteristic parameters, such as radar cross-section (RCS), including static RCS values ​​and dynamic RCS change trajectories, and may also include derived parameters such as target material and shape; as well as motion characteristic parameters, such as the relative distance and relative velocity between the target and the seeker under test, target attitude (such as pitch angle and yaw angle), and motion trajectory. This transforms the abstract simulated target into calculable and modulated quantitative data, ensuring that the characteristic model can accurately match the actual characteristics of the simulated target, and ultimately allowing the generated active echo signal to truly reflect the radar and motion characteristics of the target.

[0024] The feature model is a model constructed based on the target feature parameters of the simulated target and described by the radar echo modeling algorithm. It describes the mapping relationship between the radar echo characteristics of the target and the target parameters, and reflects the correspondence between the target's range, velocity, attitude, dynamic RCS and other parameters and the characteristics of the echo signal such as delay, Doppler frequency shift, amplitude, and phase. In this way, the quantified target feature parameters are transformed into specific instructions that can be used for signal modulation, such as range corresponding to echo delay and velocity corresponding to Doppler frequency shift. After the detection signal is modulated under the guidance of this model, it can generate an active echo signal that fits the simulated target characteristics, ensuring the simulation accuracy of the echo signal.

[0025] The detection signal is the radio frequency detection signal emitted by the seeker under test in active working mode for detecting the target.

[0026] Active echo signal is a radio frequency signal generated by the seeker after the simulated target reflects the generated simulated probe signal based on the probe signal under test and after processing such as delay, Doppler modulation, amplitude calibration, and phase compensation according to the characteristic model of the simulated target.

[0027] Based on this, by obtaining the target feature parameters corresponding to the simulated target, a feature model corresponding to the parameters is constructed. Based on the feature model, the detection signal emitted by the seeker to be tested is modulated to obtain the active echo signal.

[0028] S120. Based on the radar type corresponding to the simulated target, determine the corresponding radar features from the radar feature library, and dynamically model the radar features to obtain the corresponding signal feature parameter set.

[0029] The radar feature library refers to a pre-built and stored structured database that covers signal feature parameters of various typical radar devices. It stores the basic radar features corresponding to different radar types, so that in practical applications, the basic feature parameters can be quickly retrieved and matched according to the radar type corresponding to the simulated target, such as ground early warning radar or airborne fire control radar, thereby improving the generation efficiency of passive signals.

[0030] Radar features are static parameters inherent to various radar devices that characterize their signal waveforms and operating characteristics. By retrieving radar features from a feature library based on the radar type of the simulated target, the radar type of the simulated target can be transformed into calculable and modelable static parameters.

[0031] Dynamic modeling, in the process of passive target signal generation, uses basic radar features extracted from the radar feature library as a basis and combines them with the dynamic characteristics of radar operation in actual combat to establish a model of the dynamic changes of radar signal features over time or under operating conditions. This solves the problem that basic radar features can only characterize the static signal characteristics of radar and cannot simulate the dynamic operating state of radar in actual combat. Through dynamic modeling, radar features are made to fit the real operating scenarios of radar in actual combat, such as the change of signal amplitude with gain pattern during antenna scanning and the change of pulse arrival time with scanning sequence.

[0032] The signal feature parameter set is a structured set of parameters that can comprehensively characterize the dynamic signal characteristics of radar in actual combat, obtained after dynamic modeling of radar features. This parameter set includes basic static parameters extracted from the radar feature library, such as carrier frequency and modulation mode, as well as real-time dynamic parameters added by dynamic modeling, such as signal amplitude and phase that change with antenna scanning, real-time pulse arrival time, and configurable signal attenuation and multipath effect parameters.

[0033] Based on this, by determining the radar type corresponding to the simulated target, the corresponding radar features can be identified from the radar feature library, and the radar features can be dynamically modeled to obtain the corresponding signal feature parameter set, so as to comprehensively characterize the dynamic signal characteristics of the radar in actual combat.

[0034] S130. Directly digitally synthesize the radar feature and signal feature parameter sets to obtain the corresponding passive radar signal, and superimpose the interference signal and clutter signal to obtain the target environment signal.

[0035] Direct digital synthesis refers to a technique that uses basic radar features extracted from radar feature libraries and a set of signal feature parameters obtained through dynamic modeling. It utilizes a hardware architecture with an integrated DAC in an FPGA to generate accurate digital signals and convert them into radio frequency analog signals. It features high frequency resolution, fast signal switching, and concurrent generation of multiple signals.

[0036] Passive radar signals are radio frequency signals generated by direct digital synthesis technology, which uses radar characteristics as static features and signal characteristic parameter sets as dynamic features. They simulate the radio frequency signals emitted by various radar radiation sources in actual combat and are also the passive radiation signals corresponding to the simulated target, so that the seeker can perceive the passive radiation characteristics of the simulated target in a laboratory environment.

[0037] The target environment signal refers to the integrated radio frequency signal of the seeker, which is actually received by the seeker in actual combat, including the passive signal of the target and the complex electromagnetic environment. It is used to verify the seeker's ability to identify, locate, and track passive radiation source targets in complex combat electromagnetic environments, as well as its anti-interference and anti-clutter capabilities. Compared with a single passive radar signal, this signal is completely consistent with the actual receiving scenario of the seeker in actual combat, which can improve the real combat approximation of the laboratory hardware-in-the-loop simulation test.

[0038] Based on this, the corresponding passive radar signal is obtained by directly digitally synthesizing the radar feature and signal feature parameter sets, and the target environment signal is obtained by superimposing the interference signal and clutter signal.

[0039] S140. Based on the multi-source signal fusion algorithm, the active echo signal, passive radar signal and target environment signal are fused to obtain the target signal, and the signal test is performed on the seeker to be tested according to the target signal.

[0040] Among them, the multi-source signal fusion algorithm refers to the algorithm designed for the signal characteristics of active echo signals, passive radar signals, and target environment signals. It can integrate three independently generated radio frequency signals into a unified comprehensive signal by superimposing and integrating the signals according to the actual combat scenario. It supports two modes: linear fusion and nonlinear fusion. Linear fusion is suitable for simple electromagnetic environments. The three types of signals are linearly superimposed in a fixed ratio, preserving the original characteristics of each signal without additional nonlinear distortion. Nonlinear fusion is suitable for complex electromagnetic environments. It simulates the nonlinear effects (such as electromagnetic scattering and multipath coupling) in the signal propagation and superposition process in actual combat, making the fused signal more consistent with the signal characteristics of the real battlefield.

[0041] Signal fusion refers to the process of integrating three independent radio frequency signals—active echo signals, passive radar signals, and target environment signals—according to the spatiotemporal distribution, amplitude ratio, and superposition rules of signals in actual combat scenarios. This process aims to recreate the actual scenario in which a radar seeker simultaneously receives active target echoes, passive radiation source signals, and complex electromagnetic environment signals on a real battlefield.

[0042] Based on this, a multi-source signal fusion algorithm is used to fuse active echo signals, passive radar signals, and target environment signals to obtain the fused target signal, so as to perform signal testing on the seeker to be tested based on the target signal.

[0043] Based on the feasible implementation of S110 described above, this application further provides a method for determining a corresponding feature model based on the target feature parameters corresponding to the simulated target, including: The attitude change trajectory of the simulated target is analyzed to obtain multiple target attitudes. Based on the dynamic RCS modeling method, the reflection characteristics corresponding to each target attitude are modeled to obtain the radar cross-section value corresponding to the simulated target. Based on the distance, velocity, and radar cross-section values ​​of the simulated target, the target characteristic parameters are determined and the corresponding characteristic model is constructed.

[0044] Among them, the attitude change trajectory is the trajectory curve / data sequence of the simulated target in the preset simulated motion scenario, which continuously changes its spatial attitude over time. It describes the specific values ​​and continuous change rules of the pitch angle, yaw angle, roll angle and other attitude angles of the simulated target, such as fighter jets, missiles and ships, at different time points.

[0045] Target attitude is the spatial attitude state of a target at a specific point in time, which is analyzed from the attitude change trajectory. It is a discretized specific value of the attitude change trajectory, and is characterized by specific numerical values ​​of attitude angles such as pitch angle, yaw angle, and roll angle. It is a quantitative parameter that describes the orientation and spatial position of the target at a certain moment.

[0046] Dynamic RCS modeling refers to a method that models and calculates the radar reflection characteristics of a target in real time based on its different attitudes, combined with factors such as the target's shape, material, and radar wave incident angle. This method ultimately obtains the radar cross-section value that changes dynamically with the target's attitude. Unlike static RCS modeling, which is fixed and unchanging, the RCS value can be updated in real time as the target's attitude changes.

[0047] Reflection characteristics simulate the reflection patterns and properties of a target in a specific target attitude to radar detection waves, including the reflection intensity, reflection direction, and scattering characteristics of radar waves. These characteristics are determined by the target's attitude (radar wave incident angle), shape, structure, and material properties, and are inherent radar physical characteristics of the target that are strongly related to its attitude.

[0048] Based on this, in practical applications, a broadband data acquisition module is used to acquire the active detection signal emitted by the radar seeker, with an acquisition bandwidth ≥ 2 GHz and a sampling rate ≥ 4.8 GSPS, to ensure coverage of the signal bandwidth requirements of the high-resolution seeker; the attitude change trajectory of the simulated target is acquired and analyzed to obtain multiple target attitudes, and the reflection characteristics corresponding to each target attitude are modeled based on the dynamic RCS modeling method to obtain the radar cross-section value corresponding to the simulated target, so as to determine the target feature parameters and construct the corresponding feature model based on the distance, velocity and radar cross-section value of the simulated target.

[0049] Based on the feasible implementation of S110 described above, this application further provides a method for modulating the detection signal corresponding to the seeker to be tested according to the feature model to obtain an active echo signal, including: The initial probe signal emitted by the seeker under test is acquired, and the initial probe signal is denoised according to the adaptive wavelet threshold denoising algorithm to obtain the probe signal. Based on the characteristic model, the detection signal is modulated to obtain the active echo signal.

[0050] The initial detection signal is the raw radio frequency detection signal directly emitted by the seeker under test in active working mode without any manual processing. During the acquisition process, this signal will inevitably be mixed with useless signals such as Gaussian white noise and electromagnetic interference noise.

[0051] The adaptive wavelet threshold denoising algorithm refers to the algorithm that decomposes the noisy initial detection signal into wavelet coefficients of different scales and frequencies through wavelet transform, adaptively determines the denoising threshold based on the characteristics of the wavelet coefficients of the signal and noise, sets the noise wavelet coefficients below the threshold to zero, retains the effective signal wavelet coefficients above the threshold, and then reconstructs the clean signal through inverse wavelet transform, thereby achieving accurate separation of noise and effective signal.

[0052] Based on this, the acquired active detection signal is denoised by using an adaptive wavelet threshold denoising algorithm to remove Gaussian white noise and electromagnetic interference noise from the signal. Then, signal synchronization calibration is performed by using timestamp synchronization technology to accurately synchronize the acquired signal with the system clock to obtain the preprocessed detection signal.

[0053] Based on the feasible implementation of S120 described above, this application further provides dynamic modeling of radar characteristics to obtain a corresponding set of signal feature parameters, including: The signal amplitude and signal pulse corresponding to the radar characteristics are extracted in time sequence to obtain their respective time sequence characteristics, and the corresponding signal characteristic parameter set is determined based on the time sequence characteristics.

[0054] Among these, timing characteristics refer to the patterns of signal amplitude variation and pulse transmission / arrival time variation in passive target signals over time. In practical applications, during antenna scanning, signal amplitude exhibits periodic timing fluctuations with the antenna gain pattern, and pulse arrival time shows regular time shifts with the scanning sequence. These are all reflected through the timing characteristics of amplitude and pulse. This feature imbues the originally static radar characteristics with a dynamic time-dimensional variation pattern, accurately reproducing the operational characteristics of radar in actual combat—that it does not transmit signals at a fixed time but rather dynamically changes with antenna scanning and other operating conditions.

[0055] Based on this, by extracting the timing of the signal amplitude and signal pulse corresponding to the radar characteristics, the timing features corresponding to the signal amplitude and signal pulse are obtained, so as to further determine the corresponding signal feature parameter set based on the timing features.

[0056] Based on the feasible implementation of S140 described above, this application further provides a method that, before fusing active echo signals, passive radar signals, and target environment signals based on a multi-source signal fusion algorithm to obtain the target signal, also includes: Based on the clock synchronization module, the active echo signal, passive radar signal and target environment signal are clock synchronized, and the amplitude of the active echo signal, passive radar signal and target environment signal after clock synchronization is calibrated according to the current test scenario.

[0057] Clock synchronization refers to the strict alignment of three signals—active echo signal, passive radar signal, and target environment signal—on a time reference using a high-precision clock source and clock distribution network. In practical applications, the three types of signals—active, passive, and electromagnetic environment—are processed independently by different hardware modules. Slight differences in the crystal oscillators and processing links of each channel can cause nanosecond-level time offsets in the signals. Clock synchronization eliminates this offset to ≤1ns by using a unified reference clock, ensuring that the three types of signals are completely aligned in the time dimension.

[0058] Amplitude calibration refers to the process of adjusting and precisely calibrating the amplitude ratios of the time-aligned active echo signal, passive radar signal, and target environment signal based on the actual combat characteristics of the current test scenario, such as short-range / long-range targets, strong / weak electromagnetic interference, and low-altitude / high-altitude combat scenarios. This process adjusts the amplitude values ​​and amplitude ratios of the three types of signals according to the signal amplitude distribution patterns in the actual combat scenario, while eliminating amplitude deviations caused by differences in hardware gain between channels, ensuring that the amplitude characteristics of the signal before fusion conform to the requirements of the actual combat scenario. For example, in a strong interference combat scenario, the amplitude of the target environment signal will be calibrated to be higher than that of the active / passive target signal; in a weak clutter long-range target scenario, the amplitude of the active echo signal will be increased and the amplitude of the clutter signal will be reduced to match the characteristics of weak target signals and low clutter interference during long-range detection.

[0059] Based on this, a high-precision clock synchronization module is used to synchronize the time of the signals output from the active target signal processing channel, the passive target signal processing channel, and the electromagnetic environment and interference signal processing channel.

[0060] Based on the feasible implementation of S140 described above, this application further provides amplitude calibration of the active echo signal, passive radar signal, and target environment signal after clock synchronization, according to the current test scenario, including: Based on the test category corresponding to the current test scenario, determine the preset amplitude ratios of the active echo signal, passive radar signal, and target environment signal, and perform amplitude calibration based on the preset amplitude ratios to obtain the amplitude-calibrated active echo signal, passive radar signal, and target environment signal.

[0061] The test category refers to the classification of the seeker test missions according to the test purpose and combat scenario. Different categories correspond to different signal strength configuration strategies. Common test categories include active mode test, which focuses on verifying the seeker's active detection capability; passive mode test, which focuses on verifying the seeker's ability to intercept and identify enemy radar radiation; and anti-jamming test, which focuses on verifying the seeker's target detection capability in a strong jamming environment. Each test category corresponds to a set of preset signal amplitude proportions. After determining the test category, the corresponding proportion parameters can be directly retrieved, thereby improving the efficiency and accuracy of amplitude calibration.

[0062] The preset amplitude ratio is a pre-set ratio of the amplitudes of active echo signal, passive radar signal, and target environment signal for each test category, based on the signal propagation law, electromagnetic environment characteristics, and target signal characteristics of the corresponding actual combat scenario. For example, in a certain interference test category scenario, active: passive: target environment = 3:5:12.

[0063] Based on this, by determining the test category corresponding to the current test scenario, the preset amplitude proportions of active echo signals, passive radar signals, and target environment signals can be determined, thereby enabling amplitude calibration.

[0064] Based on the above steps, it can be seen that this application modulates the seeker's detection signal based on the target feature model, thereby accurately simulating an active echo signal containing real characteristics such as target range, velocity, and RCS, thus providing accurate active target detection excitation for the seeker. Furthermore, by dynamically modeling radar features and combining them with DDS technology to generate passive radar signals, it can reproduce the real radiation characteristics of radar antenna scanning, amplitude, and temporal dynamic changes, achieving high-fidelity simulation of passive target signals. By superimposing interference and clutter to form a target environment signal, it constructs a complex electromagnetic background close to actual combat, simulating battlefield clutter and various interference environments. Through a multi-source signal fusion algorithm, it synchronously calibrates and fuses the three types of signals, thereby generating a comprehensive target signal that simultaneously possesses active echo, passive radiation, and complex electromagnetic environment characteristics, improving the accuracy of the target simulation signal. This allows for accurate testing and verification of the radar seeker's detection performance, tracking capability, passive interception and identification capability, and anti-interference performance, improving testing accuracy.

[0065] In some embodiments, a target signal-based seeker test system is also proposed, characterized in that the system includes a radio frequency processing channel device, a system control and monitoring device, and a target signal-based seeker test device, and each device is connected to the seeker through a VPX backplane. The radio frequency processing channel device is used to provide independent radio frequency link support for the seeker test device based on the target signal; The system control and monitoring device is used to monitor for anomalies in signals from other devices based on the VPX backplane; The target signal-based seeker testing device is used to determine the corresponding feature model based on the target feature parameters corresponding to the simulated target, and modulate the detection signal corresponding to the seeker under test according to the feature model to obtain the active echo signal; determine the corresponding radar features from the radar feature library according to the radar type corresponding to the simulated target, and dynamically model the radar features to obtain the corresponding signal feature parameter set; directly digitally synthesize the radar features and signal feature parameter set to obtain the corresponding passive radar signal, and superimpose the interference signal and clutter signal to obtain the target environment signal; based on the multi-source signal fusion algorithm, fuse the active echo signal, passive radar signal and target environment signal to obtain the target signal, and perform signal testing on the seeker under test according to the target signal.

[0066] Figure 2 This is a schematic diagram of a seeker testing device based on a target signal, provided as an embodiment of this application. Figure 2 As shown, this target signal-based seeker testing device includes: a modulation module, a modeling module, a synthesis module, and a fusion module; wherein: The modulation module is used to determine the corresponding feature model based on the target feature parameters corresponding to the simulated target, and modulate the detection signal corresponding to the seeker to be tested according to the feature model to obtain the active echo signal. The modeling module is used to determine the corresponding radar features from the radar feature library based on the radar type of the simulated target, and to dynamically model the radar features to obtain the corresponding signal feature parameter set. The synthesis module is used to directly digitally synthesize radar features and signal feature parameter sets to obtain the corresponding passive radar signal, and to superimpose interference signals and clutter signals to obtain the target environment signal; The fusion module is used to fuse active echo signals, passive radar signals, and target environment signals based on a multi-source signal fusion algorithm to obtain the target signal, and to perform signal testing on the seeker to be tested based on the target signal.

[0067] In this embodiment of the application, the modulation module may also be specifically used for: The attitude change trajectory of the simulated target is analyzed to obtain multiple target attitudes. Based on the dynamic RCS modeling method, the reflection characteristics corresponding to each target attitude are modeled to obtain the radar cross-section value corresponding to the simulated target. Based on the distance, velocity, and radar cross-section values ​​of the simulated target, the target characteristic parameters are determined and the corresponding characteristic model is constructed.

[0068] In this embodiment of the application, the modulation module may also be specifically used for: The initial probe signal emitted by the seeker under test is acquired, and the initial probe signal is denoised according to the adaptive wavelet threshold denoising algorithm to obtain the probe signal. Based on the characteristic model, the detection signal is modulated to obtain the active echo signal.

[0069] In this embodiment of the application, the modeling module can also be specifically used for: The signal amplitude and signal pulse corresponding to the radar characteristics are extracted in time sequence to obtain their respective time sequence characteristics, and the corresponding signal characteristic parameter set is determined based on the time sequence characteristics.

[0070] In this embodiment of the application, other modules of this target signal-based seeker testing device may also be specifically used for: Based on the clock synchronization module, the active echo signal, passive radar signal and target environment signal are clock synchronized, and the amplitude of the active echo signal, passive radar signal and target environment signal after clock synchronization is calibrated according to the current test scenario.

[0071] In this embodiment of the application, other modules of this target signal-based seeker testing device may also be specifically used for: Based on the test category corresponding to the current test scenario, determine the preset amplitude ratios of the active echo signal, passive radar signal, and target environment signal, and perform amplitude calibration based on the preset amplitude ratios to obtain the amplitude-calibrated active echo signal, passive radar signal, and target environment signal.

[0072] Figure 3 This is a schematic diagram of the structure of an apparatus for performing a target signal-based seeker testing method according to an embodiment of this application. Figure 3 As shown, the device includes: The device may include one or more processors with processing cores, one or more computer-readable storage media such as memory, communication components, etc. The processor, memory, and communication components are connected via a bus.

[0073] In the specific implementation process, at least one processor executes computer execution instructions stored in memory, causing at least one processor to execute the above-described seeker test method based on target signals.

[0074] The specific implementation process of the processor can be found in the above method embodiments, and its implementation principle and technical effect are similar, so it will not be repeated here.

[0075] Furthermore, the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this application can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0076] The memory may include Random Access Memory (RAM) and may also include Non-volatile Memory (NVM), such as at least one disk storage device.

[0077] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0078] In some embodiments, a computer program product is also provided, including a computer program or instructions that, when executed by a processor, implement the steps in any of the target signal-based seeker testing methods described above.

[0079] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0080] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0081] Therefore, embodiments of this application provide a computer-readable storage medium storing a plurality of program codes, which can be loaded by a processor to execute the steps in any of the target signal-based seeker testing methods provided in embodiments of this application.

[0082] The storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0083] According to one aspect of this application, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium.

[0084] Since the instructions stored in the storage medium can execute the steps in any of the target signal-based seeker testing methods provided in the embodiments of this application, the beneficial effects that any of the target signal-based seeker testing methods provided in the embodiments of this application can achieve can be realized, as detailed in the preceding embodiments, and will not be repeated here.

[0085] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope of this application is indicated by the appended claims.

[0086] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A method for testing a seeker head based on a target signal, characterized in that, The method includes: Based on the target feature parameters corresponding to the simulated target, the corresponding feature model is determined, and according to the feature model, the detection signal corresponding to the seeker to be tested is modulated to obtain the active echo signal. Based on the radar type corresponding to the simulated target, the corresponding radar features are determined from the radar feature library, and the radar features are dynamically modeled to obtain the corresponding signal feature parameter set. The radar features and the set of signal feature parameters are directly digitally synthesized to obtain the corresponding passive radar signal, and the interference signal and clutter signal are superimposed to obtain the target environment signal; Based on a multi-source signal fusion algorithm, the active echo signal, the passive radar signal, and the target environment signal are fused to obtain the target signal, and the seeker to be tested is then tested based on the target signal.

2. The method according to claim 1, characterized in that, The step of determining the corresponding feature model based on the target feature parameters corresponding to the simulated target includes: The attitude change trajectory of the simulated target is analyzed to obtain multiple target attitudes. Based on the dynamic RCS modeling method, the reflection characteristics corresponding to each target attitude are modeled to obtain the radar cross-section value corresponding to the simulated target. Based on the distance, velocity, and radar cross-section value of the simulated target, the target characteristic parameters are determined and a corresponding characteristic model is constructed.

3. The method according to claim 1, characterized in that, The step of modulating the detection signal corresponding to the seeker head under test according to the feature model to obtain the active echo signal includes: The initial detection signal emitted by the seeker under test is acquired, and the initial detection signal is denoised according to the adaptive wavelet threshold denoising algorithm to obtain the detection signal. Based on the characteristic model, the detection signal is modulated to obtain the active echo signal.

4. The method according to claim 1, characterized in that, The dynamic modeling of the radar features to obtain the corresponding signal feature parameter set includes: The signal amplitude and signal pulse corresponding to the radar features are extracted in time sequence to obtain their respective time sequence features, and the corresponding signal feature parameter set is determined based on the time sequence features.

5. The method according to claim 1, characterized in that, Before fusing the active echo signal, the passive radar signal, and the target environment signal based on the multi-source signal fusion algorithm to obtain the target signal, the method further includes: Based on the clock synchronization module, the active echo signal, the passive radar signal, and the target environment signal are clock synchronized, and the amplitude of the clock synchronized active echo signal, the passive radar signal, and the target environment signal is calibrated according to the current test scenario.

6. The method according to claim 5, characterized in that, The step of performing amplitude calibration on the active echo signal, the passive radar signal, and the target environment signal after clock synchronization, based on the current test scenario, includes: Based on the test category corresponding to the current test scenario, the preset amplitude ratios of the active echo signal, the passive radar signal, and the target environment signal are determined, and amplitude calibration is performed based on the preset amplitude ratios to obtain the amplitude-calibrated active echo signal, passive radar signal, and target environment signal.

7. A seeker testing system based on target signals, characterized in that, The system includes a radio frequency processing channel device, a system control and monitoring device, and a seeker head testing device based on target signals. Each device is connected to the other via a VPX backplane. The radio frequency processing channel device is used to provide independent radio frequency link support for the target signal-based seeker test device; The system control and monitoring device is used to monitor the signals of other devices for anomalies based on the VPX backplane; The target signal-based seeker testing device is used to determine the corresponding feature model based on the target feature parameters corresponding to the simulated target, and modulate the detection signal corresponding to the seeker under test according to the feature model to obtain an active echo signal; determine the corresponding radar feature from the radar feature library according to the radar type corresponding to the simulated target, and dynamically model the radar feature to obtain the corresponding signal feature parameter set; directly digitally synthesize the radar feature and the signal feature parameter set to obtain the corresponding passive radar signal, and superimpose the interference signal and clutter signal to obtain the target environment signal; based on the multi-source signal fusion algorithm, fuse the active echo signal, the passive radar signal and the target environment signal to obtain the target signal, and perform signal testing on the seeker under test according to the target signal.

8. A seeker testing device based on a target signal as described in claim 7, characterized in that, For implementing the method as described in claim 1, the apparatus comprises: The modulation module is used to determine the corresponding feature model based on the target feature parameters corresponding to the simulated target, and modulate the detection signal corresponding to the seeker to be tested according to the feature model to obtain the active echo signal. The modeling module is used to determine the corresponding radar features from the radar feature library according to the radar type corresponding to the simulated target, and to dynamically model the radar features to obtain the corresponding signal feature parameter set. The synthesis module is used to directly digitally synthesize the radar features and the signal feature parameter set to obtain the corresponding passive radar signal, and to superimpose the interference signal and clutter signal to obtain the target environment signal. The fusion module is used to fuse the active echo signal, the passive radar signal, and the target environment signal based on a multi-source signal fusion algorithm to obtain the target signal, and to perform signal testing on the seeker head under test based on the target signal.

9. A computer device, characterized in that, include: One or more processors; Memory; One or more programs, wherein the one or more programs are stored in memory and configured to be executed by one or more processors, the one or more programs being configured to perform the method as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program code that can be called by a processor to perform the method as described in any one of claims 1 to 6.

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