Ship radar signal adaptive filtering method and device against multipath effect

Abstract

The application belongs to the technical field of radar signal processing, and specifically discloses a ship radar signal adaptive filtering method and device against multipath effect, which comprises the following steps: based on a pre-constructed signal processing model of each functional module of a radar, a radar overall filtering model is constructed, and the signal processing model is used to describe the corresponding relationship between input signal characteristics and output signal quality; according to current detection task requirements, the radar overall filtering model is reversely deduced to obtain theoretical input signals of each functional module; the theoretical input signals are subjected to multi-dimensional feature matching with pre-acquired historical signal sequences to obtain signal feature parameters; the signal feature parameters are substituted into the overall filtering model for parameter optimization to obtain an optimization result, and the optimization result is used to drive each functional module of the radar to adjust working parameters.

Description

Technical Field

[0001] This application belongs to the field of radar signal processing technology, and more specifically, relates to an adaptive filtering method and apparatus for ship radar signals that resists multipath effects. Background Technology

[0002] Existing radar signal filtering methods mainly focus on parameter estimation during target tracking. However, these methods are ineffective in complex dynamic environments, especially when direct and reflected signals (multipath signals) are intertwined, leading to decreased detection accuracy and stability. Other methods use adaptive adjustment of the CIC filter order to generate high-sampling-rate, high-precision baseband signals, solving the frequency instability problem in traditional signal generation. However, their adaptive mechanism lacks targeted processing capabilities when facing complex signal distortions caused by multipath effects, thus contributing little to improving the overall anti-multipath interference performance of radar systems.

[0003] In summary, existing technologies either focus on target tracking while neglecting in-depth processing of multipath effects, or concentrate on signal generation without effectively addressing multipath interference during signal reception. They generally lack a comprehensive solution that can dynamically and adaptively optimize filtering parameters based on real-time mission requirements and historical environmental data at the system-wide level to proactively counteract and suppress multipath effects, resulting in low multipath interference resistance of radar systems in complex multipath environments. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the purpose of this application is to provide an adaptive filtering method and apparatus for ship radar signals that resists multipath effects. This aims to solve the problem that existing radar signal filtering technologies focus on target tracking or signal generation, resulting in low anti-multipath interference capability of radar systems in complex multipath environments.

[0005] To achieve the above objectives, in a first aspect, this application provides an adaptive filtering method for ship radar signals to resist multipath effects, comprising: Based on the pre-built signal processing models of each functional module of the radar, an overall radar filtering model is constructed. The signal processing model is used to describe the correspondence between the characteristics of the input signal and the quality of the output signal. Based on the current detection mission requirements, the theoretical input signals of each functional module are obtained by reverse derivation using the overall radar filtering model. The theoretical input signal is matched with the pre-acquired historical signal sequence using multi-dimensional feature matching to obtain signal feature parameters; The signal characteristic parameters are substituted into the overall filtering model for parameter optimization to obtain the optimization results, and the operating parameters of each functional module of the radar are adjusted based on the optimization results.

[0006] This application provides a parameter adaptive optimization mechanism driven by an overall filtering model and historical signal sequences. By constructing an adaptive closed-loop mechanism driven by an overall filtering model and historical signal sequences, a complete technical path from "modeling—accumulation—analysis—matching—optimization—driving" is realized. By constructing an accurate system model and using historical data for feature matching and parameter optimization, the radar system can dynamically adjust the filtering parameters according to real-time mission requirements and historical environmental data, no longer relying on fixed parameter filtering. This solves the problems of fixed parameters and poor adaptability of traditional filtering methods in complex multipath environments, thereby significantly improving signal processing capabilities, anti-interference performance, and the real-time performance and accuracy of target detection in complex multipath environments.

[0007] According to the adaptive filtering method for ship radar signals to resist multipath effects provided in this application, the construction of an overall radar filtering model based on the pre-built signal processing models of each functional module of the radar includes: Based on the hardware parameters of each functional module of the radar, a signal processing model for each functional module of the radar is established. Based on the signal transmission paths between functional modules, the signal processing models are connected in series and integrated to form an overall filtering model.

[0008] This application obtains the hardware parameters and signal transmission paths of each functional module, establishes an accurate signal processing model for each module, and integrates them in series according to the signal flow sequence to form an overall filtering model. This allows the system to be modeled from the underlying hardware characteristics, ensuring a high degree of consistency between the model and the actual hardware. This modeling method gives the subsequent parameter optimization process clear physical meaning and engineering feasibility, avoids blind optimization that is detached from the actual hardware, and improves the reliability and executability of the optimization results.

[0009] According to the adaptive filtering method for ship radar signals to resist multipath effects provided in this application, the theoretical input signals of each functional module are obtained by reverse derivation through the overall radar filtering model based on the current detection mission requirements, including: The current exploration mission requirements are broken down into multiple sub-mission requirements; Based on the requirements of the sub-tasks, the theoretical input signals required by each functional module are derived in reverse through the overall radar filtering model.

[0010] This application obtains the operational status information of the target recognition device, decomposes the high-level detection task requirements into multiple sub-task requirements, and uses an overall filtering model to reverse-derive the theoretical input signals required by each functional module, achieving a precise mapping from the task target to the underlying signal requirements. This reverse-derivation method ensures a high degree of consistency between the signal processing process and the final detection target, overcoming the drawback of the disconnect between signal processing and task requirements in traditional methods, and improving the system's efficiency and the accuracy of target detection.

[0011] According to the adaptive filtering method for ship radar signals to resist multipath effects provided in this application, the step of performing multi-dimensional feature matching between the theoretical input signal and the pre-acquired historical signal sequence to obtain signal feature parameters includes: A parameter extraction process is performed on the theoretical input signal to obtain the first data feature of the reference signal. A parameter extraction process is then performed on multiple neighboring theoretical input signals centered on the theoretical input signal to obtain the second data feature of the reference signal. The first data feature and the second data feature are combined to form the signal feature parameters; The parameter extraction process includes: In the historical signal sequence, find the input signal that is the same as or similar to the theoretical input signal and the corresponding output signal, and use the input signal and output signal as the reference signal; The mean value of the reference signal within the same duration is calculated, and the deviation between the mean value and the output signal quality corresponding to the theoretical input signal under ideal conditions is calculated to obtain the first dynamic feature sequence. Difference analysis is performed between different reference signal segments to obtain the second dynamic feature sequence; the first dynamic feature sequence and the second dynamic feature sequence are superimposed to form the data features of the reference signal.

[0012] This application achieves a comprehensive and in-depth characterization of the signal features by substituting the theoretical input signal into a signal processing model to find a reference signal, performing multi-dimensional analysis on the reference signal, extracting a first dynamic feature sequence and a second dynamic feature sequence, and superimposing them. Simultaneously, it analyzes the second data features of multiple neighboring reference signals. This multi-dimensional, multi-timescale feature matching strategy significantly enhances the radar signal's separation capability and signal-to-noise ratio under multipath interference, providing accurate and reliable feature basis for subsequent parameter optimization.

[0013] According to the adaptive filtering method for ship radar signals to resist multipath effects provided in this application, the step of substituting the signal characteristic parameters into the overall filtering model for parameter optimization to obtain the optimization result, and driving the operating parameters of each functional module of the radar to adjust based on the optimization result, includes: The signal feature parameters are input into the overall filtering model to obtain multiple filter spectra output by the overall filtering model; The multiple filter spectra are compared and analyzed to select the optimal filter spectra; Adjust the operating parameters of each functional module of the radar to the parameters corresponding to the optimal filter spectrum.

[0014] This application generates a benchmark filter spectrum and multiple neighboring filter spectra using signal characteristic parameters. Comparative analysis is then performed using indicators such as output signal-to-noise ratio, multipath clutter suppression, and spectral fluctuation amplitude to select the parameter-driven functional module corresponding to the optimal filter spectrum. This achieves scientific screening and precise application of parameter optimization results. This spectrum comparison and analysis strategy enables the system to intelligently select the optimal solution from multiple candidate parameter combinations, effectively improving the accuracy of parameter optimization and the overall filtering performance of the system, while ensuring real-time parameter adjustment.

[0015] Secondly, this application provides an adaptive filtering device for ship radar signals that resists multipath effects, comprising: The construction module is used to build an overall radar filtering model based on the pre-built signal processing models of each functional module of the radar. The signal processing model is used to describe the correspondence between the characteristics of the input signal and the quality of the output signal. The decomposition module is used to reverse derive the theoretical input signals of each functional module by using the overall radar filtering model according to the current detection mission requirements. The matching module is used to perform multi-dimensional feature matching between the theoretical input signal and the pre-acquired historical signal sequence to obtain signal feature parameters; The adjustment module is used to substitute the signal characteristic parameters into the overall filtering model for parameter optimization, obtain the optimization result, and drive the radar's various functional modules to adjust their operating parameters based on the optimization result.

[0016] Thirdly, this application provides an electronic device, comprising: at least one memory for storing a program; and at least one processor for executing the program stored in the memory, wherein when the program stored in the memory is executed, the processor is configured to execute the adaptive filtering method for ship radar signals against multipath effects described in the first aspect or any possible implementation thereof.

[0017] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to execute the adaptive filtering method for ship radar signals against multipath effects described in the first aspect or any possible implementation of the first aspect.

[0018] Fifthly, this application provides a computer program product that, when run on a processor, causes the processor to execute the adaptive filtering method for ship radar signals against multipath effects described in the first aspect or any possible implementation of the first aspect.

[0019] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0020] Overall, the technical solutions conceived in this application have the following beneficial effects compared with the prior art: This application provides a parameter adaptive optimization mechanism driven by an overall filtering model and historical signal sequences. By constructing an adaptive closed-loop mechanism driven by an overall filtering model and historical signal sequences, a complete technical path from "modeling—accumulation—analysis—matching—optimization—driving" is realized. By constructing an accurate system model and using historical data for feature matching and parameter optimization, the radar system can dynamically adjust the filtering parameters according to real-time mission requirements and historical environmental data, no longer relying on fixed parameter filtering. This solves the problems of fixed parameters and poor adaptability of traditional filtering methods in complex multipath environments, thereby significantly improving signal processing capabilities, anti-interference performance, and the real-time performance and accuracy of target detection in complex multipath environments. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating the adaptive filtering method for ship radar signals against multipath effects provided in the embodiments of this application. Figure 2 This is a flowchart illustrating the multi-dimensional feature matching process provided in an embodiment of this application; Figure 3 This is a schematic diagram of the parameter optimization process provided in the embodiments of this application; Figure 4 This is a schematic diagram of the structure of the ship radar signal adaptive filtering device against multipath effects provided in the embodiments of this application; Figure 5 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0024] In this article, the term "and / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. The symbol " / " in this article indicates that the related objects are in an "or" relationship; for example, A / B means A or B.

[0025] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0026] In the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more, for example, multiple processing units means two or more processing units, multiple elements means two or more elements, etc.

[0027] Next, combined Figures 1-3 The adaptive filtering method for ship radar signals that resists multipath effects provided in the embodiments of this application is introduced.

[0028] Figure 1 This is a flowchart illustrating the adaptive filtering method for ship radar signals against multipath effects provided in this application embodiment, as shown below. Figure 1 As shown, the method includes the following steps: Step S1: Based on the pre-built signal processing models of each functional module of the radar, construct the overall radar filtering model. The signal processing model is used to describe the correspondence between the characteristics of the input signal and the quality of the output signal. Optionally, signal processing models for each functional module of the radar are first constructed. Each functional module of the radar can be an intermediate frequency filter module, an amplifier module, and a radar receiver module, etc.

[0029] Optionally, the signal processing model is used to describe the correspondence between the characteristics of the input signal and the quality of the output signal. For example, for an amplifier module, the signal processing model is a model of the relationship between the input signal amplitude and the output signal-to-noise ratio; for a filter module, the signal processing model is a model of the relationship between the input frequency components and the output spectral characteristics; and for a radar receiver module, the signal processing model is a model of the relationship between the input echo signal and the stability of the output target echo.

[0030] Optionally, the overall filtering model is a cascaded integration of multiple signal processing models used for system simulation. The inputs to the overall filtering model include: theoretical input signals and signal characteristic parameters. The theoretical input signals include parameters such as the center frequency, bandwidth, gain requirements, and noise level needed for target detection; the signal characteristic parameters include the dynamic characteristic sequence corresponding to historical signals, output signal quality deviation, and signal fluctuation characteristics. The outputs of the overall filtering model include: a reference filter spectrum; a neighboring filter spectrum; and parameter optimization results. The parameter optimization results include adjusted filter bandwidth, gain, and cutoff frequency values. Step S2: Based on the current detection mission requirements, the theoretical input signals of each functional module are decomposed by reverse derivation through the overall radar filtering model. Optionally, the current detection task requirements can be provided by a target identification device that communicates with the radar system. These requirements include information such as target type, target distance, target speed, and target detection accuracy requirements. For example, if the target identification device reports that the target type is a small boat, the target distance is 10 kilometers, and the target speed is 5 m / s, the current detection task can be defined as "achieving stable detection of small boats within a 10-kilometer range."

[0031] Furthermore, since the overall filtering model is formed by integrating multiple signal processing models in series according to the signal transmission path, and each signal processing model is used to describe the correspondence between the characteristics of the input signal and the quality of the output signal, the overall filtering model can simulate and analyze the signal processing process of each functional module.

[0032] Optionally, the target output signal quality corresponding to the detection mission requirements can be used as a constraint input to the overall filtering model, and the inverse solution can be performed using the correspondence between the input signal characteristics and the output signal quality in each signal processing model to determine the input signal characteristics required to satisfy the target output signal quality, so as to obtain the theoretical input signal required for each functional module to complete the detection mission requirements.

[0033] Specifically, the theoretical input signal includes parameters such as the center frequency, bandwidth, gain requirements, and noise level needed for target detection.

[0034] Step S3: Perform multi-dimensional feature matching between the theoretical input signal and the pre-acquired historical signal sequence to obtain signal feature parameters; Optionally, the system can continuously collect and store the input signal characteristics (such as amplitude and frequency) and output signal quality (such as signal-to-noise ratio) of each functional module at different times, forming a historical signal sequence as an "experience database." The input signal characteristics include: signal amplitude; signal frequency; signal phase; and bandwidth parameters. The output signal quality includes: output signal-to-noise ratio; bit error rate; target echo stability; spectral fluctuation; and multipath clutter suppression effect.

[0035] For example, if a functional module receives input signals S1, S2...Sn at multiple consecutive sampling times, and the corresponding output signal qualities are Q1, Q2...Qn respectively, then these signals are combined and arranged in chronological order to form the historical signal sequence of the functional module, which serves as a historical experience database for subsequent parameter optimization.

[0036] Optionally, the theoretical input signal is matched with the pre-acquired historical signal sequence using multi-dimensional features. The purpose is to find similar input signals and corresponding output signals from the historical signal sequence, providing a feature basis for subsequent optimization.

[0037] Step S4: Substitute the signal characteristic parameters into the overall filtering model to optimize the parameters, obtain the optimization results, and drive the radar's functional modules to adjust their operating parameters based on the optimization results.

[0038] Optionally, the inputs to the overall filtering model include: a theoretical input signal and signal characteristic parameters. The theoretical input signal includes parameters such as the center frequency, bandwidth, gain requirements, and noise level needed for target detection; the signal characteristic parameters include the dynamic characteristic sequence corresponding to historical signals, the output signal quality deviation, and signal fluctuation characteristics. The outputs of the overall filtering model include: a reference filter spectrum, a neighboring filter spectrum, and parameter optimization results. The parameter optimization results include filter bandwidth adjustment values, gain adjustment values, and cutoff frequency adjustment values.

[0039] Optionally, the operating parameters of each functional module of the radar can be adjusted to the parameter optimization results output by the overall filtering model.

[0040] This application provides an adaptive filtering method for ship radar signals to combat multipath effects, offering a parameter adaptive optimization mechanism driven by an overall filtering model and historical signal sequences. By constructing an adaptive closed-loop mechanism driven by an overall filtering model and historical signal sequences, a complete technical path from "modeling—accumulation—analysis—matching—optimization—driving" is realized. By constructing an accurate system model and utilizing historical data for feature matching and parameter optimization, the radar system can dynamically adjust filtering parameters according to real-time mission requirements and historical environmental data, no longer relying on fixed parameter filtering. This solves the problems of fixed parameters and poor adaptability in traditional filtering methods under complex multipath environments, thus significantly improving signal processing capabilities, anti-interference performance, and the real-time performance and accuracy of target detection in complex multipath environments.

[0041] In some embodiments, step S1 specifically includes: Based on the hardware parameters of each functional module of the radar, a signal processing model for each functional module of the radar is established. Based on the signal transmission paths between functional modules, the signal processing models are connected in series and integrated to form an overall filtering model.

[0042] Optionally, hardware parameters include sampling frequency, signal bandwidth, center frequency, gain range, and input / output interface characteristics. For example, for an intermediate frequency filter module, its hardware parameters include a center frequency of 60MHz, a bandwidth of 500kHz, and a cutoff frequency; for an amplifier module, its hardware parameters include a gain adjustment range of 0dB to 40dB, noise figure, and input / output impedance; for a radar receiver module, its hardware parameters include a sampling frequency of 2MHz and a signal bandwidth of 500kHz. Subsequently, corresponding signal processing models are established based on the hardware parameters of each functional module.

[0043] Optionally, the signal processing model is used to describe the correspondence between the characteristics of the input signal and the quality of the output signal: for the amplifier module, a model is established to establish the relationship between the input signal amplitude and the output signal-to-noise ratio based on the gain range and noise figure; for the filter module, a model is established to establish the relationship between the input frequency components and the output spectral characteristics based on the center frequency and bandwidth parameters; for the radar receiver module, a model is established to establish the relationship between the input echo signal and the stability of the output target echo based on the sampling frequency and signal bandwidth.

[0044] Optionally, the signal transmission path between functional modules in the radar system integrates multiple signal processing models in series according to the signal flow sequence to form an overall filtering model.

[0045] In some embodiments, step S2 specifically includes: The current exploration mission requirements are broken down into multiple sub-mission requirements; Based on the requirements of the sub-tasks, the theoretical input signals required by each functional module are derived by reverse engineering through the overall radar filtering model.

[0046] Optionally, the radar overall filtering model is first decomposed into multiple sub-task requirements based on the current detection mission requirements. These sub-task requirements include improving the signal-to-noise ratio of the target echo signal, suppressing sea surface reflection clutter, enhancing the frequency components of weak targets, and improving the stability of the target echo.

[0047] Optionally, the target output signal quality corresponding to the requirements of each sub-task is used as a constraint input to the overall filtering model, and the inverse solution is performed using the correspondence between the input signal characteristics and the output signal quality in each signal processing model to determine the input signal characteristics required to satisfy the target output signal quality, so as to obtain the theoretical input signal required for each functional module to complete the corresponding sub-task requirements.

[0048] For example, when the subtask requirement is to improve the signal-to-noise ratio (SNR) of the target echo signal, the required input signal amplitude range of the amplifier module can be determined by reverse calculation based on the correspondence between the input signal amplitude and the output SNR of the amplifier module; when the subtask requirement is to suppress sea surface reflection clutter, the required center frequency and bandwidth range of the filter module can be determined by reverse calculation based on the correspondence between the input frequency components and the output spectral characteristics of the filter module; when the subtask requirement is to improve the stability of the target echo, the required sampling stability of the radar receiver module can be determined by reverse calculation based on the correspondence between the input echo signal and the output target echo stability of the radar receiver module.

[0049] In some embodiments, step S3 specifically includes: A parameter extraction process is performed on the theoretical input signal to obtain the first data feature of the reference signal. A parameter extraction process is also performed on multiple neighboring theoretical input signals centered on the theoretical input signal to obtain the second data feature of the reference signal. The first data feature and the second data feature are combined to form the signal feature parameters; The parameter extraction process includes: Search for input signals that are the same as or similar to the theoretical input signal and their corresponding output signals in the historical signal sequence, and use the input signal and output signal as the reference signal; The mean value of the reference signal within the same duration is calculated, and the deviation between the mean value and the output signal quality corresponding to the theoretical input signal under ideal conditions is calculated to obtain the first dynamic feature sequence. Difference analysis is performed between different reference signal segments to obtain the second dynamic feature sequence; the first dynamic feature sequence and the second dynamic feature sequence are superimposed to form the data features of the reference signal.

[0050] Optionally, the theoretical input signal is substituted into the signal processing model of the corresponding functional module, and multi-dimensional signal feature matching is performed based on the historical signal sequence.

[0051] Figure 2 This is a flowchart illustrating the multi-dimensional feature matching process provided in an embodiment of this application, such as... Figure 2 As shown, firstly, based on the theoretical input signal, the same or similar input signal is searched in the historical signal sequence. For example, if the theoretical input signal corresponds to the input condition of "60MHz center frequency and 500kHz bandwidth", then historical signals that meet the same frequency range and similar bandwidth conditions are searched in the historical signal sequence.

[0052] Subsequently, the historical input signals and their corresponding output signals are marked as reference signals. Then, based on the time corresponding to the reference signals, the continuously occurring reference signals are divided into multiple reference signal segments. Each reference signal segment is then subjected to multi-dimensional analysis, including: signal-to-noise ratio variation analysis; amplitude fluctuation analysis; spectral stability analysis; and multipath interference fluctuation analysis.

[0053] Specifically, a first dynamic feature sequence is obtained by calculating the mean of the reference signal within the same duration and calculating the deviation between the output signal quality and the theoretical input signal under ideal conditions. At the same time, a difference analysis is performed between different reference signal segments to obtain a second dynamic feature sequence. Finally, the first dynamic feature sequence and the second dynamic feature sequence are superimposed to form the first data feature of the reference signal.

[0054] Furthermore, using the theoretical input signal as the center, multiple neighboring theoretical input signals are acquired as reference input signals, and the above analysis process is repeated to form the second data features of the reference signal. Finally, the first data features of the reference signal and the second data features of the reference signal are combined to form the signal feature parameters.

[0055] In some embodiments, step S4 specifically includes: Input the signal characteristic parameters into the overall filtering model to obtain multiple filter spectra output by the overall filtering model; Comparative analysis of multiple filter spectra was conducted to select the optimal filter spectra. Adjust the operating parameters of each functional module of the radar to the parameters corresponding to the optimal filter spectrum.

[0056] The signal characteristic parameters corresponding to each functional module are substituted into the overall filtering model to generate multiple filtering spectra, which are then compared and analyzed.

[0057] Figure 3 This is a schematic diagram of the parameter optimization process provided in the embodiments of this application, such as... Figure 3As shown, firstly, the first data feature of the reference signal is substituted into the overall filtering model to generate the reference filtering spectrum; then, based on the reference filtering spectrum, the second data features of multiple reference signals are substituted into the overall filtering model to generate multiple neighbor filtering spectra.

[0058] Next, the benchmark filter spectrum was compared and analyzed with each of its neighboring filter spectra. The comparison metrics included: output signal-to-noise ratio (SNR), target echo stability, multipath clutter suppression, spectral fluctuation amplitude, and bit error rate. When a filter spectrum corresponds to a high output signal-to-noise ratio, good multipath clutter suppression, and low output fluctuation, that filter spectrum was determined to be the optimal filter spectrum.

[0059] Finally, the parameters corresponding to the optimal filter spectrum are used as the parameter optimization result, and the radar's various functional modules are adjusted in real time. For example, when multipath reflection from the sea surface is enhanced, the parameter optimization unit adjusts the filter bandwidth from 500kHz to 300kHz to reduce clutter frequency components; at the same time, it adjusts the preamplifier gain from 20dB to 28dB to enhance weak target signals; or it adjusts the filter cutoff frequency to improve the transmission capability of target frequency components.

[0060] This application provides a multi-dimensional, multi-time-scale signal feature matching and spectrum comparison analysis strategy. By extracting the dynamic feature sequences of the benchmark and reference signals and generating benchmark and neighboring filter spectra for comparison, intelligent selection and dynamic adjustment of optimal processing parameters are achieved. This significantly enhances the radar signal separation capability and signal-to-noise ratio under multipath interference, while optimizing the system's real-time processing performance.

[0061] In this way, the radar system can dynamically adjust the parameters of each functional module in complex multipath environments, thereby improving target detection accuracy, enhancing anti-interference capabilities, and improving real-time processing performance.

[0062] In one embodiment of this application, the scenario involves a radar needing to detect a small target 10 kilometers away under strong multipath interference. The system derives a theoretical input signal for the intermediate frequency filter: a center frequency of 60MHz and low noise. Historical data shows that when similar strong multipath interference occurs, the strategy of "tightening the bandwidth and slightly increasing the gain" has successfully improved signal quality. The system verifies the effectiveness of this strategy through model simulation, demonstrating its ability to improve multipath suppression capability. Control commands are then generated: adjust the filter to a narrower bandwidth and increase the gain of the preceding stage. The radar operates according to these optimized parameters, effectively suppressing multipath clutter and stably acquiring the target.

[0063] The adaptive filtering device for ship radar signals that resists multipath effects provided in this application is described below. The adaptive filtering device for ship radar signals that resists multipath effects described below can be referred to in correspondence with the adaptive filtering method for ship radar signals that resists multipath effects described above.

[0064] Figure 4 This is a schematic diagram of the structure of an adaptive filtering device for ship radar signals that resists multipath effects, provided in an embodiment of this application. Figure 4 As shown, the device 400 includes: Module 410 is used to construct an overall radar filtering model based on the pre-built signal processing models of each functional module of the radar. The signal processing model is used to describe the correspondence between the characteristics of the input signal and the quality of the output signal. The decomposition module 420 is used to decompose the theoretical input signals of each functional module by reverse derivation through the overall radar filtering model according to the current detection mission requirements. Matching module 430 is used to perform multi-dimensional feature matching between the theoretical input signal and the pre-acquired historical signal sequence to obtain signal feature parameters; The adjustment module 440 is used to substitute the signal characteristic parameters into the overall filtering model for parameter optimization, obtain the optimization results, and drive the radar's various functional modules to adjust their operating parameters based on the optimization results.

[0065] It should be understood that the above-described device is used to execute the methods in the above embodiments. The implementation principle and technical effect of the corresponding program modules in the device are similar to those described in the above methods. The working process of the device can be referred to the corresponding process in the above methods, and will not be repeated here.

[0066] Based on the methods in the above embodiments, Figure 5 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 5 As shown in the illustration, this application provides an electronic device that may include a processor 510, a communication interface 520, a memory 530, and a communication bus 540. The processor 510, communication interface 520, and memory 530 communicate with each other via the communication bus 540. The processor 510 can call logical instructions from the memory 530 to execute the adaptive filtering method for ship radar signals against multipath effects described in the above embodiment.

[0067] Furthermore, the logical instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the adaptive filtering method for ship radar signals against multipath effects described in the various embodiments of this application.

[0068] Based on the methods in the above embodiments, this application provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to execute the ship radar signal adaptive filtering method against multipath effects as described in the above embodiments.

[0069] Based on the methods in the above embodiments, this application provides a computer program product that, when run on a processor, causes the processor to execute the ship radar signal adaptive filtering method against multipath effects as described in the above embodiments.

[0070] It is understood that the processor in the embodiments of this application can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor can be a microprocessor or any conventional processor.

[0071] The method steps in this application embodiment can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an ASIC.

[0072] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0073] It is understood that the various numerical designations used in the embodiments of this application are merely for the convenience of description and are not intended to limit the scope of the embodiments of this application.

[0074] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. An adaptive filtering method for ship radar signals to resist multipath effects, characterized in that, include: Based on the pre-built signal processing models of each functional module of the radar, an overall radar filtering model is constructed. The signal processing model is used to describe the correspondence between the characteristics of the input signal and the quality of the output signal. Based on the current detection mission requirements, the theoretical input signals of each functional module are obtained by reverse derivation using the overall radar filtering model. The theoretical input signal is matched with the pre-acquired historical signal sequence using multi-dimensional feature matching to obtain signal feature parameters; The signal characteristic parameters are substituted into the overall filtering model for parameter optimization to obtain the optimization results, and the operating parameters of each functional module of the radar are adjusted based on the optimization results.

2. The adaptive filtering method for ship radar signals against multipath effects according to claim 1, characterized in that, The radar overall filtering model is constructed based on the pre-built signal processing models of each functional module of the radar, including: Based on the hardware parameters of each functional module of the radar, a signal processing model for each functional module of the radar is established. Based on the signal transmission paths between functional modules, the signal processing models are connected in series and integrated to form an overall filtering model.

3. The adaptive filtering method for ship radar signals against multipath effects according to claim 1, characterized in that, Based on the current detection mission requirements, the theoretical input signals of each functional module are obtained by reverse derivation using the overall radar filtering model, including: The current exploration mission requirements are broken down into multiple sub-mission requirements; Based on the requirements of the sub-tasks, the theoretical input signals required by each functional module are derived in reverse through the overall radar filtering model.

4. The adaptive filtering method for ship radar signals against multipath effects according to claim 1, characterized in that, The step of performing multi-dimensional feature matching between the theoretical input signal and the pre-acquired historical signal sequence to obtain signal feature parameters includes: A parameter extraction process is performed on the theoretical input signal to obtain the first data feature of the reference signal. A parameter extraction process is then performed on multiple neighboring theoretical input signals centered on the theoretical input signal to obtain the second data feature of the reference signal. The first data feature and the second data feature are combined to form the signal feature parameters; The parameter extraction process includes: In the historical signal sequence, find the input signal that is the same as or similar to the theoretical input signal and the corresponding output signal, and use the input signal and output signal as the reference signal; The mean value of the reference signal within the same duration is calculated, and the deviation between the mean value and the output signal quality corresponding to the theoretical input signal under ideal conditions is calculated to obtain the first dynamic feature sequence. Difference analysis is performed between different reference signal segments to obtain the second dynamic feature sequence; the first dynamic feature sequence and the second dynamic feature sequence are superimposed to form the data features of the reference signal.

5. The adaptive filtering method for ship radar signals against multipath effects according to claim 1, characterized in that, The step of substituting the signal feature parameters into the overall filtering model for parameter optimization, obtaining the optimization result, and driving the radar's various functional modules to adjust their operating parameters based on the optimization result includes: The signal feature parameters are input into the overall filtering model to obtain multiple filter spectra output by the overall filtering model; The multiple filter spectra are compared and analyzed to select the optimal filter spectra; Adjust the operating parameters of each functional module of the radar to the parameters corresponding to the optimal filter spectrum.

6. An adaptive filtering device for ship radar signals to resist multipath effects, characterized in that, include: The construction module is used to build an overall radar filtering model based on the pre-built signal processing models of each functional module of the radar. The signal processing model is used to describe the correspondence between the characteristics of the input signal and the quality of the output signal. The decomposition module is used to reverse derive the theoretical input signals of each functional module by using the overall radar filtering model according to the current detection mission requirements. The matching module is used to perform multi-dimensional feature matching between the theoretical input signal and the pre-acquired historical signal sequence to obtain signal feature parameters; The adjustment module is used to substitute the signal characteristic parameters into the overall filtering model for parameter optimization, obtain the optimization result, and drive the radar's various functional modules to adjust their operating parameters based on the optimization result.

7. An electronic device, characterized in that, include: At least one memory for storing computer programs; At least one processor is configured to execute a program stored in the memory, wherein when the program stored in the memory is executed, the processor is configured to perform an adaptive filtering method for ship radar signals against multipath effects as described in any one of claims 1-5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is run on the processor, the processor performs the adaptive filtering method for ship radar signals against multipath effects as described in any one of claims 1-5.

9. A computer program product, characterized in that, When the computer program product is run on a processor, the processor performs the adaptive filtering method for ship radar signals against multipath effects as described in any one of claims 1-5.

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