A microwave frequency adaptive impedance matching method and system

Abstract

The present invention provides an adaptive impedance matching method and system for microwave frequency bands. The method includes: performing initial impedance measurement on multiple microwave component ports to obtain complex impedance parameters as matching benchmarks; during operation, collecting the current impedance state and reflection coefficient through a reflection monitoring mechanism to construct a dynamic impedance state vector; inputting the state information into an adaptive algorithm module, generating a matching parameter vector and configuring adjustable devices to achieve real-time adjustment of the matching network; calculating the matching error based on the current reflection state and the target reference value, and driving the parameter iterative update; after meeting the matching accuracy requirements, loading the final parameters into the controller to complete the actual configuration; at the same time, the system periodically collects environmental parameters and matching effects, and dynamically determines whether re-matching is required to maintain long-term stable operation of the system. The present invention has the ability to automatically sense, perform closed-loop optimization, and adapt to environmental changes, and can improve the matching efficiency and signal transmission quality of microwave systems in multi-band and high-dynamic conditions.

Description

Technical Field

[0001] The present invention relates to the fields of microwave technology and radio frequency circuits, and in particular to an adaptive impedance matching method and system in a microwave frequency band. Background Art

[0002] Impedance matching is a critical technical step in high-frequency scenarios, such as RF communication systems, radar antenna arrays, and microwave power amplifiers. To reduce reflection loss and improve signal integrity, traditional microwave systems often use fixed matching networks or manually adjust capacitors and inductors to achieve initial impedance alignment. However, these methods typically rely on static parameter configuration and are difficult to adapt to impedance shifts caused by temperature drift, load changes, frequency switching, and other factors during operation.

[0003] Currently, some dynamic matching solutions have attempted to incorporate controllable devices such as PIN diodes and MEMS devices for matching adjustments. However, most only support local optimization within a limited frequency range or, in actual deployment, still rely on manual intervention, failing to achieve dynamic matching control across a continuous frequency range. Furthermore, traditional control strategies are typically based on fixed rules or preset lookup tables, lacking adjustment mechanisms based on real-time state feedback, and their matching effectiveness is susceptible to degradation as the environment changes.

[0004] Therefore, there is an urgent need for an adaptive impedance matching method with real-time state perception, data-driven analysis and automatic parameter adjustment capabilities, which can model and evaluate impedance state changes and achieve rapid response and continuous optimization of the matching network through an algorithmic control mechanism to meet the matching performance requirements of microwave systems in complex environments and multi-band operating conditions. Summary of the Invention

[0005] The present invention provides a microwave frequency band adaptive impedance matching method, which includes:

[0006] S10, performing an initial measurement on the port impedances of multiple components in the microwave system, obtaining an initial impedance parameter of each node, and using the parameter as a reference for subsequent matching control;

[0007] S20, inputting the initial impedance parameters into the impedance matching module, dynamically collecting the port impedance value and signal reflection coefficient under the current operating state through the reflection monitoring mechanism, and constructing real-time impedance state information;

[0008] S30, inputting the real-time impedance information into the adaptive algorithm module, performing parameter calculation and configuration update of the adjustable elements in the matching network, so that the system reflection coefficient gradually converges to a minimum value, thereby achieving dynamic impedance matching;

[0009] S40, comparing the current reflection coefficient with a preset target reflection reference value, calculating a matching error value as a feedback signal, and driving the matching network parameters to further iterate until the set matching accuracy condition is met;

[0010] S50: After the matching conditions meet the set requirements, the optimized matching parameter group is output and loaded into the controller to complete the configuration and adjustment of the actual signal channel;

[0011] S60: Verify the optimized matching parameters in multiple frequency bands and under different environmental conditions, output the matching status evaluation results, and provide adaptive parameter support for the long-term stable operation of the system.

[0012] The microwave frequency band adaptive impedance matching method described above, wherein the port impedances of multiple components in the microwave system are initially measured to obtain initial impedance parameters of each node, and the parameters are used as a reference for subsequent matching control, includes:

[0013] Inject the calibration signal into each component port in turn as the stimulus input;

[0014] Collect the stimulus response data of each port, calculate and output the complex impedance parameters at the corresponding frequency;

[0015] The initial complex impedance data is written into the matching reference storage module for subsequent impedance modeling and comparison reference.

[0016] In the microwave frequency band adaptive impedance matching method described above, the initial impedance parameters are input into the impedance matching module, and the port impedance value and signal reflection coefficient in the current operating state are dynamically collected through the reflection monitoring mechanism to construct real-time impedance state information, including:

[0017] Inputting the incident wave and reflected wave of the microwave signal into the reflection monitoring module;

[0018] Perform amplitude and phase analysis on the reflected wave and calculate the current reflection coefficient value;

[0019] The obtained reflection coefficient and impedance measurement values are synchronously written into the timing cache queue for dynamic model construction and subsequent algorithm processing.

[0020] The microwave frequency band adaptive impedance matching method described above, wherein real-time impedance information is input into an adaptive algorithm module, and parameter calculation and configuration update of adjustable elements in the matching network are performed so that the system reflection coefficient gradually converges to a minimum value, thereby achieving dynamic impedance matching, includes:

[0021] Input the currently measured impedance value and initial impedance parameters into the adaptive algorithm module;

[0022] The matching parameter vector is calculated and generated by the adaptive algorithm module;

[0023] The configuration of adjustable capacitors, inductors or MEMS elements in the matching network is updated according to the matching parameter vector to achieve real-time dynamic adjustment of parameters.

[0024] The microwave frequency band adaptive impedance matching method described above, wherein the current reflection coefficient is compared with a preset target reflection reference value, a matching error value is calculated as a feedback signal, and the matching network parameters are driven to further iterate until the set matching accuracy conditions are met, includes:

[0025] Inputting the reflection coefficient of the current output signal and the preset target reflection coefficient into the error evaluation module;

[0026] The error evaluation module calculates the matching error value as the matching performance criterion;

[0027] If the matching error value does not meet the set convergence conditions, the system will recalculate the parameters based on the matching error value and start the next round of matching adjustment.

[0028] The microwave frequency band adaptive impedance matching method described above, wherein after the matching conditions meet the set requirements, the optimized matching parameter group is output and loaded into the controller to complete the configuration and adjustment of the actual signal channel, includes:

[0029] Write the final matching parameter group into the controller internal register through the control interface;

[0030] The controller drives the physical matching network to complete the actual configuration based on the written parameters;

[0031] Output the configuration completion status to the matching record module to record the current matching parameter group and system status.

[0032] The microwave frequency band adaptive impedance matching method described above verifies the optimized matching parameters in multiple frequency bands and under different environmental conditions, outputs matching status evaluation results, and provides adaptive parameter support for the long-term stable operation of the system, including:

[0033] Periodically collect reflection coefficient values within the set frequency band;

[0034] Input the collected frequency band environmental parameters into the adaptability judgment module to identify changes in environmental status;

[0035] When an environmental offset or matching performance degradation is detected, a rematch trigger signal is output to start the system to re-enter the matching process to maintain matching stability.

[0036] The present invention also provides a microwave frequency band adaptive impedance matching system, which includes:

[0037] An impedance measurement module is configured to inject a calibration signal into the ports of each component in the microwave system and collect the stimulus response, thereby calculating the complex impedance parameters of the output port at the target frequency as a reference for system initialization;

[0038] a signal monitoring module configured to collect the current impedance value and reflection coefficient of the port during system operation and output a time series data stream for constructing a dynamic impedance state model;

[0039] The adaptive adjustment module is connected to the signal monitoring module, receives real-time impedance data, performs parameter optimization calculations and outputs configuration instructions for the adjustable network;

[0040] The error evaluation module is connected to the adaptive adjustment module and is used to calculate the matching error between the reflection coefficient and the target value, and feed the error back to the adjustment module to form a closed-loop control;

[0041] A control execution module is configured to receive target matching parameters, control the component states of the adjustable matching network in real time, and complete the actual parameter loading and signal channel configuration;

[0042] The stability verification module is configured to periodically collect environmental status and system matching status, and output a rematch trigger signal when the recognition performance degrades or the parameters drift, so as to achieve stable adaptive adjustment of the system for long-term operation.

[0043] The present invention achieves the following beneficial effects: It proposes an adaptive impedance matching method based on multi-parameter sensing and closed-loop control, enabling automatic parameter adjustment and dynamic impedance optimization across multiple frequency bands. The system collects the current impedance state and reflection characteristics in real time, combines initial reference parameters with environmental changes, and outputs precise matching instructions, improving the signal channel's reflection suppression capability and long-term stability. This method is suitable for meeting the stringent requirements for dynamic matching performance in high-reliability RF communication scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0045] Figure 1 This is a flow chart of an adaptive impedance matching method for microwave frequency bands provided in Example 1 of the present application. DETAILED DESCRIPTION

[0046] The following is a clear and complete description of the technical solutions in the embodiments of the present invention, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0047] Example 1

[0048] like Figure 1 As shown, the first embodiment of the present application provides an adaptive impedance matching method in a microwave frequency band, comprising the following steps:

[0049] S10, performing an initial measurement on the port impedances of multiple components in the microwave system, obtaining an initial impedance parameter of each node, and using the parameter as a reference for subsequent matching control;

[0050] In this embodiment, the system first performs an impedance initialization step to measure the complex impedance parameters of multiple key RF component ports as reference inputs for subsequent adaptive matching calculations. Specifically, the system includes the following sub-steps:

[0051] S101, injecting calibration signals into the ports of each component in sequence as stimulus input;

[0052] The system control module selects the target component ports in turn and injects a standard calibration signal into each port.

[0053] The calibration signal is a frequency-adjustable, narrowband swept-frequency excitation that covers the system's primary operating frequency band. Its characteristics include smooth amplitude, linear phase, and controlled harmonic content. To avoid interference between ports, the system uses serial injection, stimulating only one component port at a time.

[0054] S102, collecting stimulus response data of each port, calculating and outputting complex impedance parameters at corresponding frequencies;

[0055] After the calibration signal is injected, the system uses a high-precision impedance sampling module to collect the excitation response returned by the port in real time. The response signal includes the complex components of the reflected wave and the transmitted wave.

[0056] The system uses the Fast Fourier Transform (FFT) algorithm to convert the time-domain sampling data into a frequency-domain expression, extract the complex components of voltage and current V(f) and I(f), and calculate the complex impedance at the current frequency point according to the following formula: , where A(f), B(f), C(f), and D(f) are the real and imaginary parts after amplitude and phase demodulation. The system performs multiple sampling on each port and uses mean filtering to eliminate occasional interference to improve measurement accuracy.

[0057] S103 , writing the initial complex impedance data into a matching reference storage module for subsequent impedance modeling and comparison reference.

[0058] The system packages the complex impedance parameters calculated at the corresponding frequency point of each port into a structured data group and marks them according to fields such as port number, frequency index, and measurement time.

[0059] The system then writes this data into the matching benchmark storage module. This module uses a frequency-band-based storage structure that supports fast indexing and historical data backtracking for subsequent matching algorithm modeling, initial value setting, and error correction benchmarks.

[0060] S20, inputting the initial impedance parameters into the impedance matching module, dynamically collecting the port impedance value and signal reflection coefficient under the current operating state through the reflection monitoring mechanism, and constructing real-time impedance state information;

[0061] After completing step S10, the system obtains the frequency of each component port at different frequency points. Complex impedance parameters under For the sake of uniformity, the static measurement impedance parameter is referred to as the initial impedance and is expressed as: The system inputs this initial parameter into the impedance matching module as a reference for impedance deviation calculation during dynamic operation. Subsequently, the system uses the reflection monitoring mechanism to collect the incident signal and reflected signal in real time during operation, and constructs dynamic impedance state information in a two-dimensional frequency-time structure. This step includes the following sub-steps:

[0062] S201, inputting the incident wave and reflected wave of the microwave signal into a reflection monitoring module;

[0063] The system sets a broadband directional coupler at each key port to extract the incident wave voltage signal in the working channel in real time. and reflected wave voltage signal and input the two synchronously into the reflection monitoring module.

[0064] To ensure the phase and sampling timing are consistent, the system uses a unified master clock Dual-channel sampling triggering is achieved, so that the two signals are aligned with nanosecond time accuracy.

[0065] S202, performing amplitude and phase analysis on the reflected wave to calculate the current reflection coefficient value;

[0066] The reflection monitoring module applies a window function weight to the input signal and then performs a fast Fourier transform to extract the frequency points. Complex voltage value on The complex reflection coefficient of this frequency point is calculated according to the following expression: ,in, are the real and imaginary parts of the reflected signal at the frequency point respectively; are the real and imaginary parts of the incident signal; is the reflection coefficient, which is used to describe the amplitude and phase deviation of the current matching state and serves as an important basis for system matching evaluation.

[0067] S203 , synchronously writing the obtained reflection coefficient and impedance measurement value into a timing cache queue for use in dynamic model construction and subsequent algorithm processing.

[0068] The system at each frequency point , time point The complex impedance value of the current port is collected in real time and recorded as: and the initial complex impedance value obtained in step S10 To comprehensively evaluate the impedance deviation degree, the trend of drastic change of reflected waves and the impact of environmental disturbances under the current working state, the system constructs a matching deviation risk intensity function: ,in, Indicates the kth frequency sampling point currently being processed; Indicates the time sampling point of the current moment; Represents the real part of the current actual impedance; Indicates the imaginary part of the current actual impedance; , Represent the real and imaginary parts of the initial impedance at the frequency point respectively; Indicates the complex reflection coefficient of the current frequency point; The modulus length represents the reflection coefficient; It represents the derivative of the reflection coefficient mode length with respect to frequency, and is used to measure the severity of reflection fluctuations in the frequency domain; Indicates frequency point Place, time Temperature offset; The reference temperature value set for the system is used to normalize the environmental impact; Frequency point ,time The power fluctuation amount; The nominal reference power value set for the system. The larger the value of the risk function, the more obvious the matching deviation in the current state of the system, the more unstable the reflection trend or the stronger the environmental disturbance, and the priority is to enter the adjustment control process. The system constructs the above parameters into an impedance state vector: , and write to the cache queue The queue adopts a cyclic sliding window structure, which supports fast access indexed by frequency and time, and serves as the input basis for the subsequent matching adjustment algorithm.

[0069] S30, inputting the real-time impedance information into the adaptive algorithm module, performing parameter calculation and configuration update of the adjustable elements in the matching network, so that the system reflection coefficient gradually converges to a minimum value, thereby achieving dynamic impedance matching;

[0070] The dynamic impedance state information obtained in step S20 includes multi-dimensional input parameters such as the current impedance value, reflection coefficient, and matching deviation risk index. The system inputs this information into the adaptive algorithm module to achieve dynamic configuration control of various adjustable components in the matching network, ensuring that the microwave system maintains the optimal matching state in each operating frequency band. This step specifically includes the following sub-steps:

[0071] S301, inputting the currently measured impedance value and initial impedance parameters into the adaptive algorithm module;

[0072] The system is at the current frequency and time Next, extract the following input data: real-time complex impedance value ; Initial complex impedance value ; Current reflection coefficient ; Matching deviation index The above data is used as feature input and sent to the adaptive algorithm module for adjustment calculation.

[0073] S302, the adaptive algorithm module calculates and generates a matching parameter vector;

[0074] The adaptive algorithm module calculates the configuration parameters required for each adjustment device in the matching network based on the input parameters and uses the following multi-factor combination function to generate the matching parameter vector: ,in, represents the target matching parameter vector, which represents the control output of n adjustable devices in the matching network; is the impedance real part deviation; is the impedance imaginary part deviation; is the derivative of the reflection coefficient mode length with respect to frequency; is the matching deviation risk intensity function value; Weight coefficient and bias value for adjusting the i-th device; represents the unit direction selection vector for the control channel of the i-th device; n represents the total number of adjustment elements involved in the matching network configuration. Through this computational model, the system can comprehensively evaluate the current matching state and evolution trend, generating a set of adjustable configuration parameters for precise control.

[0075] S303 : updating the configuration of adjustable capacitors, inductors or MEMS elements in the matching network according to the matching parameter vector to achieve real-time dynamic adjustment of parameters.

[0076] The system generates matching parameter vectors based on , sending configuration instructions to various adjustable components in the matching network through high-speed control channels to achieve dynamic control of the following devices: adjustable capacitor arrays; variable inductor modules; microelectromechanical tunable devices (MEMS tuners); microstrip phase shifters; digitally programmable electronic tuning networks and other structured adjustment units.

[0077] These components can be deployed individually or in combination, depending on the system architecture, to achieve control objectives such as impedance matching, phase adjustment, or reflection minimization. Control commands are directly loaded into the physical components via digital-to-analog converters or digital control interfaces, enabling continuous adjustment with sub-millisecond response.

[0078] The system monitors the adjusted reflection coefficient The system then analyzes the change trend to determine whether the expected convergence conditions have been met. If the deviation still does not meet the set threshold, the parameter calculation and update process is re-executed to form a stable closed-loop control mechanism and achieve continuous dynamic impedance matching.

[0079] S40, comparing the current reflection coefficient with a preset target reflection reference value, calculating a matching error value as a feedback signal, and driving the matching network parameters to further iterate until the set matching accuracy condition is met;

[0080] To continuously optimize the system's matching accuracy, after completing the initial parameter adjustment, the system further evaluates the deviation between the current reflection state and the ideal matching state and establishes a feedback control mechanism. Through error evaluation and parameter updates, multiple rounds of impedance matching are implemented until the preset accuracy threshold is reached. This step includes the following sub-steps:

[0081] S401, inputting the reflection coefficient of the current output signal and the preset target reflection coefficient into an error evaluation module;

[0082] The system is at the current frequency and time Under this condition, the actual reflection coefficient of the output signal is collected. , and load the preset target reflection coefficient value .

[0083] The two are sent as input to the error evaluation module and serve as the basic data source for subsequent error calculation. It can be obtained by fitting historical test data, calculating typical impedance standards, or setting engineering experience values.

[0084] S402, the error evaluation module calculates the matching error value as a matching performance criterion;

[0085] In the error evaluation module, the system constructs a matching error function in the following form based on the current reflection deviation and reflection trend: ,in, is the output reflection coefficient at the current frequency point; The target reflection coefficient value preset for the system; is the square of the comprehensive error of reflection amplitude and phase; is the rate of change of the reflection coefficient over time, which is used to determine whether the system is in an unstable oscillation or non-convergent state; , is the weight coefficient, which is used to control the influence ratio of static error and dynamic error; The matching error value is used as the core indicator to judge the current matching performance. The system uses the error value and the set matching convergence threshold to Compare and use as a basis for whether further adjustment is needed.

[0086] S403: If the matching error value does not meet the set convergence condition, the system re-calculates the parameters based on the matching error value and starts the next round of matching adjustment.

[0087] When the error value When the system determines that the current matching state still does not meet the accuracy requirements, it immediately feeds the error feedback to the adaptive algorithm module. The module dynamically adjusts the matching parameter weights according to the feedback error size, re-executes the matching parameter vector calculation process, and generates a new parameter output. , and drives each adjustment device in the matching network to update the configuration.

[0088] The above adjustment process forms an automatic closed-loop matching mechanism. The system will continue to perform error detection and parameter iteration until the error value is , it is determined that the matching convergence condition is reached and the matching process is terminated.

[0089] S50: After the matching conditions meet the set requirements, the optimized matching parameter group is output and loaded into the controller to complete the configuration and adjustment of the actual signal channel;

[0090] After multiple rounds of adjustment and error feedback control, the system detects that the reflection coefficient at the current frequency point meets the preset matching accuracy conditions, that is, the error index is lower than the set threshold, and the system determines that the matching process is complete. At this point, the final optimized matching parameter set is output and loaded into the controller to drive the physical matching network into actual working state. This step includes the following sub-steps:

[0091] S501, writing the final matching parameter group into the controller internal register through the control interface;

[0092] The system will set the current frequency point The matching parameter set determined by the adaptive algorithm module is output and written to a dedicated register within the controller via the control bus or high-speed interface. This parameter set contains detailed configuration data for the adjustable components, such as capacitance and inductance values, phase shift status, and channel switching instructions. The controller then prepares to execute instructions based on this data.

[0093] S502: The controller drives the physical matching network to complete the actual configuration according to the written parameters;

[0094] The controller reads the written register values and maps the parameters to the corresponding physical actuator channels. It controls the adjustable capacitors, inductor arrays, microstrip structures, MEMS components or phase-shifting networks in the matching network through voltage, current or digital pulse signals to complete the matching configuration of key nodes in the RF signal path, ensuring channel impedance continuity and minimizing signal reflections.

[0095] S503: Output the configuration completion status to the matching record module, and record the current matching parameter group and system status.

[0096] To ensure system status backtracking and matching data traceability, after completing physical configuration, the controller encapsulates the current matching parameter set and corresponding operating status information such as frequency, time, temperature, and power into a status record data packet and outputs it to the matching record module. This module stores this data in a structured manner, which can be used for subsequent model optimization, historical analysis, and anomaly reconstruction, ensuring the system's long-term evolution and failure self-diagnosis capabilities.

[0097] S60: Verify the optimized matching parameters in multiple frequency bands and under different environmental conditions, output the matching status evaluation results, and provide adaptive parameter support for the long-term stable operation of the system.

[0098] After the matching parameters are loaded and configured, the system enters the continuous operation phase. To ensure the adaptability and stability of the optimized matching parameters in actual use, the system periodically verifies the matching effect in key frequency bands and typical environmental conditions. By real-time detection of changes in reflection status and external interference, it determines whether to trigger the rematching mechanism to achieve long-term adaptive control of the system. This step includes the following sub-steps:

[0099] S601, periodically collecting reflection coefficient values within a set frequency band;

[0100] Based on the preset operating frequency range, the system performs periodic reflection coefficient measurements at multiple sampling points, including the main carrier, edge frequencies, and historically high-deviation frequency bands. This measurement is triggered periodically by the reflection monitoring module, which collects output reflection characteristics corresponding to the frequency and timestamps to construct a long-term impedance trend map. The system dynamically adjusts the sampling interval and frequency density based on historical evaluation results to optimize resource allocation efficiency.

[0101] S602: Input the collected frequency band environment parameters into the adaptability judgment module to identify changes in the environment state;

[0102] The system simultaneously collects external environmental parameters corresponding to the reflection coefficient, including temperature, humidity, input power, and voltage fluctuations, constructing a complete frequency band-environmental characteristic data pair. This data is then fed into the adaptability judgment module for real-time analysis. This module uses preset thresholds and a self-learning model to identify changing environmental trends and determine whether there are potential interference factors that could cause fluctuations in matching performance, such as power supply instability, thermal drift, and mechanical vibration.

[0103] S603: When an environmental offset or a degradation in matching performance is detected, a rematch trigger signal is output to start the system to re-enter the matching process to maintain matching stability.

[0104] If the system detects that the environmental state has deviated beyond the set stability tolerance, or detects a significant increase in the reflection coefficient trend, it will determine that matching performance is at risk of degradation and immediately output a rematch trigger signal to the main control module. Based on this, the system initiates the automatic matching process callback mechanism, re-executing the parameter measurement, error determination, and matching adjustment steps to ensure that the matching network is restored to the optimal configuration under the new operating conditions, thereby achieving long-term matching stability control in dynamic environments.

[0105] Example 2

[0106] A second embodiment of the present application provides an adaptive impedance matching system in a microwave frequency band, comprising:

[0107] An impedance measurement module is configured to inject a calibration signal into the ports of each component in the microwave system and collect the stimulus response, thereby calculating the complex impedance parameters of the output port at the target frequency as a reference for system initialization;

[0108] The impedance measurement module is used to obtain the initial impedance parameters of multiple component ports in a microwave system at target frequencies, serving as reference inputs for subsequent dynamic matching adjustments. This module is equipped with an excitation control unit that injects calibration signals into each component port sequentially according to a preset frequency list to stimulate their electrical response. The response signals are collected through measurement channels, and the signal recognition and modeling unit extracts the amplitude and phase characteristics at key frequency points to construct a complete port impedance model.

[0109] The module integrates a high-precision synchronous clock and response sampling controller, ensuring a unified time reference for stimulus and response acquisition, improving measurement consistency and reliability. To reduce the risk of error propagation during port switching, the module supports a dynamic link compensation mechanism that automatically performs calibration corrections during component polling.

[0110] All collected data is structured by port number and frequency index and written uniformly to the matching reference storage unit for subsequent dynamic comparison and adjustment. The module supports multi-channel parallel measurement strategies, allowing impedance scanning of multiple components simultaneously.

[0111] a signal monitoring module configured to collect the current impedance value and reflection coefficient of the port during system operation and output a time series data stream for constructing a dynamic impedance state model;

[0112] The signal monitoring module is configured to collect the current impedance and reflection coefficient of component ports in real time during system operation, outputting a continuous data stream used to construct a dynamic impedance state model. The module extracts the incident and reflected wave signals via a directional coupler in the signal path, and synchronizes these signals via a dual-channel high-speed sampling interface, ensuring consistent sampling data in both frequency and phase dimensions.

[0113] The module integrates reflection coefficient analysis logic and impedance state extraction units, enabling continuous acquisition of port operating status at different frequencies and time periods. All collected data is time-stamped and packaged into a sequential impedance state vector, which is then written to a cache queue to provide high-frequency input for subsequent adjustment and evaluation modules.

[0114] To adapt to complex spectrum fluctuation scenarios, the module supports a sliding window data management mechanism and has a built-in reflection fluctuation detection function. When the abnormal reflection change exceeds the set threshold, it can automatically issue an adjustment warning and intervene in the matching process in advance.

[0115] The adaptive adjustment module is connected to the signal monitoring module, receives real-time impedance data, performs parameter optimization calculations and outputs configuration instructions for the adjustable network;

[0116] The adaptive adjustment module, connected to the signal monitoring module, receives real-time impedance state information and, based on initial reference parameters, optimizes the matching network configuration parameters. The module includes a feature extraction unit, a parameter modeling engine, and an adjustment command generator. It calculates difference vectors based on real-time data and generates control outputs.

[0117] The module combines and calculates multi-dimensional features such as the real and imaginary impedance components, reflection trends, and risk indicators. Using a built-in adaptive algorithm, it generates precise configuration parameters for each adjustable component. The module automatically adjusts calculation weights based on error feedback, improving convergence speed and control stability.

[0118] The output adjustment parameters are formatted and sent to the control execution module, which supports simultaneous adjustment of multiple control channels, adapts to multi-component network structures, and achieves high-precision and high-speed matching configuration.

[0119] The error evaluation module is connected to the adaptive adjustment module and is used to calculate the matching error between the reflection coefficient and the target value, and feed the error back to the adjustment module to form a closed-loop control;

[0120] The error evaluation module, connected to the adaptive adjustment module, calculates the matching error between the current output reflection coefficient and the target reflection reference value and feeds this error back to the adjustment module, forming a closed-loop control mechanism. The module includes a reference loader, an error calculator, and a feedback channel, enabling real-time error assessment and dynamic response adjustment.

[0121] The module supports simultaneous evaluation of static deviations and dynamic trends, determining whether the current matching result has achieved the target accuracy based on the set convergence threshold. If the error is within the tolerance, the current configuration remains unchanged; if the deviation exceeds the limit, the adaptive module will immediately execute a new round of optimization calculations.

[0122] The module has the ability to adjust sensitivity and can dynamically adjust the error response strategy according to environmental factors such as frequency band, temperature, and power supply changes to ensure precision control capabilities under changing conditions.

[0123] A control execution module is configured to receive target matching parameters, control the component states of the adjustable matching network in real time, and complete the actual parameter loading and signal channel configuration;

[0124] The control execution module is configured to receive the matching parameter set output by the adaptive adjustment module and control the state of various adjustable components in the matching network in real time, completing parameter loading and final configuration of the signal path. Serving as a bridge between control logic and physical execution, the module is responsible for translating algorithm results into specific hardware control operations.

[0125] The module includes a parameter parser, a command dispatch unit, and an execution interface. It maps adjustment parameters into control signals, accurately driving capacitors, inductor arrays, MEMS tuners, microstrip phase shifters, and other devices to configure their states. It supports multipath parallel control, ensuring impedance adjustment synchronization in complex network structures.

[0126] The module also has a status recording function. After configuration is completed, the device status is automatically packaged and output to the recording module, leaving traces of the entire adjustment process.

[0127] The stability verification module is configured to periodically collect environmental status and system matching status, and output a rematch trigger signal when the recognition performance degrades or the parameters drift, so as to achieve stable adaptive adjustment of the system for long-term operation.

[0128] The stability verification module is configured to periodically collect reflection coefficient and environmental status information during system operation, continuously evaluating the effectiveness of the current matching configuration. If system performance degrades or parameter drift occurs, the module automatically outputs a rematch trigger signal, instructing the system to restart the matching process, ensuring long-term operational stability.

[0129] The module features a built-in reflection status acquisition unit and an environmental sensing interface. It periodically measures key frequencies using a frequency sweep table and simultaneously collects environmental indicators such as temperature, voltage, and electromagnetic interference. It also offers historical data window analysis capabilities, identifying performance trends and assessing matching reliability. The module supports hierarchical matching status management and a multi-level trigger mechanism, providing trend warning and self-recovery capabilities.

[0130] Corresponding to the above embodiment, an embodiment of the present invention provides a computer storage medium, comprising: at least one memory and at least one processor;

[0131] The memory is used to store one or more program instructions;

[0132] The processor is used for running one or more program instructions to execute an adaptive impedance matching method in a microwave frequency band.

[0133] Corresponding to the above embodiment, an embodiment of the present invention provides a computer-readable storage medium, which contains one or more program instructions, and the one or more program instructions are used by a processor to execute an adaptive impedance matching method in a microwave frequency band.

[0134] The embodiments disclosed in the present invention provide a computer-readable storage medium, in which computer program instructions are stored. When the computer program instructions are executed on a computer, the computer executes the above-mentioned adaptive impedance matching method for a microwave frequency band.

[0135] In the embodiments of the present invention, the processor may be an integrated circuit chip having signal processing capabilities. The processor may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0136] The methods, steps, and logic diagrams disclosed in the embodiments of the present invention can be implemented or executed. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of the present invention can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules within the decoding processor. The software modules can be located in a storage medium well-established in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. The processor reads the information from the storage medium and, in conjunction with its hardware, completes the steps of the aforementioned methods.

[0137] The storage medium may be a memory and may be, for example, a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memory.

[0138] Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory.

[0139] Volatile memory may be random access memory (RAM), which is used as an external cache memory. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct RAM bus random access memory (DRRAM).

[0140] The storage media described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0141] Those skilled in the art will appreciate that in one or more of the above examples, the functions described herein can be implemented using a combination of hardware and software. When software is used, the corresponding functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media includes any medium that facilitates the transmission of computer programs from one place to another. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0142] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solution of the present invention should be included in the scope of protection of the present invention.

Claims

1. A microwave frequency band adaptive impedance matching method, characterized in that: The following steps are involved: S10, performing an initial measurement on the port impedances of multiple components in the microwave system, obtaining an initial impedance parameter of each node, and using the parameter as a reference for subsequent matching control; S20, inputting the initial impedance parameters into the impedance matching module, dynamically collecting the port impedance value and signal reflection coefficient under the current operating state through the reflection monitoring mechanism, and constructing real-time impedance state information; The system sets up a broadband directional coupler at each key port to extract the incident wave voltage signal and the reflected wave voltage signal in the working channel in real time and input them into the reflection monitoring module. The reflection monitoring module applies a window function to the input signal and then performs a fast Fourier transform to extract the complex voltage value at the frequency point and calculate the reflection coefficient at that frequency point. The system collects the complex impedance value of the current port in real time at each frequency point and time point. To comprehensively evaluate the impedance deviation degree, the trend of drastic changes in reflected waves, and the impact of environmental disturbances under the current working state, the system constructs a matching deviation risk intensity function: ,in, Indicates the kth frequency sampling point currently being processed; Indicates the time sampling point of the current moment; Represents the real part of the current actual impedance; Indicates the imaginary part of the current actual impedance; Represent the real and imaginary parts of the initial impedance at the frequency point respectively; Indicates the reflection coefficient of the current frequency point; The modulus length represents the reflection coefficient; It represents the derivative of the reflection coefficient mode length with respect to frequency, and is used to measure the severity of reflection fluctuations in the frequency domain; Indicates frequency point Place, time Temperature offset; The reference temperature value set for the system is used to normalize the environmental impact; Frequency point ,time The power fluctuation amount; This is the nominal reference power value set for the system. The larger the value of the risk intensity function, the more obvious the matching deviation in the current state of the system, the more unstable the reflection trend, or the stronger the environmental disturbance, and the higher the priority for entering the adjustment control process. The system constructs the above parameters into an impedance state vector: , the impedance state vector represents the real-time impedance state information and is written into the cache queue ,The queue adopts a cyclic sliding window structure, which supports ,fast access indexed by frequency and time; S30, inputting the real-time impedance state information into the adaptive algorithm module, performing parameter calculation and configuration update of the adjustable elements in the matching network, so that the system reflection coefficient gradually converges to a minimum value, thereby achieving dynamic impedance matching; S40, comparing the current reflection coefficient with a preset target reflection reference value, calculating a matching error value as a feedback signal, and driving the matching network parameters to further iterate until the set matching accuracy condition is met; S50, after the matching accuracy condition meets the set requirements, output the optimized matching parameter group and load it into the controller to complete the configuration and adjustment of the actual signal channel; S60: Verify the optimized matching parameters in multiple frequency bands and under different environmental conditions, output the matching status evaluation results, and provide adaptive parameter support for the long-term stable operation of the system.

2. The microwave frequency band adaptive impedance matching method according to claim 1, wherein: Performing an initial measurement of the port impedance of multiple components in a microwave system to obtain the initial impedance parameters of each node and using the parameters as a reference for subsequent matching control includes the following steps: Inject the calibration signal into each component port in turn as the stimulus input; Collect the stimulus response data of each port, calculate and output the complex impedance parameters at the corresponding frequency; The initial complex impedance data is written into the matching reference storage module for subsequent impedance modeling and comparison reference.

3. The microwave frequency band adaptive impedance matching method according to claim 1, wherein: Input the initial impedance parameters into the impedance matching module, and dynamically collect the port impedance value and signal reflection coefficient under the current operating state through the reflection monitoring mechanism. Constructing real-time impedance status information includes the following steps: Inputting the incident wave and reflected wave of the microwave signal into the reflection monitoring module; Perform amplitude and phase analysis on the reflected wave and calculate the current reflection coefficient value; The obtained reflection coefficient and impedance measurement values are synchronously written into the timing cache queue for dynamic model construction and subsequent algorithm processing.

4. The microwave frequency band adaptive impedance matching method according to claim 1, wherein: The real-time impedance state information is input into the adaptive algorithm module, and the parameters of the adjustable components in the matching network are calculated and the configuration is updated so that the system reflection coefficient gradually converges to the minimum value. The dynamic impedance matching is achieved by the following steps: Inputting the real-time impedance state information and the initial impedance parameters into the adaptive algorithm module; The matching parameter vector is calculated and generated by the adaptive algorithm module; The configuration of adjustable capacitors, inductors or MEMS elements in the matching network is updated according to the matching parameter vector to achieve real-time dynamic adjustment of parameters.

5. The microwave frequency band adaptive impedance matching method according to claim 1, wherein: Comparing the current reflection coefficient with the preset target reflection reference value, calculating the matching error value as a feedback signal, and driving the matching network parameters to further iterate until the set matching accuracy conditions are met includes the following steps: Inputting the reflection coefficient of the current output signal and the preset target reflection coefficient into the error evaluation module; The error evaluation module calculates the matching error value as the matching performance criterion; If the matching error value does not meet the set convergence condition, the system will recalculate the parameters based on the matching error value and start the next round of matching adjustment.

6. The microwave frequency band adaptive impedance matching method according to claim 1, wherein: After the matching conditions meet the set requirements, the optimized matching parameter set is output and loaded into the controller to complete the configuration and adjustment of the actual signal channel. The following steps are included: Write the final matching parameter group into the controller internal register through the control interface; The controller drives the physical matching network to complete the actual configuration based on the written parameters; Output the configuration completion status to the matching record module to record the current matching parameter group and system status.

7. The microwave frequency band adaptive impedance matching method according to claim 1, wherein: Verify the optimized matching parameters in multiple frequency bands and under different environmental conditions, output the matching status evaluation results, and provide adaptive parameter support for the long-term stable operation of the system. The following steps are included: Periodically collect reflection coefficient values within the set frequency band; Input the collected frequency band environmental parameters into the adaptability judgment module to identify changes in environmental status; When an environmental offset or matching performance degradation is detected, a rematch trigger signal is output to start the system to re-enter the matching process to maintain matching stability.

8. A microwave frequency band adaptive impedance matching system, characterized in that: include: An impedance measurement module is configured to inject a calibration signal into the ports of each component in the microwave system and collect the stimulus response, thereby calculating the complex impedance parameters of the output port at the target frequency as a reference for system initialization; a signal monitoring module configured to collect the current impedance value and reflection coefficient of the port during system operation and output a time series data stream for constructing a dynamic impedance state model; The system sets up a broadband directional coupler at each key port to extract the incident wave voltage signal and the reflected wave voltage signal in the working channel in real time and input them into the reflection monitoring module. The reflection monitoring module applies a window function to the input signal and then performs a fast Fourier transform to extract the complex voltage value at the frequency point and calculate the reflection coefficient at that frequency point. The system collects the complex impedance value of the current port in real time at each frequency point and time point. To comprehensively evaluate the impedance deviation degree, the trend of drastic changes in reflected waves, and the impact of environmental disturbances under the current working state, the system constructs a matching deviation risk intensity function: ,in, Indicates the kth frequency sampling point currently being processed; Indicates the time sampling point of the current moment; Represents the real part of the current actual impedance; Indicates the imaginary part of the current actual impedance; Represent the real and imaginary parts of the initial impedance at the frequency point respectively; Indicates the reflection coefficient of the current frequency point; The modulus length represents the reflection coefficient; It represents the derivative of the reflection coefficient mode length with respect to frequency, and is used to measure the severity of reflection fluctuations in the frequency domain; Indicates frequency point Place, time Temperature offset; The reference temperature value set for the system is used to normalize the environmental impact; Frequency point ,time The power fluctuation amount; This is the nominal reference power value set for the system. The larger the value of the risk intensity function, the more obvious the matching deviation in the current state of the system, the more unstable the reflection trend, or the stronger the environmental disturbance, and the higher the priority for entering the adjustment control process. The system constructs the above parameters into an impedance state vector: , the impedance state vector represents the real-time impedance state information and is written into the cache queue ,The queue adopts a cyclic sliding window structure, which supports ,fast access indexed by frequency and time; The adaptive adjustment module is connected to the signal monitoring module, receives real-time impedance status information, performs parameter optimization calculations and outputs configuration instructions for the adjustable network; The error evaluation module is connected to the adaptive adjustment module and is used to calculate the matching error between the reflection coefficient and the target value, and feed the error back to the adjustment module to form a closed-loop control; A control execution module is configured to receive target matching parameters, control the component states of the adjustable matching network in real time, and complete the actual parameter loading and signal channel configuration; The stability verification module is configured to periodically collect environmental status and system matching status, and output a rematch trigger signal when the recognition performance degrades or the parameters drift, so as to achieve stable adaptive adjustment of the system for long-term operation.

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