Intermediate frequency filtering signal processing equipment based on radar transmitting power measurement

Abstract

The invention discloses intermediate frequency filtering signal processing equipment based on radar transmitting power measurement, and relates to the technical field of radar signal processing, and the equipment comprises a global tracking module which is used for optimizing a signal processing flow and tracking a plurality of targets; the global tracking module comprises a target detection unit, a target tracking unit and a resource scheduling unit. According to the invention, the global tracking module is designed, so that the state detection of multiple targets is realized, the target tracking precision and the resource utilization efficiency are improved, and the problem that the signal processing equipment cannot fuse multi-radar node information is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of radar signal processing, and in particular to an intermediate frequency filtering signal processing device based on radar transmission power measurement. Background Art

[0002] IF signal processing equipment is a crucial component of radar systems, receiving and processing the IF echo signals reflected from radar transmissions. By extracting useful information from these signals, the equipment can detect and track targets, resulting in widespread applications in radar detection, navigation, meteorology, and other fields.

[0003] Traditional equipment often lacks intelligent strategies for resource scheduling. Resource allocation is usually random and cannot be optimized based on target measurement information and environmental conditions. As a result, the radar system's resources cannot be maximized, thereby reducing resource utilization.

[0004] Patent CN114152915B discloses a radar signal processing device, a radar device and a movable platform. The above patent realizes the positioning of the end time of wavefront interference, avoids the reduction of signal-to-noise ratio, and ensures signal accuracy.

[0005] The above patent accurately locates the end time of wavefront interference and solves the problem of discarding a large amount of valid data, but there is still room for optimization in processing information of multiple radar nodes. This application realizes the status detection of multiple targets, tracks multiple targets, optimizes the system resources of the networked radar system, and solves the problem that the signal processing equipment cannot integrate information from multiple radar nodes.

[0006] To this end, the present application proposes an intermediate frequency filtering signal processing device that realizes status detection of multiple targets based on radar transmission power measurement. Summary of the Invention

[0007] The purpose of the present invention is to provide an intermediate frequency filtering signal processing device based on radar transmission power measurement to solve the technical problem that the signal processing device proposed in the above background technology cannot fuse multiple radar node information.

[0008] To achieve the above-mentioned object, the present invention provides the following technical solutions: an intermediate frequency filtering signal processing device based on radar transmit power measurement, comprising a global tracking module, wherein the global tracking module is used to optimize the signal processing process and track multiple targets;

[0009] The global tracking module includes: a target detection unit, a target tracking unit and a resource scheduling unit, wherein the target detection unit is connected to the target tracking unit via a signal, and the target detection unit is connected to the resource scheduling unit via a signal;

[0010] The target detection unit receives the echo signals from each radar node in the networked radar system, performs cluster analysis on the echo signals of each radar node using a fuzzy clustering algorithm, extracts the measurement information of the target, and obtains the environmental situation information;

[0011] The target tracking unit processes the measurement information of each radar node using a state estimation method, obtains a single radar state estimation result, selects a fusion architecture, fuses the result to obtain a global estimation result, and performs a track initiation operation on the target;

[0012] The resource scheduling unit allocates and optimizes system resources of the networked radar system according to target measurement information and environmental situation.

[0013] Preferably, the target detection unit is connected to an enumeration calculation module via a signal, and the enumeration calculation module is used to quantify the lower bound of the target tracking accuracy and accurately quantify the target tracking performance;

[0014] The enumeration calculation module includes: an enumeration detection unit, a lower bound derivation unit and a strategy allocation unit, the enumeration detection unit is connected to the lower bound derivation unit through a signal, the lower bound derivation unit is connected to the strategy allocation unit through a signal, and the target detection unit is connected to the enumeration detection unit through a signal;

[0015] The enumeration detection unit sets the detection threshold, performs target detection, compares the target's measurement information with the detection threshold, determines and traverses the target's detection and missed detection status, and generates a detection and missed detection matrix;

[0016] The lower bound derivation unit uses the measurement data and the target dynamic model to derive the lower bound of the target state estimation error under detection and missed detection conditions through the detection and missed detection matrix, and performs weighted processing on the lower bound of the target state estimation error to obtain the lower bound based on enumeration;

[0017] The strategy allocation unit will use the enumerated lower bound as the target tracking accuracy, compare the target tracking accuracy with the preset tracking accuracy requirement, generate a tracking performance evaluation report, and generate an adjustment strategy for the radar transmit power allocation.

[0018] Preferably, the target tracking unit is connected to an interference suppression module via a signal, and the interference suppression module is used to optimize the radar transmission and reception beam patterns;

[0019] The interference suppression module includes: a dynamic generation unit, a dynamic optimization unit and an output feedback unit, the dynamic generation unit is connected to the dynamic optimization unit through a signal, the dynamic optimization unit is connected to the output feedback unit and the strategy allocation unit through a signal, and the dynamic generation unit is connected to the target tracking unit through a signal;

[0020] The dynamic generation unit receives the state estimation result and the configuration information of the networked radar system, dynamically generates the transmit beam pattern, and dynamically generates the receive beam pattern by combining the transmit beam pattern and the echo signal;

[0021] The dynamic optimization unit uses an iterative optimization method to solve the transmit and receive beam pattern optimization problem based on the tracking performance evaluation report, and combines the convex approximation method to dynamically optimize the transmit and receive beam patterns.

[0022] The output feedback unit feeds back the optimized transmit and receive beam patterns to the networked radar system, updates the transmit and receive beam patterns, and guides the beam synthesis at the next moment.

[0023] Preferably, the global tracking module is connected to a signal prediction module via a signal, and the signal prediction module is used to predict the echo signal of each radar node;

[0024] The signal prediction module includes: a signal simulation unit, a prediction model unit and an error analysis unit, the signal simulation unit is connected to the prediction model unit through a signal, the prediction model unit is connected to the error analysis unit through a signal, and the lower bound derivation unit is connected to the error analysis unit through a signal;

[0025] The signal simulation unit generates the time series of the target signal and the clutter signal according to the target signal model and the clutter signal model respectively, superimposes the target signal and the clutter signal to generate the radar intermediate frequency simulation signal;

[0026] The prediction model unit uses the time-frequency analysis method to extract the time-frequency characteristics of the historical echo signals of each radar node, analyzes the time-frequency characteristics of the Doppler effect in the echo signals, and combines them with the intermediate frequency analog signals to build a signal prediction model;

[0027] The error analysis unit inputs the currently received echo signal into the signal prediction model, calculates the error between the actual signal and the predicted signal, detects the target status in the echo signal by setting the threshold value, dynamically adjusts the model parameters, and optimizes the signal prediction model.

[0028] Preferably, the global tracking module is connected to a frequency compensation module via a signal, and the frequency compensation module is used to accurately compensate the echo signal of each radar node;

[0029] The frequency compensation module includes: a factor construction unit and a signal compensation unit, wherein the factor construction unit is connected to the signal compensation unit and the prediction model unit through a signal;

[0030] The factor construction unit estimates the Doppler frequency and calculates and constructs the phase compensation factor according to the analysis results of the time-frequency characteristics;

[0031] The signal compensation unit multiplies the phase compensation factor with the echo signal of each radar node to compensate for the Doppler frequency, evaluates the compensated signal, and verifies the effectiveness of the compensation.

[0032] Preferably, the global tracking module is connected to a gain control module via a signal, and the gain control module is used to dynamically adjust the gain and dynamic range of the echo signal;

[0033] The gain control module includes: a range compression unit, a gain control unit and a feedback correction unit, the range compression unit is connected to the gain control unit via a signal, the gain control unit is connected to the feedback correction unit via a signal, the target detection unit is connected to the range compression unit via a signal, and the gain control unit is connected to the resource scheduling unit via a signal;

[0034] The range compression unit analyzes the dynamic range of the echo signal of each radar node and compresses the dynamic range of the echo signal using a nonlinear compression algorithm;

[0035] The gain control unit dynamically adjusts the gain of the intermediate frequency amplifier and the gain of the echo signal based on the system resource allocation results of the networked radar system and the transmit power measurement value of the radar node, combined with the gain control strategy;

[0036] The feedback correction unit compares the difference between the adjusted echo signal and the expected signal, evaluates the error in the gain control, and adaptively adjusts the compression algorithm and gain control strategy based on the error evaluation result.

[0037] Preferably, the interference suppression module is connected to an interference detection module via a signal, and the interference detection module is used to identify interference signals in the echo signal;

[0038] The interference detection module includes: an interference identification unit and an interference evaluation unit, the interference identification unit is connected to the interference evaluation unit through a signal, and the interference evaluation unit is connected to the dynamic optimization unit through a signal;

[0039] The interference identification unit performs spectrum analysis on the echo signals of each radar node, identifies the interference signals in the echo signals, and extracts the characteristics of the interference signals;

[0040] The interference assessment unit assesses and classifies interference according to the characteristics of the interference signal, and generates a processing strategy for suppressing interference according to the type of interference and the severity of the interference.

[0041] Preferably, the interference types include: suppression interference, deception interference, sweep frequency interference and pulse interference.

[0042] Preferably, the measurement information includes the distance between the target and each radar node, the speed of the target relative to each radar node, and the position information of the target in three-dimensional space.

[0043] Preferably, the dynamic range of the echo signal is the difference between the maximum amplitude and the minimum amplitude of the echo signal.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. The present invention realizes the status detection of multiple targets by designing a global tracking module, improves the tracking accuracy and resource utilization efficiency of the target, and solves the problem that the signal processing equipment cannot fuse the information of multiple radar nodes;

[0046] 2. The present invention, through the design of an enumeration calculation module, achieves comprehensive coverage of target detection and missed detection scenarios, provides decision-making for dynamic resource allocation and power optimization, and solves the resource waste problem of traditional fixed power allocation;

[0047] 3. The present invention incorporates an interference suppression module to dynamically generate and optimize transmit and receive beam patterns, improving the radar system's tracking performance and anti-interference capabilities. This addresses the problem of traditional radar systems using static beam patterns that cannot be flexibly adjusted based on real-time environmental information.

[0048] 4. The present invention realizes the simulation and prediction of radar intermediate frequency signals by designing a signal prediction module, improves the accuracy and efficiency of signal processing, and solves the problems of inaccurate signal prediction and poor model adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 Schematic diagram of the global tracking module of the present invention;

[0050] Figure 2 Schematic diagram of the enumeration calculation module of the present invention;

[0051] Figure 3 Schematic diagram of the interference suppression module of the present invention;

[0052] Figure 4 Schematic diagram of the signal prediction module of the present invention;

[0053] Figure 5 Schematic diagram of the frequency compensation module of the present invention;

[0054] Figure 6 Schematic diagram of the gain control module of the present invention;

[0055] Figure 7 Schematic diagram of the interference detection module of the present invention;

[0056] Figure 8 Schematic diagram of the workflow of the present invention. DETAILED DESCRIPTION

[0057] The following will clearly and completely describe 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 the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0058] Example 1, please refer to Figure 1 and Figure 8 , a medium frequency filtering signal processing device based on radar transmit power measurement, including a global tracking module, the global tracking module is used to optimize the signal processing process and track multiple targets; the global tracking module includes: a target detection unit, a target tracking unit and a resource scheduling unit, the target detection unit is connected to the target tracking unit via a signal, and the target detection unit is connected to the resource scheduling unit via a signal; the target detection unit receives the echo signal from each radar node in the networked radar system, uses a fuzzy clustering algorithm to perform cluster analysis on the echo signal of each radar node, extracts the measurement information of the target, and obtains environmental situation information; the target tracking unit uses a state estimation method to process the measurement information of each radar node, obtains a single radar state estimation result, selects a fusion architecture, fuses to obtain a global estimation result, and performs a track initiation operation on the target; the resource scheduling unit allocates and optimizes the system resources of the networked radar system according to the target measurement information and the environmental situation.

[0059] Furthermore, the target detection unit is responsible for receiving the echo signal from each radar node in the networked radar system. The echo signal is the signal reflected back after the radar signal hits the target. The target detection unit uses a fuzzy clustering algorithm to perform cluster analysis on the echo signal after filtering. For example, K initial cluster centers are randomly selected, the distance between each signal data point and each cluster center is calculated, the data point is assigned to the nearest cluster center, and the cluster center is updated to the mean of the data points within the center. This is repeated until the cluster center no longer changes or the maximum number of iterations is reached. The clustering result is obtained, thereby classifying similar echo signals into one category. Each category represents a potential target or interference source, thereby achieving the distinction between target signals and interference signals and improving the accuracy of subsequent signal processing. The membership degree of each signal belonging to each category is calculated based on the similarity of the signals to achieve signal classification. Based on the cluster analysis results, the target measurement information, such as distance, speed, azimuth, etc., is extracted from each category. The distance between the target and the radar is calculated by the delay time of the echo signal, and the speed of the target is measured by the frequency change of the echo signal. Environmental situation information, such as terrain and obstacle distribution, is obtained.

[0060] The target tracking unit receives the measurement information from the target detection unit, performs state estimation on the measurement information, and uses algorithms such as Kalman filtering to predict the target's position, speed and other state information to obtain a single radar state estimation result. The single radar state estimation result reflects the dynamic state of the target in the field of view of a single radar. A fusion architecture is selected, such as centralized fusion and distributed fusion. Centralized fusion uniformly processes the measurement information of all radar nodes, while distributed fusion first performs partial processing locally on each radar node and then fuses them to obtain a global estimation result. The global estimation result reflects the dynamic state of the target in the entire networked radar system. The target tracking unit performs a track start operation on the target to determine the target's initial motion trajectory, providing a basis for subsequent target tracking and resource scheduling.

[0061] The resource scheduling unit identifies the presence of new targets based on the target initial track information provided by the target tracking unit, and identifies the target distance based on the target measurement information and environmental situation information of the target detection unit, thereby dynamically adjusting the radar transmission power. For example, when the distance between the target and the radar increases, the radar transmission power needs to be increased to ensure the effectiveness of target detection and the rationality of system energy consumption; the position and number of radar nodes are adjusted according to the target distribution and environmental characteristics, such as adding radar nodes in areas where targets are concentrated, thereby improving the system coverage and tracking accuracy; in the case of multiple radar nodes, the resource scheduling unit allocates spectrum resources, such as using the 2-4GHz frequency band in node 1 and the 4-6GHz frequency band in node 2, etc., to avoid signal interference, ensure the rational allocation and effective utilization of spectrum resources among nodes, and achieve load balancing among multiple radar nodes by allocating and optimizing the system resources of the networked radar system to avoid overloading of some nodes while other nodes are idle.

[0062] Example 2, please refer to Figure 1 、 Figure 2 and Figure 8, a medium frequency filtering signal processing device based on radar transmission power measurement, including a global tracking module, the global tracking module is used to optimize the signal processing process and track multiple targets; the global tracking module includes: a target detection unit, a target tracking unit and a resource scheduling unit; the target detection unit is connected to an enumeration calculation module through a signal, the enumeration calculation module is used to quantify the lower bound of target tracking accuracy and accurately quantify the target tracking performance; the enumeration calculation module includes: an enumeration detection unit, a lower bound derivation unit and a strategy allocation unit, the enumeration detection unit is connected to the lower bound derivation unit through a signal, the lower bound derivation unit is connected to the strategy allocation unit through a signal, the target detection unit is connected to the lower bound derivation unit through a signal, the lower bound derivation unit is connected to the strategy allocation unit through a signal It is connected to an enumeration detection unit; the enumeration detection unit sets a detection threshold, performs target detection, compares the target's measurement information with the detection threshold, judges and traverses the target's detection and missed detection situations, and generates a detection and missed detection situation matrix; the lower bound derivation unit uses the measurement data and the target dynamic model to derive the lower bound of the target state estimation error under detection and missed detection situations through the detection and missed detection situation matrix, and performs weighted processing on the lower bound of the target state estimation error to obtain an enumeration-based lower bound; the strategy allocation unit uses the enumeration-based lower bound as the target tracking accuracy, compares the target tracking accuracy with the preset tracking accuracy requirement, generates a tracking performance evaluation report, and generates an adjustment strategy for the radar transmit power allocation.

[0063] Furthermore, the enumeration detection unit receives the target measurement information transmitted by the target detection unit. The enumeration detection unit compares the set detection threshold with the target measurement information to realize target detection. If the measurement information of a target exceeds the detection threshold, it is determined that the target is detected; otherwise, it is determined to be missed. The enumeration detection unit traverses all targets and records the detection or missed detection status of all targets. According to the traversal results, the enumeration detection unit generates a two-dimensional matrix, namely the detection and missed detection matrix. According to the detection or missed detection status recorded during the traversal process, the corresponding elements in the matrix are filled in, where the rows represent targets and the columns represent detection or missed detection status. The element 1 in the matrix indicates that the target is detected and 0 indicates missed detection, thereby achieving comprehensive coverage of target detection and missed detection scenarios.

[0064] The lower bound derivation unit calculates the lower bound of the target state estimation error under each detection and omission case based on the target dynamic model that describes the dynamic changes of the target in time and space, such as the uniform linear motion model and the uniformly accelerated linear motion model, in combination with the measurement data and based on the detection and omission case matrix. Based on the occurrence probability of the detection and omission cases, the lower bound of the target state estimation error under all possible cases is weighted by weighted summation to obtain the enumeration-based lower bound.

[0065] The strategy allocation unit receives the enumerated lower bound of the target state estimation error from the lower bound derivation unit and obtains the preset target tracking accuracy requirement from the networked radar system. The strategy allocation unit compares the enumerated lower bound with the preset tracking accuracy requirement target by target. For each tracked target, if its lower bound value is less than or equal to the accuracy requirement, it is judged that the current radar configuration can meet the tracking requirements of the target; if the lower bound value is greater than the accuracy requirement, it is judged that the tracking performance of the current radar configuration is insufficient and needs to be adjusted. Based on the comparison results, the strategy allocation unit generates a target tracking performance evaluation report, including the tracking accuracy of each target, whether the radar meets the tracking requirements of the target, etc. Based on the evaluation report, the strategy allocation unit determines the radar that needs to adjust the transmit power, and generates an adjustment strategy based on the principles of maximizing the tracking performance of the networked radar system and balancing resource utilization. The adjustment strategy includes the adjusted nodes, power increase or decrease, etc. The resource scheduling unit adjusts the corresponding radar transmit power according to the resource scheduling unit, solving the resource waste problem of traditional fixed power allocation.

[0066] Example 3, please refer to Figure 3 、 Figure 7 and Figure 8 , a medium frequency filtering signal processing device based on radar transmission power measurement, the target tracking unit is connected to the interference suppression module through a signal, and the interference suppression module is used to optimize the radar transmission and reception beam patterns; the interference suppression module includes: a dynamic generation unit, a dynamic optimization unit and an output feedback unit, the dynamic generation unit is connected to the dynamic optimization unit through a signal, the dynamic optimization unit is connected to the output feedback unit and the strategy allocation unit through a signal, and the dynamic generation unit is connected to the target tracking unit through a signal; the dynamic generation unit receives the state estimation result and the configuration information of the networked radar system, dynamically generates the transmission beam pattern, and dynamically generates the reception beam pattern by combining the transmission beam pattern and the echo signal; the dynamic optimization unit adopts an iterative optimization method to solve the transmission and reception beam pattern optimization problem according to the tracking performance evaluation report, and dynamically optimizes the transmission and reception beam patterns by combining the convex approximation method; the output feedback unit feeds back the optimized transmission and reception beam patterns to the networked radar system, updates the transmission and reception beam patterns, and guides the beam synthesis at the next moment;

[0067] The interference suppression module is connected to the interference detection module via a signal, and the interference detection module is used to identify the interference signal in the echo signal; the interference detection module includes: an interference identification unit and an interference evaluation unit, the interference identification unit is connected to the interference evaluation unit via a signal, and the interference evaluation unit is connected to the dynamic optimization unit via a signal; the interference identification unit performs spectrum analysis on the echo signal of each radar node, identifies the interference information in the echo signal, and extracts the characteristics of the interference signal; the interference evaluation unit evaluates and classifies the interference according to the characteristics of the interference signal, and generates a processing strategy for suppressing the interference according to the type of interference and the severity of the interference.

[0068] Furthermore, the dynamic generation unit receives the single radar state estimation result and the configuration information of the networked radar system transmitted by the target tracking unit, wherein the configuration information of the networked radar system includes parameters such as the layout of the radar system, the position of each radar node, and the beam width. The dynamic generation unit uses a beamforming algorithm to calculate the directivity pattern of the transmit beam, including key parameters such as the main lobe width, side lobe level, and beam pointing. For example, the beam pointing is determined by the target position information and the beam shape is optimized by the target speed information. After the transmit beam directivity pattern is generated, the dynamic generation unit combines the received echo signal and matches the echo signal with the transmit beam directivity pattern to generate a corresponding receive beam directivity pattern.

[0069] The dynamic optimization unit first receives the tracking performance evaluation report from the strategy allocation unit. The report contains the target tracking accuracy under the current beam pattern. Based on the tracking performance evaluation report and the radar system configuration information, the dynamic optimization unit performs iterative optimization to solve the transmit and receive beam pattern optimization problem. By adjusting parameters such as beam pointing and shape, it gradually approaches the optimal solution. At the same time, combined with the convex approximation method, the optimization problem is converted into a convex optimization problem for solution, thereby improving the solution efficiency. The dynamic optimization unit dynamically optimizes the beam pattern by considering factors such as changes in target status, characteristics of interference signals, and dynamic adjustment of system resources in real time. The dynamic optimization unit outputs the optimized transmit and receive beam patterns to the output feedback unit to guide beam synthesis at the next moment.

[0070] The output feedback unit feeds back the optimized transmit and receive beam patterns to the networked radar system, and performs beam synthesis at the next moment by adjusting the transmit and receive beam patterns of each node, thereby adapting to changes in target status and the characteristics of interference signals in real time, and improving radar tracking accuracy and anti-interference capability; in addition, the interference suppression module, combined with the feedback from the interference detection module, can further dynamically adjust and optimize the transmit and receive beam patterns, so that the radar can more accurately locate the target and reduce unnecessary energy loss. The interference detection module identifies and classifies interference signals, and the interference suppression module optimizes the transmit and receive beam patterns based on the interference signals, thereby effectively suppressing the interference signals and improving the anti-interference capability of the radar system.

[0071] Example 4, please refer to Figure 1 、 Figure 4 and Figure 8 , a medium frequency filtering signal processing device based on radar transmission power measurement, including a global tracking module, the global tracking module is used to optimize the signal processing process and track multiple targets; the global tracking module includes: a target detection unit, a target tracking unit and a resource scheduling unit; the global tracking module is connected to a signal prediction module through a signal, the signal prediction module is used to predict the echo signal of each radar node; the signal prediction module includes: a signal simulation unit, a prediction model unit and an error analysis unit, the signal simulation unit is connected to the prediction model unit through a signal, the prediction model unit is connected to the error analysis unit through a signal, and the lower bound derivation unit is connected to the prediction model unit through a signal. Error analysis unit; the signal simulation unit generates the time series of the target signal and the clutter signal according to the target signal model and the clutter signal model respectively, superimposes the target signal and the clutter signal to generate the radar intermediate frequency simulation signal; the prediction model unit uses the time-frequency analysis method to extract the time-frequency characteristics of the historical echo signals of each radar node, analyzes the time-frequency characteristics of the Doppler effect in the echo signal, and combines the intermediate frequency simulation signal to construct a signal prediction model; the error analysis unit inputs the currently received echo signal into the signal prediction model, calculates the error between the actual signal and the predicted signal, detects the target status in the echo signal by setting the threshold value, dynamically adjusts the model parameters, and optimizes the signal prediction model.

[0072] Furthermore, the signal simulation unit establishes a target signal model and a clutter signal model based on the historical echo signal tracking data of the radar networking system. The target signal model is used to simulate the motion trajectory and signal characteristics of the target, and the clutter signal model is used to simulate the noise, interference signals, etc. in the radar working environment. Based on the target signal model and the clutter signal model, the signal simulation unit generates time series of the target signal and the clutter signal respectively. The time series includes signal data at different time points, thereby simulating the signal reception situation of the radar in continuous time. The signal simulation unit superimposes the generated time series of the target signal and the clutter signal to generate a radar intermediate frequency simulation signal.

[0073] The prediction model unit extracts time-frequency features from the historical echo signals of the radar networking system, including Doppler frequency shift, signal intensity change, and phase information. The Doppler frequency shift reflects the relative motion state between the target and the radar, allowing the prediction model unit to determine key parameters such as the target's speed and direction. Through further analysis of the time-frequency characteristics of the Doppler effect, the prediction model unit obtains information such as the temporal trend of the Doppler frequency shift and the distribution range of the frequency shift. The Doppler effect time-frequency characteristics obtained by analysis are integrated with the radar intermediate frequency simulation signal generated by the signal simulation unit. The intermediate frequency simulation signal, as an ideal signal, provides a benchmark and reference for the establishment of the prediction model. The signal prediction model is constructed by combining statistical methods with machine learning technology.

[0074] The error analysis unit compares the predicted signal generated by the signal prediction model with the actual received radar echo signal point by point, calculates the error, and sets a threshold value. If the predicted error exceeds the threshold value, it is judged that there is a target in the signal; if it does not exceed the threshold value, it is judged that there is no target in the signal, and the model parameters are dynamically adjusted to optimize the signal prediction model.

[0075] Example 5, please refer to Figure 5 and Figure 8 , a medium-frequency filtering signal processing device based on radar transmit power measurement, wherein the global tracking module is connected to the frequency compensation module via a signal, and the frequency compensation module is used to accurately compensate the echo signal of each radar node; the frequency compensation module includes: a factor construction unit and a signal compensation unit, and the factor construction unit is connected to the signal compensation unit and the prediction model unit via a signal; the factor construction unit estimates the Doppler frequency based on the analysis results of the time-frequency characteristics, calculates and constructs the phase compensation factor; the signal compensation unit multiplies the phase compensation factor with the echo signal of each radar node to compensate for the Doppler frequency, evaluates the compensated signal, and verifies the effectiveness of the compensation.

[0076] Furthermore, before the target detection unit receives the echo signal from each radar node in the networked radar system, the factor construction unit first receives the echo signal from the networked radar system. The factor construction unit uses a time-frequency analysis method to extract the frequency component and phase change of the signal and analyzes the time-frequency characteristics of the signal. According to the analysis results of the time-frequency characteristics, the factor construction unit uses the Doppler effect to estimate the radial velocity of the target relative to the radar, thereby calculating an estimated value of the Doppler frequency. Based on the estimated value of the Doppler frequency, the factor construction unit then uses a phase compensation algorithm to calculate a phase compensation factor. The phase compensation factor is used to offset the phase offset caused by the Doppler effect and restore the original phase information of the signal. The factor construction unit constructs a phase compensation factor matrix according to the phase compensation factors calculated for each radar node.

[0077] The signal compensation unit receives the phase compensation factor matrix from the factor construction unit and the echo signal of the radar system, and performs complex multiplication operation on the phase compensation factor and the corresponding echo signal to compensate for the phase offset caused by the Doppler effect. Through the complex multiplication operation, the phase of the echo signal is adjusted, thereby realizing the compensation of the Doppler frequency of the echo signal. The compensated echo signal is closer to the real echo information of the target, thereby improving the detection accuracy and resolution of the networked radar system. When verifying the effectiveness of the compensation, the signal compensation unit uses an evaluation method to compare the echo signals before and after compensation, including calculating the signal-to-noise ratio, phase error and other indicators of the signal. If the quality of the echo signal after compensation is improved, the compensation is judged to be effective; if the quality of the echo signal after compensation does not change much, it is judged that the compensation effect is poor. The signal compensation unit will pass feedback information to the factor construction unit, and the factor construction unit will adjust the calculation method and parameter setting of the phase compensation factor, thereby improving the accuracy and stability of the compensation.

[0078] Example 6, please refer to Figure 6 and Figure 8 , a medium frequency filtering signal processing device based on radar transmit power measurement, the global tracking module is connected to a gain control module via a signal, and the gain control module is used to dynamically adjust the gain and dynamic range of the echo signal; the gain control module includes: a range compression unit, a gain control unit and a feedback correction unit, the range compression unit is connected to the gain control unit via a signal, the gain control unit is connected to the feedback correction unit via a signal, the target detection unit is connected to the range compression unit via a signal, and the gain control unit is connected to the resource scheduling unit via a signal; the range compression unit analyzes the dynamic range of the echo signal of each radar node and compresses the dynamic range of the echo signal using a nonlinear compression algorithm; the gain control unit dynamically adjusts the gain of the medium frequency amplifier and adjusts the gain of the echo signal based on the system resource allocation result of the networked radar system and the transmit power measurement value of the radar node in combination with the gain control strategy; the feedback correction unit compares the difference between the adjusted echo signal and the expected signal, evaluates the error in the gain control, and adaptively adjusts the compression algorithm and the gain control strategy according to the error evaluation result.

[0079] Furthermore, the range compression unit analyzes the dynamic range of the echo signal and determines the maximum and minimum amplitudes of the echo signal. This allows the dynamic range of the echo signal at each radar node to be analyzed, i.e., the difference between the maximum and minimum amplitudes of the echo signal. The range compression unit uses a nonlinear compression algorithm to compress the dynamic range of the echo signal. Based on the amplitude differences of the echo signal, the compression ratio of the echo signal is dynamically adjusted to make the compressed echo signal more uniform in amplitude.

[0080] The gain control unit receives the resource allocation results of the networked radar system and the transmit power of the radar nodes transmitted by the resource scheduling unit, obtains the measured transmit power value from each radar node, and calculates the gain adjustment value based on the system resource allocation results and the transmit power measurement value, combined with a preset gain control strategy, such as adaptive gain control based on signal strength and gain adjustment based on target distance. The calculated gain adjustment value is applied to the intermediate frequency amplifier to dynamically adjust the gain of the intermediate frequency amplifier, thereby adjusting the gain of the echo signal, and outputs the adjusted echo signal to the subsequent feedback correction unit.

[0081] The feedback correction unit compares the difference between the adjusted echo signal and the expected signal, calculates the difference value, evaluates the difference value, determines the error in the gain control, adaptively adjusts the compression algorithm and gain control strategy based on the error evaluation result, and updates the adjusted compression algorithm and gain control strategy to the range compression unit and the gain control unit.

[0082] Working Principle: The global tracking module extracts the target's measurement information from the echo signals of each radar node, and combines this information with the predicted structure of the echo signals from the signal prediction module to obtain the target's dynamic state in the entire networked radar system.

[0083] The enumeration calculation module uses the lower bound of the enumeration as the target tracking accuracy, identifies the radar tracking performance, and generates an adjustment strategy for radar transmit power allocation. The global tracking module optimizes the system resources of the networked radar system based on the target measurement information and the adjustment strategy.

[0084] The interference suppression module dynamically adjusts and optimizes the transmit and receive beam patterns according to the interference processing strategy feedback from the interference detection module, thereby effectively suppressing the interference signal and improving the anti-interference capability of the radar system.

[0085] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. An intermediate frequency filter signal processing device based on radar transmission power measurement, characterized in that: It includes a global tracking module, which is used to optimize the signal processing process and track multiple targets; The global tracking module includes: a target detection unit, a target tracking unit and a resource scheduling unit, wherein the target detection unit is connected to the target tracking unit via a signal, and the target detection unit is connected to the resource scheduling unit via a signal; The target detection unit receives the echo signals from each radar node in the networked radar system, performs cluster analysis on the echo signals of each radar node using a fuzzy clustering algorithm, extracts the measurement information of the target, and obtains the environmental situation information; The target tracking unit processes the measurement information of each radar node using a state estimation method, obtains a single radar state estimation result, selects a fusion architecture, fuses the result to obtain a global estimation result, and performs a track initiation operation on the target; The resource scheduling unit allocates and optimizes system resources of the networked radar system according to target measurement information and environmental situation.

2. The intermediate frequency filtering signal processing device based on radar transmission power measurement according to claim 1, characterized in that: The target detection unit is connected to an enumeration calculation module via a signal, and the enumeration calculation module is used to quantify the lower bound of the target tracking accuracy and accurately quantify the target tracking performance; The enumeration calculation module includes: an enumeration detection unit, a lower bound derivation unit and a strategy allocation unit, the enumeration detection unit is connected to the lower bound derivation unit through a signal, the lower bound derivation unit is connected to the strategy allocation unit through a signal, and the target detection unit is connected to the enumeration detection unit through a signal; The enumeration detection unit sets the detection threshold, performs target detection, compares the target's measurement information with the detection threshold, determines and traverses the target's detection and missed detection status, and generates a detection and missed detection matrix; The lower bound derivation unit uses the measurement data and the target dynamic model to derive the lower bound of the target state estimation error under detection and missed detection conditions through the detection and missed detection matrix, and performs weighted processing on the lower bound of the target state estimation error to obtain the lower bound based on enumeration; The strategy allocation unit will use the enumerated lower bound as the target tracking accuracy, compare the target tracking accuracy with the preset tracking accuracy requirement, generate a tracking performance evaluation report, and generate an adjustment strategy for the radar transmit power allocation.

3. The intermediate frequency filtering signal processing device based on radar transmission power measurement according to claim 1, characterized in that: The target tracking unit is connected to an interference suppression module via a signal, and the interference suppression module is used to optimize the radar transmission and reception beam patterns; The interference suppression module includes: a dynamic generation unit, a dynamic optimization unit and an output feedback unit, the dynamic generation unit is connected to the dynamic optimization unit through a signal, the dynamic optimization unit is connected to the output feedback unit and the strategy allocation unit through a signal, and the dynamic generation unit is connected to the target tracking unit through a signal; The dynamic generation unit receives the state estimation result and the configuration information of the networked radar system, dynamically generates the transmit beam pattern, and dynamically generates the receive beam pattern by combining the transmit beam pattern and the echo signal; The dynamic optimization unit uses an iterative optimization method to solve the transmit and receive beam pattern optimization problem based on the tracking performance evaluation report, and combines the convex approximation method to dynamically optimize the transmit and receive beam patterns; The output feedback unit feeds back the optimized transmit and receive beam patterns to the networked radar system, updates the transmit and receive beam patterns, and guides the beam synthesis at the next moment.

4. The intermediate frequency filtering signal processing device based on radar transmission power measurement according to claim 1, characterized in that: The global tracking module is connected to a signal prediction module via a signal, and the signal prediction module is used to predict the echo signal of each radar node; The signal prediction module includes: a signal simulation unit, a prediction model unit and an error analysis unit, the signal simulation unit is connected to the prediction model unit through a signal, the prediction model unit is connected to the error analysis unit through a signal, and the lower bound derivation unit is connected to the error analysis unit through a signal; The signal simulation unit generates the time series of the target signal and the clutter signal according to the target signal model and the clutter signal model respectively, superimposes the target signal and the clutter signal to generate the radar intermediate frequency simulation signal; The prediction model unit uses the time-frequency analysis method to extract the time-frequency characteristics of the historical echo signals of each radar node, analyzes the time-frequency characteristics of the Doppler effect in the echo signals, and combines them with the intermediate frequency analog signals to build a signal prediction model; The error analysis unit inputs the currently received echo signal into the signal prediction model, calculates the error between the actual signal and the predicted signal, detects the target status in the echo signal by setting the threshold value, dynamically adjusts the model parameters, and optimizes the signal prediction model.

5. The intermediate frequency filtering signal processing device based on radar transmission power measurement according to claim 1, characterized in that: The global tracking module is connected to a frequency compensation module via a signal, and the frequency compensation module is used to accurately compensate the echo signal of each radar node; The frequency compensation module includes: a factor construction unit and a signal compensation unit, wherein the factor construction unit is connected to the signal compensation unit and the prediction model unit through a signal; The factor construction unit estimates the Doppler frequency and calculates and constructs the phase compensation factor according to the analysis results of the time-frequency characteristics; The signal compensation unit multiplies the phase compensation factor with the echo signal of each radar node to compensate for the Doppler frequency, evaluates the compensated signal, and verifies the effectiveness of the compensation.

6. The intermediate frequency filtering signal processing device based on radar transmission power measurement according to claim 1, characterized in that: The global tracking module is connected to a gain control module via a signal, and the gain control module is used to dynamically adjust the gain and dynamic range of the echo signal; The gain control module includes: a range compression unit, a gain control unit and a feedback correction unit, the range compression unit is connected to the gain control unit via a signal, the gain control unit is connected to the feedback correction unit via a signal, the target detection unit is connected to the range compression unit via a signal, and the gain control unit is connected to the resource scheduling unit via a signal; The range compression unit analyzes the dynamic range of the echo signal of each radar node and compresses the dynamic range of the echo signal using a nonlinear compression algorithm; The gain control unit dynamically adjusts the gain of the intermediate frequency amplifier and the gain of the echo signal based on the system resource allocation results of the networked radar system and the transmit power measurement value of the radar node, combined with the gain control strategy; The feedback correction unit compares the difference between the adjusted echo signal and the expected signal, evaluates the error in the gain control, and adaptively adjusts the compression algorithm and gain control strategy based on the error evaluation result.

7. The intermediate frequency filtering signal processing device based on radar transmission power measurement according to claim 3, characterized in that: The interference suppression module is connected to the interference detection module via a signal, and the interference detection module is used to identify interference signals in the echo signal; The interference detection module includes: an interference identification unit and an interference evaluation unit, the interference identification unit is connected to the interference evaluation unit through a signal, and the interference evaluation unit is connected to the dynamic optimization unit through a signal; The interference identification unit performs spectrum analysis on the echo signals of each radar node, identifies the interference signals in the echo signals, and extracts the characteristics of the interference signals; The interference assessment unit assesses and classifies interference according to the characteristics of the interference signal, and generates a processing strategy for suppressing interference according to the type of interference and the severity of the interference.

8. The intermediate frequency filtering signal processing device based on radar transmission power measurement according to claim 7, characterized in that: The interference types include: suppression interference, deception interference, sweep frequency interference and pulse interference.

9. The intermediate frequency filtering signal processing device based on radar transmission power measurement according to claim 1, characterized in that: The measurement information includes the distance between the target and each radar node, the speed of the target relative to each radar node, and the position information of the target in three-dimensional space.

10. The intermediate frequency filtering signal processing device based on radar transmission power measurement according to claim 6, characterized in that: The dynamic range of the echo signal is the difference between the maximum amplitude and the minimum amplitude of the echo signal.

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