CN113050045A - Intelligent comprehensive main and side lobe interference resisting system and method - Google Patents
Intelligent comprehensive main and side lobe interference resisting system and method Download PDFInfo
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Abstract
The invention provides a method for an intelligent comprehensive anti-main lobe and anti-side lobe interference system, which comprises the following steps: the radar control module of the intelligent processing unit executes function setting on a radar antenna array surface, the comprehensive anti-interference processing module finishes detection on an interference environment by acquiring a broadband detection channel, extracts interference characteristics of various interference signals and stores the interference characteristics into an interference characteristic library, and the comprehensive anti-interference processing module performs refined interference classification on the received interference signals and generates anti-interference measures corresponding to the characteristic information of the received interference signals; according to the interference environment cognition, the waveform design and the algorithm design, anti-interference measures are provided in a self-adaptive mode, interference counter effect evaluation and self-adaptive anti-interference measure optimization are executed, and anti-interference measures are further optimized and adjusted according to the interference counter effect evaluation.
Description
Technical Field
The invention belongs to the technical field of radar signal anti-interference processing, and particularly relates to an intelligent comprehensive main and side lobe interference resisting system and method.
Background
The radar intelligent anti-interference system research of the Wang Feng of the Nanjing electronic technology research institute is proposed in the first phase of 2014 of the modern radar:
the development of the radar to cognition and intelligence is a necessary trend. With the increasing deterioration of electromagnetic environment in battlefield, the demand of radar for the perception capability of electromagnetic environment is increasing. The electromagnetic interference comes from intentional interference of enemies or unintentional interference of other parties, and the radar detection performance is seriously reduced. With the development of interference technology, the interference types present diversity and complexity, and at present, the anti-interference technical means commonly used in radars, such as side lobe cancellation, side lobe concealment, adaptive frequency agility, etc., cannot meet the technical requirements of anti-interference. For a modern radar anti-interference technology, the following problems exist. The cognition of external interference environment is not enough, and the radar is interfered and the type of interference lacks the equipment of automatic discrimination. And the interference suppression performance of the side lobe is limited. The main lobe interference is difficult to take effect.
The anti-interference mechanism of the radar system needs to be intelligentized, the anti-interference problem of the radar of an intelligent anti-interference system can not be solved by depending on a certain algorithm or a certain subsystem technology, but needs to be solved by comprehensively coordinating various technologies of the radar system. Under the design criterion of anti-interference, comprehensive anti-interference design is carried out aiming at each link of antenna, receiving, signal processing and data processing, an optimized anti-interference system is formed, and the anti-interference problem of the radar is solved from a higher level. Because the related radar anti-interference technology and algorithm are excessive, the use of the traditional single anti-interference measure is difficult to select, and the selection of an artificial anti-interference measure is replaced by intelligent processing. The intelligent anti-interference technology has the concept and technology of cognitive radar to a certain extent.
Disclosure of Invention
In order to solve the technical problems, the invention provides an intelligent comprehensive anti-main-side lobe interference system which comprises a radar antenna, a radio frequency front end and an intelligent processing unit, wherein the intelligent processing unit comprises a radar control module, a plurality of DBF modules, a comprehensive anti-interference processing module and a memory module, and an interference information characteristic library and an algorithm strategy library of an anti-interference algorithm are built in the memory module.
Furthermore, the interference information feature library stores feature parameters of interference signals extracted through a broadband detection channel and a narrowband detection channel, and a Bayesian neural network is used for intelligently classifying the detected interference signals; and for the interference signal which is verified, storing the extracted interference characteristics into an interference characteristic library, wherein the interference characteristic library is continuously updated and perfected along with the use of the interference system.
Furthermore, in the interference information feature library, the interference pattern directly corresponds to the interference feature.
Furthermore, the comprehensive anti-interference processing module comprehensively senses interference signals, after the interference types are identified, anti-interference processing is carried out by automatically matching corresponding emission waveforms and modes in combination with an anti-interference intelligent decision-making means, and corresponding active anti-interference measures and interference suppression technologies are adopted for suppressing different interference types.
The invention also provides an anti-interference method of the intelligent comprehensive anti-main-side lobe interference system, which comprises the following steps:
step 1, a radar control module of an intelligent processing unit executes function setting on a radar antenna array surface, and a plurality of detection beams and a plurality of deception beams are formed on the radar antenna array surface;
step 2, the comprehensive anti-interference processing module detects an interference environment through a broadband detection channel and extracts interference characteristics of various interference signals by combining signals detected and received by a narrow-band detection channel; aiming at the interference signal characteristics, storing the extracted interference signal characteristics into an interference characteristic library, and establishing a corresponding relation between an interference pattern and the interference characteristics;
step 3, the comprehensive anti-interference processing module performs refined interference classification on the received interference signals, obtains corresponding interference signal characteristic information from an interference signal characteristic library, and generates anti-interference measures corresponding to the received interference signal characteristic information by using an anti-interference algorithm stored in an anti-interference algorithm library;
step 3.1, the interference signals are subjected to refined interference classification, and the interference signals are divided into ten types:
firstly, secondary lobe continuous wave interference;
the interference of the sidelobe pulses is avoided;
the first type of composite side lobe interference with continuous wave interference as a main and pulse interference as an auxiliary;
fourth, second-class composite side lobe interference with impulse interference serving as a main interference and continuous wave interference serving as an auxiliary interference is obtained;
fifthly, carrying out third composite side lobe interference with the same intensity and ratio of the continuous waves and the pulse interference;
sixthly, main lobe continuous wave interference;
main lobe forward interference;
and continuous wave interference is primary and repeated interference is secondary first-class composite main lobe interference;
a second type of composite main lobe interference with the self-skin forwarding interference as main and continuous wave interference as auxiliary;
the third type of composite main lobe interference with equivalent continuous wave and forwarding interference intensity and ratio is obtained;
step 3.2, using the interference characteristic parameters extracted by the broadband detection channel and the narrow-band detection channel as the input of a Bayesian neural network, constructing an active interference signal classifier formed by the Bayesian neural network, using the Bayesian neural network to identify active interference signals, firstly training the whole Bayesian neural network, inputting different types of interference signals into the trained Bayesian neural network, enabling the output of the network to be within a required error range, then inputting unknown interference signals into the Bayesian neural network, and distinguishing the types of the interference signals;
step 4, the comprehensive anti-interference processing module provides anti-interference measures in a self-adaptive manner according to interference environment cognition, waveform design and algorithm design, and self-adaptive comprehensive countermeasure of interference by the radar is realized;
wherein, the active anti-interference measure comprises: agility, deception (masking pulses), complex waveforms;
the main lobe interference suppression method comprises the following steps: a blind source separation algorithm, a waveform entropy anti-interference algorithm, a sparse signal processing anti-interference algorithm, an amplitude-phase analysis reconstruction algorithm and a trace point filtering anti-interference algorithm;
the side lobe interference suppression method comprises the following steps: side lobe cancellation, side lobe shadow elimination and trace point filtering algorithm;
for passive main lobe anti-interference measures, the selection of the algorithm sequence depends on the interference type judged by the system:
step 4.1, judging the interference as main lobe continuous wave interference, then
Step 4.11, adopting a blind source separation algorithm to evaluate the interference suppression effect;
step 4.12, when the interference suppression effect is lower than a threshold value, adopting an interference suppression algorithm based on sparse processing and an interference suppression algorithm based on amplitude-phase analysis reconstruction;
step 4.2, judging that the interference is main lobe forwarding interference, and adopting an amplitude-phase analysis reconstruction algorithm, a waveform entropy anti-interference algorithm and/or a trace filtering anti-interference algorithm;
step 4.3, if the interference is judged to be the first type of composite main lobe interference, the interference is judged to be the first type of composite main lobe interference
Step 4.31, adopting a blind source separation algorithm to evaluate the interference suppression effect;
step 4.32, adopting a waveform entropy anti-interference algorithm and a trace filtering anti-interference algorithm; performing interference suppression effect evaluation;
step 4.33, when the interference suppression effect is lower than a threshold value, adopting an interference suppression algorithm based on sparse processing and an interference suppression algorithm based on amplitude-phase analysis reconstruction;
step 4.4, if the interference is judged to be the second type composite main lobe interference, the interference is judged to be the second type composite main lobe interference
Step 4.41, adopting a waveform entropy anti-interference algorithm and a trace filtering anti-interference algorithm to evaluate the interference suppression effect;
step 4.42, adopting a blind source separation algorithm; performing interference suppression effect evaluation;
step 4.43, when the interference suppression effect is lower than the threshold value, adopting an interference suppression algorithm based on sparse processing and an interference suppression algorithm based on amplitude-phase analysis reconstruction;
step 4.5, judging that the interference is the third type of composite main lobe interference, and returning to the step 4.3;
and 5, the comprehensive anti-interference processing module executes interference countermeasure effect evaluation and adaptive anti-interference measure optimization, and further optimizes and adjusts anti-interference measures according to the interference countermeasure effect evaluation.
Further, the interference characteristic parameters in step 2 include: main and side lobe decision factors, pulse pressure front time width, pulse pressure back time width, frequency, angle, interference bandwidth, interference repetition frequency, interference signal form, pulse width, arrival time, interference intra-pulse modulation parameters and variance.
Further, the step 5 of evaluating the interference countermeasure effect by the integrated anti-interference processing module includes:
calculation of the final probability value:
P(Y|X,L)＝∫P(Y|X,W)P(W|L)dW
where Y is the output, which is a 10-dimensional vector, Y — 1, Y — 2, …, Y — 10,
y _ i represents a probability corresponding to the i-th interference pattern of the 10 interference patterns;
x denotes a sequentially connected interference characteristic parameter vector, where L denotes a training sample set,
wherein wijRepresents the weight of the jth neuron at the ith layer, NwRepresenting the total number of layers of the neural network, MiRepresenting the number of neurons of the i-th layer neural network;
p (Y | X, L) represents the conditional probability of the predicted output given the input X and the neural network weight W, and P (W | L) represents the posterior distribution with weight W, which can be expressed as
P (W) represents the distribution of weights, P (L | W) represents the likelihood function of the training sample set, and P (L) is the edge likelihood function.
The method of the invention is adopted to carry out anti-interference evaluation by using a neural network in artificial intelligence for reference, and the incidence relation between the anti-interference efficiency and the influence elements is analyzed through a probability theory and a multi-level network relation. The anti-interference evaluation network can be modeled into an appointed multi-level causal network structure, and the anti-interference efficiency can be comprehensively evaluated according to uncertain or incomplete radar anti-interference indexes.
Drawings
FIG. 1 is a flow chart of anti-interference processing according to the present invention;
FIG. 2 illustrates the principle of radar broadband detection according to the present invention;
fig. 3 shows the principle of radar interference classification according to the present invention.
Fig. 4 is a block diagram of an intelligent comprehensive anti-interference system provided by the invention.
Detailed Description
The invention provides a radar comprehensive main and side lobe interference resisting system and an anti-interference method based on interference cognition and anti-interference effect feedback. The design adopts a cognitive architecture to realize sensing, decision making and closed loop processing, interference analysis is completed through a broadband detection channel and a narrowband detection channel, interference characteristics are extracted, and interference classification recognition processing is completed through comparison with an interference characteristic library. The anti-interference processing is used for recognizing the interference environment in real time, and an anti-interference strategy is selected in a self-adaptive manner by combining an interference characteristic library and an interference algorithm library; the anti-interference processing realizes the intelligent anti-interference of the system by evaluating the interference suppression effect and adaptively optimizing an anti-interference strategy and an interference suppression algorithm. The intelligent anti-interference processing flow of the system is shown in fig. 1.
Referring to fig. 4, the circuit block of the intelligent comprehensive anti-main-side lobe interference system includes a radar antenna, a radio frequency front end, an intelligent processing unit, a broadband receiving channel and a narrow-band detecting channel; the radio frequency front ends of the two channels process signals received by the radar antenna of the channel and perform analog/digital conversion (A/D) on the radio frequency signals; outputting the digital signal to an intelligent processing unit; the intelligent processing unit comprises a radar control module, a plurality of DBF modules, a comprehensive anti-interference processing module and a memory module, wherein an interference information characteristic library and an algorithm strategy library of an anti-interference algorithm are built in the memory module.
Furthermore, the interference information feature library stores feature parameters of interference signals extracted through a broadband detection channel and a narrowband detection channel, and stores the extracted interference features into the interference feature library, wherein the interference feature library is continuously updated and perfected along with the use of the interference system.
The radar comprehensive anti-main-side lobe interference system based on interference cognition and anti-interference effect feedback comprises a broadband receiving channel and a narrow-band detection channel, wherein the narrow-band detection channel at least comprises a reconfigurable receiving and transmitting front end, the reconfigurable receiving and transmitting front end comprises an antenna array, a plurality of receiving and transmitting radio frequency front ends, a plurality of adaptive channels, and the adaptive channels comprise a plurality of receiving and transmitting digital channels. The method comprises the steps of defining functions of an antenna array surface through software to form a plurality of detection and deception wave beams, sensing interference environment through a wide-band channel and a narrow-band channel, and performing adaptive resource scheduling and transceiving processing according to the result of sensing the interference environment. The specific extraction method is shown in FIG. 2.
The intelligent anti-interference technology is adopted to complete interference detection, receiving, analysis, classification and interference self-adaptive suppression functions, the broadband detection and receiving channel completes detection and receiving of interference signals, the narrow band channel receives and transmits detection signals and completes analysis of the interference signals, and the wide band channel and the narrow band channel are combined to complete extraction of interference characteristics. The broadband interference detecting and receiving system adopts antenna subarrays to form a plurality of space coverage beams with azimuth dimensions. And completing the acquisition of broadband signals by adopting high-speed AD, performing digital channelization processing, and forming a plurality of channels which are mutually overlapped aiming at the broadband. And detecting the interference source signal of each channel, and completing parameter measurement and feature extraction of the interference signal to form interference source description information. It can be summarized as the following steps:
1. the function definition of the antenna array surface is carried out through software, and a plurality of detection and deception beams are formed.
2. The broadband detection channel detects the interference environment and extracts the interference characteristics by combining the signals detected by the narrow band.
3. And interference fine classification is carried out, a library is built for interference characteristics, anti-interference measures are enhanced, the self-adaptive capacity to an external interference environment is improved, and the response time is shortened.
4. The system is self-adaptive and anti-jamming, and comprehensive interference resistance is realized through interference environment cognitive waveform design and algorithm design.
5. And evaluating the interference countermeasure effect and optimizing the self-adaptive anti-interference measures, optimizing and adjusting the anti-interference measures according to the evaluation condition of the interference countermeasure effect, and ensuring the optimal anti-interference effect.
The characteristics of the interfering signal to a typical radar include: pre-pulse pressure time width, post-pulse pressure time width, frequency, angle, interference bandwidth, interference repetition frequency, interference signal form, pulse width, arrival time, interference intra-pulse modulation parameters, variance, etc.
And (3) estimating an interference signal by utilizing narrow-band detection, and extracting and estimating main lobe interference characteristic parameters. Typical interference characteristics include: pre-pulse pressure time width, post-pulse pressure time width, frequency, angle, interference bandwidth, interference repetition frequency, interference signal form, pulse width, arrival time, interference intra-pulse modulation parameters, variance, etc.
Sensing the environment interference situation in real time through broadband interception, guiding the working mode of the radar and improving the detection efficiency of the radar; monitoring the interference state in real time, and guiding anti-interference measures such as radar frequency agility and waveform agility; interference characteristics are collected on line, an interference characteristic library is perfected, and support is provided for a radar anti-interference algorithm.
And interference classification and interference library building are carried out, anti-interference measures are enhanced, the self-adaptive capacity to an external interference environment is improved, and the reaction time is shortened. Intelligently classifying the interference signals by adopting a Bayesian neural network through interference characteristics obtained by interference environment perception; and for the interference signals which are verified, the interference characteristics are recorded into an interference characteristic library, the database establishes the corresponding relation between the interference pattern and the interference characteristics, and the database is continuously updated and perfected along with the use of the system.
The self-adaptive anti-interference method has the advantages that the self-adaptive anti-interference state is obtained according to the interference environment sensing, the system is self-adaptive anti-interference, and the comprehensive countermeasure of interference is realized through the interference environment cognitive waveform design and the anti-interference algorithm design.
According to the evaluation condition of the interference countermeasure effect, the evaluation object comprises signal level point track quality, flight path quality and the like, the anti-interference measures are optimized and adjusted, and the optimal anti-interference effect is ensured.
The interference scenarios faced by a typical radar are: self-defense interference, satellite interference, and support interference. The corresponding interference types can be classified as: main lobe suppression, main lobe forwarding, side lobe pulse, side lobe continuous wave and the like. In the actual interference signals, the combination of the continuous wave interference and the repeater interference is mostly adopted, the difference is that the strength and the ratio of the continuous wave interference and the repeater interference in the interference signals are different, and the strength and the ratio of the continuous wave interference and the repeater interference in the interference determine the use of the later anti-interference measures.
The classification of interference is the physical basis for realizing active interference cognition. The interference signal classification technology provides an interference signal classification result for intelligent radar anti-interference, and is a precondition for adaptive anti-interference processing of the system. The invention adopts the Bayesian neural network to realize the classification of refined interference types. Under the refined interference classification, the invention divides the types of interference signals into 10 types:
firstly, secondary lobe continuous wave interference;
the interference of the sidelobe pulses is avoided;
the first type of composite side lobe interference with continuous wave interference as a main and pulse interference as an auxiliary;
fourth, second-class composite side lobe interference with impulse interference serving as a main interference and continuous wave interference serving as an auxiliary interference is obtained;
fifthly, carrying out third composite side lobe interference with the same intensity and ratio of the continuous waves and the pulse interference;
sixthly, main lobe continuous wave interference;
main lobe forward interference;
and continuous wave interference is primary and repeated interference is secondary first-class composite main lobe interference;
a second type of composite main lobe interference with the self-skin forwarding interference as main and continuous wave interference as auxiliary;
the third type of composite main lobe interference with equivalent continuous wave and forwarding interference strength and ratio is obtained.
The refined classification of the interference types is shown in fig. 3, and the refined interference signal classification provided by the invention has great significance for selecting the later anti-interference measures.
The characteristic parameters of the interference signals extracted by the broadband detection channel and the narrowband detection channel, including a main lobe decision factor, a time width before pulse pressure, a time width after pulse pressure, a frequency, an angle, an interference bandwidth, an interference repetition frequency, an interference signal form, a pulse width, an arrival time, an interference intra-pulse modulation parameter and the like are used as the input of a Bayesian neural network, an active interference signal classifier formed by the Bayesian neural network is constructed, the active interference signals are identified by the Bayesian neural network, the whole network is trained firstly, the interference signals of different types are input into the trained error range which enables the output of the network to reach the requirement, and then the type of the interference signals is judged in the unknown Bayesian neural network.
The probability values of the final algorithm can be obtained by:
P(Y|X,L)＝∫P(Y|X,W)P(W|L)dW (1)
where Y is the output, which is a 10-dimensional vector, Y ═ Y1,y2,…,y10]，yiThe probability corresponding to the ith interference pattern is shown, and the ten interference patterns are sequentially and respectively sidelobe continuous wave interference, sidelobe impulse interference, first-class composite sidelobe interference with main continuous wave interference and auxiliary impulse interference, second-class composite sidelobe interference with main impulse interference and auxiliary continuous wave interference, third-class composite sidelobe interference with strength and occupation ratio of continuous wave and impulse interference equivalent to that of main lobe continuous wave interference, main lobe forward interference, second-class composite main lobe interference with main continuous wave interference and auxiliary forward interference equivalent to that of first-class composite main lobe interference, and third-class composite main lobe interference with strength and occupation ratio equivalent to that of continuous wave and forward interference; x represents a sequentially connected feature vector, namely: main and side lobe decision factors, pulse pressure front time width, pulse pressure back time width, frequency, angle, interference bandwidth, interference repetition frequency, interference signal form, pulse width, arrival time and interference intra-pulse modulation parameters. Where L represents a set of training samples,
wherein wijRepresents the weight of the jth neuron at the ith layer, NwRepresenting the total number of layers of the neural network, MiAnd the number of the neural network neurons of the i-th layer is represented.
P (Y | X, L) represents the conditional probability of the predicted output given the input X and the neural network weights W. P (W | L) represents a posterior distribution of weight W, which can be expressed as
P (W) represents the distribution of weights, P (L | W) represents the likelihood function of the training sample set, and P (L) is the edge likelihood function.
Self-adaptation interference suppression realizes intelligent anti-interference, adopts corresponding initiative anti-interference measure and interference suppression technique to restrain to different interference types:
active anti-interference includes: frequency agility, deception/masking pulses, complex waveforms, etc.
The main lobe interference suppression method comprises the following steps: blind source separation algorithm, waveform entropy anti-interference algorithm, sparse signal processing anti-interference algorithm, amplitude-phase analysis reconstruction algorithm, point trace filtering anti-interference algorithm and the like;
the side lobe interference suppression method comprises the following steps: side lobe cancellation, side lobe shadow masking, a point trace filtering algorithm and the like.
The interference suppression technology based on waveform design and adaptive waveform optimization comprises the steps of using low intercepted probability signals such as broadband/ultra-wideband, intra-pulse/inter-pulse agility, complex modulation and the like in order to change the passive situation of the radar in countermeasure, so that the signals are difficult to detect and identify by an interference/detection receiver, and the radar is protected from electronic interference.
The invention introduces the concept of cognitive radar into the anti-interference field, optimizes the transmitting waveform according to the detected active interference and the specific situation of the target, can make the radar adaptively react to the current electromagnetic environment, and has the optimal anti-interference capability. The air information radar carries out comprehensive perception on interference signals, after the interference type is identified, the anti-interference intelligent decision means is combined, and corresponding transmitting waveforms and modes are automatically matched for anti-interference processing.
The self-adaptive main lobe interference suppression technology adopts corresponding main lobe interference suppression technology to suppress different interference types:
the interference suppression technology based on blind source separation is the blind separation technology in the radar anti-interference technology, which must adapt to the severe conditions of signal number change, overdetermination/underdetermined/single channel, signal correlation and the like, can quickly and stably track the change of an electromagnetic environment, and finally extracts a target echo through target characteristic parameters to achieve the purpose of suppressing interference. Firstly, preprocessing a received signal to form a plurality of paths of positive definite signals; then, a new dynamic order-fixing estimation algorithm is researched on the basis of the existing estimation criterion, and the fast order fixing of the received signals is quickly and stably realized; then, the separation of the echo and the interference is completed through a blind separation technology; and finally, identifying the target echo and the interference according to the characteristic difference of the target and the interference, and realizing the extraction of the target echo.
An interference suppression algorithm based on waveform entropy: the waveform entropy can well represent the fluctuation degree of a plurality of pulse echoes of the radar and can be used for eliminating asynchronous forwarding/spoofing interference.
An interference suppression algorithm based on sparse processing: the sparse characteristic of the intermittent compression system interference is utilized, and the undisturbed data recovery signal is adopted, so that the interference is well inhibited.
An interference suppression algorithm based on amplitude and phase analysis reconstruction comprises the following steps: when the radar is subjected to noise frequency modulation interference, and the amplitude of an interference signal is far larger than the amplitudes of an echo signal and a noise signal, the amplitude characteristic of the interference is prominent; the signal amplitude and phase are estimated to reconstruct the signal, and then the effect of interference is eliminated from the received signal.
An interference suppression algorithm based on point trace filtering: the deception jamming machine carries out delay forwarding on the received radar main lobe signal, and a false target can be formed on the radar. Real targets typically have rotating parts and have a certain volume or dimension. The two have certain difference in the aspects of echo amplitude, phase, Doppler frequency spectrum, point trace space envelope and the like, and the characteristics are extracted and analyzed, so that deception interference can be identified and inhibited.
And the anti-interference processing is carried out by evaluating the interference suppression effect and adaptively optimizing an interference strategy and an interference suppression algorithm. The anti-interference performance index mainly includes signal quality, trace point quality and track quality. And performing optimized interference strategy self-adaptive optimization by evaluating the anti-interference efficiency index.
The existing anti-interference evaluation method is basically that an index system is established firstly, and then evaluation is carried out through an analytic hierarchy process or an expert scoring method. The subjectivity of the evaluation method is too strong, and dynamic evaluation cannot be carried out along with the change of an interference environment and the adjustment of anti-interference measures. Therefore, an intelligent anti-interference evaluation method is urgently needed in actual combat, and the anti-interference effect is dynamically evaluated and inferred according to the electromagnetic environment situation.
And inputting the authenticated interference signals into an interference feature library according to the interference classification condition and the extracted interference features, and updating the interference feature library. The interference characteristic library provides data support for radar anti-interference strategies, has an updating function, is a core component of intelligent anti-interference, and has different interference characteristic signals corresponding to different interference signals.
And adopting corresponding active anti-interference measures and interference suppression technologies to suppress different interference types:
active anti-interference includes: frequency agility, deception (masking pulses), complex waveforms, etc.
The main lobe interference suppression method comprises the following steps: blind source separation algorithm, waveform entropy anti-interference algorithm, sparse signal processing anti-interference algorithm, amplitude-phase analysis reconstruction algorithm, point trace filtering anti-interference algorithm and the like;
the side lobe interference suppression method comprises the following steps: side lobe cancellation, side lobe shadow masking, a point trace filtering algorithm and the like.
For passive main lobe anti-interference measures, the algorithm sequence has an influence on the anti-interference effect, and the selection of the algorithm sequence depends on the interference type researched by the system:
1) if the interference is judged to be main lobe continuous wave interference, firstly adopting a blind source separation algorithm, and after the interference suppression effect is evaluated, if further interference resistance is needed, adopting an interference suppression algorithm based on sparse processing and an interference suppression algorithm based on amplitude-phase analysis reconstruction;
2) if the interference is judged to be main lobe forwarding interference, adopting an amplitude-phase analysis reconstruction algorithm, a waveform entropy anti-interference algorithm and a trace filtering anti-interference algorithm;
3) if the interference is judged to be the composite main lobe interference 1, firstly adopting a blind source separation algorithm, and then adopting a waveform entropy anti-interference algorithm and a trace filtering anti-interference algorithm; after the interference suppression effect is evaluated, if further interference suppression is needed, an interference suppression algorithm based on sparse processing and an interference suppression algorithm based on amplitude-phase analysis reconstruction are adopted;
4) if the interference is judged to be the composite main lobe interference 2, firstly adopting a waveform entropy anti-interference algorithm and a trace filtering anti-interference algorithm, and then adopting a blind source separation algorithm; after the interference suppression effect is evaluated, if further interference suppression is needed, an interference suppression algorithm based on sparse processing and an interference suppression algorithm based on amplitude-phase analysis reconstruction are adopted;
5) if the interference is judged to be the composite main lobe interference 3, the same as 3).
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the embodiments of the present invention and not for limiting, and although the embodiments of the present invention are described in detail with reference to the above preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the embodiments of the present invention without departing from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (7)
1. The intelligent comprehensive anti-main-side lobe interference system comprises a radar antenna, a radio frequency front end and an intelligent processing unit, and is characterized in that the intelligent processing unit comprises a radar control module, a plurality of DBF modules, a comprehensive anti-interference processing module and a memory module, and an interference information characteristic library and an anti-interference algorithm strategy library are built in the memory module.
2. The intelligent integrated mainlobe jamming resistance system of claim 1, wherein: the interference information feature library stores feature parameters of interference signals extracted through a broadband detection channel and a narrowband detection channel, and a Bayesian neural network is used for intelligently classifying the detected interference signals; and for the interference signal which is verified, storing the extracted interference characteristics into an interference characteristic library, wherein the interference characteristic library is continuously updated and perfected along with the use of the interference system.
3. The intelligent integrated mainlobe interference rejection system of claim 2, wherein the interference patterns and the interference signatures directly correspond in the interference information signature library.
4. The intelligent integrated mainlobe jamming resistance system of claim 1, wherein: the comprehensive anti-interference processing module comprehensively senses interference signals, after the interference types are identified, anti-interference processing is carried out by automatically matching corresponding emission waveforms and modes by combining an anti-interference intelligent decision-making means, and corresponding active anti-interference measures and interference suppression technologies are adopted for suppressing interference according to different interference types.
5. An interference rejection method for an intelligent integrated anti-main-side lobe interference system, for operating the intelligent integrated anti-main-side lobe interference system according to claims 1-4, comprising the steps of:
step 1, a radar control module of an intelligent processing unit executes function setting on a radar antenna array surface, and a plurality of detection beams and a plurality of deception beams are formed on the radar antenna array surface;
step 2, the comprehensive anti-interference processing module detects an interference environment through a broadband detection channel and extracts interference characteristics of various interference signals by combining signals detected and received by a narrow-band detection channel; aiming at the interference signal characteristics, storing the extracted interference signal characteristics into an interference characteristic library, and establishing a corresponding relation between an interference pattern and the interference characteristics;
step 3, the comprehensive anti-interference processing module performs refined interference classification on the received interference signals, obtains corresponding interference signal characteristic information from an interference signal characteristic library, and generates anti-interference measures corresponding to the received interference signal characteristic information by using an anti-interference algorithm stored in an anti-interference algorithm library;
step 3.1, the interference signals are subjected to refined interference classification, and the interference signals are divided into ten types:
firstly, secondary lobe continuous wave interference;
the interference of the sidelobe pulses is avoided;
the first type of composite side lobe interference with continuous wave interference as a main and pulse interference as an auxiliary;
fourth, second-class composite side lobe interference with impulse interference serving as a main interference and continuous wave interference serving as an auxiliary interference is obtained;
fifthly, carrying out third composite side lobe interference with the same intensity and ratio of the continuous waves and the pulse interference;
sixthly, main lobe continuous wave interference;
main lobe forward interference;
and continuous wave interference is primary and repeated interference is secondary first-class composite main lobe interference;
a second type of composite main lobe interference with the self-skin forwarding interference as main and continuous wave interference as auxiliary;
the third type of composite main lobe interference with equivalent continuous wave and forwarding interference intensity and ratio is obtained;
step 3.2, using the interference characteristic parameters extracted by the broadband detection channel and the narrow-band detection channel as the input of a Bayesian neural network, constructing an active interference signal classifier formed by the Bayesian neural network, using the Bayesian neural network to identify active interference signals, firstly training the whole Bayesian neural network, inputting different types of interference signals into the trained Bayesian neural network, enabling the output of the network to be within a required error range, then inputting unknown interference signals into the Bayesian neural network, and distinguishing the types of the interference signals;
step 4, the comprehensive anti-interference processing module provides anti-interference measures in a self-adaptive manner according to interference environment cognition, waveform design and algorithm design, and self-adaptive comprehensive countermeasure of interference by the radar is realized;
wherein, the active anti-interference measure comprises: agility, deception (masking pulses), complex waveforms;
the main lobe interference suppression method comprises the following steps: a blind source separation algorithm, a waveform entropy anti-interference algorithm, a sparse signal processing anti-interference algorithm, an amplitude-phase analysis reconstruction algorithm and a trace point filtering anti-interference algorithm;
the side lobe interference suppression method comprises the following steps: side lobe cancellation, side lobe shadow elimination and trace point filtering algorithm;
for passive main lobe anti-interference measures, the selection of the algorithm sequence depends on the interference type judged by the system:
step 4.1, judging the interference as main lobe continuous wave interference, then
Step 4.11, adopting a blind source separation algorithm to evaluate the interference suppression effect;
step 4.12, when the interference suppression effect is lower than a threshold value, adopting an interference suppression algorithm based on sparse processing and an interference suppression algorithm based on amplitude-phase analysis reconstruction;
step 4.2, judging that the interference is main lobe forwarding interference, and adopting an amplitude-phase analysis reconstruction algorithm, a waveform entropy anti-interference algorithm and/or a trace filtering anti-interference algorithm;
step 4.3, if the interference is judged to be the first type of composite main lobe interference, the interference is judged to be the first type of composite main lobe interference
Step 4.31, adopting a blind source separation algorithm to evaluate the interference suppression effect;
step 4.32, adopting a waveform entropy anti-interference algorithm and a trace filtering anti-interference algorithm; performing interference suppression effect evaluation;
step 4.33, when the interference suppression effect is lower than a threshold value, adopting an interference suppression algorithm based on sparse processing and an interference suppression algorithm based on amplitude-phase analysis reconstruction;
step 4.4, if the interference is judged to be the second type composite main lobe interference, the interference is judged to be the second type composite main lobe interference
Step 4.41, adopting a waveform entropy anti-interference algorithm and a trace filtering anti-interference algorithm to evaluate the interference suppression effect;
step 4.42, adopting a blind source separation algorithm; performing interference suppression effect evaluation;
step 4.43, when the interference suppression effect is lower than the threshold value, adopting an interference suppression algorithm based on sparse processing and an interference suppression algorithm based on amplitude-phase analysis reconstruction;
step 4.5, judging that the interference is the third type of composite main lobe interference, and returning to the step 4.3;
and 5, the comprehensive anti-interference processing module executes interference countermeasure effect evaluation and adaptive anti-interference measure optimization, and further optimizes and adjusts anti-interference measures according to the interference countermeasure effect evaluation.
6. The tamper-resistant method of claim 5, wherein: the interference characteristic parameters in the step 2 comprise: main and side lobe decision factors, pulse pressure front time width, pulse pressure back time width, frequency, angle, interference bandwidth, interference repetition frequency, interference signal form, pulse width, arrival time, interference intra-pulse modulation parameters and variance.
7. The method of combating interference of claim 6, wherein: the step 5 of evaluating the interference countermeasure effect by the integrated anti-interference processing module comprises the following steps:
calculation of the final probability value:
P(Y|X,L)＝∫P(Y|X,W)P(W|L)dW
where Y is the output, which is a 10-dimensional vector, Y — 1, Y — 2, …, Y — 10,
y _ i represents a probability corresponding to the i-th interference pattern of the 10 interference patterns;
x denotes a sequentially connected interference characteristic parameter vector, where L denotes a training sample set,
wherein wijRepresents the weight of the jth neuron at the ith layer, NwRepresenting the total number of layers of the neural network, MiRepresenting the number of neurons of the i-th layer neural network;
p (Y | X, L) represents the conditional probability of the predicted output given the input X and the neural network weight W, and P (W | L) represents the posterior distribution with weight W, which can be expressed as
P (W) represents the distribution of weights, P (L | W) represents the likelihood function of the training sample set, and P (L) is the edge likelihood function.
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