CN113189550B - Array radar anti-noise interference method and system based on multi-unknown signal extraction - Google Patents
Array radar anti-noise interference method and system based on multi-unknown signal extraction Download PDFInfo
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Abstract
The invention discloses an array radar anti-noise interference method and system based on multi-unknown signal extraction, and relates to the technical field of radar anti-interference. The scheme of the invention comprises the following steps: establishing a received signal model according to an array structure of the radar; estimating the number of information sources of an array receiving signal of the radar; extracting multiple unknown signals to obtain a separation signal; and calculating the time domain moment kurtosis according to the separation signals, and determining a signal channel. The method is applied to radar anti-interference and is also suitable for array models with various structures. The information source number estimation is carried out firstly, DOA estimation is not needed, and the arrival direction of the expected signal and the interference does not need to be accurately obtained. And then, a multi-unknown signal extraction method is adopted, so that the waveforms of the expected signal and the interference can be obtained, and the subsequent processing is facilitated. When the array receiving data contains the expected signals, the anti-interference performance of the array receiving data is superior to that of a traditional side lobe and main lobe anti-interference method, and the larger the signal-to-noise ratio is, the better the separation effect is.
Description
Technical Field
The invention relates to the technical field of radar anti-interference, in particular to an array radar anti-noise interference method and system based on multi-unknown signal extraction.
Background
Modern information systems such as radar, navigation, communication and sonar realize the transmission of information and the detection, positioning, tracking, imaging and identification of target objects by the modulation, transmission and reception of electromagnetic waves or sound waves. For example, radar radiates energy into space by electromagnetic waves, detects an echo signal reflected by a target object, while suppressing unnecessary clutter, interference, noise, and the like in the background, and realizes detection, tracking, imaging, and the like of a target of interest. With the development of electronic information technology, the working environment of modern information systems is increasingly complex, and a large amount of active interference seriously hinders the normal work of the information systems. For an interference party, the suppression interference is widely applied because the generation mode is simple, the bandwidth and the carrier wave can be flexibly changed, and larger power can be transmitted, and the suppression interference has better interference effect on radars with various working modes. Noise type interference such as noise amplitude modulation and frequency modulation is one of the important forms of suppression type interference, and can enable target echoes to be completely submerged by interference, so that the detection of a target by a radar is seriously influenced.
In modern electronic warfare, improving the anti-interference capability of the radar is a key research task of scholars. At present, the modern information system generally adopts array antenna systems such as phased arrays, auxiliary channels and the like, and scholars propose various anti-interference algorithms aiming at different interferences. Most classical is Adaptive Beamforming (ABF), which can adaptively change weight vectors to make the main lobe of the antenna directional pattern in the desired signal direction and form nulls in the interference direction, and is widely used in the fields of radar, navigation, communication, sonar, ultrasonic diagnosis, seismic detection, etc.
However, when the training sample contains the desired signal, the phenomenon of cancellation of the desired signal occurs, and in practical situations, it is difficult to obtain the received data without the desired signal; adaptive beamforming requires that the steering vector of the desired signal be accurately known and, when there is a steering vector error, the desired signal is treated as interference suppression. In addition, when the interference is located in the main lobe, the adaptive beamforming method may cause the problems of directional diagram main lobe distortion and peak value shift, so that the output signal-to-interference-and-noise ratio is reduced. The scholars also propose a series of methods for suppressing the main lobe interference, such as a blocking matrix preprocessing algorithm (BMP), but need to know the angle information of the main lobe interference accurately, which is difficult to satisfy in practical situations and can lose the degree of freedom of the system. There are also feature projection preprocessing algorithms (EMPs) that also result in the loss of the desired signal when the received data contains the desired signal.
When the received data of the radar array contains the expected signal, a good anti-interference effect is achieved, and the method is not available in the existing anti-interference method.
Disclosure of Invention
In view of this, the invention provides an array radar anti-noise interference method and system based on multi-unknown signal extraction, which do not need to predict the direction of the expected signal and interference, and have better anti-interference performance when the received data contains the expected signal.
In order to achieve the purpose, the technical scheme of the invention is as follows: an array radar anti-noise interference method based on multi-unknown signal extraction comprises the following steps: establishing a received signal model according to an array structure of the radar; estimating the number of information sources of array receiving signals of the radar; extracting multiple unknown signals to obtain a separation signal; and calculating the time domain moment kurtosis according to the separation signals, and determining a signal channel.
Further, the method for estimating the number of the source of the array receiving signals of the radar specifically comprises the following steps:
estimating a covariance matrix of an array received signal of a radar to obtain a covariance matrix estimate
And decomposing the eigenvalue of the covariance matrix estimated value to obtain:
wherein, { λ i I =1,2, \ 8230;, M } is
M is the total number of eigenvalues, and the eigenvalue series are sorted from large to small, namely, the lambda is satisfied 1 ≥λ 2 ≥…≥λ M 。
Sequentially taking the difference value of two adjacent characteristic values to obtain eta 1 ～η M-1 And the total number of the characteristic value differences is M-1.
And if M is an even number, taking the mean value of the latter M/2 characteristic value difference values as mu, if M is an odd number, taking the mean value of the (M-1)/2 characteristic value difference values as mu, sequentially judging whether the former characteristic value difference values are greater than or equal to 2 mu from back to front, and judging that the sequence number corresponding to the first characteristic value difference value greater than or equal to 2 mu is the estimated information source number L.
Further, the method for extracting the separation signal through the multiple unknown signals specifically comprises the following steps:
(1) and aiming at the last M-L characteristic values in the characteristic value sequence as small characteristic values (the others are large characteristic values), and taking the average value of all the small characteristic values as the noise power.
(2) And constructing a whitening matrix and carrying out pre-whitening processing on the received signal.
The whitening matrix B is:
wherein L is the information source number estimated in the previous step; lambda 1 ～λ L 1 st to Lth characteristic values; u. of 1 ～u L Is λ 1 ～λ L Corresponding feature vectors;
is the noise power.
And performing pre-whitening processing on the array received signal of the radar by using the whitening matrix to obtain a pre-whitened signal Z (t).
(3) Carrying out zero equalization on the signal Z (t) after pre-whitening and solving a time delay covariance matrix R Z (τ), time delay covariance matrix
The matrix composed of the eigenvectors obtained by the characteristic decomposition is C 0 ；C 0 Is a linear permutation matrix of the unitary matrix C from which the unitary matrix C is estimated.
(4) The separation signal is Y (t) = C 0 H Z(t)；
The separated signal includes a waveform of the separated desired signal and a waveform of the noise-type interference.
And further, calculating time domain moment kurtosis according to a pulse compression result of the separation signal, if the difference between the time domain moment kurtosis and 3 is within a set range, determining the channel as a noise type interference channel, and if the difference between the time domain moment kurtosis and 3 is greater than a set threshold, determining the channel as an expected signal channel.
Further, a received signal model is established according to the array structure of the radar, specifically:
X(t)＝A(θ)S(t)+N(t)
wherein X (t) is a received signal and t is a time variable; a (theta) is a steering matrix, and theta is a wave arrival angle; s (t) is a combined waveform of the expected signal and the interference; n (t) is a noise signal.
Has the advantages that:
the method is applied to radar anti-interference and is also suitable for array models with various structures. Firstly, the information source number estimation is carried out, DOA estimation is not needed, and the arrival direction of the expected signal and the interference does not need to be accurately obtained. And then, a multi-unknown signal extraction method is adopted, so that the waveforms of the expected signal and the interference can be obtained, and the subsequent processing is facilitated. When the array received data contains the expected signals, the anti-interference performance of the array received data is superior to that of the traditional side lobe and main lobe anti-interference method, and the larger the signal-to-noise ratio is, the better the separation effect is.
Drawings
FIG. 1 is a flow chart of an array radar anti-noise interference method based on multi-unknown signal extraction according to the invention;
FIG. 2 is a time domain waveform and a pulse pressure waveform of a received signal before interference rejection;
FIG. 3 is a time domain waveform and a pulse pressure waveform of an anti-interference signal channel;
FIG. 4 is a graph of detection probability versus interference azimuth;
fig. 5 is a plot of interference rejection ratio versus interference azimuth;
fig. 6 is a graph of different immunity methods.
Detailed Description
The invention is described in detail below by way of example with reference to the accompanying drawings.
The invention provides an array radar anti-noise interference method based on multi-unknown signal extraction. Firstly, a signal model is established according to array element distribution to obtain array receiving data. Calculating a sampling covariance matrix according to array receiving data, decomposing an eigenvalue, and estimating the number of information sources by setting a threshold according to the obtained eigenvalue so as to determine the number of separated channels; then, pre-whitening is carried out on the received data by utilizing a multi-unknown signal extraction method, and the unitary matrix is estimated, so that waveform data of each separation channel is obtained; and finally, determining a signal channel and an interference channel through the time domain moment kurtosis parameter. The invention is suitable for electronic defense application such as multi-channel radar anti-interference in the field of electronic countermeasure, and can also be applied to the anti-interference field of information systems such as navigation, sonar and communication.
The invention is described in detail below by way of example with reference to the accompanying drawings.
The anti-noise interference method for the array radar is characterized in that a processing flow chart is shown in figure 1, and the method comprises the following specific steps:
the method is simultaneously suitable for array models with various structures such as a one-dimensional linear array, a two-dimensional area array and the like, and a signal model is constructed by taking a one-dimensional uniform linear array as an example.
Assuming that there is a uniform linear array composed of M antenna elements, the distance between each element is d, the far-field incoming wave signal is a narrow-band signal, the wavelength of the signal is λ, and the arrival angle of the signal is θ, then the steering vector of the incident signal is:
the array receiving signal comprises three parts of a desired signal, interference and noise, and the existence of a desired signal s is assumed 0 (t) the arrival angle of which is θ 0 Total P interferers s p (t), P is more than or equal to 1 and less than or equal to P, and the arrival angle is theta p P is more than or equal to 1 and less than or equal to P, and the noise is independent white Gaussian noise, the array received signal can be expressed as
Wherein X s (t)、X p (t)、X n (t) represents a desired signal, interference and noise, respectively, a (θ) 0 ) Is the steering vector of the desired signal, a (θ) p ) And P is more than or equal to 1 and less than or equal to P represents the guide vector of the P-th interference. The array received signal models may be unified into
X(t)＝A(θ)S(t)+N(t)
Wherein the steering matrix A (theta) = [ a (theta) = 0 ),a(θ 1 ),…,a(θ P )](ii) a Waveform S (t) = [ S ] composed of desired signal and interference 0 (t),s 1 (t),…,s P (t)] T ；
because the number of information sources cannot be known in actual conditions, information source number estimation needs to be carried out on received data, and the method provides an information source number estimation method based on covariance matrix eigenvalues.
Under ideal conditions, the array receive covariance matrix is
R X Is an array receive covariance matrix, R s Is a covariance matrix, R, of the desired signal received by the array i+n Is a covariance matrix of interference plus noise;
signal power, interference power, noise power, I-identity matrix;
in practical cases, however, the covariance matrix can only be estimated from the snapshot data
Wherein K is the number of snapshots; x (n) receive signal sample values.
Performing eigenvalue decomposition on the estimated covariance matrix to obtain
Wherein λ is m M =1,2, \ 8230;, M is
And satisfies lambda 1 ≥λ 2 ≥…≥λ M Namely, the eigenvalue sequence is arranged according to the sequence from big to small; generally, the eigenvalues of the signal corresponding to interference are larger than the eigenvalues corresponding to noise.
Calculating the difference between two adjacent characteristic values
η i ＝λ i -λ i-1 ,i＝1,2,…,M-1
Assume M is an even number, order
Is last M/2 eta i Mean of (2) to 1 η of the preceding M/2 i Sequentially judging whether the requirements are met from back to front
η i ≥2μ；
When M is an odd number, the value is the mean value of the difference values of the last (M-1)/2 characteristic values.
First η satisfying the above formula i The corresponding i is the estimated information source number L; l = P +1, the total number of desired signals and interference.
the multi-unknown signal extraction refers to the recovery of each relatively independent source signal s from the array observation signal X (t) i (t) Process.
(1) Firstly, the mean value of small eigenvalues obtained in the characteristic decomposition is used to estimate the eigenvalue corresponding to the noise, namely the noise power
L is the estimated information source number, the last M-L eigenvalues are ranked as small eigenvalues,
the large eigenvalues remain
(2) And constructing a whitening matrix and carrying out pre-whitening processing on the received signal.
Whitening matrix is
The pre-whitened signal is
Z(t)＝BX(t)＝B(AS(t)+N(t))＝CS(t)+BN(t)
Where C = BA is a unitary matrix.
(3) Estimating unitary matrix C
Carrying out zero equalization on the signal Z (t) after pre-whitening to obtain
Selecting a time delay τ > 0, for->
Time delay covariance matrix
Wherein K is the number of snapshots; in receiving discrete data, τ =1 may be generally taken.
To R Z (tau) is decomposed into characteristic values
The obtained characteristic vector constitutes a matrix C 0 Is a linear permutation matrix of the unitary matrix C, i.e. C 0 The order of each column with C may not be the same and each column may be scaled. The Λ eigenvalues form a diagonal matrix.
(4) Obtaining a separation signal
The separation signal is:
wherein Y (t) = [ Y = 1 (t),y 2 (t),…,y L (t)] T Including the waveform of the separated desired signal and the waveform of the noisy disturbance.
And 4, calculating the time domain moment kurtosis and determining a signal channel.
Each channel obtained by separating the signals comprises an expected signal and noise-type interference, and the expected signal and the noise-type interference are distinguished by adopting time domain moment kurtosis. The time domain waveform after the expected signal pulse compression can have a peak value, and the time domain waveform after the noise type interference pulse compression is relatively stable, so that the signals of each separation channel are subjected to pulse compression, and the time domain moment kurtosis of the signals is calculated
Wherein, X is an input signal for calculating the time domain moment kurtosis, and in the embodiment of the present invention, X is a pulse compression result Y of the separated signal obtained in the previous step pc (t)＝[y pc1 (t),y pc2 (t),…,y pcL (t)] T Wherein y is pcl (t) is for the l signal y l (t) as a result of the pulse compression; μ is the mean value of X; σ is the standard deviation of X.
When the noise-type interference follows gaussian distribution, the difference between the time domain moment kurtosis and 3 is within a set range (i.e. the time domain moment kurtosis is approximately equal to 3, and the difference between the time domain moment kurtosis and 3 is small), and the noise-type interference does not change greatly with the dry-to-noise ratio, and if the difference between the time domain moment kurtosis and 3 of the expected signal is greater than a set threshold (specifically, the time domain moment kurtosis is a numerical value far greater than 3), the expected signal and the interference can be distinguished, so that a signal channel is determined for later detection. Meanwhile, the approximate waveform of the interference can be obtained, and the subsequent research on the interference is facilitated. And if the difference value between the time domain moment kurtosis and 3 is within a set range, the channel is a noise type interference channel, and if the difference value between the time domain moment kurtosis and 3 is greater than a set threshold value, the channel is an expected signal channel.
The embodiment of the invention also provides an array radar anti-noise interference system based on multi-unknown signal extraction, which comprises a model construction module, an information source number estimation module, a separation signal extraction module and a signal channel determination module. The above functional modules may be functional modules integrated on a processor or FPGA chip on a computer.
The model building module is used for building a received signal model according to the array structure of the radar, and the received signal model is input to the information source number estimation module;
the signal source number estimation module is used for determining array receiving signals of the radar according to the receiving signal model, estimating the signal source number by using the array receiving signals of the radar, and inputting the estimated signal source number to the separation signal extraction module;
the separation signal extraction module is used for extracting separation signals through multiple unknown signals by combining the estimated information source number, and the separation signals are input to the signal channel determination module;
and the signal channel determining module is used for calculating the time domain moment kurtosis according to the separation signal and determining a signal channel.
The embodiment of the present invention further provides a computer-readable storage medium, on which computer instructions are stored, where the computer instructions, when executed by a processor, can implement the steps in the method for detecting accuracy of business opportunity data provided in the foregoing embodiment. In practical applications, the computer readable medium may be included in the apparatus/device/system described in the above embodiments, or may exist alone without being assembled into the apparatus/device/system. The computer-readable storage medium carries one or more programs.
According to embodiments disclosed herein, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example and without limitation: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing, without limiting the scope of the present disclosure. In the embodiments disclosed herein, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
In addition, the method steps described in this application may be implemented by hardware, for example, logic gates, switches, application Specific Integrated Circuits (ASICs), programmable logic controllers, embedded microcontrollers, and the like, in addition to data processing programs. Such hardware capable of implementing the methods described herein may also constitute the present application.
Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not explicitly recited in the present application. In particular, the features recited in the various embodiments and/or claims of the present application may be combined and/or coupled in various ways, all of which fall within the scope of the present disclosure, without departing from the spirit and teachings of the present application.
Example one
In order to verify the anti-noise type interference method for the array radar based on multi-unknown signal extraction, anti-interference processing is carried out on actual measurement data obtained by an actual radar and an interference machine. The radar array is a two-dimensional area array and is divided into 12 sub-array channels, radar transmission signals are linear frequency modulation signals, and interference transmitted by an interference machine is noise amplitude modulation type interference. The difference between the azimuth of the desired signal and the interference is 2 °, which is the main lobe interference. The experimental parameters are shown in the following table.
Table 1 experimental parameter settings
Fig. 2 is a time domain waveform and a pulse pressure waveform of a received signal before interference resistance, wherein (a) in fig. 2 is a single-channel time domain waveform of the received signal, and (b) in fig. 2 is a single-channel pulse pressure diagram of the received signal; fig. 3 shows an anti-interference signal channel time domain waveform and a pulse pressure waveform, where (a) in fig. 3 is the anti-interference signal channel time domain waveform, and (b) in fig. 3 is the anti-interference signal channel pulse pressure waveform. It can be seen that before interference rejection, the power is too low, and the desired signal is still buried by the noise-type interference after pulse compression, and cannot be detected. After interference resistance, the power of the interference is obviously weakened, the waveform of the expected signal is shown, a high peak value appears in the expected signal after pulse compression, and the expected signal can be detected by CFAR. The interference rejection ratio before and after interference resistance is calculated to be 34.8dB, and the interference resistance performance is good.
Changing the azimuth angle of the interference, keeping other parameters unchanged, carrying out CFAR detection on the signal channel subjected to interference resistance, counting the change of the detection probability and the interference rejection ratio along with the azimuth angle of the interference, and carrying out Monte Carlo simulation for 500 times. Fig. 4 is a diagram of a relationship between a detection probability and an interference azimuth, and it can be seen that, under a main lobe condition, when the interference azimuth is greater than 1.2 °, the detection probability is higher than 80%, and under a side lobe condition, the detection probability can almost reach 100%. Fig. 5 is a diagram of interference suppression ratio versus azimuth angle of interference, and it can be seen that in the main lobe condition, the interference suppression ratio gradually increases with the increase of the azimuth angle of interference, and when the azimuth angle is greater than 1 °, the interference suppression ratio is greater than 20dB, and in the side lobe condition, the interference suppression ratio can be higher than 30dB. In conclusion, the method has better anti-interference performance under the conditions of the main lobe and the side lobe.
Example two
In order to compare the performance of the method of the present invention with the performance of the conventional adaptive beamforming method, the following simulation experiment was designed: the simulation adopts a uniform linear array, and the simulation parameters are shown in the following table.
Table 2 simulation parameter settings
Fig. 6 is a diagram of Conventional Beamforming (CBF), adaptive beamforming with and without desired signals (ABF), and the patterns of the method of the present invention. The following table shows the output signal-to-noise ratio (SNR) versus the output signal-to-interference-and-noise ratio (SINR) for different interference rejection methods. For output SNR, ABF (no desired signal) and the inventive method are slightly lower than CBF because they form nulls in the main beam to suppress main lobe interference, resulting in peak shifts and hence a slight decrease in gain in the direction of the desired signal, whereas ABF (with desired signal) undergoes desired signal cancellation and hence the output SNR is lowest. For the output SINR, CBF has the lowest SINR since it has no interference suppression capability, whereas ABF (no desired signal) and the output SINR of the method of the present invention are approximately equal to the output SNR and form a deep null in the interference direction, indicating that the interference can be completely suppressed. In practical situations, the received signal often contains a desired signal, and the cancellation of the desired signal can happen by using the ABF, so in practical situations, the anti-interference performance of the method is better than that of the ABF.
TABLE 3 comparison of output SNR and output SINR for different methods
| Output SNR | Output SINR | |
| CBF | 32.1dB | -12.4dB |
| No expected signal for ABF | 28.7dB | 28.7dB |
| ABF has desired signal | 16.6dB | 16.5dB |
| The method of the invention | 28.6dB | 28.0dB |
In summary, the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (4)
1. The array radar anti-noise interference method based on multi-unknown signal extraction is characterized by comprising the following steps of:
establishing a received signal model according to an array structure of the radar;
estimating the number of information sources of array receiving signals of the radar;
extracting multiple unknown signals to obtain a separation signal;
calculating time domain moment kurtosis according to the separation signals, and determining a signal channel;
the method for estimating the number of the information sources of the array receiving signals of the radar specifically comprises the following steps:
estimating a covariance matrix of an array received signal of a radar to obtain a covariance matrix estimated value
And decomposing the eigenvalue of the covariance matrix estimated value to obtain:
wherein, { lambda ] i I =1,2, \8230, M } is
M is the total number of eigenvalues, and the eigenvalue series are sorted from large to small, namely, the lambda is satisfied 1 ≥λ 2 ≥…≥λ M ；
Sequentially taking the difference value of two adjacent characteristic values to obtain eta 1 ～η M-1 The total number of the characteristic value differences is M-1;
if M is an even number, taking the mean value of the following M/2 characteristic value difference values as mu, if M is an odd number, taking the mean value of the following (M-1)/2 characteristic value difference values as mu, sequentially judging whether the previous characteristic value difference values meet the requirement that the previous characteristic value difference values are more than or equal to 2 mu from back to front, and judging that the sequence number corresponding to the obtained first characteristic value difference value more than or equal to 2 mu is the estimated information source number L.
2. The method of claim 1, wherein the extracting the separate signal from the multiple unknown signals comprises:
(1) aiming at the M-L characteristic values sequenced in the characteristic value sequence, the characteristic values are small characteristic values, the others are large characteristic values, and the mean value of all the small characteristic values is used as the noise power;
(2) constructing a whitening matrix, and performing pre-whitening processing on a received signal;
the whitening matrix B is:
wherein L is the information source number estimated in the previous step; lambda [ alpha ] 1 ～λ L 1 st to Lth characteristic values; u. of 1 ～u L Is λ 1 ～λ L Corresponding feature vectors;
is the noise power;
carrying out pre-whitening processing on an array receiving signal of the radar by using the whitening matrix to obtain a pre-whitened signal Z (t);
(3) carrying out zero equalization on the pre-whitened signal Z (t), and solving a time delay covariance matrix R Z (τ), the time-delay covariance matrix R Z The matrix composed of eigenvectors obtained by (tau) eigen decomposition is C 0 ；C 0 Is a linear permutation matrix of the unitary matrix C, from which the unitary is estimatedA matrix C;
(4) the separation signal is Y (t) = C 0 H Z(t)；
The separation signal includes a waveform of the separated desired signal and a waveform of the noise-type interference.
3. The method of claim 2, wherein said computing a time domain kurtosis from said separated signals determines a signal path by:
and pulse compression is carried out on the separated signals, time domain moment kurtosis is obtained, if the difference value between the time domain moment kurtosis and 3 is within a set range, a noise type interference channel is formed, and if the difference value between the time domain moment kurtosis and 3 is larger than a set threshold value, an expected signal channel is formed.
4. The array radar anti-noise interference system based on multi-unknown signal extraction is characterized by comprising a model building module, an information source number estimation module, a separation signal extraction module and a signal channel determination module:
the model building module is used for building a received signal model according to the array structure of the radar, and the received signal model is input to the information source number estimation module;
the signal source number estimation module is used for determining array receiving signals of the radar according to the receiving signal model, estimating the signal source number by using the array receiving signals of the radar, and inputting the estimated signal source number to the separation signal extraction module;
the separation signal extraction module is used for extracting separation signals through multiple unknown signals by combining the estimated information source number, and the separation signals are input to the signal channel determination module;
the signal channel determining module is used for calculating time domain moment kurtosis according to the separation signal and determining a signal channel;
the method for estimating the number of the information sources by using the array receiving signals of the radar specifically comprises the following steps:
the covariance matrix of the array receiving signals of the radar is estimatedCovariance matrix estimate
And decomposing the eigenvalue of the covariance matrix estimated value to obtain:
wherein, { lambda ] i I =1,2, \8230, M } is
M is the total number of eigenvalues, and the eigenvalue series are sorted from large to small, namely, the lambda is satisfied 1 ≥λ 2 ≥…≥λ M ；
Sequentially taking the difference value of two adjacent characteristic values to obtain eta 1 ～η M-1 The total number of the characteristic value differences is M-1;
if M is an even number, taking the mean value of the following M/2 characteristic value difference values as mu, if M is an odd number, taking the mean value of the following (M-1)/2 characteristic value difference values as mu, sequentially judging whether the previous characteristic value difference values meet the requirement that the previous characteristic value difference values are more than or equal to 2 mu from back to front, and judging that the sequence number corresponding to the obtained first characteristic value difference value more than or equal to 2 mu is the estimated information source number L.
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