阅读说明：本技术 一种基于独立成分分析的盲源分离实时抗主瓣干扰方法 (Blind source separation real-time main lobe interference resisting method based on independent component analysis ) 是由 黄柏圣 刘光杰 李宝龙 于 2021-09-22 设计创作，主要内容包括：本发明公开了一种基于独立成分分析的盲源分离实时抗主瓣干扰方法,属于雷达抗干扰信号处理技术领域。其利用雷达M个子阵输入通道AD信号,首先应用基于ICA的盲源分离处理获取主瓣干扰的参考信号,同时应用M通道子阵信号合成和通道信号数据、方位差通道信号数据及俯仰差通道信号数据,然后对和通道信号数据、方位差通道信号数据及俯仰差通道信号数据进行干扰对消完成干扰抑制,利于后续雷达目标检测跟踪。本发明利用子阵通道的高自由度,通过导向矢量形成多个角度的和通道数据,可同时抑制主瓣干扰,稳健实时性好。(The invention discloses a blind source separation real-time main lobe interference resisting method based on independent component analysis, and belongs to the technical field of radar anti-interference signal processing. According to the method, M subarrays of a radar are used for inputting channel AD signals, firstly, ICA-based blind source separation processing is applied to obtain reference signals of main lobe interference, M channel subarray signal synthesis and channel signal data, azimuth difference channel signal data and pitch difference channel signal data are applied, then interference cancellation is carried out on the sum channel signal data, the azimuth difference channel signal data and the pitch difference channel signal data to complete interference suppression, and follow-up radar target detection and tracking are facilitated. The invention utilizes the high degree of freedom of the subarray channel to form sum channel data of a plurality of angles through the guide vector, can simultaneously restrain the main lobe interference, and has robustness and good real-time performance.)

1. A blind source separation real-time main lobe interference resisting method based on independent component analysis is characterized by comprising the following steps:

step 1, radar receiving subarray echo signal data:

m sub-arrays are arranged in a radar to receive radar echo signal data, and the radar echo signal data received by the sub-arrays comprise interference signal data; wherein M is the total number of sub-arrays in the radar and satisfies the power of 2;

step 2, acquiring signal data of a sum channel, a azimuth difference channel and a pitch difference channel:

multiplying the M sub-array received radar signals by respective sub-array guide vectors, eliminating the influence of the wave path difference and obtaining M channel signal data d_MMultiplying the sum, the azimuth difference and the pitching difference weight matrix to obtain signal data of a sum channel, an azimuth difference channel and a pitching difference channel;

step 3, obtaining main lobe interference signal estimation data by adopting a blind source separation method of Independent Component Analysis (ICA);

step 4, interference cancellation is respectively carried out on the sum channel, the azimuth difference channel and the pitch difference channel by utilizing the interference signal estimation data;

and 5, estimating and weighting the interference signal by using the cancellation weight to obtain a main lobe interference suppression result of the sum channel, the azimuth difference channel and the pitch difference channel.

2. The blind source separation real-time mainlobe interference resisting method according to claim 1, wherein in step 2, the sum weight matrix, the azimuth difference weight matrix and the pitch difference weight matrix are multiplied to obtain signal data of a sum channel, an azimuth difference channel and a pitch difference channel as follows:

d_M＝[d₁，d₂，L，d_M]′

s_sum＝[1，1，1，1，1，1，1，1.1，L，1，1，1，1，1，1，1]*d_M

s_a＝[1，1，1，1，1，L，1，1，1，-1，-1，-1，-1，-1，L，-1，-1，-1]*d_M

S_e＝[1，-1，1，1，-1，-1，L，1，-1，1，1，-1，-1，L，1，-1，1，-1]*d_M

wherein s is_sumFor sum channel signal data, s_aFor azimuth channel informationNumber data, s_eIs pitch difference channel signal data.

3. The blind source separation real-time main lobe interference resisting method according to claim 2, wherein in step 3, the blind source separation method using Independent Component Analysis (ICA) obtains the main lobe interference signal estimation data by the following steps:

1) performing cross-correlation operation on the azimuth difference channel signal data, the pitch difference channel signal data and the sum channel signal data to obtain the separation weight of blind source separation;

w_sum＝0

w_a＝∑conj(s_a)*-s_sum/∑conj(S_sum)*s_sum

w_e＝∑conj(s_e)*s_sum/∑conj(s_sum)*S_sum

wherein, w_sumTo separate from the channel, w_aIs the channel separation power of azimuth difference, w_eFor the pitch difference channel separation weight, conj represents taking the complex conjugate;

2) respectively carrying out weighted summation on the separation weight and the sum channel signal data, the azimuth difference channel signal data and the pitch difference channel signal data to obtain an interference signal estimation value J_E：

J_E＝w_a*s_a+w_e*s_e

Wherein, J_EIs an interference signal estimate.

4. The blind source separation real-time mainlobe interference resisting method according to claim 3, wherein in step 4, the specific steps of utilizing the interference signal estimation data to respectively perform interference cancellation on the sum channel, the azimuth difference channel and the pitch difference channel are as follows:

1) performing cross-correlation operation on the interference estimation value, sum channel signal data, azimuth difference channel signal data and pitch difference channel signal data to obtain the following cancellation power;

W_sc＝∑conj(s_sum)*J_E/∑conj(J_E)*J_E

w_ac＝∑conj(s_a)*J_E/∑conj(J_E)*J_E

W_ec＝∑conj(s_e)*J_E/∑conj(J_E)*J_E

wherein, w_scFor sum channel cancellation, w_acFor azimuth channel depoise, w_ecAnd the right is cancelled for the pitch difference channel.

2) Weighting the interference signals by using the cancellation weights to obtain interference estimation results of each corresponding channel;

J_Es＝w_sc*J_E

J_Ea＝w_ac*J_E

J_Ee＝w_ec*J_E

wherein, J_EsFor sum channel interference estimation, J_EaAs an estimate of the azimuth difference channel interference, J_EeIs the estimated value of the channel interference of the pitch difference.

5. The blind source separation real-time mainlobe interference rejection method according to claim 4, wherein in step 5, the mainlobe interference suppression result is as follows:

o_sum＝s_sum-J_Es

o_a＝S_a-J_Ea

o_e＝S_e-J_Ee

wherein o is_sumFor sum channel main lobe interference suppression results, o_aAs a result of the azimuth difference channel main lobe interference suppression, o_eObtaining a main lobe interference suppression result of a pitch difference channel;

6. the blind source separation real-time mainlobe interference resisting method according to claim 5, wherein the step 5 further comprises performing pulse pressure, MTD, CFAR processing on the interference-cancelled sum channel signal data, azimuth difference channel signal data, and pitch difference channel signal data to obtain distance angle information of a target, and the specific steps are as follows:

1) constructing a distance pulse pressure function, and carrying out pulse pressure processing on the sum channel signal data, the azimuth difference channel signal data and the pitch difference channel signal data:

o_sp＝IFFT{FFT(o_sum(n，m))·FFT[x(n)·h(n)]}

o_ap＝IFFT{FFT(o_a(n，m))·FFT[x(n)·h(n)]}

o_ep＝IFFT{FFT(o_e(n，m))·FFT[x(n)·h(n)]}

wherein o is_spFor channel-to-channel pulse pressure data, o_apIs azimuth difference channel distance pulse pressure data, o_epIs the pitch difference channel distance pulse pressure data, x (n) ═ rect (n.DELTA.t/T)_p)exp(-jπbn²Δt²) As a function of pulse pressure, h (n) ═ 1-cos (2 π n/(n +1))]The/2 is a Hanning window function, b is a linear frequency modulation, delta t is a sampling interval, n is a distance gate number, and m is a pulse sequence number;

2) performing FFT on the sum channel, the azimuth difference channel and the pitch difference channel distance pulse pressure data in the azimuth direction, namely completing the MTD processing of moving target detection;

o_sp1＝FFT{o_sp}，o_ap1＝FFT{o_ap}，o_ep1＝FFT{o_ep}；

3) constant false alarm detection CFAR processing

The CFAR processing mode adopts unit average processing, namely a fast threshold, and the shutter limit CFAR is realized by adopting a method of unit average selection, namely, a unit near a detection point is taken as a reference unit, the average value of a left reference unit and a right reference unit is selected as a threshold, and the reference unit is D_i、D_i-1、D_i+1After the average value of the left reference unit and the right reference unit is selected to be large, the left reference unit and the right reference unit output judgment through a comparator;

4) obtaining a distance angle of a target

The target distance is the distance corresponding to the distance between the distance door and the front edge of the distance door, and the azimuth difference and the pitch difference angle are calculated by the following formula:

wherein d is_∑To sum the channel target value, d_ΔaIs the azimuth difference channel target value, d_ΔeIs the pitch difference target value.

Technical Field

The invention relates to a blind source separation real-time main lobe interference resisting method based on independent component analysis, and belongs to the technical field of radar anti-interference signal processing.

Background

Many array processing techniques rely on digital models of the array response matrix, for which parameters can be obtained by physical modeling assumptions or direct measurements of the array. However, in many experimental applications, the values of these parameters are often not available, in which case the separation of the multiple independent sources is studied from a new point of view, completely departing from the modeling of the phenomenon of signal physics transmission, i.e. without giving the mixing matrix elements any meaning of physical constants. This is the main direction of investigation for blind source separation.

Blind source separation is the recovery of a signal of interest from observations of a set of unknown source signals mixed to produce the signal. In the design of the existing radar, the interference from the secondary main lobe of the side lobe is mainly considered, and the interference from the secondary main lobe of the side lobe is suppressed by adopting methods such as side lobe hiding shadow, side lobe cancellation and the like. For interference entering from a radar main lobe, because the distance between the interference and a signal is very short, the interference can not be separated in a frequency domain and a space domain by using a traditional self-adaption method, and can not be inhibited by using a traditional side lobe hiding method and a traditional side lobe cancellation method.

At present, the domestic research on the blind signal separation problem makes great progress in the aspects of theory and application, but a plurality of problems are still needed to be further researched and solved. The existing blind source separation method can realize the separation of signals and interference to a certain extent, but has large calculation amount, is very unfavorable for a radar with precious time resources, and is not favorable for radar target detection and stable tracking.

Disclosure of Invention

The invention aims to provide a blind source separation real-time main lobe interference resisting method based on independent component analysis aiming at the defects of the prior art method, so that the radar target tracking stability and precision under the condition of main lobe interference are improved, the calculation amount is reduced, the algorithm robustness and real-time performance are improved, and the engineering realization is facilitated.

The technical scheme of the invention is as follows:

in order to realize the purpose of the invention, the technical idea scheme is as follows: the method comprises the steps of inputting channel AD signals by using 16 sub-arrays of a radar, firstly, obtaining reference signals of main lobe interference by applying ICA-based blind source separation processing, simultaneously, applying 16-channel sub-array signal synthesis and beam main channel signals, and then, carrying out interference cancellation on the main channel signals to finish interference suppression, thereby being beneficial to follow-up radar target detection and tracking.

The invention relates to a blind source separation real-time main lobe interference resisting method based on independent component analysis, which comprises the following steps of:

step 1, radar receiving subarray echo signal data:

m sub-arrays are arranged in a radar to receive radar echo signal data, and the radar echo signal data received by the sub-arrays comprise interference signal data; wherein M is the total number of sub-arrays in the radar and satisfies the power of 2;

step 2, acquiring signal data of a sum channel, a azimuth difference channel and a pitch difference channel:

multiplying the M sub-array received radar signals by respective sub-array guide vectors, eliminating the influence of the wave path difference and obtaining M channel signal data d_MMultiplying the sum, the azimuth difference and the pitching difference weight matrix to obtain signal data of a sum channel, an azimuth difference channel and a pitching difference channel;

step 3, obtaining main lobe interference signal estimation data by adopting a blind source separation method of Independent Component Analysis (ICA);

step 4, interference cancellation is respectively carried out on the sum channel, the azimuth difference channel and the pitch difference channel by utilizing the interference signal estimation data;

and 5, estimating and weighting the interference signal by using the cancellation weight to obtain a main lobe interference suppression result of the sum channel, the azimuth difference channel and the pitch difference channel.

Further, in step 2, the sum weight matrix, the azimuth difference weight matrix, and the pitch difference weight matrix are multiplied to obtain signal data of a sum channel, an azimuth difference channel, and a pitch difference channel as follows:

d_M＝[d₁，d₂，L，d_M]′

S_sum＝[1，1，1，1，1，1，1，1，1，L，1，1，1，1，1，1，1]*d_M

s_a＝[1，1，1，1，1，L，1，1，1，-1，-1，-1，-1，-1，L，-1，-1，-1]*d_M

s_e＝[1，-1，1，1.-1，-1，L，1，-1，1，1，-1，-1，L，1，-1，1，-1]*d_M

wherein s is_sumIs sum channel signal data，s_aFor azimuth difference channel signal data, s_eIs pitch difference channel signal data.

Further, in step 3, the step of obtaining the main lobe interference signal estimation data by using the blind source separation method of independent component analysis ICA is as follows:

1) performing cross-correlation operation on the azimuth difference channel signal data, the pitch difference channel signal data and the sum channel signal data to obtain the separation weight of blind source separation;

w_sum＝0

w_a＝∑conj(s_a)*s_sum/∑conj(s_sum)*s_sum

w_e＝∑conj(s_e)*s_sum/∑conj(s_sum)*s_sum

wherein, w_sumTo separate from the channel, w_aIs the channel separation power of azimuth difference, w_eFor the pitch difference channel separation weight, conj represents taking the complex conjugate;

2) respectively carrying out weighted summation on the separation weight and the sum channel signal data, the azimuth difference channel signal data and the pitch difference channel signal data to obtain an interference signal estimation value J_E：

J_E＝w_a*s_a+w_e*s_e

Wherein, J_EIs an interference signal estimate.

Further, in step 4, the specific steps of performing interference cancellation on the sum channel, the azimuth difference channel, and the pitch difference channel respectively by using the interference signal estimation data are as follows:

1) performing cross-correlation operation on the interference estimation value, sum channel signal data, azimuth difference channel signal data and pitch difference channel signal data to obtain the following cancellation power;

w_sc＝∑conj(s_sum)*J_E/∑conj(J_E)*J_E

w_ac＝∑conj(s_a)*J_E/∑conj(J_E)*J_E

w_ec＝∑conj(s_e)*J_E/∑conj(J_E)*J_E

wherein, w_scFor sum channel cancellation, w_acFor azimuth channel depoise, w_ecAnd the right is cancelled for the pitch difference channel.

2) Weighting the interference signals by using the cancellation weights to obtain interference estimation results of each corresponding channel;

J_Es＝w_sc*J_E

J_Ea＝w_ac*J_E

J_Ee＝w_ec*J_E

wherein, J_EsFor sum channel interference estimation, J_EaAs an estimate of the azimuth difference channel interference, J_EeIs the estimated value of the channel interference of the pitch difference.

Further, in step 5, the main lobe interference suppression result is as follows:

o_sum＝s_sum-J_Es

o_a＝S_a-J_Ea

o_e＝s_e-J_Ee

wherein o is_sumFor sum channel main lobe interference suppression results, o_aAs a result of the azimuth difference channel main lobe interference suppression, o_eObtaining a main lobe interference suppression result of a pitch difference channel;

further, step 5 further includes performing pulse pressure, MTD, and CFAR processing on the interference-cancelled sum channel signal data, azimuth difference channel signal data, and pitch difference channel signal data to obtain distance angle information of the target, specifically including the following steps:

1) constructing a distance pulse pressure function, and carrying out pulse pressure processing on the sum channel signal data, the azimuth difference channel signal data and the pitch difference channel signal data:

o_sp＝IFFT{FFT(o_sum(n，m))·FFT[x(n)·h(n)]}

o_ap＝IFFT{FFT(o_a(n，m))·FFT[x(n)·h(n)]}

o_ep＝IFFT{FFT(o_e(n，m))·FFT[x(n)·h(n)]}

wherein the content of the first and second substances,o_spfor channel-to-channel pulse pressure data, o_apIs azimuth difference channel distance pulse pressure data, o_epIs the pitch difference channel distance pulse pressure data, x (n) ═ rect (n.DELTA.t/T)_p)exp(-jπbn²Δt²) As a function of pulse pressure, h (n) ═ 1-cos (2 π n/(n +1))]The/2 is a Hanning window function, b is a linear frequency modulation, delta t is a sampling interval, n is a distance gate number, and m is a pulse sequence number;

2) performing FFT on the sum channel, the azimuth difference channel and the pitch difference channel distance pulse pressure data in the azimuth direction, namely completing the MTD processing of moving target detection;

o_sp1＝FFT{o_sp}，o_ap1＝FFT{o_ap}，o_ep1＝FFT{o_ep}；

3) constant false alarm detection CFAR processing

The CFAR processing mode adopts unit average processing, namely a fast threshold, and the shutter limit CFAR is realized by adopting a method of unit average selection, namely, a unit near a detection point is taken as a reference unit, the average value of a left reference unit and a right reference unit is selected as a threshold, and the reference unit is D_i、D_i-1、D_i+1After the average value of the left reference unit and the right reference unit is selected to be large, the left reference unit and the right reference unit output judgment through a comparator;

4) obtaining a distance angle of a target

The target distance is the distance corresponding to the distance between the distance door and the front edge of the distance door, and the azimuth difference and the pitch difference angle are calculated by the following formula:

wherein d is_∑To sum the channel target value, d_ΔaIs the azimuth difference channel target value, d_ΔeIs the pitch difference target value.

Advantageous effects

(1) According to the invention, the high degree of freedom of the subarray channel is utilized, and sum channel signal data of a plurality of angles is formed through the guide vector, so that main lobe interference of different angles can be inhibited at the same time;

(2) the method utilizes sum channel signal data of a plurality of angles, has rich sample information, can effectively extract interference signals, and has the advantages of good stability and real-time performance and high main lobe interference rejection ratio as shown by a simulation result;

(3) the blind source separation real-time main lobe interference resisting method based on the independent component analysis has the advantages of simple algorithm, low calculation amount and high real-time performance, and is beneficial to engineering realization.

Drawings

FIG. 1 is a flow chart of an implementation of a blind source separation real-time mainlobe interference resisting method based on independent component analysis according to the present invention;

FIG. 2 is a flow chart of ICA blind source separation in FIG. 1;

FIG. 3 is a block diagram of a unit-averaged CFAR process;

FIG. 4 shows the interference data of the sum channel, the azimuth difference channel and the pitch difference channel;

FIG. 5 shows the input signals of the sum channel, the azimuth difference channel, and the pitch difference channel;

FIG. 6 shows the input noise input signals of the sum channel, the azimuth difference channel, and the pitch difference channel;

FIG. 7 is a schematic diagram of blind source separation results;

FIG. 8 shows the interference suppression output signals of the sum channel, the azimuth difference channel, and the pitch difference channel;

fig. 9 shows the pulse pressure output before and after channel interference suppression.

Detailed Description

The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.

It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.

The invention discloses a blind source separation real-time main lobe interference resisting method based on independent component analysis, which comprises the following specific implementation steps as shown in figures 1 and 2:

step 1, radar receiving subarray echo signal data:

m sub-arrays are arranged in a radar to receive radar echo signal data, and the radar echo signal data received by the sub-arrays comprise interference signal data; wherein, M is the total number of the sub-arrays in the radar, and satisfies the power of 2, such as M is 8, 16, 32, 64, and the like.

Step 2: acquiring sum channel signal data, azimuth difference channel signal data and pitch difference channel signal data:

multiplying the M sub-array received radar signals by respective sub-array guide vectors, eliminating the influence of the wave path difference and obtaining M channel signal data d_M. The spatial phase difference of adjacent channels in the subarray is as follows:

wherein, theta is the direction angle of the echo signal pointing to each array element, d is the distance between adjacent array elements, and lambda is the signal wavelength.

S_iThe signal in the θ direction received by the ith array element can be expressed as:

s_i＝A₀e^j(iΔφ+ψ)

wherein A is₀Psi is the reference channel phase for the echo signal amplitude.

If the beam of the receiving channel is to be directed to theta_BAnd then the compensation value of the spatial phase difference of the adjacent channels in the subarray is as follows:

to S_iThe array output obtained by adding after phase compensation is:

wherein M is the total number of sub-arrays in the radar.

Then, multiplying the sum weight matrix, the azimuth difference weight matrix and the pitch difference weight matrix to obtain sum channel signal data, azimuth difference channel signal data and pitch difference channel signal data:

d_M＝[d₁，d₂，L，d_M]′

s_sum＝[1，1，1，1，1，1，1，1，1，L，1，1，1，1，1，1，1]*d_M

s_a＝[1，1，1，1，1，L，1，1，1，-1，-1，-1，-1，-1，L，-1，-1，-1]*d_M

s_e＝[1，-1，1，1，-1，-1，L，1，-1，1，1，-1，-1，L，1，-1，1，-1]*d_M

wherein s is_sumFor sum channel signal data, s_aFor azimuth difference channel signal data, s_eIs pitch difference channel signal data.

And step 3: obtaining main lobe interference signal estimation data by adopting a blind source separation method of Independent Component Analysis (ICA):

as shown in fig. 2, the steps of obtaining the main lobe interference estimation signal by ICA blind source separation are as follows:

1) performing cross-correlation operation on the azimuth difference channel signal data, the pitch difference channel signal data and the sum channel signal data to obtain the separation weight of blind source separation;

w_sum＝0

w_a＝∑conj(s_a)*s_sum/∑conj(s_sum)*s_sum

w_e＝∑conj(s_e)*s_sum/∑conj(s_sum)*s_sum

wherein, w_sumTo separate from the channel, w_aIs the channel separation power of azimuth difference, w_eFor the pitch difference channel separation weight, conj represents taking the complex conjugate;

2) respectively carrying out weighted summation on the sum channel separation weight, the azimuth difference channel separation weight and the pitch difference channel separation weight with sum channel signal data, the azimuth difference channel signal data and the pitch difference channel signal data to obtain an interference signal estimation value JE:

J_E＝w_a*s_a+w_e*s_e

wherein, J_EIs an interference signal estimate.

Step 4, interference cancellation:

the specific steps of utilizing the interference signal estimation data to respectively carry out interference cancellation on the sum channel, the azimuth difference channel and the pitch difference channel are as follows:

1) performing cross-correlation operation on the interference estimation value, sum channel signal data, azimuth difference channel signal data and pitch difference channel signal data to obtain the following cancellation power;

w_sc＝∑conj(s_sum)*J_E/∑conj(J_E)*J_E

w_ac＝∑conj(s_a)*J_E∑conj(J_E)*J_E

w_ec＝∑conj(s_e)*J_E/∑conj(J_E)*J_E

wherein, w_scFor sum channel cancellation, w_acFor azimuth channel depoise, w_ecAnd the right is cancelled for the pitch difference channel.

2) Weighting the interference signals by using the cancellation weights to obtain interference estimation results of each corresponding channel;

J_Es＝w_sc*J_E

J_Ea＝w_ac*J_E

J_Ee＝w_ec*J_E

wherein, J_EsTo sum channel interference estimates, d_EaAs an estimate of the azimuth difference channel interference, J_EeIs the estimated value of the channel interference of the pitch difference.

And 5: and estimating and weighting the interference signal by using the cancellation weight to obtain a main lobe interference suppression result of the sum channel, the azimuth difference channel and the pitch difference channel:

o_sun＝s_sum-J_Es

o_a＝s_a-J_Ea

o_e＝s_e-J_Ee

wherein o is_sumFor sum channel main lobe interference suppression results, o_aAs a result of the azimuth difference channel main lobe interference suppression, o_eObtaining a main lobe interference suppression result of a pitch difference channel;

continuing pulse pressure, MTD and CFAR processing on the interference-cancelled sum channel signal data, azimuth difference channel signal data and pitch difference channel signal data to acquire distance angle information of the target, and specifically comprising the following steps:

1) constructing a distance pulse pressure function, and carrying out pulse pressure processing on the sum channel signal data, the azimuth difference channel signal data and the pitch difference channel signal data:

o_sp＝IFFT{FFT(o_sum(n，m))·FFT[x(n)·h(n)]}

o_ap＝IFFT{FFT(o_a(n，m))·FFT[x(n)·h(n)]}

o_ep＝IFFT{FFT(o_e(n，m))·FFT[x(n)·h(n)]}

wherein o is_spFor channel-to-channel pulse pressure data, o_apIs azimuth difference channel distance pulse pressure data, o_epIs the pitch difference channel distance pulse pressure data, x (n) ═ rect (n.DELTA.t/T)_p)exp(-jπbn²Δt²) As a function of pulse pressure, h (n) ═ 1-cos (2 π n/(n +1))]The/2 is a Hanning window function, b is a linear frequency modulation, delta t is a sampling interval, n is a distance gate number, and m is a pulse sequence number;

2) performing FFT on the sum channel, the azimuth difference channel and the pitch difference channel distance pulse pressure data in the azimuth direction, namely completing the MTD processing of moving target detection;

o_sp1＝FFT{o_sp}，o_ap1＝FFT{o_ap}，o_ep1＝FFT{o_ep}；

3) constant false alarm detection CFAR processing

The purpose of CFAR processing is to provide a detection threshold that can relatively avoid the influence of noise background clutter and interference, so that the target detection has a constant false alarm probability;

the CFAR processing mode adopts unit average processing, namely fast threshold, the shutter limit CFAR mainly acts on a clutter area and is used for inhibiting the influence of residual clutter, and the CFAR processing mode is realized by adopting a method of unit average selection, namely, a detection point is attachedThe near cell is a reference cell, and the average value of the left and right reference cells is selected as the threshold, as shown in fig. 3, which is a processing block diagram of the CFAR of the cell average, in which the reference cell is D_i、D_i-1、D_i+1After the average value of the left reference unit and the right reference unit is selected to be large, the left reference unit and the right reference unit output judgment through a comparator;

4) obtaining a distance angle of a target

The target distance is the distance corresponding to the distance between the distance door and the front edge of the distance door, and the azimuth difference and the pitch difference angle are calculated by the following formula:

wherein d is_∑To sum the channel target value, d_ΔaIs the azimuth difference channel target value, d_ΔeIs the pitch difference target value.

The algorithm simulation analysis result of the blind source separation real-time main lobe interference resisting method based on the independent component analysis is as follows:

simulation parameters: and the channel input dry-to-noise ratio JNR is 30dB, the signal-to-noise ratio SNR is 20dB, and the channel noise is 30dB, and the target signal is incident from the normal of the array surface, the interference incidence direction is different from the target azimuth by 0.2 degrees, the pitch difference is 0.2 degrees, and the number of the sub-arrays is 16.

1) Mainlobe interference data simulation

Under the above simulation parameter setting conditions, 16 subarray channel echo interference noise signals are multiplied by their corresponding subarray steering vectors, and multiplied by a weight matrix to form sum channel, azimuth difference channel, and pitch difference channel echo signals, interference data, input signals, and noise input signals of the sum channel, azimuth difference channel, and pitch difference channel are input, as shown in fig. 4, the input signals of the sum channel, azimuth difference channel, and pitch difference channel are input, as shown in fig. 5, the input signals of the sum channel, azimuth difference channel, and pitch difference channel are input, and as shown in fig. 6, the input signals of the sum channel, azimuth difference channel, and pitch difference channel are input.

2) ICA blind source separation and interference signal estimation simulation

When the target signal is incident from the normal of the wavefront, the interference incidence direction is 0.2 ° different from the target azimuth and the pitch is 0.2 °, the blind source separation result is shown in fig. 7, and the interference signal estimation result is shown in fig. 8, which shows the interference suppression output signals of the sum channel, the azimuth difference channel and the pitch difference channel. From the processing results, it can be seen that: the algorithm provided by the invention can effectively separate the target signal and the main lobe interference signal.

3) Interference suppression front and back pulse pressure simulation

The interference suppression results are shown in fig. 9. From the processing results, it can be seen that: when the interference is incident in the same direction as the target, the interference suppression method obtains the signal-to-interference-and-noise ratio SJNR processing gain of about 20dB for the sum channel signal.

From the above simulation processing results, it can be seen that: the method can effectively separate the target signal and the main lobe interference signal, has obvious main lobe interference suppression effect, has the suppression ratio of more than 20dB (the larger the difference between the interference incident direction and the target angle is, the higher the suppression ratio is), has simple algorithm, low calculation amount, good real-time performance and easy engineering realization, and improves the tracking stability and precision of the radar target under the condition of main lobe interference.

According to the invention, 16 sub-array input channel AD signals of the radar are utilized, firstly, a reference signal of main lobe interference is obtained by applying ICA-based blind source separation processing, meanwhile, 16-channel sub-array signal synthesis and beam main channel signals are applied, then interference cancellation is carried out on the main channel signals to complete interference suppression, and the follow-up radar target detection and tracking are facilitated.

The above description is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the embodiments of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.