CN103728595A - Subspace-projection-based networked radar inhibition pressing type main lobe interference method - Google Patents

Abstract

The invention discloses a subspace-projection-based networked radar inhibition pressing type main lobe interference method which mainly solves the problem that single station radars only can restrain one type of interference. The subspace-projection-based networked radar inhibition pressing type main lobe interference method comprises steps of 1, computing base band receiving signals of all node radars, enabling the base band receiving signals to be aligned on the time domain with interference signals serving as the standard and obtaining a node radar array signal; 2, estimating the covariance matrix of the node radar array signal, performing characteristic decomposition on the covariance matrix, constructing noise subspace according to a characteristic decomposition result and computing the projection matrix of the projection of the noise subspace; 3, projecting the node radar array signal to the noise subspace and obtaining a projection vector which is used for target detection; 4, constructing a generalized likelihood ratio function according to the projection vector which is used for the target detection; 5, setting the detection threshold, comparing values of the generalized likelihood ratio function at any moment with the detection threshold and obtaining an output result of the target detection. The subspace-projection-based networked radar inhibition pressing type main lobe interference method can effectively restrain the interference signals in different types and can be used to a networked radar system.

Description

Networking radar based on subspace projection suppresses pressing type main lobe interference method

Technical field

The present invention relates to Radar Technology field, be particularly related to a kind of method that resistance to compression standard main lobe disturbs, can be used for networking radar system, under pressing type main lobe disturbed condition, the undesired signal entering from each node radar antenna main lobe of networking radar is effectively suppressed, improve the target detection performance of networking radar under pressing type main lobe disturbed condition.

Background technology

It is a kind of common radar jamming pattern that pressing type disturbs, by frequency directing, make the centre frequency of jamming transmitter close with the centre frequency of radar, high-power undesired signal is entered radar receiver, thereby flood target echo, make radar be difficult to target information be detected from receive echo.Pressing type disturbs and is divided into the interference of active suppressing formula and the interference of passive pressing type.Active suppressing formula is disturbed and is divided into again spot jamming and barrage jamming; It is mainly a large amount of chaff deliverys that passive pressing type disturbs, and causes strong interference, covers echo signal.

For the pressing type that is positioned at target proximity, disturb, undesired signal can enter from radar receiving antenna main lobe conventionally; When radar detection distant object, even if jammer range target is far away, within undesired signal also can fall into the main lobe of radar receiving antenna, thereby form pressing type main lobe, disturb.Disappear mutually technology, adaptive beam formation technology etc. of existing adaptive side-lobe can only suppress the interference entering from radar receiving antenna secondary lobe, main lobe interference can cause serious wave beam distortion or the biasing of main lobe peak value, cannot when suppressing interference, to target, effectively detect.

Networking radar, refers to the organic radar netting consisting of the identical or different node radar of multi-section system, and it has mode of operation and collaborative detection mode flexibly, can provide abundant sky, time, resource frequently.

Array along with potato masher antenna, jammer has the ability that multi-beam forms and system resource is dispatched, can to multi-section radar, implement to disturb simultaneously, networking radar is faced with the threat that main lobe disturbs, in network, each node radar all can be subject to the impact that main lobe disturbs, and the detection performance of networking radar can decline rapidly.

For pressing type main lobe, disturb, people have proposed some monostatic radars and have suppressed the method that pressing type main lobe disturbs, and by its thinking, can be divided three classes: (1) utilizes parametric method to recover undesired signal, in time domain, completes interference cancellation; (2) utilize frequency domain trap or time-frequency filtering filtering interfering; (3) utilize the subspace projection based on time domain data vector to realize interference inhibition.But said method is all that time domain, frequency domain or the time-frequency domain architectural feature according to undesired signal designs, and has stronger interference type specific aim.When interference type mismatch, its interference rejection capability will reduce even inefficacy.

Summary of the invention

The object of the invention is to disturb for above-mentioned inhibition pressing type main lobe the problem existing, propose a kind of networking radar based on subspace projection and suppress pressing type main lobe method, to realize the inhibition that networking radar pressing type main lobe is disturbed, improve the target detection performance of networking radar.

For achieving the above object, technical scheme of the present invention comprises the steps:

(1) hypothetical network radar is comprised of M node radar, first node radar is operated in sending and receiving state, and all the other node radars are only operated in accepting state, the wave beam main lobe of each node radar all points to jammer and target region, in radar detection area, only exist a pressing type to disturb, near disturbing, pressing type there is Q real goal, according to target, disturb the space geometry position with respect to networking radar, the baseband receiving signals r of n node radar of calculating _n(t):

r _n(t)＝r _nT(t)+r _nJ(t)+w _n(t)，

Wherein, r _nT(t) be the baseband receiving signals of n node radar target, $r_{\mathrm{nT}}\left(t\right)=\underset{k=1}{\overset{Q}{\mathrm{\&Sigma;}}}{\mathrm{\&alpha;}}_{k,n}\&CenterDot;s_{\mathrm{TX}}(t-\frac{R_{k,1}^{\left(T\right)}+R_{k,n}^{\left(T\right)}}{c})\&CenterDot;e^{-\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000457777150000021.png"\; state="\{\{state\}\}">j</figure-callout>}2\mathrm{\&pi;}(R_{k,1}^{\left(T\right)}+R_{k,n}^{\left(T\right)})/\mathrm{\&lambda;}},$ α _k,nthe echo of k the target complex magnitude while arriving n node radar, k=1,2 ..., Q, Q is the number of extraterrestrial target, n=1,2 ..., M, M is the number of node radar, M>=2, s _tX(t) be base band transmit, the distance that k target arrives the 1st node radar,

be k target to the distance of n node radar, c is the light velocity, λ is radar operation wavelength, r _nJ(t) be the baseband receiving signals of n node radar jamming,

β _nbe the complex magnitude of undesired signal while arriving n node radar, J (t) is baseband interference signal,

the distance that jammer arrives n node radar, w _n(t) be n node noise of radar receiver signal, and the noise signal of each node radar receiver is uncorrelated mutually;

(2) baseband receiving signals of each node radar step (1) being obtained, in time domain, with the interference echo signal of first node radar, be as the criterion and align, signal after alignment is represented by vectorial form, obtains node array radar signal vector r (t):

r(t)＝[r ₁(t),r ₂(t-τ ₁₂),…,r _M(t-τ _1M)] ^T＝S(t)+J(t)+w(t),

Wherein, τ _1nbe the interference echo signal of n node radar with respect to the time delay of the interference echo signal of first node radar,

variate-value while representing to make objective function get maximal value,

represent convolution algorithm, () ^*represent to get conjugation, () ^trepresent to get transposition, S (t) is the target echo signal vector of node radar array, a _tbe target with respect to the steering vector of node radar array, $a_T={[e^{-j4\mathrm{\&pi;}{R}_{k,1}^{\left(T\right)}/\mathrm{\&lambda;}},e^{-\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000457777150000021.png"\; state="\{\{state\}\}">j</figure-callout>}2\mathrm{\&pi;}(R_{k,1}^{\left(T\right)}+R_{k,2}^T)/\mathrm{\&lambda;}},\&CenterDot;\&CenterDot;\&CenterDot;,e^{-2\mathrm{\&pi;}(R_{k,1}^{\left(T\right)}+R_{k,M}^{\left(T\right)})/\mathrm{\&lambda;}}]}^T,$ α _kthe complex amplitude vector of node radar array target echo, α _k=[α _{k, 1}, α _{k, 2}..., α _k,M] ^t,

represent point multiplication operation, J (t) is the interference echo signal phasor of node radar array,

a _jthat jammer is with respect to the steering vector of node radar array

β is the complex amplitude vector of node radar array interference echo, β=[β ₁..., β _m] ^t, w (t) is the noise signal vector of node radar array;

(3) the node array radar signal vector r (t) obtaining according to step (2), estimates its covariance matrix

$\hat{R}=\underset{i=1}{\overset{T}{\mathrm{\&Sigma;}}}r\left(i\right)r^H\left(i\right),$

Wherein, i=1,2 ..., T, T is for training covariance matrix

time-domain sampling sample number, () ^hrepresent conjugate transpose;

(4) covariance matrix by following formula, step (3) being obtained

carry out feature decomposition, obtain covariance matrix

eigenvalue of maximum characteristic of correspondence vector v ₁with all the other eigenwert characteristic of correspondence vector v ₂～v _m:

$\hat{R}=({\mathrm{\&sigma;}}_{\mathrm{<figure-callout\; id="2"\; label="J"\; filenames="HDA0000457777150000021.png"\; state="\{\{state\}\}">J</figure-callout>}}^2+{\mathrm{\&sigma;}}_w^2)v_1{v}_1^H+\underset{j=2}{\overset{M}{\mathrm{\&Sigma;}}}{\mathrm{\&sigma;}}_w^2{v}_j{v}_j^H,$

Wherein, j=2 ..., M, v _jrepresent j proper vector;

(5) the proper vector v that utilizes step (4) to obtain ₂～v _m, structure noise subspace, calculates the projection matrix P to noise subspace projection _nr;

(6) the node array radar signal vector r (t) step (2) being obtained projects in the noise subspace of step (5) structure, obtains the projection vector z for target detection _r(t):

z _r(t)＝P _nrr(t)，

Wherein, P _nrit is the projection matrix to noise subspace projection;

(7) projection vector z step (6) being obtained _r(t) with Generalized Likelihood Ratio detecting device, carry out target detection, structure Generalized Likelihood Ratio function Λ (t):

$\mathrm{\&Lambda;}\left(t\right)=r{\left(t\right)}^H{P}_{\mathrm{nr}}r\left(t\right);$

(8) set detection threshold: $\mathrm{\&delta;}=({\mathrm{\&sigma;}}_{\mathrm{<figure-callout\; id="2"\; label="w"\; filenames="HDA0000457777150000021.png"\; state="\{\{state\}\}">w</figure-callout>}}^2/2)F_{{\mathrm{\&chi;}}_{2(M-1)}^2}^{-1}(1-P_{\mathrm{fa}}),$

Wherein,

represent that degree of freedom is card side's distribution of 2 (M-1),

for

the inverse function of distribution function, P _fait is false-alarm probability;

(9) functional value of each moment point of Generalized Likelihood Ratio function Λ (t) and detection threshold δ are compared, obtain the Output rusults of target detection: if Λ (t) < is δ, represent driftlessness, if Λ (t) > is δ, indicate target.

The present invention compared with prior art tool has the following advantages:

1, the method for disturbing with monostatic radar inhibition pressing type main lobe is compared, the present invention is owing to having adopted networking radar configuration node radar array, utilization suppresses the interference of pressing type main lobe to the method for subspace projection, therefore do not rely on the time-frequency structure feature of undesired signal, can be applicable to dissimilar undesired signal, when interference type mismatch, also there is good interference rejection capability.

2, the present invention is owing to having adopted the method for subspace projection, when application without knowing the geometric parameter of networking radar node array and the parameters such as amplitude phase error between each node radar, therefore the variation of pair array inner structure has stronger adaptive ability.

Accompanying drawing explanation

Fig. 1 is realization flow figure of the present invention;

Fig. 2 is the scene schematic diagram that the present invention uses;

Fig. 3 suppresses FM Noise Jamming Signal by the inventive method, the likelihood ratio function output map of emulation;

Fig. 4 suppresses the dexterous undesired signal of noise convolution by the inventive method, the likelihood ratio function output map of emulation.

Embodiment

With reference to Fig. 1, specific embodiment of the invention step is as follows:

Step 1: according to target, disturb the locus with respect to networking radar, calculate the baseband receiving signals r of n node radar _n(t).

1a) the locus with respect to networking radar according to target, the baseband receiving signals r of n node radar target of calculating _nT(t):

$r_{\mathrm{nT}}\left(t\right)=\underset{k=1}{\overset{Q}{\mathrm{\&Sigma;}}}{\mathrm{\&alpha;}}_{k,n}\&CenterDot;s_{\mathrm{TX}}(t-\frac{R_{k,1}^{\left(T\right)}+R_{k,n}^{\left(T\right)}}{c})\&CenterDot;e^{-\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000457777150000021.png"\; state="\{\{state\}\}">j</figure-callout>}2\mathrm{\&pi;}(R_{k,1}^{\left(T\right)}+R_{k,n}^{\left(T\right)})/\mathrm{\&lambda;}},$

Wherein, α _k, _nthe echo of k the target complex magnitude while arriving n node radar, α _k, _nobeying average is 0, and variance is σ _α ²multiple Gaussian random process, σ _α ²the average power of target echo, k=1,2 ..., Q, Q is the number of extraterrestrial target, n=1,2 ..., M, M is the number of node radar, M>=2, s _tX(t) be base band transmit,

the distance that k target arrives the 1st node radar,

be k target to the distance of n node radar, c is the light velocity, λ is radar operation wavelength;

1b) according to the locus of disturbing with respect to networking radar, calculate the baseband receiving signals r of n node radar jamming _nJ(t):

$r_{\mathrm{nJ}}\left(t\right)={\mathrm{\&beta;}}_n\&CenterDot;J(t-\frac{R_n^{\left(J\right)}}{c})\&CenterDot;e^{-\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000457777150000021.png"\; state="\{\{state\}\}">j</figure-callout>}2\mathrm{\&pi;}{R}_n^{\left(J\right)}/\mathrm{\&lambda;}},$

Wherein, β _nbe the complex magnitude of undesired signal while arriving n node radar, J (t) is baseband interference signal,

it is the distance that jammer arrives n node radar;

1c) according to the baseband receiving signals r of n node radar target _nTand the baseband receiving signals r disturbing (t) _nJ, and the noise signal w of n node radar (t) _n(t), obtain the baseband receiving signals r of n node radar _n(t):

r _n(t)＝r _nT(t)+r _nJ(t)+w _n(t)，

Wherein, noise signal w _n(t) obeying average is 0, and variance is σ _w ²multiple Gaussian random process, σ _w ²be noise power, and the noise signal of each node radar is uncorrelated mutually.

Step 2: the baseband receiving signals of each node radar that step (1) is obtained, in time domain, with the interference echo signal of first node radar, be as the criterion and align, obtain node array radar signal vector r (t):

r(t)＝[r ₁(t),r ₂(t-τ ₁₂),…r _n(t-t _1n)…,rM(t-τ _1M)] ^T，

Wherein, τ _1nbe the interference echo signal of n node radar with respect to the time delay of the interference echo signal of first node radar,

τ while representing to make objective function get maximal value _1n,

represent convolution algorithm, () ^*represent to get conjugation, () ^trepresent to get transposition.

Step 3: be comprised of signal, interference and noise three parts according to node array radar signal vector signal, the node array radar signal vector r (t) that step (2) is obtained is rewritten as following expression:

r(t)＝S(t)+J(t)+w(t)，

Wherein, S (t) is the target echo signal vector of node radar array,

aT be target with respect to the steering vector of node radar array, $a_T={[e^{-j4\mathrm{\&pi;}{R}_{k,1}^{\left(T\right)}/\mathrm{\&lambda;}},e^{-\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000457777150000021.png"\; state="\{\{state\}\}">j</figure-callout>}2\mathrm{\&pi;}(R_{k,1}^{\left(T\right)}+R_{k,2}^T)/\mathrm{\&lambda;}},\&CenterDot;\&CenterDot;\&CenterDot;,e^{-2\mathrm{\&pi;}(R_{k,1}^{\left(T\right)}+R_{k,M}^{\left(T\right)})/\mathrm{\&lambda;}}]}^T,$ α _kthe complex amplitude vector of node radar array target echo, α _k=[α _{k, 1}, α _{k, 2}, ... α _k,M] ^t,

represent point multiplication operation, J (t) is the interference echo signal phasor of node radar array, a _jbe jammer with respect to the steering vector of node radar array,

β is the complex amplitude vector of node radar array interference echo, β=[β ₁..., β _m] ^t, w (t) is the noise signal vector of node radar array.

Step 4: the node array radar signal vector r (t) obtaining according to step (3), estimates its covariance matrix

$\hat{R}=\underset{i=1}{\overset{T}{\mathrm{\&Sigma;}}}r\left(i\right)r^H\left(i\right),$

Wherein, i=1,2 ..., T, T is for training covariance matrix

time-domain sampling sample number, () ^hthe conjugate transpose of expression to matrix;

Step 5: covariance matrix step (4) being obtained by following formula

carry out feature decomposition, obtain covariance matrix

eigenvalue of maximum characteristic of correspondence vector v ₁with all the other eigenwert characteristic of correspondence vector v ₂～v _m:

$\hat{R}=({\mathrm{\&sigma;}}_{\mathrm{<figure-callout\; id="2"\; label="J"\; filenames="HDA0000457777150000021.png"\; state="\{\{state\}\}">J</figure-callout>}}^2+{\mathrm{\&sigma;}}_w^2)v_1{v}_1^H+\underset{j=2}{\overset{M}{\mathrm{\&Sigma;}}}{\mathrm{\&sigma;}}_w^2{v}_j{v}_j^H,$

Wherein, j=2 ..., M, v _jrepresent j proper vector,

represent jamming power;

Step 6: the proper vector v that utilizes step (5) to obtain ₂～v _m, structure noise subspace U _nr:

U _nr＝[v ₂,…,v _M]。

Step 7: utilize noise subspace U _nr, calculate the projection matrix P to noise subspace projection _nr:

$P_{\mathrm{nr}}=U_{\mathrm{nr}}{U}_{\mathrm{nr}}^H.$

Step 8: the projection matrix P obtaining according to step (7) _nr, the node array signal phasor r (t) that step (3) is obtained projects in the noise subspace of step (6) structure, obtains the projection vector z for target detection _r(t):

z _r(t)＝P _nrr(t)。

Step 9: the projection vector z that step (8) is obtained _r(t) with Generalized Likelihood Ratio detecting device, carry out target detection, structure Generalized Likelihood Ratio function Λ (t).

Projection vector z 9a) obtaining according to step (8) _r(t), obtain the data x (t) after dimensionality reduction:

x(t)=Tz _r(t)，

Wherein, T is dimensionality reduction matrix,

9b), according to the data x after dimensionality reduction (t), target detection is summed up as to following Hypothesis Testing Problem:

H ₀:x(t)＝TP _nrw(t)，

H ₁:x(t)＝TP _nr?S(t)+TP _nrw(t)，

Wherein, H ₀represent driftlessness, H ₁indicate target;

9c) according to the Hypothesis Testing Problem for target detection, structure Generalized Likelihood Ratio function Λ (t):

$\mathrm{\&Lambda;}\left(t\right)=\log \frac{P\left[x\left(t\right)\right|{\mathrm{<figure-callout\; id="1"\; label="H"\; filenames="HDA0000457777150000021.png"\; state="\{\{state\}\}">H</figure-callout>}}_1,S\left(t\right)]}{P\left[x\left(t\right)\right|H_0]}=r{\left(t\right)}^H{P}_{\mathrm{nr}}r\left(t\right),$

Wherein, $P\left[x\left(t\right)\right|H_0]=\frac{1}{{\left(2\mathrm{\&pi;}\right)}^{(M-1)/2}|{{\mathrm{\&sigma;}}_w}^2\&CenterDot;T{P}_{\mathrm{nr}}{T}^H|}{e}^{-\frac{x{\left(t\right)}^H{\left(T{P}_{\mathrm{nr}}{T}^H\right)}^{-1}x\left(t\right)}{2{{\mathrm{\&sigma;}}_w}^2}},$ Be illustrated in H ₀under supposing, the probability density function of x (t), () ^-1representing matrix is inverted, P[x (t) | H ₁, S (t)] and be illustrated in H ₁under supposing, the probability density function of x (t), $P\left[x\left(t\right)\right|H_1,S\left(t\right)]=\frac{1}{{\left(2\mathrm{\&pi;}\right)}^{(M-1)/2}|{{\mathrm{\&sigma;}}_w}^2\&CenterDot;T{P}_{\mathrm{nr}}{T}^H|}{e}^{-\frac{\left[x{\left(t\right)-T{P}_{\mathrm{br}}S\left(t\right)]}^H{\left(T{P}_{\mathrm{nr}}{T}^H\right)}^{-1}\right[x\left(t\right)-T{P}_{\mathrm{nr}}S\left(t\right)]}{2{{\mathrm{\&sigma;}}_w}^2}},$ S (t) is the target echo signal vector of node radar array, and when structure Generalized Likelihood Ratio function, S (t) uses its maximal possibility estimation conventionally

replace, $\hat{S}\left(t\right)=P_{\mathrm{nr}}{U}_{\mathrm{nr}}{\left(T{U}_{\mathrm{nr}}\right)}^{-1}x\left(t\right).$

Step 10: set detection threshold δ.

10a) according to the Generalized Likelihood Ratio function Λ (t) of step (9) structure, calculate Generalized Likelihood Ratio function Λ in aimless situation ₁(t):

${\mathrm{\&Lambda;}}_1\left(t\right)=w{\left(t\right)}^H{U}_{\mathrm{nr}}{U}_{\mathrm{nr}}^{H}w\left(t\right),$

10b) according to step 10a) Generalized Likelihood Ratio function Λ in the driftlessness situation that obtains ₁(t) and the noise signal vector w (t) of node radar array obey multiple Gaussian distribution, obtain Λ ₁(t) obey card side's distribution that degree of freedom is 2 (M-1);

10c) according to step 10b) Λ that obtains ₁(t) obey card side's distribution that degree of freedom is 2 (M-1), obtain false-alarm probability P _faexpression formula as follows:

$\begin{array}{c}{P}_{\mathrm{fa}}=P[{\mathrm{\&Lambda;}}_1\left(t\right)>\mathrm{\&delta;}|H_0]\\ =P[{\mathrm{\&chi;}}_{2(M-1)}^2>(2\mathrm{\&delta;}/{\mathrm{\&sigma;}}_w^2)]\end{array},$

Wherein,

represent that degree of freedom is card side's distribution of 2 (M-1);

10d) according to step 10c) the false-alarm probability P that obtains _faexpression formula, calculate detection threshold δ:

$\mathrm{\&delta;}=({\mathrm{\&sigma;}}_{\mathrm{<figure-callout\; id="2"\; label="w"\; filenames="HDA0000457777150000021.png"\; state="\{\{state\}\}">w</figure-callout>}}^2/2)F_{{\mathrm{\&chi;}}_{2(M-1)}^2}^{-1}(1-P_{\mathrm{fa}}),$

Wherein,

for

the inverse function of distribution function.

Step 11: the functional value of each moment point of Generalized Likelihood Ratio function Λ (t) and detection threshold δ are compared, obtain the Output rusults of target detection: if Λ (t) < is δ, represent driftlessness, if Λ (t) > is δ, indicate target.

The rejection that the present invention disturbs pressing type main lobe can further be verified by following emulation.

1. experiment scene:

As shown in Figure 2, networking radar consists of six node radars, first node radar is operated in receipts, the state of sending out, and all the other node radars are only operated in accepting state, each node radar is isomorphism radar, running parameter is identical, signal waveform is all linear FM signal, frequency of operation 1.4286GHz, sampling rate 12MHz, signal bandwidth 10MHz, pulse repetition rate 20KHz, pulsewidth 50us, 3.5294 ° of beam angles, the coordinate of six node radars is respectively: (0, 0) km, (2.21, 3.90) km, (5.13, 4.47) km, (0,-4.24) km, (4.13,-1.26) km and (2.37,-0.97) km, signal interference ratio is respectively :-40dB, 40.99dB,-40.07dB,-41.91dB,-41.32dB and-41.22dB, signal to noise ratio (S/N ratio) is 15dB, the coordinate of jammer is (0, 38) km, there are two real goals, target 1 coordinate is (0, 38.9) km, target 2 coordinates are (0, 37.1) km.

2. experiment content:

Experiment 1, when undesired signal is noise FM interference, adopt the inventive method, by node array radar signal vector to noise subspace projection, obtain the projection vector for target detection, the projection vector of target detection is carried out to Generalized Likelihood Ratio detection, the likelihood ratio function output map of emulation, as shown in Figure 3.

Experiment 2, when undesired signal is the dexterous interference of noise convolution, adopt the inventive method, by node array radar signal vector to noise subspace projection, obtain the projection vector for target detection, the projection vector of target detection is carried out to Generalized Likelihood Ratio detection, the likelihood ratio function output map of emulation, as shown in Figure 4.

3. interpretation:

From Fig. 3 and Fig. 4, can see, the echoed signal of the echoed signal of two real goals and jammer itself all clearly shows, show the inhibition that the present invention has not only carried out effectively FM Noise Jamming Signal, and dexterous interference of noise convolution also carried out to effective inhibition, make target detection become possibility.

In sum, method of the present invention can effectively suppress dissimilar undesired signal, has improved the detection performance of target.

Claims (3)

1. the networking radar based on subspace projection suppresses a pressing type main lobe interference method, comprises the steps:

(1) hypothetical network radar is comprised of M node radar, first node radar is operated in sending and receiving state, and all the other node radars are only operated in accepting state, the wave beam main lobe of each node radar all points to jammer and target region, in radar detection area, only exist a pressing type to disturb, near disturbing, pressing type there is Q real goal, according to target, disturb the space geometry position with respect to networking radar, the baseband receiving signals r of n node radar of calculating _n(t):

r _n(t)＝r _nT(t)+r _nJ(t)+w _n(t)，

Wherein, r _nT(t) be the baseband receiving signals of n node radar target, $r_{\mathrm{nT}}\left(t\right)=\underset{k=1}{\overset{Q}{\mathrm{\&Sigma;}}}{\mathrm{\&alpha;}}_{k,n}\&CenterDot;s_{\mathrm{TX}}(t-\frac{R_{k,1}^{\left(T\right)}+R_{k,n}^{\left(T\right)}}{c})\&CenterDot;e^{-j2\mathrm{\&pi;}(R_{k,1}^{\left(T\right)}+R_{k,n}^{\left(T\right)})/\mathrm{\&lambda;}},$ α _k,nthe echo of k the target complex magnitude while arriving n node radar, k=1,2 ..., Q, Q is the number of extraterrestrial target, n=1,2 ..., M, M is the number of node radar, M>=2, sTX (t) is base band transmit,

the distance that k target arrives the 1st node radar,

be k target to the distance of n node radar, c is the light velocity, λ is radar operation wavelength,

the baseband receiving signals of n node radar jamming,

β _nbe the complex magnitude of undesired signal while arriving n node radar, J (t) is baseband interference signal,

the distance that jammer arrives n node radar, w _n(t) be n node noise of radar receiver signal, and the noise signal of each node radar receiver is uncorrelated mutually;

(2) baseband receiving signals of each node radar step (1) being obtained, in time domain, with the interference echo signal of first node radar, be as the criterion and align, signal after alignment is represented by vectorial form, obtains node array radar signal vector r (t):

r(t)＝[r ₁(t),r ₂(t-τ ₁₂),…,r _M(t-τ _1M)] ^T＝S(t)+J(t)+w(t),

Wherein, τ _1nbe the interference echo signal of n node radar with respect to the time delay of the interference echo signal of first node radar,

variate-value while representing to make objective function get maximal value,

represent convolution algorithm, () * represents to get conjugation, () ^trepresent to get transposition, S (t) is the target echo signal vector of node radar array,

a _tbe target with respect to the steering vector of node radar array, $a_T={[e^{-j4\mathrm{\&pi;}{R}_{k,1}^{\left(T\right)}/\mathrm{\&lambda;}},e^{-j2\mathrm{\&pi;}(R_{k,1}^{\left(T\right)}+R_{k,2}^T)/\mathrm{\&lambda;}},\&CenterDot;\&CenterDot;\&CenterDot;,e^{-2\mathrm{\&pi;}(R_{k,1}^{\left(T\right)}+R_{k,M}^{\left(T\right)})/\mathrm{\&lambda;}}]}^T,$ α _kthe complex amplitude vector of node radar array target echo, α k=[α k, 1, α k, 2 ..., α k, M] ^t,

represent point multiplication operation, J (t) is the interference echo signal phasor of node radar array,

a _jthat jammer is with respect to the steering vector of node radar array

β is the complex amplitude vector of node radar array interference echo, β=[β ₁..., β _m] ^t, w (t) is the noise signal vector of node radar array;

(3) the node array radar signal vector r (t) obtaining according to step (2), estimates its covariance matrix

$\hat{R}=\underset{i=1}{\overset{T}{\mathrm{\&Sigma;}}}r\left(i\right)r^H\left(i\right),$

Wherein, i=1,2 ..., T, T is for training covariance matrix

time-domain sampling sample number, () ^hrepresent conjugate transpose;

(4) covariance matrix by following formula, step (3) being obtained

carry out feature decomposition, obtain covariance matrix

eigenvalue of maximum characteristic of correspondence vector v ₁with all the other eigenwert characteristic of correspondence vector v ₂～v _m:

$\hat{R}=({\mathrm{\&sigma;}}_J^2+{\mathrm{\&sigma;}}_w^2)v_1{v}_1^H+\underset{j=2}{\overset{M}{\mathrm{\&Sigma;}}}{\mathrm{\&sigma;}}_w^2{v}_j{v}_j^H,$

Wherein, j=2 ..., M, v _jrepresent j proper vector;

(5) the proper vector v that utilizes step (4) to obtain ₂～v _m, structure noise subspace, calculates the projection matrix P to noise subspace projection _nr;

(6) the node array radar signal vector r (t) step (2) being obtained projects in the noise subspace of step (5) structure, obtains the projection vector z for target detection _r(t):

z _r(t)＝P _nrr(t)，

Wherein, P _nrit is the projection matrix to noise subspace projection;

(7) projection vector z step (6) being obtained _r(t) with Generalized Likelihood Ratio detecting device, carry out target detection, structure Generalized Likelihood Ratio function Λ (t):

Λ(t)＝r(t) ^HP _nrr(t)；

(8) set detection threshold: $\mathrm{\&delta;}=({\mathrm{\&sigma;}}_w^2/2)F_{{\mathrm{\&chi;}}_{2(M-1)}^2}^{-1}(1-P_{\mathrm{fa}}),$

Wherein,

represent that degree of freedom is card side's distribution of 2 (M-1),

for

the inverse function of distribution function, P _fait is false-alarm probability;

(9) functional value of each moment point of Generalized Likelihood Ratio function Λ (t) and detection threshold δ are compared, obtain the Output rusults of target detection: if Λ (t) < is δ, represent driftlessness, if Λ (t) > is δ, indicate target.

2. the networking radar based on subspace projection according to claim 1 suppresses pressing type main lobe interference method, and wherein the calculating described in step (5) is to the projection matrix P of noise subspace projection _nr, be calculated as follows:

$P_{\mathrm{nr}}=U_{\mathrm{nr}}{U}_{\mathrm{nr}}^H,$

Wherein, U _nr=[v ₂..., v _m], v ₂..., v _mthe proper vector that represents node array radar signal vector covariance matrix, M is the number of node radar, M>=2, () ^hthe conjugate transpose of expression to matrix.

3. networking radar based on subspace projection according to claim 1 suppresses pressing type main lobe interference method, and the described structure Generalized Likelihood Ratio function Λ (t) of step (7) wherein carries out as follows:

Projection vector z 7a) obtaining according to step (6) _r(t), obtain the data x (t) after dimensionality reduction:

x(t)=Tz _r(t)，

Wherein, T is dimensionality reduction matrix,

7b), according to the data x after dimensionality reduction (t), target detection is summed up as to following Hypothesis Testing Problem:

H ₀:x(t)＝TP _nrw(t)，

H ₁:x(t)＝TP _nr?S(t)+TP _nrw(t)，

Wherein, H ₀represent driftlessness, H ₁indicate target, P _nrbe the projection matrix to noise subspace projection, w (t) is the noise signal vector of node radar array, and S (t) is the target echo signal vector of node radar array;

7c) according to the Hypothesis Testing Problem for target detection, structure Generalized Likelihood Ratio function Λ (t):

$\mathrm{\&Lambda;}\left(t\right)=\log \frac{P\left[x\left(t\right)\right|H_1,S\left(t\right)]}{P\left[x\left(t\right)\right|H_0]}=r{\left(t\right)}^H{P}_{\mathrm{nr}}r\left(t\right),$

Wherein, P[x (t) | H ₀] be illustrated in H ₀under supposing, the probability density function of x (t), P[x (t) | H ₁, S (t)] and be illustrated in H ₁under supposing, the probability density function of x (t), $P\left[x\left(t\right)\right|H_0]=\frac{1}{{\left(2\mathrm{\&pi;}\right)}^{(M-1)/2}|{{\mathrm{\&sigma;}}_w}^2\&CenterDot;T{P}_{\mathrm{nr}}{T}^H|}{e}^{-\frac{x{\left(t\right)}^H{\left(T{P}_{\mathrm{nr}}{T}^H\right)}^{-1}x\left(t\right)}{2{{\mathrm{\&sigma;}}_w}^2}},$ $P\left[x\left(t\right)\right|H_1,S\left(t\right)]=\frac{1}{{\left(2\mathrm{\&pi;}\right)}^{(M-1)/2}|{{\mathrm{\&sigma;}}_w}^2\&CenterDot;T{P}_{\mathrm{nr}}{T}^H|}{e}^{-\frac{\left[x{\left(t\right)-T{P}_{\mathrm{br}}s\left(t\right)]}^H{\left(T{P}_{\mathrm{nr}}{T}^H\right)}^{-1}\right[x\left(t\right)-T{P}_{\mathrm{nr}}s\left(t\right)]}{2{{\mathrm{\&sigma;}}_w}^2}},$ () ^-1represent inversion operation, S (t) is the target echo signal vector of node radar array, and when structure Generalized Likelihood Ratio function, S (t) uses its maximal possibility estimation conventionally

replace, $\hat{S}\left(t\right)=P_{\mathrm{nr}}{U}_{\mathrm{nr}}{\left(T{U}_{\mathrm{nr}}\right)}^{-1}x\left(t\right).$

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