CN102221696A - Sparse microwave imaging method - Google Patents

Abstract

The invention discloses a sparse microwave imaging method, relating to the information acquisition and processing technology. By utilizing sparsity of microwave imaging observation, through introducing sparse signal processing theory into microwave imaging technology, after signal processing and information extraction, the geometrical and physical characteristics of an observation object such as space position, scattering characteristic and motion characteristic and the like are obtained, wherein the sparse signal processing theory is through searching sparse representation field of the observation object, obtaining sparse microwave signal of the observation object in space, time, frequency spectrum or polarizing field sparse sampling. According to the sparse microwave imaging method in the invention, the bottleneck problems existed in the prior art such as difficult system realization, complex imaging processing method, redundant information and difficult feature extraction and the like based on Nyquist sampling theorem and classic digital signal processing theory are solved, thus microwave imaging system structure and imaging complexity is reduced.

Description

Sparse microwave imaging method

Technical field

The information of the present invention relates to is obtained and processing technology field, it is a kind of sparse microwave imaging method, utilize the sparse property of microwave imaging observation scene data set, when signal is launched in conjunction with observation scene sparse property, and utilize antenna to be lower than the data sampling of Nyquist's theorem when data are obtained, it is integrated to be embodied as picture and information extraction when signal Processing.

Background technology

Microwave Imaging Technique be electromagnetic wave with the microwave spectral coverage as detection means, utilize the microwave imaging sensor to obtain the technology of object being observed scattering signatures and relevant information.Compare with optical image technology, microwave imaging is not subjected to the restriction of sunshine and weather condition, has round-the-clock, round-the-clock ability to target or scene observation, has developed into the important means of remote sensing applications such as resources survey, environmental monitoring and disaster assessment.In over half a century in the past, microwave imaging system resolution develops into a centimetre magnitude by tens meters magnitudes; The imaging system develops into bunching type, scan-type, slip pack, two/multistation and the three-dimensional imaging that faces the future and moving-target imaging pattern by initial band imaging; Polarization mode is developed to complete polarization from single polarization; Application mode develops into DEM measurement, atural object parameter measurement, ocean wave parameter measurement and parameters of target motion measurement etc. by the qualitative decipher of single image.

Existing microwave imaging system is to be based upon radar to differentiate on theoretical and the nyquist sampling theorem basis.Radar is differentiated theoretical resolution=light velocity/(2 * signal bandwidth), and the sample frequency during recovery that nyquist sampling theorem is undistorted is greater than 2 times bandwidth.Radar is differentiated theoretical and nyquist sampling theorem is the theoretical foundation of the universality of microwave imaging, can not break through.It has determined the scale and the complexity of microwave imaging system.Resolution is high more, the mapping bandwidth is big more, multisystem is just complicated more more for port number.

The core that existing microwave imaging is handled is to make up distance-Doppler's two dimension according to the microwave system observing matrix to be decoupled into the picture operator, thereby obtains the microwave imagery of object of observation.Obtaining distance, generally all adopt the pulse compression system to aspect the high resolving power; Obtaining the orientation to aspect the high resolving power, the main employing reduced the method for orientation to antenna length.

It is one of most active branch in signal Processing field in this century that sparse signal is handled.The goal in research of this branch is to extract the least possible observation data from original signal, keeps contained information in the original signal simultaneously to greatest extent, and original signal is effectively approached and recovered.

1986, Santosa and Symes clearly proposed the notion of sparse signal the earliest, and the basic mathematic model that sparse signal is handled is a linear regression model (LRM).Suitably select sparse observing matrix Ф, then the sparse signal transaction module can be described with following equation,

Y＝ФX+N

Wherein Y is the observed samples data, and N is a noise, and X is a vector to be estimated.In general, the non-zero entry number of X seldom that is to say that X has sparse characteristic.Simultaneously, the dimension of Y is much smaller than the dimension of X.Therefore, this is a underdetermined equation, and infinite a plurality of separating arranged generally speaking.The effective ways of finding the solution above-mentioned " sparse linear recurrence " problem are to use low-order mode that common second order error is carried out regularization, promptly to objective function || and y-Φ x|| ₂+ λ || x|| _lBe optimized, this optimization problem can be converted into following problem and find the solution:

min||x|| _l?s.t.||y-Фx|| ₂≤ε，l＝0

Here || || _lThe l rank norm of expression variable, min () expression minimizes operation, s.t. expression makes and satisfies, ε is the threshold value that is provided with, and the zero norm of x is represented the non-zero entry number of x, can be used to describe the sparse characteristic for the treatment of estimate vector, under existing theory of foundations of mathematics condition, finding the solution of zero norm is very difficult, is similar to l ∈ [0,2] rank norm usually.

2006, Donoho has systematically discussed the association between l 1 (being l=1) optimization and the sparse property, also used the notion of Compressed Sensing (being called for short CS) first, at treating characteristic that estimated signal can sparse sign in certain space, adopt specific dimensionality reduction compression sampling, utilizing optimization method to realize signal reconstruction, signals sampling, recovery and information extraction directly are based upon on the signal sparse characteristic Foundation of Representation.2006, Candes and Tao proved that the RIP condition is l 1 and l 0 optimization problem adequate condition of equal value, has disclosed the association between l 1 optimization and the sparse signal reconstruction.On their working foundation, the scholars of signal Processing circle have carried out deeply and extensive studies in the relevant issues of in recent years sparse signal being handled.At present, the numerical solution of l 1 optimization problem mainly contains two big class methods: a class is based on the method for iteration optimizing technology, as protruding optimization and linear programming etc.; The another kind of method that is based on greedy algorithm (Greedy Algorithm).

Achievement in research that the sparse signal treatment theory is recent and the breakthrough that obtains make it obtain comparatively successful application at optical imagery (as single pixel optics camera of Duarte, Davenport, Takhar etc.).In these were used, object of observation had tangible sparse characteristic, can obtain comparatively ideal reconstructed results by sparse base commonly used, adopted sample relation between sparse property and the sparse property of object being observed of statistical method approximate description.

Existing microwave imaging system is not considered following factor:

At first, existing microwave imaging system does not consider that microwave imaging has sparse characteristic.The sparse property of microwave imaging can be embodied directly in the transform domain of microwave imagery or image, and this is because the scene itself that is observed often has stronger correlativity.Microwave imagery is under the specific microwave observation condition, the scene echoes data are through the relevant synthetic sign of handling back electromagnetic scattering characteristic, as the different observation data collection that are observed scene (the microwave imaging raw data, through the part imaging processing data, image or at its transform domain) have a possibility of rarefaction sign.

Next, obtain and imaging processing, imaging processing and the information extraction of existing algorithm data are separate between any two.Because the observation complicacy of scene and the diversity of microwave imaging system, existing microwave imaging theory are not still supported to conduct a research from the obtaining of microwave imaging data, processing and the incorporate angle of information extraction systemicly.

The 3rd, the application of sparse signal treatment theory in microwave imaging at present all is from existing radar system and working system, be mainly used in and address the problem: in real work, because situations such as condition is limited or be interfered, when the data of obtaining can not satisfy sampling constraint or segmental defect, by interpolation and extrapolation, recover to satisfy the default sampling of Nyquist's theorem to the default signal; Utilize the classical signals disposal route to be embodied as picture and information extraction again.The sparse signal of introducing on this meaning is handled, still can't consider when the microwave imaging data are obtained that application demand carries out on purpose sparse sampling, can't solve fundamentally that application demand and microwave imaging system realize that difficulty, magnanimity information are redundant, signal Processing and characteristic information extract the contradiction between the difficulty.

List of references

[1]F.Santosa，and?W.W.Symes，Linear?inversion?of?band-limited?reflectionseismograms，SIAM?J.Sci.Statist?Comput?7(1986)，1307-1330.

[2]D.L.Donoho，Compressed?Sensing，IEEE?Trans.Inform.Theory，52，no.4，(2006)，pp.1289-1306.

[3]D.L.Donoho，For?most?large?underdetermined?systems?of?linear?equationsthe?minimal?l?1-norm?solution?is?also?the?sparsest?solution，Comm.PureAppl.Math.59，no.6(2006)，pp.797-829.

[4]E.J.Candès?and?T.Tao，Decoding?by?linear?programming，IEEE?Trans.Inform.Theory?51，4203-4215，2006.

Summary of the invention

The objective of the invention is to disclose a kind of sparse microwave imaging method, utilize the sparse property of microwave imaging, by reducing data transfer rate, make algorithm quick, easy, reduced the complexity that system realizes, system cost reduced, and be embodied as picture and feature extraction integrated.

For achieving the above object, technical solution of the present invention is:

A kind of sparse microwave imaging method, it is handled sparse signal and introduces microwave imaging, and organically combines the microwave imaging that forms;

By seeking the sparse sign territory of object being observed, in the space, time, frequency spectrum or polarizing field sparse sampling, obtain the sparse microwave signal of object being observed, through sparse microwave signal is handled and information extraction, obtain locus, scattering signatures and the kinetic characteristic of object being observed, by optimized Algorithm, the restoration scenario target information;

According to the sparse microwave imaging method of the sparse property structure of microwave imaging observation data collection, the data volume of collection is lacked than the data volume based on the systematic sampling of nyquist sampling theorem, thereby reduces the complexity of microwave imaging system.

Described sparse microwave imaging method, its described time is sparse, is that the microwave imaging middle distance is sparse to the sampling time two dimension to sampling time, orientation.

Described sparse microwave imaging method, its described space is sparse, is and position of platform, the array element relevant sparse characteristic that distributes, and by the overall treatment to each passage echoed signal, the information that realizes being observed the zone is recovered and the extraction of target signature information.

Described sparse microwave imaging method, its described frequency spectrum is sparse, be that sparse spectrum signal is adopted in radar emission and reception, handle the recovery broadband signal by sparse signal, and the super-resolution image and the multiple spectra characteristic information of object being observed are obtained in the sparse microwave imaging of sparse microwave imaging of binding time or space.

Described sparse microwave imaging method, its described polarization is sparse, is to utilize polarization, the mixed polarization technology of condensing, and realizes that by sparse POLARIZATION CHANNEL the full polarimetric SAR data of target is obtained, to reduce the requirement to radar system transceiver channel number and pulse repetition rate.

Described sparse microwave imaging method, its described joint sparse is to utilize wherein both or both above sparse characteristic of space, time, frequency spectrum or polarization, realizes the joint sparse microwave imaging.

Described sparse microwave imaging method, it comprises step:

Step S1: the signal of emission is a linear FM signal, or the random series signal, or bidimensional coded signal when empty, and the signal of emission is:

In the formula, t is the time, and a (t) is the amplitude that transmits, and f is the carrier frequency that transmits, It is the phase place that transmits;

Step S2: signal is launched by the antenna of sparse microwave imaging radar system after amplifying by power amplifier;

Step S3: set up echo model according to the geometric relationship between the form that transmits and Texas tower and the target;

Step S4: the sample mode of sparse microwave imaging radar system is even low speed sampling, stochastic sampling or process pre-service sampling afterwards;

Step S5: set up sparse microwave observation equation, to observing the recovery of scene:

S51: for the scene X with obvious sparse characteristic, the observation equation of sparse microwave is:

Y＝ФX?+N

Wherein, Y is an echo data, and Ф is an observing matrix, and N is a noise; According to sparse signal treatment theory, l ₁Optimization can well provide K element of scene X intermediate value maximum

$\hat{X}=\arg \min {\left|\right|X\left|\right|}_{1}s.t.{\left|\right|Y-\mathrm{\ΦX}\left|\right|}_2\≤\mathrm{\ϵ}$

Wherein, || || _lThe l rank norm of expression variable, min () expression minimizes operation, and ε is to be to optimize the threshold value that convergence is set when existing for noise;

S52: for there not being obviously sparse scene X, must comprise space, time, frequency spectrum, polarization or various dimensions joint sparse characteristic according to its sparse base, the sparse map table of scene X is shown:

X＝Ψθ

Wherein Ψ is sparse transformation matrix, and θ is the expression of X under the Ψ transformation matrix, and therefore the observation equation of sparse microwave imaging radar is expressed as:

Y＝ФΨθ+N

According to l ₁Optimum theory, K maximum sparse base system number of value provided by following formula:

$\widehat{\mathrm{\&theta;}}=\arg \min {\left|\right|\mathrm{\&theta;}\left|\right|}_{1}s.t.{\left|\right|Y-\mathrm{\&Phi;\&Psi;\&theta;}\left|\right|}_2<\mathrm{\&epsiv;}$

Here, || || _lThe l rank norm of expression variable, min () expression minimizes operation, and s.t. represents to make satisfied; Wherein, ε is when existing for noise, for optimizing the thresholding that convergence is set, recovers after the sparse coefficient, and scene is expressed as:

$X=\mathrm{\&Psi;}\widehat{\mathrm{\&theta;}};$

S53: obtain among the scene information X positions of elements corresponding to the locus of object being observed according to above-mentioned steps, among the X value of element corresponding to the scattering properties and the kinetic characteristic of object being observed, under the transform domain among the θ value of element corresponding to characteristic information.

Described sparse microwave imaging method, the sparse microwave imaging among its described step S5, it is relevant with the sparse property in the observation data set transformation territory of sparse property of the observation data collection of observing scene or observation scene that observation scene sparse table is levied; Utilize observation data collection space, time, frequency, polarization or the associating various dimensions of observation scene sparse; Its transform domain relation is unit transformation, discrete cosine transform, wavelet transformation or Walsh transform.

Described sparse microwave imaging method, the observation data collection of its described observation scene comprises the transform domain data of microwave imaging raw data, the data through the part imaging processing, view data or above-mentioned data.

Described sparse microwave imaging method, the waveform that transmits among its described step S1 are to depend on the observation scene characteristics, this waveform be linear FM signal, random series signal, nonlinear frequency modulation signal or when empty the bidimensional coded signal one of them.

Described sparse microwave imaging method, data capture method among its described step S4 is sampled for being lower than Nyquist rate ADC, and there not to be the fuzzy target information of recovering, its sampling rate is that strong more its ADC sampling rate of the sparse property of scene is low more according to the decision of the complexity of scene; This data capture method adopts stochastic sampling, uniform sampling or the pretreated sampling of process.

Described sparse microwave imaging method, observing matrix Ф among its described step S51 is by the waveform that transmits, antenna parameter and position, data capture method, operation wavelength, pulse repetition rate, operating distance, work visual angle, position of platform, the decision of platform motion characteristic.

Described sparse microwave imaging method, sparse microwave signal among its described step S5 is handled, be to adopt protruding optimization and linear programming, or based on the method for greedy algorithm, by the coefficient in transform domain of this signal Processing with the radar scattering characteristic or the scene objects radar scattering characteristic of restoration scenario target, thereby realization imaging processing and feature extraction are integrated.

Described sparse microwave imaging method, its be used to observe the scene target information extract and imaging integrated, utilize the sparse property of observation scene target signature, sparse microwave imaging method observing matrix and described sparse microwave signal processing.

Described sparse microwave imaging method, it is used to carry out moving object detection, utilizes the sparse property of moving target, sparse microwave imaging method observing matrix and described sparse microwave signal to handle.

Described sparse microwave imaging method, it is used for wide swath ocean target imaging, utilizes the sparse property of target on the ocean, sparse microwave imaging method observing matrix and described sparse microwave signal to handle.

Described sparse microwave imaging method, it is used to carry out synthetic aperture radar image-forming, utilizes the sparse property of the observation data set transformation domain coefficient of observation scene, sparse microwave imaging method observing matrix and described sparse microwave signal to handle.

Described sparse microwave imaging method, it is used to carry out inverse synthetic aperture radar imaging, utilizes the sparse property of aerial target, sparse microwave imaging method observing matrix and described sparse microwave signal to handle.

Described sparse microwave imaging method, it is used to carry out circumferential synthetic aperture radar imaging (CSAR), utilizes the sparse property of the observation data set transformation domain coefficient of observation scene, sparse microwave imaging method observing matrix and described sparse microwave signal to handle.

Described sparse microwave imaging method, it is used for the hyperchannel radar imagery, comprise that interference synthetic aperture radar imaging, three-dimensional imaging, MIMO (Multiple-Input Multiple-Out-put) imaging, station-keeping radar satellite imagery, multistatic radar imaging, digital beam form, space-time adaptive is handled, utilize the sparse property of each passage observation data set transformation domain coefficient of observation scene, the correlativity of observing each passage observation data of scene, sparse microwave imaging method observing matrix and described sparse microwave signal to handle.

Sparse microwave imaging method of the present invention has reduced data transfer rate, makes algorithm quick, easy, has reduced system cost, and be embodied as the picture and feature extraction carry out simultaneously.

Description of drawings

Fig. 1 is a kind of sparse microwave imaging method synoptic diagram of the present invention;

Fig. 2 is a kind of sparse microwave imaging schematic flow sheet of the present invention.

Embodiment

Sparse microwave imaging is meant to be introduced the sparse signal treatment theory microwave imaging and organically combines microwave imaging new theory, New System and the new method that forms, promptly by seeking the sparse sign territory of object being observed, in the space, time, frequency spectrum or polarizing field sparse sampling obtain the sparse microwave signal of object being observed, through signal Processing and information extraction, obtain how much of locus, scattering signatures and kinetic characteristics etc. and the physical features of object being observed.

Specific embodiment one: based on sparse sparse microwave imaging method (seeing also Fig. 1) of time

In sparse microwave imaging, exist to have obviously sparse scene X, that is to say that the most seldom in other words element of nonzero element among the X is all much larger than zero, for N element and obvious sparse scene are arranged:

||X|| ₀＜＜N

Wherein, || X|| _p, the p norm of expression X.

In sparse microwave imaging,, therefore also has sparse property though there is information redundancy in the not obvious sparse very strong correlativity that often has of the scene that is observed.We will observe scene be expressed as X, and there is sparse territory in it, that is to say that there is sparse transformation matrix Ψ in it, make

X＝Ψ·α

||α|| ₀＜＜N

It is the sparse sign of X that sparse coefficient vector α just is called, and it is a nonzero element vector seldom.The conversion of this sparse transformation matrices correspondence can be unit transformation, discrete cosine transform, wavelet transformation or Walsh transform.Show and the microwave power image of decipher being generally used for, can directly adopt wavelet field or discrete cosine transform domain etc. as sparse territory.But because power diagram similarly is that complex pattern has been carried out result after the Nonlinear Processing (mould square), complex pattern is sought suitable Ψ, make complex pattern available sparse coefficient vector α under Ψ come sparse sign.In the data acquisition of microwave imaging, the different observation data collection of object of observation comprise microwave imaging raw data, the X as a result after the data of part imaging processing (as distance to the data after the matched filter processing), image or their transform domain (can be unit transformation, discrete cosine transform, wavelet transformation or Walsh transform) data can be thought X carried out conversion process _f=f (X) just can be considered as these data sets to observe the function of scene, therefore also contained object of observation intrinsic sparse characteristic, but to the X in the different application _f, seek different sparse transformation matrix Ψ _f, to satisfy

X _f＝f(X)＝Ψ _f·α

||α|| ₀＜＜N

In a word, in actual treatment, can carry out conversion, also can carry out conversion, also can carry out conversion the microwave data that has passed through section processes to complex pattern to the microwave power image.

Step S1: the signal of emission is a bidimensional coded signal in the time of can being linear FM signal or random series signal or sky, and the signal of emission is

In the formula, a (t) is the amplitude that transmits, and f is the carrier frequency that transmits,

It is the phase place that transmits.

Step S2: signal is launched by the antenna of sparse microwave imaging radar system after amplifying by power amplifier.

Step S3: set up echo model according to the geometric relationship between the form that transmits and Texas tower and the target.For being positioned at distance by radar is the target of R, and the available echo shaping of system is

In the formula, C is the light velocity; ρ and φ are respectively the amplitude and the phase place of target; 2R/C is that transmitting through target range is the two-way time of R; N (t) is an observation noise.

So, after the echoed signal demodulation be for the scene that has I target

Step S4: the sample mode of sparse microwave imaging radar system can be the sampling after even low speed sampling, stochastic sampling and the process pre-service.Mode with even low rate sampling is the observation equation that example makes up sparse microwave imaging radar below.

For sparse microwave imaging radar, the signals sampling rate is determined according to the sparse property of signal.Degree of rarefication is defined as nonzero element shared proportion in the element of observation scene,, is defined as nonzero element and the ratio of observing situation elements in the sparse sign vector of scene for not obvious sparse scene.

p＝K/N

The sampling rate of writing sufficient nyquist sampling theorem all over is F _N, and the sampling rate of sparse microwave imaging radar is determined for following formula can be arranged:

F _C＝O(plog(1/p))·F _N

Its sampling interval is Δ T=1/F _C, then the point target sampling equation for unit amplitude can be expressed as

The form of being write as column vector is,

Because the radar return of diverse location can be regarded the time delay that transmits as and take advantage of amplitude and phase place in target, therefore adopt uniform sampling each column vector of observing matrix Ф be the time delay after the discretize of transmitting, if only fetch the limited echo of object scene region in the wave datum, adopt the observation equation of the sparse microwave imaging radar of uniform sampling to be

Wherein, the column vector number of Ф is the number behind the object scene uniform sampling.Object scene note X is

X＝[x ₁ x ₂?…?x _M] ^T

Wherein, M is the length of X.x _i＝ρ _iexp(jφ _i)。But the scattering properties of representing the i scene objects.Therefore, the signal acquisition based on sparse microwave imaging radar can be expressed as

$y\left(n\right)=S\&CircleTimes;X=\underset{m=1}{\overset{M}{\mathrm{\&Sigma;}}}s(n-m)x\left(m\right)=\underset{m=1}{\overset{M}{\mathrm{\&Sigma;}}}s(n-m){\mathrm{\&rho;}}_M\exp \left(j{\mathrm{\&phi;}}_M\right)$

Wherein

The expression convolution, the form that is expressed as matrix is

Therefore, for scene X sparse on transform domain Ψ, its observation equation can be described as

Y＝ФX＝ФΨα

Step S5: to the recovery of observation scene

S51: for the scene X with obvious sparse characteristic, the observation equation of sparse microwave is

Y＝ФX+N

According to sparse signal treatment theory, l ₁Optimization can well provide K element of scene X intermediate value maximum

$\hat{X}=\arg \min {\left|\right|X\left|\right|}_1\mathrm{st}.{\left|\right|Y-\mathrm{\&Phi;X}\left|\right|}_2\&le;\mathrm{\&epsiv;}$

Wherein ε is when existing for noise, is to optimize the thresholding that convergence is set.Recover

Can well represent scene.

S52: for there not being obviously sparse scene X, must comprise space, time, frequency spectrum, polarization or various dimensions joint sparse characteristic according to its sparse base, the sparse conversion of scene X can be expressed as

X＝Ψθ

The observation equation of sparse microwave imaging radar can be expressed as

Y＝ФΨθ+N

According to l ₁Optimum theory, K maximum sparse base system number of value can be provided by following formula:

$\widehat{\mathrm{\&theta;}}=\arg \min {\left|\right|\mathrm{\&theta;}\left|\right|}_{1}s.t.{\left|\right|Y-\mathrm{\&Phi;\&Psi;\&theta;}\left|\right|}_2\&le;\mathrm{\&epsiv;}$

Wherein ε is when existing for noise, and s.t. represents to make and satisfies, and is to optimize the thresholding that convergence is set.Recover after the sparse coefficient, scene can be expressed as

$X=\mathrm{\&Psi;}\widehat{\mathrm{\&theta;}}$

Specific embodiment two: based on the sparse microwave imaging of time and space joint sparse

Step 1-step 4: adopt the echo data step S5 that obtains a plurality of passages with embodiment one identical method, can obtain the echo samples formula according to step 1-step 4

Y＝ФX+N

Following formula is described is the observed quantity obtain manner of time during sparse sampling, below sparse and sparse signals collecting mode and the disposal route in space of analysis joint time.Consider that sparse microwave imaging system has I sampled aperture (space).The data of each sampled aperture are obtained equation

T _i＝Ф _iX+N _i?i＝1，…，I

Wherein, Y _i, Ф _iAnd N _iBe respectively observed quantity, observing matrix and the observation noise in i aperture.All sampled points can be expressed as a new vector

$Y^{\&prime;}=\left[\begin{array}{c}{Y}_1\\ {\mathrm{<figure-callout\; id="2"\; label="Y"\; filenames="HSA00000083437400021.png"\; state="\{\{state\}\}">Y</figure-callout>}}_2\\ .\\ .\\ .\\ T_I\end{array}\right]=\left[\begin{array}{c}{\mathrm{\&Phi;}}_1\\ {\mathrm{\&Phi;}}_2\\ .\\ .\\ .\\ {\mathrm{\&Phi;}}_I\end{array}\right]X+\left[\begin{array}{c}{N}_1\\ {\mathrm{<figure-callout\; id="2"\; label="N"\; filenames="HSA00000083437400021.png"\; state="\{\{state\}\}">N</figure-callout>}}_2\\ .\\ .\\ .\\ N_I\end{array}\right]$

The space is sparse to be meant that being used for is some apertures of sparse distribution on the space to the scene observed samples.Sampled data after the sparse sampling of space can be expressed as

Wherein, Δ is a space sparse sampling matrix, is used to describe space sparse sampling problem.Δ _iBe and Ф _iEqual-sized matrix, it is unit matrix or null matrix, it is a unit matrix when i channel sample, is null matrix when not participating in sampling.If order

Then the data of room and time joint sparse are obtained the step that can be expressed as among the S5.

Y＝ФX+N

Step S6: to the recovery of observation scene.The implementation method of this step is identical with step S5 among the embodiment one.

Claims (20)

1. a sparse microwave imaging method is characterized in that, sparse signal is handled introduced microwave imaging, and organically combine the microwave imaging that forms;

Sparse sign territory by object being observed, in the space, time, frequency spectrum or polarizing field sparse sampling, obtain the sparse microwave signal of object being observed, through sparse microwave signal is handled and information extraction, obtain locus, scattering signatures and the kinetic characteristic of object being observed, by optimized Algorithm, the restoration scenario target information;

According to the sparse microwave imaging method of the sparse property structure of microwave imaging observation data, the data volume of collection is lacked than the data volume based on the systematic sampling of nyquist sampling theorem, thereby reduces the complexity of microwave imaging system.

2. sparse microwave imaging method as claimed in claim 1 is characterized in that the described time is sparse, is that the microwave imaging middle distance is sparse to the sampling time two dimension to sampling time, orientation.

3. sparse microwave imaging method as claimed in claim 1, it is characterized in that described space is sparse, be and position of platform, the relevant sparse characteristic of array element distribution, by overall treatment, realize being observed the information recovery in zone and the extraction of target signature information to each passage echoed signal.

4. sparse microwave imaging method according to claim 1, it is characterized in that, described frequency spectrum is sparse, be that sparse spectrum signal is adopted in radar emission and reception, handle the recovery broadband signal by sparse signal, and the super-resolution image and the multiple spectra characteristic information of object being observed are obtained in the sparse microwave imaging of sparse microwave imaging of binding time or space.

5. sparse microwave imaging method according to claim 1, it is characterized in that, described polarization is sparse, be to utilize polarization, the mixed polarization technology of condensing, full polarimetric SAR data by sparse POLARIZATION CHANNEL realization target is obtained, to reduce the requirement to radar system transceiver channel number and pulse repetition rate.

6. sparse microwave imaging method according to claim 1 is characterized in that, described joint sparse is to utilize wherein both or both above sparse characteristic of space, time, frequency spectrum or polarization, realizes the joint sparse microwave imaging.

7. sparse microwave imaging method as claimed in claim 1 is characterized in that, comprises step:

Step S1: the signal of emission is a linear FM signal, or the random series signal, or bidimensional coded signal when empty;

Step S2: signal is launched by the antenna of sparse microwave imaging radar system after amplifying by power amplifier;

Step S3: set up echo model according to the geometric relationship between the form that transmits and Texas tower and the target;

Step S4: the sample mode of sparse microwave imaging radar system is even low speed sampling, stochastic sampling or process pre-service sampling afterwards;

Step S5: set up sparse microwave observation equation, realize recovery to the observation scene:

S51: for the scene X with obvious sparse characteristic, the observation equation of sparse microwave is:

Y＝ΦX+N

Wherein, Y is an echo data, and Φ is an observing matrix, and N is a noise; According to sparse signal treatment theory, l ₁Optimization can well provide K element of scene X intermediate value maximum:

$\hat{X}=\arg \min {\left|\right|X\left|\right|}_l\mathrm{st}.{\left|\right|Y-\mathrm{\&Phi;X}\left|\right|}_2\&le;\mathrm{\&epsiv;}$

Wherein, || || _lThe l rank norm of expression variable, min () expression minimizes operation, and s.t. represents to make and satisfies, and ε is to be to optimize the threshold value that convergence is set when existing for noise;

S52: for there not being obviously sparse scene X, must comprise space, time, frequency spectrum, polarization or various dimensions joint sparse characteristic according to its sparse base, the sparse map table of scene X is shown:

X＝Ψθ

Wherein Ψ is sparse transformation matrix, and θ is the expression of X under the Ψ transformation matrix, and therefore the observation equation of sparse microwave imaging radar is expressed as:

Y＝ΦΨθ+N

According to l ₁Optimum theory, K maximum sparse base system number of value provided by following formula:

$\widehat{\mathrm{\&theta;}}=\arg \min {\left|\right|\mathrm{\&theta;}\left|\right|}_l\mathrm{st}.{\left|\right|Y-\mathrm{\&Phi;\&Psi;\&theta;}\left|\right|}_2<\mathrm{\&epsiv;}$

Here, || || _lThe l rank norm of expression variable, min () expression minimizes operation, and s.t. represents to make satisfied; Wherein, ε is when existing for noise, for optimizing the thresholding that convergence is set, recovers after the sparse coefficient, and scene is expressed as:

$X=\mathrm{\&Psi;}\widehat{\mathrm{\&theta;}};$

S53: obtain among the scene information X positions of elements corresponding to the locus of object being observed according to above-mentioned steps, among the X value of element corresponding to the scattering properties and the kinetic characteristic of object being observed, under the transform domain among the θ value of element corresponding to characteristic information.

8. sparse microwave imaging method as claimed in claim 7, it is characterized in that, it is relevant with the sparse property in the observation data set transformation territory of sparse property of the observation data collection of observing scene or observation scene that sparse microwave imaging among the described step S5, observation scene sparse table are levied; Utilize observation data collection space, time, frequency, polarization or the associating various dimensions of observation scene sparse; Its transform domain relation, or unit transformation, discrete cosine transform, wavelet transformation or Walsh transform.

9. sparse microwave imaging method as claimed in claim 8 is characterized in that, the observation data collection of described observation scene comprises the transform domain data of microwave imaging raw data, the data through the part imaging processing, view data or above-mentioned data.

10. sparse microwave imaging method according to claim 7, it is characterized in that, the waveform that transmits among the described step S 1 is to depend on the observation scene characteristics, this waveform be linear FM signal, random series signal, nonlinear frequency modulation signal or when empty the bidimensional coded signal one of them.

11. sparse microwave imaging method according to claim 7, it is characterized in that, data capture method among the described step S4, for being lower than Nyquist rate ADC sampling, and there not to be the fuzzy target information of recovering, its sampling rate is that strong more its ADC sampling rate of the sparse property of scene is low more according to the decision of the complexity of scene; This data capture method adopts stochastic sampling, uniform sampling or the pretreated sampling of process.

12. sparse microwave imaging method according to claim 7, it is characterized in that, observing matrix Φ among the described step S51 is by the waveform that transmits, antenna parameter and position, data capture method, operation wavelength, pulse repetition rate, operating distance, work visual angle, position of platform, the decision of platform motion characteristic.

13. sparse microwave imaging method as claimed in claim 7, it is characterized in that, sparse microwave signal among the described step S5 is handled, be to adopt protruding optimization and linear programming, or based on the method for greedy algorithm, by the coefficient in transform domain of this signal Processing with the radar scattering characteristic or the scene objects radar scattering characteristic of restoration scenario target, thereby realization imaging processing and feature extraction are integrated.

14. sparse microwave imaging method according to claim 1, it is characterized in that, be used to observe extraction of scene target information and imaging integrated, utilize the sparse property of observation scene target signature, sparse microwave imaging method observing matrix and described sparse microwave signal to handle.

15. sparse microwave imaging method according to claim 1 is characterized in that, is used to carry out moving object detection, utilizes the sparse property of moving target, sparse microwave imaging method observing matrix and described sparse microwave signal to handle.

16. sparse microwave imaging method according to claim 1 is characterized in that, is used for wide swath ocean target imaging, utilizes the sparse property of target on the ocean, sparse microwave imaging method observing matrix and described sparse microwave signal to handle.

17. sparse microwave imaging method according to claim 1, it is characterized in that, be used to carry out synthetic aperture radar image-forming, utilize the sparse property of the observation data set transformation domain coefficient of observation scene, sparse microwave imaging method observing matrix and described sparse microwave signal to handle.

18. sparse microwave imaging method according to claim 1 is characterized in that, is used to carry out inverse synthetic aperture radar imaging, utilizes the sparse property of aerial target, sparse microwave imaging method observing matrix and described sparse microwave signal to handle.

19. sparse microwave imaging method according to claim 1, it is characterized in that, be used to carry out the circumferential synthetic aperture radar imaging, utilize the sparse property of the observation data set transformation domain coefficient of observation scene, sparse microwave imaging method observing matrix and described sparse microwave signal to handle.

20. sparse microwave imaging method according to claim 1, it is characterized in that, be used for the hyperchannel radar imagery, comprise that interference synthetic aperture radar imaging, three-dimensional imaging, MIMO (Multiple-Input Multiple-Out-put) imaging, station-keeping radar satellite imagery, multistatic radar imaging, digital beam form, space-time adaptive is handled, utilize the sparse property of each passage observation data set transformation domain coefficient of observation scene, the correlativity of observing each passage observation data of scene, sparse microwave imaging method observing matrix and described sparse microwave signal to handle.

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