CN104898119A - Correlation function-based moving-target parameter estimation method - Google Patents

Abstract

The invention discloses a correlation function-based moving-target parameter estimation method. The method comprises steps of 1: reading in original moving target echo data and related imaging parameters; 2: performing Fourier transform processing for azimuth; 3: performing compensation by multiplying azimuth and CS factors; 4: performing Fourier transform processing for range distance; 5: multiplying the range distance and distance compensation factors so as to process distance compressing processing; 6: performing Fourier inversion processing for range distance; 7: performing correlation function processing for distance-Doppler domain; 8: performing Fourier inversion processing for azimuth; 9: performing zero filling for frequency domain, and adding sampling in time domain so as to perform interpolation processing or related processing results and get the maximum value; and 10: estimating target speed via the related maximum value. According to the invention, estimation of target azimuth and radial speed are simultaneously achieved, precision for estimation speed of moving targets of a current satellite born SAR is increased, and a novel detection method is provided.

Description

A kind of moving target parameter estimation method based on related function

Technical field

The invention belongs to signal transacting field, particularly a kind of synthetic-aperture radar (Synthetic Aperture Radar, SAR) field is based on the moving target parameter estimation method of related function.

Background technology

SAR is a kind of high-resolution imaging radar, can obtain large-area high-resolution radar image.It is a kind of active remote sensing device being operated in microwave frequency band simultaneously, do not limit by region, energy round-the-clock, round-the-clock enforcement remote sensing of the earth imaging observation task, and natural vegetation, artificial camouflage etc. can be penetrated, compared with conventional optical image, significantly improve the information capture ability of radar.Therefore, SAR is widely used.Ground and movement overseas target detection technique are modern radar tactical reconnaissance field important directions both at home and abroad.As far back as during the Second World War, the Doppler shift just successfully utilizing moving target to produce is to distinguish static target and moving target, and along with the development of Radar Technology, the Detection for Moving Target also development of SAR, is widely used in military affairs, the every field such as civilian.

Current existing moving target detecting method is mainly divided into following a few class:

1. based on the moving target detect of single channel system, its principle directly detects according to the echoed signal characteristic of moving-target, but to single channel system, clutter recognition is difficult to realize, and clutters a large amount of in echo makes moving-target be difficult to detect;

2. based on the moving target detect of multi-channel system, its principle first carries out clutter recognition, moving target detect is carried out again according to the signal after clutter recognition, main disposal route has displaced phase center antenna technology DPCA, Along-track interferometry ATI, space-time adaptive treatment S TAP etc., but there is the problem of blind speed and velocity ambiguity in this detection method, and system is more complicated;

3. look side ways dualbeam (Bi-Directional SAR forwards, backwards based on orientation, BIDI) moving target detect, its principle is the wave beam that single star single antenna produces two different directions simultaneously, realize the repeatedly observation to target and imaging, the change of moving-target position between multiple image is utilized to detect and estimate moving-target speed, utilize in the method single star can realize target orientation to velocity measuring and estimation, utilize double star can realize target radial velocity detect and estimate.

Because target azimuth causes image defocus to speed, radial velocity causes Doppler shift, and when target has larger speed, image is difficult to focus on, and increases the difficulty of detection.

Summary of the invention

The object of the invention is to solve the problem, propose a kind of moving target parameter estimation method based on related function, the present invention is based on single star one channel model, in conjunction with the method for traditional matched filtering and refocusing, system is simple, the present invention's definition take target velocity as the related function of independent variable, traversal post analysis associated processing outcomes is carried out to independent variable, when independent variable is equal with target true velocity, associated processing outcomes is optimum, achieves the detection of moving target, velocity estimation and refocusing.

Based on a moving target parameter estimation method for related function, comprise following step:

Step one: read in raw radar data and dependent imaging parameter;

Read in the moving-target satellite-borne SAR two dimension original echo complex data S based on positive side-looking band ₀and corresponding imaging parameters, specifically comprise: orientation is to sampling number N _a, distance is to sampling number N _r, signal sampling rate f _s, signal bandwidth Bw, pulse width τ, chirp rate k, pulse repetition rate PRF, with reference to oblique distance R _ref, doppler centroid f _d0, doppler frequency rate f _r0, satellite velocities V _p, signal wavelength lambda, aspect is to bandwidth B _a, light velocity c.

Step 2: orientation is to Fourier transform process;

By two-dimentional echo simulation complex data S ₀carry out orientation to Fourier transform process: complex data S ₀carry out Fast Fourier Transform (FFT) (FFT) along each distance to (by row), obtain azimuth spectrum (distance-Doppler territory) complex data S ₁;

Step 3: CS factor treatment is multiplied by distance-Doppler territory;

The complex data S that step 2 is obtained ₁with the CS factor Ψ of counterparty's bit frequency ₁be multiplied, the complex data S after being compensated ₂;

Step 4: distance is to Fourier transform process;

The complex data S that step 3 is obtained ₂carry out Fast Fourier Transform (FFT) (FFT) along each orientation to (by row), obtain 2-d spectrum complex data S ₃;

Step 5: two-dimensional frequency carries out compensated distance process;

The complex data S that step 4 is obtained ₃with corresponding compensated distance factor Ψ ₂be multiplied, slightly focused on complex data S ₄;

Step 6: distance is to inverse Fourier transform process;

The complex data S that step 5 is obtained ₄carry out inverse fast Fourier transform (IFFT) along each orientation to (by row), obtain orientation frequency domain (distance-Doppler territory) complex data S ₅;

Step 7: relevant treatment is carried out in distance-Doppler territory;

Definition related function Ψ ₃, related function independent variable is that target azimuth is to speed V _awith target radial speed V _r, by related function Ψ ₃the complex data S obtained with step 6 ₅carry out relevant treatment, obtain complex data S ₆, as independent variable V _a, V _rwith the true bearing of target to speed V _sawith radial velocity V _srtime equal, relevant treatment reaches optimum.

Step 8: orientation is to inverse Fourier transform process;

The complex data S that step 7 is obtained ₆carry out inverse fast Fourier transform (IFFT) along each distance to (by row), obtain time domain complex data S ₇;

Step 9: to imaging results S ₇carry out interpolation processing maximizing;

To imaging results S ₇carry out frequency domain zero padding, time domain increases the interpolation processing of sampling, ask more accurate maximal value; Return step 7, complete independent variable V _aand V _rtraversal;

Step 10: utilize relevant treatment maximal value to estimate moving-target speed;

To in step 9 based on independent variable V _aand V _rthe relevant treatment maximal value result mapping obtained, estimates the speed of moving target.

The invention has the advantages that:

(1) the present invention proposes a kind of moving target parameter estimation method based on related function, can simultaneously realize target orientation to the detection with radial velocity.

(2) the present invention proposes a kind of moving target parameter estimation method based on related function, there is higher velocity measuring precision.The step-length estimated to speed and radial velocity due to target azimuth in the present invention can self-definedly be arranged, and therefore can realize high-precision speed parameter and estimate, and can carry out high-precision focal imaging according to the velocity to moving target detected to target.

(3) the present invention proposes a kind of moving target parameter estimation method based on related function, have the advantages that applicability is strong, airborne and different imaging patterns that are satellite-borne SAR can be applicable to simultaneously.

Accompanying drawing explanation

Fig. 1 is the process flow diagram of a kind of moving target parameter estimation method based on related function that the present invention proposes;

Fig. 2 is that embodiment moving target has the orientation of 2m/s to two-dimensional result figure during speed;

Fig. 3 is the two-dimensional result figure of embodiment moving target when having a radial velocity of 2m/s;

Fig. 4 is that embodiment moving target has the orientation of 2m/s respectively to three-dimensional result figure when speed and radial velocity;

Fig. 5 is that embodiment moving target has the orientation of 8m/s to two-dimensional result figure during speed;

Fig. 6 is the two-dimensional result figure of embodiment moving target when having a radial velocity of 8.6m/s;

Fig. 7 is that embodiment moving target has the orientation of 8m/s respectively to three-dimensional result figure when speed and 8.6m/s radial velocity;

Fig. 8 is that embodiment moving target has the orientation of 15m/s to two-dimensional result figure during speed;

Fig. 9 is the two-dimensional result figure of embodiment moving target when having a radial velocity of 16.2m/s;

Figure 10 is that embodiment moving target has the orientation of 15m/s respectively to three-dimensional result figure when speed and 16.2m/s radial velocity;

Embodiment

Below in conjunction with drawings and Examples, the present invention is described in further detail.

The present invention proposes a kind of moving target parameter estimation method based on related function, process to as if the spaceborne stripmap SAR pattern of positive side-looking under moving-target raw radar data, the result obtained be estimate moving target orientation to speed and radial velocity, as shown in Figure 1, concrete steps are as follows for flow process:

Step one: read in raw radar data and dependent imaging parameter;

Read in based on moving-target two dimension original echo emulation complex data S under the spaceborne stripmap SAR pattern of positive side-looking ₀and corresponding imaging parameters.Wherein S ₀be a two-dimensional complex number group, size is N _a× N _r, imaging parameters specifically comprises: orientation is to sampling number N _a, distance is to sampling number N _r, signal sampling rate f _s, signal bandwidth Bw, pulse width τ, chirp rate k, pulse repetition rate PRF, with reference to oblique distance R _ref, doppler centroid f _d0, doppler frequency rate f _r0, satellite platform speed V _p, signal wavelength lambda, aspect is to bandwidth B _a, light velocity c.

Step 2: orientation is to Fourier transform process;

By original complex according to S ₀(i, j) carries out Fast Fourier Transform (FFT) (FFT) along each distance to (by row), obtains orientation frequency domain (distance-Doppler territory) complex data S ₁(i, j);

S ₁(:,j)＝FFT(S ₀(:,j)) (1)

Wherein, S ₁(:, j) represent S ₁jth row, S ₀(:, j) represent S ₀jth row, FFT () represent Fast Fourier Transform (FFT) is carried out to one-dimension array.

Step 3: CS factor treatment is multiplied by distance-Doppler territory;

The complex data S that step 2 is obtained ₁(i, j) is with CS factor Ψ ₁(i, j) is multiplied, the complex data S after being compensated ₂(i, j);

(1) construct two one-dimensional sequence i, j, wherein i represents orientation to sequence (OK), and j represents distance to sequence (row);

i＝[1,2,…,N _a] (2)

j＝[1,2,…,N _r]

(2) distance-Doppler territory two-dimensional complex number is obtained according to S ₁the orientation frequency f that (i, j) each row is corresponding _ai the distance of () and each row correspondence is to moment τ (j);

$f_a\left(i\right)=\frac{i-N_a/2}{N_a}\·\mathrm{PRF}-f_{d0}---\left(3\right)$

$\mathrm{\τ}\left(j\right)=\frac{j-N_r/2}{f_s}+\frac{2R_{\mathrm{ref}}}{c}---\left(4\right)$

(3) velocity equivalent V is calculated by imaging parameters _refwith equivalent squint angle φ _ref;

$V_{\mathrm{ref}}=\sqrt{\frac{\mathrm{\λ}{R}_{\mathrm{ref}}{f}_{r0}}{2}+{\left(\frac{\mathrm{\λ}{f}_{d0}}{2}\right)}^2}---\left(5\right)$

${\mathrm{\φ}}_{\mathrm{ref}}=\arccos [-\frac{\mathrm{\λ}{f}_{d0}}{2V_{\mathrm{ref}}}]---\left(6\right)$

(4) CS factor Ψ is obtained ₁(τ, f) is as follows;

Ψ ₁(τ,f _a)＝exp{-jπk _rC _s[τ-τ _ref(f _a)] ²} (7)

Wherein: a=λ f _a/ 2V _ref, $k_r=\frac{k}{1+k{R}_{\mathrm{ref}}\sin {\mathrm{\φ}}_{\mathrm{ref}}\frac{2\mathrm{\λ}}{c^2}\·\frac{a^2}{{[1-a^2]}^{3/2}}},C_s=\frac{\sin {\mathrm{\φ}}_{\mathrm{ref}}}{\sqrt{1-a^2}}-1,{\mathrm{\τ}}_{\mathrm{ref}}\left(f\right)=\frac{2}{c}{R}_{\mathrm{ref}}[1+C_s]$

5) by S in step 2 ₁(i, j) and CS fac-tor, obtain complex data S ₂(i, j);

S ₂(i,j)＝S ₁(i,j)·Ψ ₁(τ,f _a) (8)

Step 4: distance is to Fourier transform process;

The complex data S that step 3 is obtained ₂(i, j) carries out distance to Fast Fourier Transform (FFT) (FFT) along each orientation to (by row), obtains two-dimensional frequency complex data S ₃(i, j);

S ₃(i,:)＝FFT(S ₂(i,:)) (9)

Wherein, S ₂(i :) represent S ₂the i-th row, S ₃(i :) represent S ₃the i-th row.

Step 5: two-dimensional frequency carries out compensated distance process;

The complex data S that step 4 is obtained ₃(i, j) is with corresponding compensated distance factor Ψ ₂(f _a, f _τ) be multiplied, obtain Range compress complex data S ₄.

(1) two-dimensional frequency complex data S is obtained ₃(i, j) orientation frequency f that often row is corresponding _a(i) and the frequency of distance f often arranging correspondence _τ(j);

$f_a\left(i\right)=\frac{i-N_a/2}{N_a}\·\mathrm{PRF}-f_{d0}$

$f_{\mathrm{\τ}}\left(j\right)=\frac{j-N_r/2}{\mathrm{Nr}}\·f_s---\left(10\right)$

(2) compensated distance factor Ψ ₂(f _a, f _τ) as follows;

${\mathrm{\Ψ}}_2(f_a,f_{\mathrm{\τ}})=\exp \{-\mathrm{j\π}[\frac{{f_{\mathrm{\τ}}}^2}{k_r[1+C_s]}-\frac{4R_{\mathrm{ref}}}{c}{f}_{\mathrm{\τ}}{C}_s\left]\right\}---\left(11\right)$

(3) by step 4 complex data S ₃(i, j) same distance compensating factor Ψ ₂(f _a, f _τ) be multiplied, obtain the two-dimensional complex number after Range compress according to S ₄(i, j)

S ₄(i,j)＝S ₃(i,j)·Ψ ₂(f _a,f _τ) (12)

Step 6: distance is to inverse Fourier transform process;

The complex data S that step 5 is obtained ₄(i, j) carries out inverse fast Fourier transform (IFFT) along each orientation to (by row), obtains orientation frequency domain (distance-Doppler territory) complex data S ₅(i, j);

S ₅(i,:)＝IFFT(S ₄(i,:)) (13)

Wherein, S ₄(i :) represent S ₄the i-th row, S ₅(i :) represent S ₅the i-th row, IFFT () represent inverse fast Fourier transform is carried out to one-dimension array.

Step 7: relevant treatment is carried out in distance-Doppler territory;

The complex data S that step 6 is obtained ₅(i, j) and related function Ψ ₃(f _a, τ, V _a, V _r) be multiplied, obtain the complex data S after relevant treatment ₆(i, j);

(1) Offered target orientation is first distinguished to speed V _awith radial velocity V _rinitial value V _{a, min}, V _{r, min}, span [V _{a, min}, V _{a, max}], [V _{r, min}, V _{r, max}], and step delta V _a, Δ V _r;

(2) according to arrange orientation to speed V _acalculate the relative velocity V of equivalence _{s_t}; According to the radial velocity V arranged _rcomputer azimuth spectrum position shifted by delta f _d;

V _{s_t}＝V _ref-V _a(14)

$\mathrm{\Δ}{f}_d=\frac{2V_r}{\mathrm{\λ}}---\left(15\right)$

(3) according to the change of azimuth spectrum position and orientation to corresponding orientation frequency f (i) of bandwidth calculation;

In orientation to counting $i\∈[(1-\frac{B_a+2\mathrm{\Δ}{f}_a}{\mathrm{PRF}})\frac{N_a}{2},(1+\frac{B_a-2\mathrm{\Δ}{f}_a}{\mathrm{PRF}})\frac{N_a}{2}]---\left(16\right)$

Get orientation frequency $f\left(i\right)=\frac{i-N_a/2}{N_a}\mathrm{PRF}-f_{d0}-\mathrm{\Δ}{f}_d---\left(17\right)$

F (i)=0 is got at all the other bearing point places

Illustrate: if only need detect target azimuth to speed, then only need Offered target orientation to speed initial value, scope and step-length, count in orientation i ∈ [0, N _a] time get orientation frequency all the other same above-mentioned steps; If only target radial speed need be detected, then only need Offered target radial velocity initial value, scope and step-length, then the relative velocity V of equivalence _{s_t}=V _ref, all the other same above-mentioned steps.Can estimate that moving target orientation is to speed or radial velocity respectively based on this.

(4) definition is with V _a, V _rfor the related function Ψ of independent variable ₃(f, τ, V _a, V _r) as follows;

${\mathrm{\Ψ}}_3=(f,\mathrm{\τ},V_a,V_r)=\exp \{-\mathrm{j\π}[\frac{4R}{\mathrm{\λ}}{C}_s\_1-\frac{4k_r}{c^2}({C_s}^2+C_s){(R-R_{\mathrm{ref}})}^2-\frac{2\mathrm{Rf}}{V_{\mathrm{ref}}}\cos {\mathrm{\φ}}_{\mathrm{ref}}\left]\right\}---\left(18\right)$

Wherein $R=\frac{\mathrm{c\τ}}{2},a^{\′}=\frac{\mathrm{\λf}}{2V_{s\_t}},C_s\_1=1-\sin {\mathrm{\φ}}_{\mathrm{ref}}\sqrt{1-a^{\′2}}$

(5) complex data S step 6 obtained ₅(i, j) and related function Ψ ₃(f, τ, V _a, V _r) be multiplied, obtain the complex data S after being correlated with ₆(i, j):

S ₆(i,j)＝S ₅(i,j)·Ψ ₃(f,τ,V _a,V _r) (19)

Step 8: orientation is to inverse Fourier transform process;

The complex data S that step 7 is obtained ₆(i, j) carries out inverse fast Fourier transform (IFFT) along each distance to (by row), obtains final imaging results S ₇(i, j);

S ₇(:,j)＝IFFT(S ₆(:,j)) (20)

Wherein, S ₆(:, j) represent S ₆jth row, S ₇(:, j) represent S ₇jth row.

Step 9: to imaging results S ₇carry out interpolation processing maximizing;

Frequency domain zero padding, time domain increases the method for sampling to imaging results S ₇(i, j) carries out interpolation processing;

(1) the complex data S asking step 8 to obtain ₇maximal value and position (m, n) thereof;

(2) centered by (m, n), intercept small block data and to its carry out orientation to distance to Fourier transform, carry out zero padding process at frequency domain, then the chunk data obtained is carried out orientation to distance to inverse Fourier transform process;

(3) the maximal value Max of time domain chunk data is asked, i.e. relevant treatment maximal value Max;

Return step 7 (1) step, complete independent variable V _aand V _rtraversal, obtain based on independent variable V _aand V _rrelevant treatment maximal value matrix M ax (V _a, V _r);

Illustrate: when an estimating target orientation is to speed, the relevant treatment maximal value matrix obtained is Max (V _a); During estimating target radial velocity, the relevant treatment maximal value matrix obtained is Max (V _r); When estimating target orientation is to speed and radial velocity simultaneously, the relevant treatment maximal value matrix obtained is Max (V _a, V _r);

Step 10: utilize relevant treatment maximal value Matrix Estimation velocity to moving target;

This step divides following three kinds of situations to carry out velocity to moving target estimation:

(1) when an estimating target orientation is to speed, to the relevant treatment maximal value matrix M ax (V obtained _a) draw X-Y scheme, speed corresponding to figure maximal value is the moving target orientation of estimation to speed;

(2) during estimating target radial velocity, to the relevant treatment maximal value matrix M ax (V obtained _r) draw X-Y scheme, speed corresponding to figure maximal value is the moving target radial velocity of estimation;

(3) when estimating target orientation is to speed and radial velocity, to the relevant treatment maximal value matrix M ax (V obtained simultaneously _a, V _r) drawing 3 D graphics, the speed that curve map peak is corresponding is the moving target orientation of estimation to speed and radial velocity.

Embodiment:

The present embodiment proposes a kind of moving target parameter estimation method based on related function, and simulating scenes is 1 × 1 dot matrix, and according to point target actual speed and direction, point three kinds of situations detect: hypothetical target only has orientation to speed; Hypothetical target only has radial velocity; Hypothetical target has orientation to speed and radial velocity simultaneously.The imaging parameters related in its imaging process is as shown in table 1.

Table 1 embodiment parameter

The present embodiment specifically comprises the following steps:

Step one: read in based on moving-target two dimension original echo emulation complex data S under spaceborne positive side-looking stripmap SAR pattern ₀and corresponding imaging parameters.Wherein, S ₀be two-dimensional complex number group, size is 8192 × 4096, and concrete imaging parameters is as shown in table 1;

Step 2: by original complex according to S ₀(i, j) carries out Fast Fourier Transform (FFT) (FFT) along each distance to (by row), obtains orientation frequency domain (distance-Doppler territory) complex data S ₁(i, j), concrete enforcement is undertaken by formula (1);

Step 3: the complex data S that step 2 is obtained ₁(i, j) is with CS factor Ψ ₁(τ, f _a) be multiplied, the complex data S after being compensated ₂(i, j), concrete operation step:

(1) two one-dimensional sequence i, j are constructed, shown in (2), i=[1,2 ..., 8192], j=[1,2 ..., 4096];

(2) distance-Doppler territory two-dimensional complex number is obtained according to S ₁the orientation frequency f that (i, j) each row is corresponding _ai the distance of () and each row correspondence is to moment τ (j), concrete enforcement is undertaken by formula (3) and formula (4);

(3) velocity equivalent V is calculated by imaging parameters _refwith equivalent squint angle φ _ref, concrete enforcement is undertaken by formula (5) and formula (6);

(4) CS factor Ψ is asked ₁(τ, f _a), concrete enforcement is undertaken by formula (7);

(5) by S in step 2 ₁complex data S after (i, j) and CS fac-tor are compensated ₂(i, j), concrete enforcement is undertaken by formula (8);

Step 4: the complex data S that step 3 is obtained ₂(i, j) carries out distance to Fast Fourier Transform (FFT) (FFT) along each orientation to (by row), obtains two-dimensional frequency complex data S ₃(i, j), concrete enforcement is undertaken by formula (9);

Step 5: the complex data S that step 4 is obtained ₃(i, j) is with corresponding compensated distance factor Ψ ₂(f _a, f _τ) be multiplied, obtain Range compress complex data S ₄, concrete operation step:

(1) two-dimensional frequency complex data S is obtained ₃(i, j) often arranges corresponding frequency of distance f _τ(j), concrete enforcement is undertaken by formula (10);

(2) compensated distance factor Ψ is asked ₂(f _a, f _τ), concrete enforcement is undertaken by formula (11);

(3) complex data S step 4 obtained ₃(i, j) and compensated distance factor Ψ ₂(f _a, f _τ) be multiplied, concrete enforcement is undertaken by formula (12).

Step 6: the complex data S that step 5 is obtained ₄(i, j) carries out inverse fast Fourier transform (IFFT) along each orientation to (by row), obtains range-Dopler domain complex data S ₅(i, j), concrete enforcement is undertaken by formula (13).

Step 7: the complex data S that step 6 is obtained ₅(i, j) and related function Ψ ₃(f _a, τ, V _a, V _r) be multiplied, obtain the complex data S after relevant treatment ₆(i, j), concrete operation step:

(1) Offered target orientation is first distinguished to speed V _awith radial velocity V _rinitial value V _{a, min}, V _{r, min}, span [V _{a, min}, V _{a, max}], [V _{r, min}, V _{r, max}], and step delta V _a, Δ V _r.In the present embodiment, orientation is [-30m/s, 30m/s] to speed and radial velocity scope, and an estimating target orientation is 0.1m/s to establishing its step-length when speed or target radial speed; When estimating target orientation is to speed and radial velocity, if its step-length is respectively 1m/s simultaneously;

(2) calculate the relative velocity V of equivalence to speed according to the target azimuth arranged _{s_t}, have and implement to be undertaken by formula (14); According to the target radial speed V arranged _rcomputer azimuth spectrum position shifted by delta f _d, concrete enforcement is undertaken by formula (15);

(3) according to the change of azimuth spectrum position and orientation to orientation frequency f (i) corresponding to bandwidth calculation, concrete enforcement is undertaken by formula (16) and formula (17).

(4) calculate with speed V _a, V is the related function Ψ of independent variable ₃(f, τ, V _a, V _r), concrete enforcement is undertaken by formula (18);

(5) complex data S step 6 obtained ₅(i, j) and above-mentioned compensating factor Ψ ₃(f, τ, V _a, V _r) being multiplied obtains complex data S ₆(i, j), concrete enforcement is undertaken by formula (19);

Step 8: the complex data S that step 7 is obtained ₆(i, j) carries out inverse fast Fourier transform (IFFT) along each distance to (by row), obtains final imaging results S ₇(i, j), concrete enforcement is undertaken by formula (20).

Step 9: to imaging results S ₇(i, j) carries out frequency domain zero padding, and time domain increases sample interpolation, asks relevant treatment maximal value matrix M ax (V _a, V _r), concrete operation step:

(1) the complex data S asking step 8 to obtain ₇maximal value and position (m, n) thereof;

(2) with (m, n) centered by intercept 32 × 32 small block datas and to its carry out orientation to distance to Fourier transform, carry out zero padding process at frequency domain and obtain 256 × 256 long data blocks, carry out 8 times of interpolation processing, to chunk data carry out orientation to distance to inverse Fourier transform process;

(3) the maximal value Max of time domain chunk data is asked, i.e. imaging results maximal value Max;

Return step 7 (1) step, until circulation terminates, obtain based on the target azimuth arranged to speed V _awith radial velocity V _rrelevant treatment maximal value matrix M ax (V _a, V _r);

Step 10: utilize relevant treatment maximal value estimating target speed;

This step divides following three kinds of situations:

(1) when an estimating target orientation is to speed, to the relevant treatment maximal value matrix M ax (V obtained _a) draw X-Y scheme, speed corresponding to figure maximal value is the moving target orientation of estimation to speed, as shown in Fig. 2, Fig. 5, Fig. 8;

(2) during estimating target radial velocity, to the relevant treatment maximal value matrix M ax (V obtained _r) draw X-Y scheme, speed corresponding to figure maximal value is the moving target radial velocity of estimation, as shown in Fig. 3, Fig. 6, Fig. 9;

(3) when estimating target orientation is to speed and radial velocity, to the relevant treatment maximal value matrix M ax (V obtained simultaneously _a, V _r) drawing 3 D graphics, the speed that curve map peak is corresponding is the moving target orientation of estimation to speed and radial velocity, as shown in Fig. 4, Fig. 7, Figure 10.

Velocity estimation result as table 2, shown in table 3.

The explanation of table 2 Fig. 2, Fig. 5, Fig. 8 result

V A_ is true(m/s)	V A_ estimates(m/s)	Step delta V a(m/s)	V A_ error(m/s)
				2.0	2.1	0.1	0.1
8.0	8.3	0.1	0.3
				15.0	15.6	0.1	0.6

The explanation of table 3 Fig. 3, Fig. 6, Fig. 9 result

V R_ is true(m/s)	V R_ estimates(m/s)	Step delta V r(m/s)	V R_ error(m/s)
				2.1	2.2	0.1	0.1
8.6	8.7	0.1	0.1
				16.2	16.3	0.1	0.1

Because estimating target orientation while that Fig. 4, Fig. 7 and Figure 10 being is to speed and radial velocity, in order to save computing time, step-length arranges comparatively large, causes estimating speed error larger, first larger step-length can be set and detect target velocity scope, then among a small circle development rate fine estimation.Simultaneously target true velocity is integer, and arranging speed is also integer, causes evaluated error to be 0 or 1m/s.The while that Fig. 4, Fig. 7 and Figure 10 illustrating and use method of the present invention to realize, estimating target orientation is to speed and radial velocity, and evaluated error is relevant to the speed step-length of setting.Above data illustrate, along with target true bearing is to the increase of speed, target defocuses seriously, and evaluated error can along with increase, but still within error allowed band; Along with the increase of the true radial velocity of target, only affect target location change, defocusing degree change is less, then evaluated error change is little, and this invention better effects if when detecting target radial speed is described.

Claims (4)

1., based on a moving target parameter estimation method for related function, concrete steps are as follows:

Step one: read in raw radar data and dependent imaging parameter;

Read in based on moving-target two dimension original echo emulation complex data S under the spaceborne stripmap SAR pattern of positive side-looking ₀and corresponding imaging parameters, wherein S ₀for two-dimensional complex number group, size is N _a× N _r, imaging parameters specifically comprises: orientation is to sampling number N _a, distance is to sampling number N _r, signal sampling rate f _s, signal bandwidth Bw, pulse width τ, chirp rate k, pulse repetition rate PRF, with reference to oblique distance R _ref, doppler centroid f _d0, doppler frequency rate f _r0, satellite platform speed V _p, signal wavelength lambda, aspect is to bandwidth B _a, light velocity c;

Step 2: orientation is to Fourier transform process;

By original complex according to S ₀(i, j), along each distance to carrying out Fast Fourier Transform (FFT), obtains orientation domain complex according to S ₁(i, j);

S ₁(:,j)＝FFT(S ₀(:,j)) (1)

Wherein, S ₁(:, j) represent S ₁jth row, S ₀(:, j) represent S ₀jth row, FFT () represent Fast Fourier Transform (FFT) is carried out to one-dimension array;

Step 3: CS factor treatment is multiplied by distance-Doppler territory;

The complex data S that step 2 is obtained ₁(i, j) is with CS factor Ψ ₁(i, j) is multiplied, the complex data S after being compensated ₂(i, j);

Step 4: distance is to Fourier transform process;

The complex data S that step 3 is obtained ₂(i, j) to carrying out distance to Fast Fourier Transform (FFT), obtains two-dimensional frequency complex data S along each orientation ₃(i, j);

S ₃(i,:)＝FFT(S ₂(i,:)) (9)

Wherein, S ₂(i :) represent S ₂the i-th row, S ₃(i :) represent S ₃the i-th row;

Step 5: two-dimensional frequency carries out compensated distance process;

The complex data S that step 4 is obtained ₃(i, j) is with corresponding compensated distance factor Ψ ₂(f _a, f _τ) be multiplied, obtain Range compress complex data S ₄;

Step 6: distance is to inverse Fourier transform process;

The complex data S that step 5 is obtained ₄(i, j), along each orientation to carrying out inverse fast Fourier transform, obtains orientation domain complex according to S ₅(i, j);

S ₅(i,:)＝IFFT(S ₄(i,:)) (13)

Wherein, S ₄(i :) represent S ₄the i-th row, S ₅(i :) represent S ₅the i-th row, IFFT () represent inverse fast Fourier transform is carried out to one-dimension array;

Step 7: relevant treatment is carried out in distance-Doppler territory;

The complex data S that step 6 is obtained ₅(i, j) and related function Ψ ₃(f _a, τ, V _a, V _r) be multiplied, obtain the complex data S after relevant treatment ₆(i, j);

Step 8: orientation is to inverse Fourier transform process;

The complex data S that step 7 is obtained ₆(i, j), along each distance to carrying out inverse fast Fourier transform, obtains final imaging results S ₇(i, j);

S ₇(:,j)＝IFFT(S ₆(:,j)) (20)

Wherein, S ₆(:, j) represent S ₆jth row, S ₇(:, j) represent S ₇jth row;

Step 9: to imaging results S ₇carry out interpolation processing maximizing;

Frequency domain zero padding, time domain increases the method for sampling to imaging results S ₇(i, j) carries out interpolation processing;

(1) the complex data S asking step 8 to obtain ₇maximal value and position (m, n) thereof;

(2) centered by (m, n), intercept small block data and to its carry out orientation to distance to Fourier transform, carry out zero padding process at frequency domain, then the chunk data obtained is carried out orientation to distance to inverse Fourier transform process;

(3) the maximal value Max of time domain chunk data is asked, i.e. relevant treatment maximal value Max;

Return step 7 (1) step, complete independent variable V _aand V _rtraversal, obtain based on independent variable V _aand V _rrelevant treatment maximal value matrix M ax (V _a, V _r);

Illustrate: when an estimating target orientation is to speed, the relevant treatment maximal value matrix obtained is Max (V _a); During estimating target radial velocity, the relevant treatment maximal value matrix obtained is Max (V _r); When estimating target orientation is to speed and radial velocity simultaneously, the relevant treatment maximal value matrix obtained is Max (V _a, V _r);

Step 10: utilize relevant treatment maximal value Matrix Estimation velocity to moving target;

This step divides following three kinds of situations to carry out velocity to moving target estimation:

(1) when an estimating target orientation is to speed, to the relevant treatment maximal value matrix M ax (V obtained _a) draw X-Y scheme, speed corresponding to figure maximal value is the moving target orientation of estimation to speed;

(2) during estimating target radial velocity, to the relevant treatment maximal value matrix M ax (V obtained _r) draw X-Y scheme, speed corresponding to figure maximal value is the moving target radial velocity of estimation;

(3) when estimating target orientation is to speed and radial velocity, to the relevant treatment maximal value matrix M ax (V obtained simultaneously _a, V _r) drawing 3 D graphics, the speed that curve map peak is corresponding is the moving target orientation of estimation to speed and radial velocity.

2. a kind of moving target parameter estimation method based on related function according to claim 1, described step 3 specifically comprises:

(1) construct two one-dimensional sequence i, j, wherein i represents orientation to sequence, and j represents distance to sequence;

i＝[1,2,…,N _a]

j＝[1,2,…,N _r] (2)

(2) distance-Doppler territory two-dimensional complex number is obtained according to S ₁the orientation frequency f that (i, j) each row is corresponding _ai the distance of () and each row correspondence is to moment τ (j);

$f_a\left(i\right)=\frac{i-N_a/2}{N_a}\·\mathrm{PRF}-f_{d0}---\left(3\right)$

$\mathrm{\τ}\left(j\right)=\frac{j-N_r/2}{f_s}+\frac{2R_{\mathrm{ref}}}{c}---\left(4\right)$

(3) velocity equivalent V is calculated by imaging parameters _refwith equivalent squint angle φ _ref;

$V_{\mathrm{ref}}=\sqrt{\frac{\mathrm{\λ}{R}_{\mathrm{ref}}{f}_{r0}}{2}+{\left(\frac{\mathrm{\λ}{f}_{d0}}{2}\right)}^2}---\left(5\right)$

${\mathrm{\φ}}_{\mathrm{ref}}=\arccos [-\frac{\mathrm{\λ}{f}_{d0}}{2V_{\mathrm{ref}}}]---\left(6\right)$

(4) CS factor Ψ is obtained ₁(τ, f) is as follows;

Ψ ₁(τ,f _a)＝exp{-jπk _rC _s[τ-τ _ref(f _a)] ²} (7)

Wherein: a=λ f _a/ 2V _ref, $k_r=\frac{k}{1+k{R}_{\mathrm{ref}}\sin {\mathrm{\φ}}_{\mathrm{ref}}\frac{2\mathrm{\λ}}{c^2}\·\frac{a^2}{{[1-a^2]}^{3/2}}},C_s=\frac{\sin {\mathrm{\φ}}_{\mathrm{ref}}}{\sqrt{1-a^2}}-1,{\mathrm{\τ}}_{\mathrm{ref}}\left(f\right)=\frac{2}{c}{R}_{\mathrm{ref}}[1+C_s]$

5) by S in step 2 ₁(i, j) and CS fac-tor, obtain complex data S ₂(i, j);

S ₂(i,j)＝S ₁(i,j)·Ψ ₁(τ,f _a) (8)

。

3. a kind of moving target parameter estimation method based on related function according to claim 1, described step 5 specifically comprises:

(1) two-dimensional frequency complex data S is obtained ₃(i, j) orientation frequency f that often row is corresponding _a(i) and the frequency of distance f often arranging correspondence _τ(j);

$\begin{array}{c}{f}_a\left(i\right)=\frac{i-N_a/2}{N_a}\·\mathrm{PRF}-f_{d0}\\ f_{\mathrm{\τ}}\left(j\right)=\frac{j-N_r/2}{\mathrm{Nr}}\·f_s\end{array}---\left(10\right)$

(2) compensated distance factor Ψ ₂(f _a, f _τ) as follows;

${\mathrm{\ψ}}_2(f_a,f_{\mathrm{\τ}})=\exp \{-\mathrm{j\π}[\frac{{f_{\mathrm{\τ}}}^2}{k_r[1+C_s]}-\frac{{4R}_{\mathrm{ref}}}{c}{f}_{\mathrm{\τ}}{C}_s\left]\right\}---\left(11\right)$

(3) by step 4 complex data S ₃(i, j) same distance compensating factor Ψ ₂(f _a, f _τ) be multiplied, obtain the two-dimensional complex number after Range compress according to S ₄(i, j)

S ₄(i,j)＝S ₃(i,j)·Ψ ₂(f _a,f _τ) (12)

。

4. a kind of moving target parameter estimation method based on related function according to claim 1, described step 7 specifically comprises:

(1) Offered target orientation is first distinguished to speed V _awith radial velocity V _rinitial value V _{a, min}, V _{r, min}, span [V _{a, min}, V _{a, max}], [V _{r, min}, V _{r, max}], and step delta V _a, Δ V _r;

(2) according to arrange orientation to speed V _acalculate the relative velocity V of equivalence _{s_t}; According to the radial velocity V arranged _rcomputer azimuth spectrum position shifted by delta f _d;

V _{s_t}＝V _ref-V _a(14)

$\mathrm{\Δ}{f}_d=\frac{2V_r}{\mathrm{\λ}}---\left(15\right)$

(3) according to the change of azimuth spectrum position and orientation to corresponding orientation frequency f (i) of bandwidth calculation;

In orientation to counting $i\∈[(1-\frac{B_a+2\mathrm{\Δ}{f}_d}{\mathrm{PRF}})\frac{N_a}{2},(1+\frac{B_a-2\mathrm{\Δ}{f}_d}{\mathrm{PRF}})\frac{N_a}{2}]---\left(16\right)$

Get orientation frequency $f\left(i\right)=\frac{i-N_a/2}{N_a}\mathrm{PRF}-f_{d0}-\mathrm{\Δ}{f}_d---\left(17\right)$

F (i)=0 is got at all the other bearing point places

Illustrate: if only need detect target azimuth to speed, then only need Offered target orientation to speed initial value, scope and step-length, count in orientation i ∈ [0, N _a] time get orientation frequency all the other same above-mentioned steps; If only target radial speed need be detected, then only need Offered target radial velocity initial value, scope and step-length, then the relative velocity V of equivalence _{s_t}=V _ref, all the other same above-mentioned steps; Can estimate that moving target orientation is to speed or radial velocity respectively based on this;

(4) definition is with V _a, V _rfor the related function Ψ of independent variable ₃(f, τ, V _a, V _r) as follows;

${\mathrm{\ψ}}_3(f,\mathrm{\τ},V_a,V_r)=\exp \{-\mathrm{j\π}[\frac{4R}{\mathrm{\λ}}{C}_s\_1-\frac{4k_r}{c^2}({C_s}^2+C_s){(R-R_{\mathrm{ref}})}^2-\frac{2\mathrm{Rf}}{V_{\mathrm{ref}}}\cos {\mathrm{\φ}}_{\mathrm{ref}}\left]\right\}---\left(18\right)$

Wherein $R=\frac{\mathrm{c\τ}}{2},a^{\′}=\frac{\mathrm{\λf}}{2V_{s\_t}},C_s\_1=1-\sin {\mathrm{\φ}}_{\mathrm{ref}}\sqrt{1-a^{\′2}}$

(5) complex data S step 6 obtained ₅(i, j) and related function Ψ ₃(f, τ, V _a, V _r) be multiplied, obtain the complex data S after being correlated with ₆(i, j):

S ₆(i,j)＝S ₅(i,j)·Ψ ₃(f,τ,V _a,V _r) (19)

。