RANGE AND AZIMUTH RESOLUTION ENHANCEMENT FOR REAL-BEAM RADAR

• US 20100066586A1
• Filed: 09/18/2008
• Published: 03/18/2010
• Est. Priority Date: 09/18/2008
• Status: Active Grant

• Current Assignee: BAE Systems Controls Incorporated (BAE Systems Plc)
• Original Assignee: BAE Systems Controls Incorporated (BAE Systems Plc)
• Inventors: Yang, Ken, Liu, Guoqing
• Granted Patent: US 8,026,841 B2
• Time in Patent Office: Days
• US Class Current: 342/33
• CPC Class Codes: G01S 13/34 using transmission of continuous, frequency-modulated waves while heterodyning the received signal, or a signal derived therefrom, with a locally-generated signal related to the contemporaneously transmitted signal
• CPC Class Codes: G01S 13/913 for landing purposes
• CPC Class Codes: G01S 3/74 Multi-channel systems specially adapted for direction-finding, i.e. having a single antenna system capable of giving simultaneous indications of the directions of different signals systems in which the directions of different signals are determined sequentially and displayed simultaneously G01S3/04, G01S3/14

Abstract

Disclosed is a method, means for and computer program for enhancing range and azimuth resolution in a two-dimensional (2D) image generated by a frequency modulated continuous-wave (FMCW) radar for providing enhanced situational awareness in autonomous approach and landing guidance (AALG) system by forming and displaying a two-dimensional (2D) model of landing conditions from received range and azimuth real beam radar (RBR) signals by rendering one or more target locations and amplitudes in both range and azimuth, selecting a region of interest from the displayed 2D model to enhance the one or more target locations in the selected region of interest, selectively applying range and azimuth resolution enhancement using a first and second beamforming approach or applying azimuth only resolution enhancement by using just the second beamforming approach to obtain an one or more accurate target location estimations and combining the enhanced one or more target locations to render an enhanced 2D image.

Claims

1. A method of enhancing range and azimuth resolution in a two-dimensional (2D) image generated by a frequency modulated continuous-wave (FMCW) radar for providing enhanced situational awareness in autonomous approach and landing guidance (AALG) system, comprising the steps of:

forming and displaying a two-dimensional (2D) model of landing conditions from received range and azimuth real beam radar (RBR) signals by rendering one or more target locations and amplitudes in both range and azimuth;

selecting a region of interest from said displayed 2D model to enhance said one or more target locations and amplitudes in said selected region of interest;

applying both range and azimuth resolution enhancement to enhanced the rendered one or more target locations and amplitudes in both range and azimuth; and

combining the enhanced one or more target locations and amplitudes in range and azimuth to render an enhanced 2D image.

2. The method of claim 1, wherein said range resolution is enhanced by generating one or more accurate target location estimations of said rendered one or more target locations in range by applying a first beamforming approach and said azimtuth resolution enhancement is enhanced by generating one or more accurate target location estimations in azimuth by applying a second beamforming approach.

3. The method of claim 2, wherein said first beamforming approach adaptively applies a standard Capon beamforming approach comprising the steps of:

reproducing the original range and azimuth RBR signals in said selected region of interest; and

beamforming said original range RBR signal to render one or more accurate target locations in range by passing a frequency f of interest undistorted while attenuating all other frequencies.

4. The method of claim 2, wherein said second beamforming approach adaptively applies a generalized Capon beamforming approach on said original azimuth RBR signal to render one or more second accurate target locations in azimuth by estimating a plurality of central angles and angular spreads of one or more hypothetical sources, said central angles and angular spreads derived from a azimuth sampling of a plurality of antenna beam pattern scans output from said real beam radar to determine which among said one or more hypothetical sources is of interest while rejecting all other hypothetical sources.

5. The method of claim 3, wherein the one or more accurate target amplitude estimations of said rendered one or more target amplitudes in range and azimuth, are generated by a linear least square approach.

6. The method of claim 4, wherein said azimuth sampling of a plurality of antenna beam pattern scans output from said real beam radar can be obtained by a linear stepped scan, a non-linear stepped scan or a sinusoidal scan, said linear and non-linear stepped scans are configured so that an azimuth sampling is taken at the same angle locations in each scan and said sinusoidal scan configured so that azimuth samples are taken at even and odd azimuth angle positions for different scans.

7. The method of claim 6, wherein said generalized Capon beamforming approach farther includes the steps of:

estimating a data covariance matrix by employing a spatial average and temporal average on said received azimuth samples, a target location in range data, and inputted user parameters;

calculating a signal covariance matrix from said plurality of antenna beam pattern scans output and said inputted user parameters;

generating an inverse matrix from said estimated data covariance matrix; and

estimating by adaptively applying a generalized Capon beamformer on combined said generated inverse matrix and said calculated signal covariance matrix, and

outputting an azimuth resolved data matrix,

wherein, if said azimuth sampling is obtained by sinusoidal scan said azimuth sampling is preprocessed by an interpolation approach to obtain a plurality of azimuth sample values at the same set of azimuth angle location as the first scan.

8. The method of claim 6, wherein, said target location in range data is the generated one or more accurate target location estimations in range.

9. The method of claim 2, wherein said step of selecting a region of interest farther includes the step of selecting by one of an automatic and a manual selection approach.

10. The method of claim 9, wherein said automatic selection approach employs a constant false alarm rate (CFAR) detection-processing algorithm.

11. The method of claim 9, wherein said manual selection approach permits a user to select the region of interest of the potential target from an image display.

12. The method of claim 3, wherein said standard Capon beamforming (CB) approach solves the following optimization problem,

$\underset{h}{\min }$



h H 

R ^ 

h , subject 



to 



h H 

a 

( f ) = 1 , 

where $a<br><br>\left(f\right)={\left[\begin{array}{ccc}1& \mathrm{\dots <br><br>}& {\mathrm{<br><br>}}^{\mathrm{j2\pi <br><br>}<br><br><br><br>f<br><br>\left(K-1\right)}\end{array}\right]}^T,f\in <br><br>\left[0,1\right)$

is a steering vector, wherein said CB approach comprises the steps of;

determining a spectral weight vector

${\hat{h}}_{\mathrm{CB}}<br><br>\left(f\right)=\frac{R^{-1}<br><br>a<br><br>\left(f\right)}{a^H<br><br>\left(f\right)<br><br>R^{-1}<br><br>a<br><br>\left(f\right)};<br><br>$

and

calculating the output of the CB approach by substituting said determined spectral weight vector into said FIR output power to estimate of the filter output power at frequency f

$\hat{P}<br><br>\left(f\right)=\frac{1}{a^H<br><br>\left(f\right)<br><br>R^{-1}<br><br>a<br><br>\left(f\right)}$

wherein said CB approach passes said frequency f of interest undistorted while attenuating all other frequencies.

13. The method of claim 7, wherein said step of estimating a data covariance matrix further includes:

defining a data sample covariance matrix,

${\hat{R}}_1=\frac{1}{\mathrm{LM}}<br><br>\underset{m=0}{\overset{M-1}{\sum <br><br>}}<br><br>\underset{l=0}{\overset{L-1}{\sum <br><br>}}<br><br>x_m<br><br>\left(l\right)<br><br>x_m^H<br><br>\left(l\right)$

where x_m(n)=a_m(θ

_n−

θ

_s)α

_s+e_k(n), n=0, . . . , m=0, . . . , M−

1 such that θ

_s and θ

_n denote an azimuth angel location of a target scatterer s and an azimuth angle where an azimuth sample is taken, respectively, a_m(θ

_n−

θ

_s) represents a target response at azimuth sample angle θ

_s from the scatterer s, x_k(n) corresponding to a data sample value obtained from the n-th azimuth angle position within an m-th azimuth scan and where N and M are a total number of azimuth samples within a section of interest and total number of scans, respectively, and α

_s is an amplitude of the target scatterer s and a sub-vector of length M,

x_m(l)=[x_m(l) . . . x_m(l+K−

1)]^T,l=0, . . . , L=1;

L=N−

K+1

14. The method of claim 7, wherein said step of calculating said signal covariance matrix further including the sub-steps of:

defining for an m-th scan

${\hat{R}}_{\mathrm{sm}}=\frac{1}{L}<br><br>\underset{l=0}{\overset{L-1}{\sum <br><br>}}<br><br>a_m<br><br>\left(l\right)<br><br>a_m^H<br><br>\left(l\right)$

where a_m(l) is a vector formed from the sequence a_m(θ

_n−

θ

_s) in the same way as x_m(l) is formed from x_m(n).

15. The method of claim 7, further including adaptively applying a generalized Capon Beamformer approach includes:

maximizing a signal-to-noise ratio (SNR)

$\underset{h}{\max }<br><br><br><br>\frac{h_1^H<br><br>{\hat{R}}_{\mathrm{sm}}<br><br>h_1}{h_1^H<br><br>{\hat{R}}_n<br><br>b_1}$

where {circumflex over (R)}_n denotes a noise covariance matrix, ĥ

₁=p_max({circumflex over (R)}₁<sup>−

1</sup>{circumflex over (R)}_sm), and p_max( ) denotes a principal eigenvector corresponding to a largest eigenvalue of a matrix;

calculating a power received from the target scatterer by estimation

$P_1=\frac{1}{\mathrm{LM}}<br><br>\underset{m=0}{\overset{M-1}{\sum <br><br>}}<br><br>\underset{l=0}{\overset{L-1}{\sum <br><br>}}<br><br>{<br><br>{\hat{h}}_1^H<br><br>x_m<br><br>\left(l\right)<br><br>}^2={\hat{h}}_1^H<br><br>{\hat{R}}_1<br><br>{\hat{h}}_1.$

16. The method of claim 7, wherein said input user parameters are a filter length, azimuth subsection size and scan number.

17. An autonomous landing guidance (AALG) radar system in which situational awareness is enhanced by enhanced resolution in a two-dimensional (2D) image generated by a frequency modulated continuous-wave (FMCW) radar system, comprising:

means for forming and displaying a two-dimensional (2D) model of landing conditions from received range and azimuth real beam radar (RBR) signals by rendering one or more target locations and amplitudes in both range and azimuth;

means for selecting a region of interest from said displayed 2D model to enhance said one or more target locations and amplitudes in said selected region of interest;

means for selectively applying both range and azimuth resolution enhancement as a first option to enhanced the rendered one or more target locations and amplitudes in both range and azimuth or applying azimuth resolution enhancement only as a second option to enhance the rendered one or more target locations and amplitudes in azimuth only; and

means for combining the enhanced one or more target locations and amplitudes to render an enhanced 2D image,

wherein said first option applies said range resolution enhancement by generating one or more accurate target location estimations of said rendered one or more target locations in range by applying a first beamforming approach and applies said azimuth resolution enhancement by generating one or more accurate target location estimations in azimuth by applying a second beamforming approach, and said second option accepts the rendered one or more target locations in range and applies said azimuth resolution enhancement by generating one or more accurate target location estimations in azimuth by applying said second beamforming approach.

18. A program storage device readable by a machine, tangibly embodying a program of instructions executable by the machine to perform a method of enhancing resolution in a two-dimensional (2D) image generated by a frequency modulated continuous-wave (FMCW) radar for providing enhanced situational awareness in autonomous approach and landing guidance (AALG) system, comprising the steps of:

forming and displaying a two-dimensional (2D) model of landing conditions from received range and azimuth real beam radar (RBR) signals by rendering one or more target locations and amplitudes in both range and azimuth;

selecting a region of interest from said displayed 2D model to enhance said one or more target locations and amplitudes in said selected region of interest;

selectively applying both range and azimuth resolution enhancement as a first option to enhance the rendered one or more target locations and amplitudes in both range and azimuth or applying azimuth resolution enhancement only as a second option to enhance the rendered one or more target locations and amplitudes in azimuth only; and

combining the enhanced one or more target locations and amplitudes to render an enhanced 2D image,

wherein said first option applies said range resolution enhancement by generating one or more accurate target location estimations of said rendered one or more target locations in range by applying a first beamforming approach and applies said azimuth resolution enhancement by generating one or more second accurate target location estimations in azimuth by applying a second beamforming approach, and said second option accepts the rendered one or more target locations in range and applies said azimuth resolution enhancement by generating one or more accurate target location estimation in azimuth by applying said second beamforming approach.

19. A method of enhancing resolution in a two-dimensional (2D) image generated by a frequency modulated continuous-wave (FMCW) radar for providing enhanced situational awareness in autonomous approach and landing guidance (AALG) system, comprising the steps of:

forming and displaying a two-dimensional (2D) model of landing conditions from received range and azimuth real beam radar CUR) signals by rendering one or more target locations and amplitudes in both range and azimuth;

selecting a region of interest from said displayed 2D model to enhance said one or more target locations and amplitudes in said selected region of interest;

applying azimuth resolution enhancement to enhance the rendered one or more target locations and amplitudes in azimuth; and

combining the one or more target locations and amplitudes in range and azimuth to render an enhanced 2D image,

wherein said azimuth resolution enhancement is enhanced by generating one or more accurate target location estimations in azimuth by applying a beamforming approach to said target location estimations in azimuth.