Spatial Array Digital Beamforming and Filtering

Slide1 l.jpg
1 / 19
0
0
1241 days ago, 386 views
PowerPoint PPT Presentation
Spatial Cluster Computerized Beamforming and Separating. Tim D. Reichard, M.S. L-3 Correspondences Coordinated Frameworks Laurel, Texas 972.205.8411 Timothy.D.Reichard@L-3Com.com. Spatial Cluster Advanced Beamforming and Sifting. Layout. Engendering Plane Waves Review Preparing Areas

Presentation Transcript

Slide 1

Spatial Array Digital Beamforming and Filtering Tim D. Reichard, M.S. L-3 Communications Integrated Systems Garland, Texas 972.205.8411 Timothy.D.Reichard@L-3Com.com

Slide 2

Spatial Array Digital Beamforming and Filtering OUTLINE Propagating Plane Waves Overview Processing Domains Types of Arrays and the Co-Array Function Delay and Sum Beamforming Narrowband Broadband Spatial Sampling Minimum Variance Beamforming Adaptive Beamforming and Interference Nulling Some System Applications and General Design Considerations Summary

Slide 3

Using Maxwell's conditions on an E-M field in free space, the Wave Equation is characterized as: Monochromatic Plane Wave (far-field) : ¶ 2 s + ¶ 2 s + ¶ 2 s = 1 . ¶ 2 s k ¶ x 2 ¶ y 2 ¶ z 2 c 2 ¶ t 2 x Governs how signals go from a transmitting source to a detecting exhibit Linear - such a variety of plane waves in varying headings can exist all the while => the Superposition Principal Planes of steady stage with the end goal that development of d x after some time d t is consistent Speed of engendering for a lossless medium is | d x |/d t = c Slowness vector: a = k/w and | a | = 1/c Sensor set at the inception has just a worldly recurrence connection: k = Wavenumber Vector = course of proliferation x = Sensor position vector where wave is watched s( x o ,t) = Ae j( w t - k . x o ) Temporal Freq. Spatial Freq. (| k | = 2p/l ) s([0,0,0], t) = Ae j w t Propagating Plane Waves Notation : Lowercase Underline shows 1-D lattice ( k ) Uppercase Underline demonstrates 2-D framework ( R ) ¢ or H demonstrates network conjugate-transpose

Slide 4

s( x , t) = s(t - a . x ) s( x , t) Space-Time e - j w t e j k . x e j w t e - j k . x S( x , w ) S( k , t) Wavenumber - Time Space-Freq e - j k . x e j w t e j k . x e - j w t (or beamspace) Wavenumber - Frequency S( k , w ) Processing Domains

Slide 5

# Redundancies Uniform Linear Array (ULA) Co-Array 6 m= 0 1 2 3 4 5 6 4 x 2 d M = 7 source 0 1d 2d 3d 4d 5d 6d x 2-D Array Co-Array Function: C ( c ) = S w m1 w * m2 m1,m2 x where; m1 and m2 are an arrangement of lists for x m2 – x m1 = c d Desire to limit redundancies and Choose dividing to forestall associating Sparse Linear Array (SLA) # Redundancies 4 Co-Array "A Perfect Array" m= 0 1 2 3 2 x d 1 M = 4 0 1d 2d 3d 4d 5d 6d x Some Array Types and the Co-Array Function

Slide 6

s( x ,t) = e j( w o t - k o . x ) k o y 0 (t) w* 0 Delay D 0 y 1 (t) w* 1 Delay D 1 z(t) . . . S . . . y M-1 (t) w* M-1 Delay D M-1 Time Domain: M-1 M-1 z(t) = S w* m y m (t - D m ) = e j w o t S w* m e - j( w o D m + k o . x m ) = w H y m=0 Freq Domain: M-1 M-1 Z( w ) = S w* m Y m ( w, x m ) e - j( w o D m ) = S w* m Y m ( w, x m ) e j( k o . x m ) = e H WY m=0 let D m = (- || k o || . x m )/c e is a Mx1 guiding vector Ð - || k o || Delay and Sum Beamformer (Narrowband)

Slide 7

y 1 (n) z - 1 z - 1 z - 1 w* 1,0 w* 1,1 w* 1,L-1 . . . y 2 (n) z - 1 z - 1 z - 1 w* 2,0 w* 2,1 w* 2,L-1 . . . y J (n) z - 1 z - 1 z - 1 . . . w* J,0 w* J,1 w* J,L-1 . . . Postponement and Sum Beamformer (Broadband) . . . z(n) S . . . . . . J = number of sensor channels L = number of FIR channel tap weights J L-1 z(n) = S w* m,p y m (n - p) = w H y (n) m=1 p=0

Slide 8

M-Sensor ULA Interpolation Beamformer (at area x o ) : y 0 (n) u' 0 (n) w 0 Delay D 0 I y 1 (n) w 1 Delay D 1 z(n) I . . . S LPF ( p/I) . . . I Down-example y M-1 (n) u' M-1 (n) w M-1 Delay D M-1 I Up-test M-1 z(n) = S w m S y m ( k ) * h((n-k )T-D m ) k m=0 Spatial Sampling Motivation: Reduce abnormalities presented by defer quantization Postbeamforming insertion is represented with polyphase channel

Slide 9

Apply a weight vector w to sensor yields to underline a controlled course ( z ) while stifling different bearings to such an extent that at w = w o : Real { e ¢ w } = 1 Hence: min E [ | w ¢ y | 2 ] yields => w select = R - 1 e/[ e ¢ R - 1 e ] Conventional (Delay & Sum Beamformer) Steered Response Power: P CONV ( e ) = [ e ¢ WY ] [ Y ¢ W ¢ e ] = e ¢ R e for solidarity weights Minimum Variance Steered Response Power: P MV ( e ) = w ¢ pick R w select = [ e ¢ R - 1 e ] - 1 w Minimum Variance (MV) Beamformer MVBF weights alter as the guiding vector changes Beampattern shifts as indicated by SNR of approaching signs Sidelobe structure can deliver nulls where different signal(s) might be available MVBF gives "superb" flag determination wrt controlled shaft over the Conventional Delay & Sum beamformer MVBF heading estimation precision for a given flag increments as SNR builds R = spatial relationship lattice = YY ¢

Slide 10

ULA Beamformer Comparison ; w = w o P CONV ( z ) = [ e ¢ ( z ) R e ( z )] P MV ( z ) = [ e ¢ ( z ) R - 1 e ( z )] - 1

Slide 11

Constrained Optimization: min w ¢ Rw subject to Cw = c Frost GSC † Setup: w y 0 ( l ) Adaptive Algorithm y 1 ( l ) . . . . . . . . . z( l ) S Non-Adaptive w c Adaptive w y M-1 ( l ) Adaptive (Iterative) Portion: z( l ) = w ¢ ( l ) y ( l ) w ( l +1) = w c + P [ w ( l ) - m z * ( l ) y ( l )] † - General Sidelobe Canceller Adaptive Beamformer Example #1 - Frost GSC Architecture For Minimum Variance let C = e ¢ , c = 1 e = Array Steering Vector prompted to SOI R is Spatial Correlation Matrix = y ( l ) y ¢ ( l ) R perfect = ss ¢ + I s 2 = Signal Est. + Noise Est. Decide Step Size ( m ) utilizing R perfect : m = 0.1*(3*trace[ PR perfect P ]) - 1 P = I - C ¢ ( CC ¢ ) - 1 C w c = C ¢ ( CC ¢ ) - 1 c w ( l= 0) = w c

Slide 12

Coherent Interference Signal (7 deg away & 5dB down from SOI) Signal of Interest (SOI) area Shows Signals Resolvable Beam Steered to SOI with 0.4 degree directing blunder Setup Info utilized: N = 500 specimens M = 9 sensors, ULA with d = l/2 separating SOI beat display in tests 100 toward 300 Co-Interference beat introduce in tests 250 to 450 Aperture Size (D) = 8d Array Gain = M for solidarity w m " m Example Scenario for a Digital Minimum Variance Beamformer P MV ( z ) = [ e ¢ ( z ) R - 1 e ( z )] - 1 M-1 W( k ) = S w m e j( k . x ) m=0

Slide 13

Example of Frost GSC Adaptive Beamformer Performance Results † - by means of Matlab reenactment

Slide 14

Constrained Optimization: min w ¢ Rw subject to Cw = c and || B ¢ w a || 2 < b 2 - || w c || 2 where b is limitation put on adjusted weight vector Robust GSC y 0 ( l ) Setup: w* c (0) Delay D 0 For Minimum Variance let C = e ¢ , c = 1 e = Array Steering Vector prompted to SOI B is Blocking Matrix with the end goal that B ¢ C = 0 Determine Step Size ( m ) utilizing R perfect : m = 0.1*(max l BR perfect B ) - 1 w a = B ¢ w a w c = C ¢ ( CC ¢ ) - 1 c y 1 ( l ) w* c (1) Delay D 1 . . . z( l ) + . . . S _ y M-1 ( l ) w* c (M-1) Delay D M-1 ~ w a w* a,0 ( l ) Adaptive (Iterative) Portion: S B . . . y B ( l ) = By ( l ) v ( l ) = w a ( l ) + m z * ( l ) B ¢ y B ( l ) w a ( l +1) = v ( l ), || v ( l )|| 2 < b 2 - || w c || 2 ( b 2 - || w c || 2 ) 1/2 v ( l )/|| v ( l )||, generally z( l ) = [ w c - w a ( l )] ¢ y ( l ) w* a,M-1 ( l ) ~ w a ~ LMS Algorithm ~ Adaptive Beamformer Example #2 - Robust GSC Architecture

Slide 15

Example of Robust GSC Adaptive Beamformer Performance Results † - through Matlab recreation

Slide 16

RMS Phase Noise = 136 mrad RMS Phase Error = 32 mrad Adaptive Beamformer Relative Performance Comparisons SOI Pulsewidth held for both; Robust has better reaction Robust technique's blocking grid segregates versatile weighting to nonsteered reaction Good stage blunder reaction for the separated beamformer comes about Amplitude diminishments because of commitments from exhibit design and versatile bits The bigger the progression measure ( m ), the quicker the adjustment Additional imperatives can be utilized with these calculations min l PRP is corresponding to commotion change => adjustment rate is generally corresponding to SNR

Slide 17

y 0 (t) DCM BPF Digitizer y 1 (t) Signal Detection and Parameter Encoding DCM BPF Digitizer Adaptive Beamformer . . . . . . Guiding Vector y M-1 (t) DCM BPF Digitizer Applications to Passive Digital Receiver Systems Sparse Array helpful for lessening FE equipment while endeavoring to hold opening size - > spatial determination Aperture Size (D) = 17d in the event that with d = l/2 and sensor spacings of {0, d, 3d, 6d, 2d, 5d} Co-exhibit calculation used to confirm no spatial associating for picked sensor spacings Tradeoff less HW for somewhat bring down cluster increase Further diminishments conceivable with subarray averaging at cost of pillar controlling reaction and determination execution

Slide 18

Summary Digital beamforming gives extra adaptability to spatial separating and concealment of undesirable signs, including intelligible interferers Various sorts of clusters can be utilized to suit particular applications Minimum Variance beamforming gives fantastic spatial determination execution over traditional BF and alters as indicated by SNR of approaching signs Adaptive calculations, actualized iteratively can give direct to quick monopulse union and give extra decrease of undesirable signs in respect to client characterized ideal requirements forced on the plan Adaptive, dynamic beamforming helps in maintenance of craved flag qualities for exact flag parameter estimations utilizing both plentifulness and complex stage data Linear Arrays can be used from multiple points of view contingent upon application and execution needs

Slide 19

References D. Johnson

SPONSORS