SHARPENING TECHNIQUES FOR SENSOR FEATURE ENHANCEMENT Larry Marple School of EECS Oregon State University 26 May 200

1. SHARPENING TECHNIQUES FOR SENSOR FEATURE ENHANCEMENT Larry Marple School of EECS Oregon State University 26 May 2005

2. EMERGING SENSOR EXPLOITATION OPPORTUNITY • INCREASED DIMENSIONALITY & CHANNELS (multiple sensors/platform and • multiple platforms) • 3-D IMAGERY DATA • Non-Computed Imaging: Hyperspectral Imaging (HSI) • Computed Imaging: Interferometric Synthetic Aperture Radar (IFSAR) • Scanning LADAR • Video Frame Sequence • 3-D NON-IMAGERY DATA • STAP (arrays or synthetic aperture) • Micro-Doppler (more general: nonstationary micro-motion effects) • MULTI-CHANNEL • Fusion of Coherent RF Sensors of Different Operational Frequencies • Waveform Diversity MIMO of Multiple Platform Scenarios • CREATES LIKELIHOOD OF CORRELATIVE RELATIONSHIPS AMONG • DIMENSIONS AND CHANNELS • SHARPEN 3-D Multi-Channel DETAIL WITHOUT DECREASING TRANSMISSION RATE • RESTORE DETAIL WHILE DECREASING TRANSMISSION RATE • MOTIVATION EXAMPLE FOR A 2-D MICRO-DOPPLER CASE & A 2-D,2-Ch RADAR • FUSION CASE

3. 3-D MICRO-MOTION, NOT 2-D MICRO-DOPPLER ! Targets in motion illuminated by broadband radar exhibit micro-motion simultaneous effects in: LOCALIZED: TIME -- SPACE -- FREQUENCY SHORT-DWELL SAR LONG-DWELL SAR 3-D CUBIC MICRO-MOTION FEATURE SPACE Thin Slice: temporally localized (low gain) Thick Slice: spatially averaged (high gain) Micro-Temporal (localized time variations) 2-D RANGE-DOPPLER CHIPS or SLICES 2-DTIME- FREQUENCY CHIPS or SLICES Micro-Doppler [Micro-Spectral] (localized frequency variations) Micro-Range (localized range variations) Thick Slice: temporally averaged (high gain) Thin Slice: spatially localized (low gain) BTR-80 Time-Range (HRR) 2-D Slice Whole 3-D Cube dB 2-D Range- Doppler Profile Range-Doppler 2-D Slice Time-Doppler 2-D Slice Wheel 2-D T-vs-F Profile (Micro-Doppler)

4. Micro-Temporal (localized time variations) Micro-Spectral (localized frequency variations) Micro-Spatial (localized range cell variations) AMTI MICRO-MOTION FEATURES: HELICOPTER ILLUMINATED BY WIDE BANDWIDTH RADAR • Micro-Frequency (Doppler) x Micro-Temporal (slow time) x Micro-Range (3-D) • after clutter cancellation/suppression/mitigation ( used MRC algorithm ) • Targets in motion generate cyclo-periodic 3-D micro-patterns that can uniquely • characterize ground/airborne targets Simulated Helicopter Time-Doppler Plots for 16 Range Bins ( from V.Chen, NRL )

5. Micro-Temporal (localized time variations) Micro-Spectral (localized frequency variations) Micro-Spatial (localized range cell variations) GMTI MICRO-MOTION FEATURES Back Rim Tire Treads Tire Treads No Doppler 4.3 m 5.8 m 7.0 m 9.0 m

6. Generic Sharpening / Enhancement Concept 1-D, 2-D, 3-D, 4-D 1-C, M-C (MIMO) Reverse Domain Change 1-D, 2-D, 3-D, 4-D 1-C, M-C (MIMO) Domain Change Sharpening Algorithms Signal Entity In Sharpened / Enhanced Signal Entity Out Estimate/Predict Missing High “Frequency’ Content Inverse Transform Special Transform • CURRENT STATE: 1-D, 2-D, and 1-D/Multi-Ch Sharpening Algorithms • SHARPENING PROCEDURE REQUIRES TWO FUNDAMENTAL COMPONENTS: • Selection of Appropriate Transform (may not be the same in all dimensions) • Predictive Transform Extrapolation Techniques in 1/2/3-D & MIMO Versions

7. NOTIONAL EXAMPLES OF 1-D,2-D,3=D SHARPENING RESULTS

8. NOTIONAL EXAMPLE OF SHARPENING APPROACH

9. PROOF BY EXAMPLE: MICRO-MOTION FEATURES • 2-D MICRO-DOPPLER IS REALLY 3-D MICRO-MOTION SIGNATURE • FOR BROADBAND RADAR • LARGE DYNAMIC RANGE (>60 dB) OF EXPLOITABLE SIGNATURES • FOR SUPPLEMENTAL TARGET ID • GMTI: DIFFICULT TO RECOVER WITHOUT FRONT-END CLUTTER • SUPPRESSION • AMTI: SURRENDIPITOUS 1991 16-bit CW DOPPLER COLLECTION • WITHOUT GROUND CLUTTER AND INTERFERENCE

10. AMTI Vs GMTI COLLECTION CONDITIONS Radar Platform Plane, Helicopter Radar Truck, Tank, Vehicle • PROCESSING COMPARISONS: • Short-Time Fourier Transform (FFT-based baseline) • Wigner-Ville T-vs-F Quadratic Representation/Distribution • Predictive Time-Bandwidth Extrapolation (Sharpening)

11. AMTI: AIRBORNE RADAR TARGETS German UH – 1D Huey Eurocopter BO - 105 • X-band ( ~ 10 GHz ) homodyne CW radar returns off helicopters in flight CREATES ONLY A • A 2-D RESPONSE RATHER THAN A 3-D RESPONSE ( no movement through range bins ) • Doppler signatures in baseband ( 0 Hz IF ) I/Q signals after complex demodulation; • + frequencies toward radar and – frequencies away from radar • Baseband sampled at 48000 sps with 16-bit A/D conversion precision ( 96 dB DNR ) • Up to 70 dB signal component level range may be observed in data, since negligible clutter • Nonstationary components contributing doppler signatures: • Fuselage skin line Hub signature Alias of JEM • Main rotor modulations & blade flash Multi-path bounces Cross feed • Tail rotor modulations & blade flash Stabilizer bar (Huey) • Note that –500 to +500 Hz region replaced with time code signal for accessing taped data

12. STFT T-vs-F GRAM of 4-BLADE BO-105 HELICOPTER NOTE THIS European BO-105 Helicopter

13. STFT T-vs-F GRAM of 2-BLADE UTILITY HELICOPTER Stabilizer Bar Cross Feed Tail Rotor Flashes Time Code Skin Line Main Blade Two Multipath NOISE? NO !!! FM aliases JEM Alias Main Rotor Flash Main Blade One

14. BILINEAR TIME-FREQ. REPRESENTATION PROCESSING FLOW CHART Filtered WVF Estimate Choi-William Estimate

15. WINDOWED - CAF* WIGNER TFA GRAM OF BO-105 *CAF = complex ambiguity function

16. MOTIVATION FOR SHARPENING ALGORITHM: TRADITIONAL STFT TFA PROCESSING FLOW DIAGRAM Traditional STFT X(t,t) Alternative Path Motivated by Quadratic TFAs H(t,t)

17. ALTERNATIVE STFT TFA PROCESSING FLOW CHART CREATES 2-D DATA ARRAY AND 2-D COMPLEX TRANSFORM ARRAY MAPPINGS FOR FINITE DATA Traditional STFT X(t,t) Alternative Path Motivated by Quadratic TFAs H(t,t)

18. SHARPENING 2-D T-vs-F PROCESSING FLOW CHART X(t,t) H(t,t)

19. SHARPENED 2-D MINIMUM VARIANCE T-vs-F GRAM of BO-105 HELICOPTER

20. GMTI : GROUND-BASED TANK TARGET • X-BAND SAR SYSTEM PHASE HISTORY DATA (DARPA project) • DPCA RECEIVE • CLUTTER CANCELLATION BIG INGREDIENT IN PROCESSING, but not discussed here

21. GMTI BEFORE/AFTER SHARPENING OF GROUND-BASE TARGET (TANK)

22. EXAMPLE OF 2-D/2-CHANNEL SHARPENING

23. ASIDE: MICRO-MOTION DEMO IMPLICATIONS FOR WAVEFORM DIVERSITY DESIGN • DEVISE TRANSMIT WAVEFORMS THAT CAN BETTER “TUNE” TO TARGET • SPECIFIC MICRO-MOTION SIGNATURES • DEVISE 3-D CYCLO-MOTION TRANSFORM THAT INTEGRATES THE CYCLIC • FEATURES TO PROVIDE IMPROVED DETECTABILITY AND FEATURE EMPHASIS

24. RECOMMENDATIONS • Algorithms currently available: 1-D and corrected 2-D parametric, stochastic • approximation LP, MV, and eigenanalysis/subspace techniques • Develop the 3-D critical algorithms with highest payoff: AR, LP, MV for starters • Develop the even more critical fast computational algorithms (looking for • reductions greater than factor 1000 (better exploitation of multi-dimensional • relationships) • Adapt sharpening processing chain specifically for: • HSI • 3-D micro-motion features • Develop the multi-channel (MIMO waveform diversity) for 2-D sharpening application • Combining SAR imagery on stationary targets • Fusing micro-Doppler features on non-stationary in-motion targets

25. SUPPLEMENTARY SLIDES

26. BASELINE STFT LINEAR TFA (SPECTRO)GRAM where t is the analysis window center time x(t) t t T

27. THIRD TARGET: ARBEITSGEMEINSCHAFT TRANSALL C-160 TWIN-ENGINE TURBOPROP TRANSPORT

28. GRAYSCALE STFT TFA GRAM: C-160 TURBOPROP

29. SHARPENED LOCALIZED TIME-FREQUENCY ANALYSIS PLOTS BY QUADRATIC TFRs Create 2-D time-time instantaneous correlation function from 1-D temporal signal

30. Linear Prediction Signal Subspace Technique Technique APPLY ESTIMATE ESTIMATE UNIFORM FOR.& BACK. AR Model FOR.& BACK. Nonstationary Order; No. ANALYSIS LIN. PRED. LIN. PRED. or Signal of Estimated WINDOW PARAMS. BY PARAMS. BY Signals AT EACH TRUNCATED COVARIANCE CTR. TIME SVD LP METHOD CALCULATE COMBINE FORWARD & ORIGINAL BACKWARD DATA + LIN. PRED. FOR. & BACK. DATA EXTENDED EXTENSIONS DATA Time - Frequency APPLY Representation by WINDOWED WINDOW TO SQUARED FFT OF STFT with Linear DATA EXTENDED MAGNITUDE EACH ROW Prediction FUNCTION DATA AT OF STFT OF WDF or Signal Subspace (WDF) OF CENTER Data Extrapolations EXT. DATA TIME Agorithm A: FLOW DIAGRAM OF STFT WITH 2X SIGNAL SUBSPACE EXTRAPOLATION (1-D solution) Original Data Within Analyis Window 1X Backward LP Extrapolation Forward LP Extrapolation 2X

31. LINEAR PREDICTION SOLUTION BY NOISE EXCISION (De-Noising) • Rectangular Toeplitz Data Matrix of • Covariance Case of Linear Prediction • for N Data Samples • Least Squares Normal Equations for • Forwardf and Backwardb Linear • Prediction Filters of Order p (note • that is the squared error) • SVD of Data Matrix • Excise (delete) “Noise” Eigenvectors • Leaving M Dominant Eigenvectors • (assume singular values are ordered • by magnitude ) • Use in lieu of to • Compute Linear Prediction Parameters and Reduced Rank Data Matrix

32. STFT + Noise Excision (via SVD) + Extrapolation TFA GRAM OF BO-105 LINEAR

33. FLOW DIAGRAM OF TFA BY 2-D MINIMUM VARIANCE (TFMV) Sampled Nonstationary Signal TFA Estimate X CWT WDF 2-D AR PARAM. ESTs. (Q1, Q4) FORM 2-D DATA ARRAY FORM WINDOWED DATA FUNC. ARRAY h[n] INVERSE FFT of WDF COLUMNS 2-D MINVAR SPECTRAL ESTIMATE x[n] S[mF,nT] Use 2-D Quarter-Plane Lattice LP (published in June 2000 IEEE Sig. Proc. Letters) Modified For (f,t) Functions Rather Than (t,t) Functions (fast computational algorithm recently published with Stoica & Jakobsson in September 2000 IEEE Sig. Proc. Transactions) CAN BE PERFORMED ONE LINE AT A TIME WITH PERSISTENT NONSTATIONARY SIGNALS (sliding analysis window).

34. STFT & Wigner (linear plots) ADDITIONAL TFA TECHNIQUES FOR FINE STRUCTURE 2-D TIME vs DOPPLER- FREQUENCY PATTERNS STFT & Wigner (logarithmic plots) 1-D ST-AR & ST-MV (logarithmic) (frequency only Sharpening) HIGH DNR & HIGH RESOLUTION TFA TECHNIQUES 2-D TFAR & TFMV (logarithmic) (time & frequency Sharpening)