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

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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

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3-D CUBIC MICRO-MOTION FEATURE SPACE Micro-Temporal (localized time variations) Micro-Doppler [Micro-Spectral] (localized frequency variations) Micro-Range (localized range variations) 2-D RANGE- DOPPLER CHIPS or SLICES Thin Slice: temporally localized (low gain) Thick Slice: temporally averaged (high gain) 2-D TIME- FREQUENCY CHIPS or SLICES Thick Slice: spatially averaged (high gain) Thin Slice: spatially localized (low gain) Whole 3-D Cube Time-Range (HRR) 2-D Slice Range-Doppler 2-D SliceTime-Doppler 2-D Slice BTR-80 dB SHORT-DWELL SAR LONG-DWELL SAR Wheel 2-D T-vs-F Profile (Micro-Doppler) 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 2-D Range- Doppler Profile

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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 ) AMTI MICRO-MOTION FEATURES: HELICOPTER ILLUMINATED BY WIDE BANDWIDTH RADAR Micro-Temporal (localized time variations) Micro-Spectral (localized frequency variations) Micro-Spatial (localized range cell variations)

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GMTI MICRO-MOTION FEATURES 4.3 m5.8 m7.0 m9.0 m Back Rim Tire Treads No Doppler Tire Treads Micro-Temporal (localized time variations) Micro-Spectral (localized frequency variations) Micro-Spatial (localized range cell variations)

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Generic Sharpening / Enhancement Concept 1-D, 2-D, 3-D, 4-D 1-C, M-C (MIMO) Domain Change Sharpening Algorithms 1-D, 2-D, 3-D, 4-D 1-C, M-C (MIMO) Reverse Domain Change Signal Entity In Sharpened / Enhanced Signal Entity Out Special Transform Inverse Transform Estimate/Predict Missing High “Frequency’ Content 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

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

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NOTIONAL EXAMPLE OF SHARPENING APPROACH

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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 bit CW DOPPLER COLLECTION WITHOUT GROUND CLUTTER AND INTERFERENCE

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AMTI Vs GMTI COLLECTION CONDITIONS Plane, Helicopter Radar Platform 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)

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AMTI: AIRBORNE RADAR TARGETS 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 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 lineHub signatureAlias of JEM Main rotor modulations & blade flashMulti-path bouncesCross feed Tail rotor modulations & blade flashStabilizer bar (Huey) Note that –500 to +500 Hz region replaced with time code signal for accessing taped data German UH – 1D HueyEurocopter BO - 105

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

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

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

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

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

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

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

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

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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

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

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EXAMPLE OF 2-D/2-CHANNEL SHARPENING

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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

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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

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SUPPLEMENTARY SLIDES

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

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

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GRAYSCALE STFT TFA GRAM: C-160 TURBOPROP

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

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

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Rectangular Toeplitz Data Matrix of Covariance Case of Linear Prediction for N Data Samples Least Squares Normal Equations for Forward f and Backward b 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 LINEAR PREDICTION SOLUTION BY NOISE EXCISION (De-Noising)

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

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FLOW DIAGRAM OF TFA BY 2-D MINIMUM VARIANCE (TFMV) FORM 2-D DATA ARRAY FORM WINDOWED DATA FUNC. ARRAY h[n] INVERSE FFT of WDF COLUMNS 2-D AR PARAM. ESTs. (Q1, Q4) 2-D MINVAR SPECTRAL ESTIMATE Sampled Nonstationary Signal TFA Estimate WDF X x[n] S[mF,nT] CWT Use 2-D Quarter-Plane Lattice LP (published in June 2000 IEEE Sig. Proc. Letters) Modified For (f,t) Functions Rather Than (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).

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

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