FR1.L09 -	PREDICTIVE QUANTIZATION OF DECHIRPED SPOTLIGHT-MODE SAR RAW DATA IN TRANSFORM DOMAIN
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FR1.L09 - PREDICTIVE QUANTIZATION OF DECHIRPED SPOTLIGHT-MODE SAR RAW DATA IN TRANSFORM DOMAIN

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FR1.L09 -	PREDICTIVE QUANTIZATION OF DECHIRPED SPOTLIGHT-MODE SAR RAW DATA IN TRANSFORM DOMAIN FR1.L09 - PREDICTIVE QUANTIZATION OF DECHIRPED SPOTLIGHT-MODE SAR RAW DATA IN TRANSFORM DOMAIN Presentation Transcript

  • Predictive Quantization of Dechirped Spotlight-Mode SAR Raw Data in Transform Domain Takeshi Ikuma, Mort Naraghi-Pour* Department of Electrical and Computer Engineering Louisiana State University Baton Rouge, LA Thomas Lewis Air Force Research Laboratory Dayton, OH
  • 2 Presentation Outline  Circular spotlight-mode SAR, Motivation  Previous work  Autoregressive modeling of IDFT transformed SAR data  Predictive encoding  Block predictive quantization: scalar, vector  Predictive trellis coded quantization  Numerical results  Conclusions
  • 3 Circular Spotlight-Mode SAR  We are interested in circular spotlight-mode SAR  Radar periodically emits a linear FM chirp pulse and receives, dechirps, and samples the reflected pulses  A large volume of data is generated that must be downlinked for processing and archiving  Downlink channel has limited bandwidth z  Need on-board compression q: azimuth angle of SAR RAW* data q * Not SAR Image Compression y x
  • 4 Previous Work  Block Adaptive Quantization (BAQ)  Simple scalar quantizer, adapted to the signal power  Implemented in exiting systems  NASA Magellan Mission  NASA Shuttle Imaging Radar Mission C  More Effective Method?  Samples of both I and Q channels of SAR raw data are largely uncorrelated  However, SAR image exhibits some correlation  Transformed data may exhibit some correlation
  • 5 Previous Work, cont’d Paper Method Pre-Proc. Quantize Post-Proc. r Kwon (1989) BAQ Normalization SQ Arnold (1988) CCT VQ Franceschetti SC-SAR 1-bit SQ (1991) Benz (1995) FFT-BAQ Normalization & SQ w/bit 2-D FFT allocation Bolle (1997) R & AZ comp DCT SQ Huffman Owens (1999) TCVQ Trellis coding VQ Baxter (1999) Gabor/TCQ Gabor trans. VQ Huffman Trellis coding Poggi (2000) Range VQ compression Magli (2003) NPAQ LPC SQ Arithmetic
  • 6 Spotlight CSAR Data  CSAR data samples are uncorrelated  Zero-mean Gaussian distributed  Signal power varies slowly over time Example: AFRL Gotcha data set (about 42,000 returns from full 360°) Magnitude of Raw Data Formed SAR Image (CBP, 512 returns)
  • 7 Transformed Data  If there are strong reflectors in the scene, range-wise IDFT of CSAR data exhibits correlation along azimuth.  Isotropic reflectors appear as sinusoidal traces in the transformed data. Anisotropic reflectors appear as partial sinusoidal traces. IDFT of SAR data High magnitude sinusoidal trace from metallic cylinder object
  • 8 Transformed Data, cont’d  Develop block adaptive AR model for IDFT data across returns (azimuth) for each fixed IDFT bin  AR model can capture strong reflectors and homogeneous field Example: AR(1) Model of Gotcha Data  Blocks with higher signal power  AR poles close to the unit circle Companion Paper: T. Ikuma, M. Naraghi-Pour and T. Lewis, “Autoregressive Modeling of Dechirped Spotlight-Mode Raw SAR Data in Transform Domain,” Poster presentation today.
  • 9 Block Predictive Coding  Using the AR modeling, we develop predictive coding techniques for compression of SAR data  AR Estimator: Burg’s method  Predictive Encoder:  Predictive quantization Encoder  Scalar: TD-BPQ (DPCM)  Vector: TD-BPVQ  Predictive Trellis Coded Quantization Decoder
  • 10 Transform Domain Block Predictive Quantization  All signals are complex-valued ikMv i ,q  Q(x) : 2 identical scalar quantizers rkMb i ,q Q(x) ˆ ekMb i ,q for I and Q channels  Designed for zero-mean 2 2 k ,q Gaussian input with variance k ,q  Predictor states initialization rkMb i ,q  First block : BAQ encoded. A(z) ˆ rkMb i ,q  Subsequent blocks: Last L a k ,q coded samples of previous block DPCM Encoder L: Predictor Order
  • 11 TD-Block Predictive VQ  There is some correlation between neighboring IDFT bins  Code multiple (Nb) IDFT bins together to take advantage of this correlation Predictive VQ  Model a block of data as a vector AR process  Treat each IDFT bin as a separate channel. Use generalized AR estimators for vector process. Each AR coefficient is now a matrix  Innovation process comprises independent circular complex Gaussian processes with zero mean and different variances  Vector quantizer codebook  Basic codebook designed with LBG algorithm for circular complex Gaussian training samples with zero mean and unit variance  For each data block, basic codebook is transformed according to estimated covariance matrix given by vector AR estimator
  • 12 Predictive Trellis Coded Quantization  We have also applied predictive trellis-coded quantization (PTCQ) for coding of IDFT data Two design considerations: Trellis and codebook  Amplitude Modulation Trellises  Exhibit reasonable resistance against error propagation  Codebook Design:  Based on 32QAM symbol constellation  Scaled according to variance estimation from AR analysis  (Can be optimized by training it with LBG)  Viterbi algorithm is used for encoding
  • 13 Example: 2-Bit/S PTCQ Configuration Codebook Structure Trellis Structure B0 0 0 32QAM 1 2 3 B1 1 3 0 1 2 B0 2 3 2 0 TCQ set partitioning 1 1 B1 3 2 3 0 0 B0 B0 4 1 2 3 3 D0 D2 D4 D6 B1 5 1 0 2 2 3 B0 6 0 B1 1 2 1 D1 D3 D5 D7 B1 3 0 7
  • 14 Numerical Results  Numerical Results are obtained for AFRL Gotcha dataset  Performance measure: Average SNR of formed SAR images  119 images are formed each from 352 returns (roughly 3° azimuth) using convolution back-projection algorithm  Bit Rate: Fixed to 2 bits per real sample  TD-BPQ, TD-BPVQ, TD-BPTCQ, & BAQ are compared in terms of  Average SNR  Per-Image SNR as prediction order and block size are varied
  • 15 TD-BPQ: SNR vs. Predictor Order M = 256 SNR improves by 2.5 dB by introducing prediction (from L = 0 to L = 1)
  • 16 TD-BPQ: SNR vs. Block Size Prediction order L = 4 Reasons for SNR loss: Mb small – poor AR estimates Mb large – data non-stationary
  • 17 Performance Results L = 4, M = 256, TD-BPVQ: 2 IDFT bins together 1 dB 1.5 dB BAQ TD-BPQ 5 dB TD-BPVQ TD-BPTCQ SNR for each of the three schemes experiences fluctuations across images due to the anisotropic nature of the scene
  • 18 Formed SAR Image Comparison BAQ TD-BPVQ 9.7 dB 14.4dB Original TD-BPQ TD-BPTCQ 13.5 dB 14.9 dB
  • 19 Conclusions  Significant correlation is observed in IDFT of dechirped CSAR data  Three predictive encoding algorithms are applied to transformed data:  TD-BPQ: Scalar DPCM coding in IDFT domain  TD-BPVQ: Vector predictive coding in IDFT domain  TD-PTCQ: Predictive Trellis Coded Quantization  The predictive quantization can provide up to 6 dB improvement in average SNR Any Questions?