FR1.L09 -PREDICTIVE QUANTIZATION OF DECHIRPED SPOTLIGHT-MODE SAR RAW DATA IN TRANSFORM DOMAIN
description
Transcript of FR1.L09 -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
Predictive Quantization of
Dechirped Spotlight-Mode SAR Raw Data in
Transform Domain
Presentation Outline
2
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
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
Need on-board compression
of SAR RAW* data
* Not SAR Image Compression
3
x
y
q
z
q: azimuth angle
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
4
Previous Work, cont’d
5
Paper Method Pre-Proc. Quantize
r
Post-Proc.
Kwon (1989) BAQ Normalization SQ
Arnold (1988) CCT VQ
Franceschetti
(1991)
SC-SAR 1-bit SQ
Benz (1995) FFT-BAQ Normalization &
2-D FFT
SQ w/bit
allocation
Bolle (1997) R & AZ comp DCT SQ Huffman
Owens (1999) TCVQ Trellis coding VQ
Baxter (1999) Gabor/TCQ Gabor trans.
Trellis coding
VQ Huffman
Poggi (2000) Range
compression
VQ
Magli (2003) NPAQ LPC SQ Arithmetic
CSAR data samples are uncorrelated Zero-mean Gaussian distributed Signal power varies slowly over time
6
Example: AFRL Gotcha data set (about 42,000 returns from full 360°)
Magnitude of Raw Data Formed SAR Image (CBP, 512 returns)
Spotlight CSAR 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.
7
Transformed Data
IDFT of SAR data
High magnitude sinusoidal trace from metallic cylinder object
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
8
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.
Transformed Data, cont’d
Block Predictive Coding
Using the AR modeling, we develop predictive coding techniques
for compression of SAR data
9
Encoder
Decoder
AR Estimator: Burg’s method
Predictive Encoder:
Predictive quantization
Scalar: TD-BPQ (DPCM)
Vector: TD-BPVQ
Predictive Trellis Coded Quantization
Transform Domain Block Predictive Quantization
All signals are complex-valued
Q(x) : 2 identical scalar quantizers
for I and Q channels
Designed for zero-mean
Gaussian input with variance
Predictor states initialization
First block : BAQ encoded.
Subsequent blocks: Last L
coded samples of previous
block
10
,bkM i qr Q(x)
A(z)
,ˆbkM i qe
,ˆbkM i qr
,bkM i qr
,k qa
2,k q
,vkM i qi
DPCM Encoder L: Predictor Order
2,k q
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
11
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
12
Predictive Trellis Coded Quantization
Example: 2-Bit/S PTCQ Configuration
13
D6 D0
D1
D2
D3
D4
D5 D7
32QAM
1
0
1
2
3
4
5
6
7
B0
B1
B0
B1
B0
B1
B0
B1
0
3
2
1
0
3 2
1
0
3
2
1
0
3
2
0
3
2
1
0
2
1
0
3
2
1
0
3 2
1
3
B0
B1
Codebook Structure
TCQ set partitioning
Trellis Structure
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
14
TD-BPQ: SNR vs. Predictor Order
15
SNR improves by 2.5 dB by introducing prediction (from L = 0 to L = 1)
M = 256
TD-BPQ: SNR vs. Block Size
16
Reasons for SNR loss:
Mb small – poor AR estimates
Mb large – data non-stationary
Prediction order L = 4
Performance Results
17
L = 4, M = 256, TD-BPVQ: 2 IDFT bins together
SNR for each of the three schemes experiences fluctuations across
images due to the anisotropic nature of the scene
5 dB
1 dB 1.5 dB
BAQ
TD-BPQ
TD-BPVQ
TD-BPTCQ
Formed SAR Image Comparison 18
Original
BAQ
TD-BPQ
TD-BPVQ
TD-BPTCQ
9.7 dB
13.5 dB
14.4dB
14.9 dB
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
19
Any Questions?