Compression for Synthetic Aperture Sonar Signals Thomas Higdon MDDSP Feb. 25, 2008

Outline What is synthetic aperture sonar? Why do we need compression? Some current research Prospective research directions

What is synthetic aperture processing? Collect sensor data at a series of physical locations. Aggregate the data and process it to form an image.

Circular Synthetic Aperture Data is collected at equidistant points in a circle, pointing towards the center.

Projection-Slice Theorem The Fourier transform of a projection is a slice of the 2D Fourier transform.

Image Formation Project the image to polar coordinates and then inverse 2D FFT to yield the object’s image.

Why compression is needed Data for a typical sonar array might arrive at many gigabytes/sec. Storage on autonomous vehicles is limited. Compression might allow data to be reasonably transmitted via underwater communication links.

Radar and ultrasound techniques Applied to radar and ultrasound images Sonar shares noise characteristics with radar and ultrasound. Application of wavelet-based techniques proposed for ultrasound and radar to sonar.

Ultrasound Technique [Gupta, et al. 2005] Logarithm to convert multiplicative speckle noise to additive noise. Perform wavelet transform and estimate quantizer thresholds and subdivide coefficients into classes based on activity level. Threshold and quantize the coefficients in each class using an adaptive uniform threshold quantizer.

Results – 40:1 compression

Image Processing The reduction of noise that does not contain image information will allow more efficient compression. Conversion to log space to allow reduction of multiplicative noise.

Compression Techniques The performance of DCT (JPEG-style) and wavelet-based compression will be investigated. Correlation of images received by different sensors

Image Assessment Evaluation of the performance of different techniques requires a metric. Sonar images typically processed by human operators and/or object-detection algorithms. PSNR will be used to evaluate the mean squared difference between the compressed and uncompressed images.

Questions