1. Coherent Noise Suppression Divided into two components – Near-DC Doppler component Always present We do a very good job of removing There can be artifacts from removal, but are very rare – Noise that is broadband in Doppler, but is made up of many narrowband fast time subbands Only always detectable at certain ranges to target Very broadband in Doppler domain with occasional bursts of very high values that affect all ranges Current method looks for peaks in fast-time frequency domain and suppresses these. The current method for removal of this non-DC component is not compatible with our deconvolution method. We currently only use this method on land ice missions.

2. Sidelobe Suppression System response is time varying (i.e. there isn’t just one deconvolution filter that works everywhere) – Rising edge sidelobes are much more stable than falling edge sidelobes A basic deconvolution method was developed – It is only moderately robust to the use of non-specular targets (just coherently averages 128 range lines to suppress non-specular scattering energy and noise) – Automated On several segments, it required manual adaptation of thresholds and even some hand picking of specular targets – Occasionally noise floor raises by several dB because of a bad estimate of the system response – Focusses on sidelobe removal from 3 m before peak to 1.6 m after peak – Very good rising edge sidelobe suppression (generally cannot detect any sidelobe) – Poor falling edge sidelobe suppression, although generally achieves some level of suppression – Filter response has to be time varying because system response is time varying  therefore suppression level is time varying! This may not be a problem except when trying to detect snow over a specular ice interface since the sidelobes are generally >30 dB down.

3. 2x Fast-time sampling Currently implemented However: Multi-looking in along-track violates Nyquist in that dimension and therefore will affect any fast-time resampling – Do we want to do 2x sampling in the along-track dimension as well?

4. Elevation Compensation Data are now elevation compensated before software presumming/coherent averaging Small SNR improvement most of the time, but very noticeable improvement when aircraft is changing altitude quickly

5. Potential Future Improvements: Do we want these? Estimate noise floor to implement MMSE version of deconvolution (would allow better coherent noise suppression) – Would potentially allow near-optimal removal of non-DC component of coherent noise. Most valuable to sea ice results since currently no effort is made to remove the non-DC component for sea ice. – Should we invest more time??? Need to improve system response estimation and quality metric for estimations (MMSE-based system estimation and/or improved manual interface) – Would potentially allow near-optimal sidelobe suppression – Should we invest more time??? SAR processing – On Global Hawk for Snow Radar project schedule to implement by March 2015

6. Coherent Noise Suppression Examples

7. CN: Good Suppression (zero detectability of coherent noise)

8. CN: Artifact Most common on very short data segments or on edges of segments

9. CN: Range Dependent (worst at short ranges) Non-DC Component

10. CN: Burst In some cases, can be much worse

11. Sidelobe Suppression Examples These are taken from real data collected over leads.

12. SL: Very good suppression example Shows two nearby lines

13. SL: Good/Bad Suppression Blue shows typical good response Red shows bad suppression with leading edge sidelobe

14. SL: Only Good Rising Edge Suppression (note peak does not have as good of a SNR as previous examples so noise floor is higher too)

15. SL: Very bad suppression example (note peak does not have as good SNR so noise floor is higher too)

16. SL removal caused noise floor to worsen Fairly rare… and I think we can improve this with minor modifications to our algorithm