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Rank Ordered Mean Noise Blanker or Sliding Median Noise Blanker PowerPoint Presentation

Rank Ordered Mean Noise Blanker or Sliding Median Noise Blanker

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### Rank Ordered Mean Noise BlankerorSliding Median Noise Blanker

(or how NB2 works!)

Phil Harman VK6APH

How Median Filtering works

- Record the values nearby
7, 9, 11, 12, 14, 15, 17, 18, 200

- Sort (Rank) the values
7, 9, 11, 12, 14, 15, 17, 18, 200

- The median is the middle of a distribution: half the scores are above the median and half below*.
7, 9, 11, 12, 14, 15, 17, 18, 200

- The median is much less sensitive to extreme values and makes it a better measure than the mean for highly skewed distributions e.g. the mean is 34
* For an even number of values use the average of centre values

Median Filtering Example - recap

- Look for samples that are outside the norm
- Sort (Rank) the samples either side in Order
- Calculate the median value
- Replace the suspect sample with the median
- Slide along to the next suspect sample and repeat
- Issues:
- Processor intensive
- Distortion if applied too aggressively
- Only effective on impulse noise
- Simpler technique gives equally good results.

Median Filtering Example

- Q. How do we detect suspect samples?
- A. Keep an average of all samples and look for samples that are greater than the average by some amount
e.g. average = 0.999last_sample + 0.001current_sample

- Code:
If sample > (threshold x average)

apply median filter

Pseudo Code

for i < buffer_size

mag = mag(signal,i)

“median” = 0.75median + 0.25(signal,i)

average = 0.999average + 0.001mag

if mag > (threshold x average)

(signal,i) = median

next i

SDR1000 Code

void

SDROMnoiseblanker(NB nb) {

int i;

for (i = 0; i < CXBsize(nb->sigbuf); i++) {

REAL cmag = Cmag(CXBdata(nb->sigbuf, i));

nb->average_sig = Cadd(Cscl(nb->average_sig, 0.75),

Cscl(CXBdata(nb->sigbuf, i), 0.25));

nb->average_mag = 0.999 * (nb->average_mag) + 0.001 * cmag;

if (cmag > (nb->threshold * nb->average_mag))

CXBdata(nb->sigbuf, i) = nb->average_sig;

}

}

Future Techniques

- Noise “Subtraction” (N4HY)
- Detect the pulse
- Determine what the receiver has done to it
- Create a model of the pulse
- Subtract the model from the signal
- Completely linear process
- If you get it wrong it will add a noise pulse!

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