Rank Ordered Mean Noise Blankeror

Sliding Median Noise Blanker

(or how NB2 works!)

Phil Harman VK6APH

The Problem

Conventional (Analogue) Solutions

Noise Clipper Noise Blanker

A DSP Solution

An image processing technique

An image processing technique

Original Image Image + Impulse noise

Median Filtering

Median Filtered Image Image + Impulse noise

How Median Filtering works

7 17 18

15 200 9

12 11 14

How Median Filtering works

• Record the values nearby7, 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

Median Filtering Example

Median Noise Blanker

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Sample

Val

ue

Value

Value 0 17 34 50 64 150 87 94 98 100

1 2 3 4 5 6 7 8 9 10

Median Filtering Example

Median Noise Blanker

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Sample

Val

ue Value

MNB

Value 0 17 34 50 64 150 87 94 98 100

MNB 0 17 34 50 64 76 87 94 98 100

1 2 3 4 5 6 7 8 9 10

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

voidSDROMnoiseblanker(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!

Questions?

Rank Order Mean (ROM) Noise Banker

Sliding ROM Noise Blanker

Median Impulse Reduction