Rank ordered mean noise blanker or sliding median noise blanker
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Rank Ordered Mean Noise Blanker or Sliding Median Noise Blanker. (or how NB2 works!) Phil Harman VK6APH. The Problem. Conventiona l (Analogue) Solutions. Noise Blanker. Noise Clipper. A DSP Solution. An image processing technique. An image processing technique. Original Image.

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Rank Ordered Mean Noise Blanker or Sliding Median Noise Blanker

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Rank ordered mean noise blanker or sliding median noise blanker
Rank Ordered Mean Noise BlankerorSliding Median Noise Blanker

(or how NB2 works!)

Phil Harman VK6APH



Conventiona l analogue solutions
Conventional (Analogue) Solutions

Noise Blanker

Noise Clipper




An image processing technique1
An image processing technique

Original Image

Image + Impulse noise


Median filtering
Median Filtering

Image + Impulse noise

Median Filtered Image



How median filtering works1
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
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 example3
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
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
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
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
Questions?

Rank Order Mean (ROM) Noise Banker

Sliding ROM Noise Blanker

Median Impulse Reduction


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