Extracting near constant elements from an array
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I have a vector 'data(1).IAS' , which looks like as follows.
data(1).IAS
ans =
288
294
296
294
.
. % length(data(1).IAS) = 153.
I am trying to extract the near constant elements from a vector as shown in the figure (The figure is IAS plotted against its index number).
The problem is that my data.IAS has 693 arrays and it is not possible to do it one by one. Does anyone have any idea how to extract the only near constant parts?
Thanks.
UPDATE: Code
data = load('SSR_20140103.mat'); % Variable name is 'data' with various fields.
newData = data.data;
for i = 1: length(data.data)
x = data.data(i).IAS;
h = [1 1 1 1 1 1 1 1 1 1];
if(length(x)>1)
x1 = [ones(length(h),1)*x(1);x];
x2 = filter(h,1,x1)/length(h);
x2 = x2(length(h):length(x2)-1);
diffx2 = diff(x2);
diffx2 = [ones(length(h),1)*diffx2(1);diffx2];
meandiffx2 = filter(h,1,diffx2)/length(h);
meandiffx2 = abs(meandiffx2(length(h):length(meandiffx2)));
constantIndex = find(meandiffx2<0.25);
constantData.index = constantIndex;
constantData.data = x(constantIndex);
constantValues(i) = {constantData};
newData(i).time = data.data(i).time(constantIndex);
newData(i).lat = data.data(i).lat(constantIndex);
newData(i).lng = data.data(i).lng(constantIndex);
newData(i).altitude = data.data(i).altitude(constantIndex);
newData(i).selected_altitude = data.data(i).selected_altitude(constantIndex);
newData(i).BPS = data.data(i).BPS(constantIndex);
newData(i).RA = data.data(i).RA(constantIndex);
newData(i).TTA = data.data(i).TTA(constantIndex);
newData(i).GS = data.data(i).GS(constantIndex);
newData(i).TAR = data.data(i).TAR(constantIndex);
newData(i).TAS = data.data(i).TAS(constantIndex);
newData(i).heading = data.data(i).heading(constantIndex);
newData(i).IAS = data.data(i).IAS(constantIndex);
newData(i).Mach = data.data(i).Mach(constantIndex);
newData(i).BAR = data.data(i).BAR(constantIndex);
newData(i).IVV = data.data(i).IVV(constantIndex);
end
end
采纳的回答
Star Strider
2015-7-10
1 个投票
Your airplane appears to be a jet flying a STAR. When it enters Class C airspace, it is told to slow to 250 kts, then is cleared for the approach and flies the approach at 130 kts.
I didn’t run the code, however it seems to be two 10-element moving average filters to create first ‘x2’ and then ‘meandiffx2’. It then thresholds ‘meandiffx2’ and returns the indices of those values that are <0.25 to ‘constantIndex’. (If it doesn’t find any, the find function will return an empty value. This usually throws an error when concatenated with an existing vector, and will obviously not function as an index in the next line, so I don’t know how the code handles this when it does the ‘constantData.index’ and constantData.data assignments.) It then stores the associated values in the designated structures.
This is a powerful argument for documenting code with comments.
4 个评论
bio lim
2015-7-10
Star Strider
2015-7-10
It’s definitely not obvious. I recognised the moving average (using the filter function) because it’s a relatively commonly-used technique, and here it’s used to detect the constant-airspeed segments. I understand part of it because I’m an Instrument-Rated Private Pilot. The rest is honestly mystifying.
bio lim
2015-7-10
Star Strider
2015-7-10
My pleasure!
I’m knowledgeable and experienced in certain aspects of MATLAB but I’d not consider myself an ‘expert’, except in certain very restrictive applications (medicine, biomedical engineering, aviation).
更多回答（2 个）
First who have to define an formal criterion of what you consider "near constant". For example, more than 20 points that vary my less than 15 units. Than you loop through your data:
pseudo code:
i_start = 1, i_end = i_end + 20
if the range of values(i_start:i_end) < 15?
then increment i_end
until the range is > 15:
1. you've found a constant area from i_start:i_end
2. continue checking with i_start = i_end+1
else
continue checking with i_start = i_start + 1
If you post your data and define your criteria, we could provide further help.
Image Analyst
2015-7-10
1 个投票
You can use stdfilt() in the Image Processing Toolbox. It will work on a 1-D signal. Basically it scans the array computing the standard deviation in a moving window. If the values don't vary much in the window, then the stdev will be low and if they change a lot the stdev will be high.
Alternatively you can scan the signal and call polyfit() from each point onwards. Fit a line and if the slope is reasonably low, like 0 to 0.2 or whatever, then the portion is fairly flat. If the slope is higher, then trending up or down.
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