In post mission data graphs we encounter several unnecessary and irrelevant data that gets mapped onto the post mission analysis graphs due to noise. So, an algorithm is proposed to eliminate the unnecessary data points that gets crept into the post mission analysis graph due to random noises and tries to minimize the same.

1. Elimination of unusable data
And
Minimization of noise in post
mission data graphs
Presented By:-
Rohit Baghel
712CS1033

2. Abstract
 In post mission data graphs we
encounter several unnecessary and
irrelevant data that gets mapped onto
the post mission analysis graphs due
to noise. So, an algorithm is proposed
to eliminate the unnecessary data
points that gets crept into the post
mission analysis graph due to random
noises and tries to minimize the same.

3. Introduction
 A fundamental challenge in dealing
with the newly available observational
data describing the trajectory for post
mission analysis, is that the data is
often of dubious quality – it is noisy
and incomplete – and hence before
any analysis method can be applied,
the data must be cleaned, and missing
information inferred.

4. Algorithm
 The two static curves forming the Impact
Limit Lines for the expected flight path (as
shown in fig.1) are available for the filtering
purpose .
 These two curves will be input to the
proposed algorithm.
 Then filter out positions which don’t fall into
the area created by Impact line limit and the
expected flight path, so that the spikes
induced by noise can be reduced and we can
obtain a cleaner graph for analysis.
 The algorithm tries to fit the curve closest to
the expected flight path.

5. Fig. 1

6. Pseudo Code
 Algorithm curve_filter (RILL1, RILL2, X_Center, Y_Center, XPos[],
YPos[])
 // Computes if the distance of a point (x,y) is greater than RILL1
and less than RILL2 from the center point.
 // Input: constant radius value RILL1 and RILL2, center coordinate
and array of both x and y coordinate of same dimension n.
 // output: Norm[] containing the normalized coordinate values after
 // applying the algorithm, of data type Position, where Position is a
user defined datatype, having two values Position->x and Position-
>y
 // Also assume XNorm and YNorm to be two temporary arrays to
hold the normalized coordinate values.

7. Continued…
 Count =0;
 For i=0 to n do
 Dist <- Distance(X_center,Y_Center,XPos[i],YPos[i])
 If ((Dist > RILL1) && (Dist < RILL2))
 XNorm[count] <- XPos[i];
 YNorm[count] <- YPos[i];
 Count = count+1;
 Position Norm[count];
 For j=0 to count do
 Norm[j]->x = XNorm[j];
 Norm[j]->y = YNorm[j];
 Return Norm;
 Algorithm Distance(X_Center, Y_Center, X,Y)
 // this algorithm returns the distance between points(X,Y) and (X-Center and Y-Center)
 // It uses the square root function in calculation.
 Dist = sqrt(((X-center-X)^2 )+((Y_center-Y)^2));
 Return Dist;

8. Simulation Results
Fig.2 Input trajectory

9. Continued..
Fig.3 Filtered Graph

10. Limitations
 Though the algorithm was able to filter
some of the unnecessary points, it is
clear that it was still not able to
smoothen the graph completely as it can
be seen in fig 3 that still some of the
spikes were left.
 Also the algorithm was not able to
perform well at the ends of the graphs,
because of the difference between
curvature of the input graph and the
curvatures of the Impact Limit Lines.

11. Conclusion
 Hence, the proposed algorithm was successfully
able to give a cleaner set of data points, which had
less noise for the post mission analysis.

12. Thank you!