Optimal Transport Based Filtering with Nonlinear State ...
Transcript of Optimal Transport Based Filtering with Nonlinear State ...
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Optimal Transport Based Filtering withNonlinear State Equality Constraints
Niladri Das & Dr. Raktim Bhattacharya
Dept. of Aerospace Engineering,Intelligent Systems Research Laboratory,
Texas A&M University,College Station, TX
June 13, 2020
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Table of Contents
1. Introduction to the problem
2. Contributions
3. Filter model
4. Optimal Transport Filtering
5. Problem formulation
6. Constrained Filtering
7. Numerical example
8. Conclusion
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Section 1
1. Introduction to the problem
2. Contributions
3. Filter model
4. Optimal Transport Filtering
5. Problem formulation
6. Constrained Filtering
7. Numerical example
8. Conclusion
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Introduction
We consider the problem of Optimal Transport based Bayesianfiltering (OTF) in presence of state dependent nonlinearequality-constraints.
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Section 2
1. Introduction to the problem
2. Contributions
3. Filter model
4. Optimal Transport Filtering
5. Problem formulation
6. Constrained Filtering
7. Numerical example
8. Conclusion
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Contributions
Our main contribution is in proposing a framework to extend theOTF, where nonlinear state equality constraints are present. Tothe best of our knowledge, there is no prior work on OTF withstate constraints.
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Section 3
1. Introduction to the problem
2. Contributions
3. Filter model
4. Optimal Transport Filtering
5. Problem formulation
6. Constrained Filtering
7. Numerical example
8. Conclusion
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Filter model
The recursion starts from the initial distribution p(x0). In the predictionstep, we evaluate the prior distribution of xk, given y1, · · · ,yk−1 as
p(xk|y1, · · · ,yk−1) =
∫p(xk|xk−1)p(xk−1|y1, · · · ,yk−1)dxk−1 (1)
where p(xk|y1, · · · ,yk−1) is the posterior distribution at kth time step,p(xk|xk−1) encapsulates the state transition model from k − 1 to k, andp(xk−1|y1, · · · ,yk−1) is the posterior state distribution at (k − 1)th timestep.In the next update step, given the new measurement yk at time step k,the updated posterior distribution of the state xk is computed using theBayes rule as
p(xk|y1, · · · ,yk) =p(yk|xk)p(xk|y1, · · · ,yk−1)∫p(yk|xk)p(xk|y1, · · · ,yk−1)dxk
. (2)
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Section 4
1. Introduction to the problem
2. Contributions
3. Filter model
4. Optimal Transport Filtering
5. Problem formulation
6. Constrained Filtering
7. Numerical example
8. Conclusion
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Optimal Transport Filtering I
I Equally weighted prior ensemble at time step k is denoted byX−
k ∈ [x−1,k,x
−2,k, ...,x
−N,k] and the equally weighted posterior
ensemble by X+k ∈ [x+
1,k,x+2,k, ...,x
+N,k].
I The matrix T := [tij ] is a coupling between X−k and X+
k ,
N∑i=1
tij = 1/N,
N∑j=1
tij = wi, and tij ≥ 0; (3)
where wi ∝ likelihood-function(yi,x−i,k).
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Optimal Transport Filtering II
Optimal matrix T is solved by optimizing the following linearprogramming problem,
T ∗ = argminT
N∑i=1
N∑j=1
tijD(x−i,k, x
+j,k) (4)
subjected to conditions in (3), where D(x−i,k,x
+j,k) is a distance metric
between x−i,k and x+
j,k. The x+j,k denotes posterior samples with sample
weights equal to wi. The equally weighted prior and unequally weighted(weighted with wi) posterior have the same sample locations.
Posterior samples:
X+k = X−
k NT (5)
(interpreted as a re-sampling).
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Initial Sample Generation
Based upon the OT filtering step: X+k = X−
k NT we present analternative sampling technique.
Figure 1: Uniform samplesgenerated from an annulus
Figure 2: Histogram generatedfrom Bi-modal distribution
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Section 5
1. Introduction to the problem
2. Contributions
3. Filter model
4. Optimal Transport Filtering
5. Problem formulation
6. Constrained Filtering
7. Numerical example
8. Conclusion
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Problem formulation
Assume that for all k ≥ 1, the state vectors xk satisfies the equalityconstraint
g(xk) = dk (6)
where g : Rn → Rs and dk ∈ Rs are known. The objective of a nonlinearequality constrained Bayesian filtering is to evaluate (5), such that it alsosatisfies (6).
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Section 6
1. Introduction to the problem
2. Contributions
3. Filter model
4. Optimal Transport Filtering
5. Problem formulation
6. Constrained Filtering
7. Numerical example
8. Conclusion
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Constrained Filtering
Common techniques1:
I Nonlinear equality-constrained filtering (NLeq)
I Projected filtering (Proj)
I Measurement-Augmented filtering (MA)
We adapt these techniques for OTF and name them: OTNLeq, OTProj,and OTMA respectively.
We reinforce the nonlinear equality constraints using the concepts ofmeasurement-augmentation and projection feedback together. This isessentially OTNLeq combined with OTMA, which is named asOTNLeqMA.
1Bruno O. Soares Teixeira et al. “Unscented filtering for equality-constrainednonlinear systems”. In: 2008 American Control Conference. IEEE, 2008. doi:10.1109/acc.2008.4586463. url:https://doi.org/10.1109%2Facc.2008.4586463.
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Section 7
1. Introduction to the problem
2. Contributions
3. Filter model
4. Optimal Transport Filtering
5. Problem formulation
6. Constrained Filtering
7. Numerical example
8. Conclusion
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Numerical example
We consider a simple pendulum with a point mass at the end.
I We use a non-minimal co-ordinates x = [x, y] to denote the locationof the point mass on a 2-dimensional plane.
I The positive x-direction point towards the right, while the positivey-direction points downwards.
The kinematics of this pendulum is modeled as:
x =1
L2(−gxy − x(x2 + y2)) (7a)
y =1
L2(gx2 − y(x2 + y2)) (7b)
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Experimental Setup I
I L is the length of the pendulum taken to be 1 meter, and g is thegravity term, which is taken to be 9.8 m/sec2.
I Since the length of the pendulum is fixed we have x(t)2 + y(t)2 = L2
for t ≥ 0 as our state dependent equality constraint.
I We set real initial location of the pendulum point massx0 = [x(0), y(0)] as x0 = [Lcos(30o), Lsin(30o)].
I The samples are generated by sampling uniformly from ±5o aboutthe mean position.
I We assume no process noise in this numerical study.
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Experimental Setup II
We assume that we can measure the location of the point mass usingvisual measurement with measurement noise and that we do not haveaccess to the angular measurements. The measurement model is:
yk = h(xk) + vk, (8)
where h(xk) := Hxk with H =
[1 0 0 00 1 0 0
]. The stochastic noise
variable , vk ∼ N(0,R), where R = diag([0.01 0.01])m2. Themeasurements are available at every discrete time step of ∆t = 0.05 secs.
Constraint — g(xk) = x2k + y2k = 1, where xk and yk denotes the
position estimates of the point mass.
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Results I
-1 -0.5 0 0.5 1
X axis [m]
-1.2
-1
-0.8
-0.6
-0.4
-0.2
0
Y a
xis
[m
]
0 1 2 3 4 5
Time [secs]
-1
-0.5
0
0.5
1
X p
ositio
n [m
]
real
filtered
0 1 2 3 4 5
Time [secs]
0
0.05
0.1
0.15
0.2
0.25
Length
err
or
in the p
endulu
m
[m]
0 1 2 3 4 5
Time [secs]
0
0.01
0.02
0.03
0.04
0.05 E
rror
in X
positio
n [m
]
0 1 2 3 4 5
Time [secs]
0
0.2
0.4
0.6
0.8
1
1.2
Y p
ositio
n [m
]
real
filtered
0 1 2 3 4 5
Time [secs]
0
0.02
0.04
0.06
0.08
0.1
Err
or
in Y
positio
n [m
]
Figure 3: OT filter responses for a simple pendulum without compensatingfor the length constraint, with 10 samples
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Results II
-1 -0.5 0 0.5 1
X axis [m]
-1
-0.8
-0.6
-0.4
-0.2
Y a
xis
[m
]
0 1 2 3 4 5
Time [secs]
0
1
2
3
4
5
6
Length
err
or
in the p
endulu
m
[m]
10-3
0 1 2 3 4 5
Time [secs]
-1
-0.5
0
0.5
1
X p
ositio
n [m
]
real
filtered
0 1 2 3 4 5
Time [secs]
0.2
0.4
0.6
0.8
1
Y p
ositio
n [m
]
real
filtered
0 1 2 3 4 5
Time [secs]
0
0.01
0.02
0.03
0.04
0.05
Err
or
in X
positio
n [m
]
0 1 2 3 4 5
Time [secs]
0
0.02
0.04
0.06
0.08
0.1
Err
or
in Y
positio
n [m
]
Figure 4: OT filter responses for a simple pendulum after compensating forthe length constraint using OTNLeqMA
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Result III
0 0.5 1 1.5 2 2.5
Pendulum Period [=2.0071 secs]
10-10
10-5
100
105
log
10
(Avg. R
MS
E)
OT
OTProj
OTMA
OTNLeq
OTNLeqMA
Figure 5: Average RMS constraint error over 100 Monte Carlo runs
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Section 8
1. Introduction to the problem
2. Contributions
3. Filter model
4. Optimal Transport Filtering
5. Problem formulation
6. Constrained Filtering
7. Numerical example
8. Conclusion
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Conclusion
I We addressed the nonlinear equality-constrained filtering problem fornonlinear systems when OT filtering is used for its state estimation.
I Three existing methodologies for equality-constrained filteringproblems using KF and UKF, were coupled with OT filtering andtheir performances were evaluated using a numerical example.
I We further showed numerically, that the proposed OTNLeqMA filterprovided the least constraint error compared to others.
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Acknowledgment
Research sponsored by Air Force Office of Scientific Research, DynamicData Driven Applications Systems grant FA9550-15-1-0071
Thank You