T = 100, observation stddev = 1 Known initial position (x y) 5.50000000 3.00000000 Ground truth positions at t=1:T, each row one data point (x y) 5.50000000 3.25000000 5.50000000 3.50000000 5.50000000 3.75000000 5.50000000 4.00000000 5.50000000 4.25000000 5.50000000 4.50000000 5.50000000 4.75000000 5.50000000 5.00000000 5.50000000 5.25000000 5.50000000 5.50000000 5.50000000 5.75000000 5.50000000 6.00000000 5.50000000 6.25000000 5.50000000 6.50000000 5.50000000 6.75000000 5.50000000 7.00000000 5.50000000 7.25000000 5.50000000 7.50000000 5.50000000 7.75000000 5.50000000 8.00000000 5.50000000 8.25000000 5.50000000 8.50000000 5.50000000 8.75000000 5.50000000 9.00000000 5.50000000 9.25000000 5.50000000 9.50000000 5.50000000 9.75000000 5.50000000 10.00000000 5.50000000 10.25000000 5.50000000 10.50000000 5.50000000 10.50000000 5.50000000 10.50000000 5.50000000 10.50000000 5.50000000 10.50000000 5.50000000 10.50000000 5.66666667 10.50000000 5.83333333 10.50000000 6.00000000 10.50000000 6.16666667 10.50000000 6.33333333 10.50000000 6.50000000 10.50000000 6.66666667 10.50000000 6.83333333 10.50000000 7.00000000 10.50000000 7.16666667 10.50000000 7.33333333 10.50000000 7.50000000 10.50000000 7.66666667 10.50000000 7.83333333 10.50000000 8.00000000 10.50000000 8.16666667 10.50000000 8.33333333 10.50000000 8.50000000 10.50000000 8.66666667 10.50000000 8.83333333 10.50000000 9.00000000 10.50000000 9.16666667 10.50000000 9.33333333 10.50000000 9.50000000 10.50000000 9.66666667 10.50000000 9.83333333 10.50000000 10.00000000 10.50000000 10.16666667 10.50000000 10.33333333 10.50000000 10.50000000 10.50000000 10.50000000 10.50000000 10.50000000 10.50000000 10.50000000 10.50000000 10.50000000 10.50000000 10.50000000 10.50000000 10.50000000 10.25000000 10.50000000 10.00000000 10.50000000 9.75000000 10.50000000 9.50000000 10.50000000 9.25000000 10.50000000 9.00000000 10.50000000 8.75000000 10.50000000 8.50000000 10.50000000 8.25000000 10.50000000 8.00000000 10.50000000 7.75000000 10.50000000 7.50000000 10.50000000 7.25000000 10.50000000 7.00000000 10.50000000 6.75000000 10.50000000 6.50000000 10.50000000 6.25000000 10.50000000 6.00000000 10.50000000 5.75000000 10.50000000 5.50000000 10.50000000 5.25000000 10.50000000 5.00000000 10.50000000 4.75000000 10.50000000 4.50000000 10.50000000 4.25000000 10.50000000 4.00000000 10.50000000 3.75000000 10.50000000 3.50000000 10.50000000 3.25000000 10.50000000 3.00000000 Noisy observations at t=1:T, each row one data point (x y) 5.68322726 2.22023246 6.44922183 3.80706192 5.63517494 4.26524634 5.76140632 3.05851423 5.33766233 4.10394537 4.96798862 6.18210359 4.62427065 4.26618495 4.78799545 3.82578767 5.30776048 4.97592977 7.03007251 5.25097526 4.43578659 7.35345730 6.73467915 5.77037355 3.99384030 5.80537218 5.34405896 6.77606825 5.23883635 7.19342191 5.89189421 5.74932109 4.55203908 6.50889391 4.99218245 7.17942449 5.51246904 4.72082266 5.04298536 9.24244841 4.43329860 9.18372816 5.85032100 8.47099424 5.68245217 7.18494399 5.41546052 10.60394635 5.59834777 9.29137361 4.76583089 9.46918627 5.73234701 10.17638756 5.12719126 9.76354542 7.52369089 7.99164603 7.72944568 10.83756370 6.50006082 8.83583553 4.90996544 10.22193584 5.92271569 8.82979930 5.97163433 9.28715280 5.56619005 11.15235589 5.99372663 11.58263350 6.83941044 9.84909226 6.25705616 9.55562219 4.84487815 11.42482593 6.33338318 10.44508109 7.41112727 11.09458370 7.01686784 11.75025123 7.76312279 10.73976326 6.30963890 9.84844636 8.35876854 8.88816961 7.30887140 8.55115282 8.52049801 11.36171630 7.66782875 10.42916279 5.34704941 11.08117232 5.80756508 8.18071969 8.24660038 9.55151902 8.74482395 11.17697781 9.35773255 9.80884087 9.11604429 10.60063335 9.65940333 11.03615708 9.89788843 10.36806213 9.01946521 11.50777341 7.20967787 9.99541359 8.22940555 10.11741520 10.31534593 11.32572715 8.81838969 10.02893009 10.13702487 10.20813662 10.46848522 10.89993094 9.40337177 10.32316973 8.36790540 11.64536171 9.87090924 9.29615003 10.24605532 9.07135314 10.47914238 9.93933500 12.67777871 11.63846539 8.00311350 10.94132693 9.10186212 9.99494482 10.66440407 10.74773403 10.22695305 11.32630015 10.01906285 9.82751212 11.16473412 9.33518859 11.38095279 9.32321314 9.71585382 6.94462665 12.35859295 7.89546991 10.60335972 8.81316696 10.61359700 7.09527379 10.03228542 7.62511005 11.97895849 6.63918431 11.28466847 7.55862314 10.26613996 5.94302725 10.21585905 6.66330972 9.03060493 6.69218224 9.67770672 6.15575941 10.83621334 5.09534594 10.21174364 6.10006276 8.66414086 6.53597591 12.92446114 6.20940051 10.18422800 5.42862268 9.46401522 6.62786546 11.44070440 5.28734578 9.62412574 4.56994913 9.94170572 3.68857058 9.92999008 2.72426638 9.59125441 3.29010267 8.80113592 3.85760058 10.38220171 3.69916033