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====================================
Test of filter design routines
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Stabilisation of real filters
Polynomial, p = [0.7 3 -0.4]
p2 = polystab(p) = [0.7 0.067948 -0.0205197]
Polynomial, p = [0.764873 -1.13073 0.452336 -0.451817 1.36468 0.355365 -0.556704]
p2 = polystab(p) = [0.764873 -0.374762 0.0760962 -0.387954 0.216522 0.194292 -0.11944]
Polynomial, p = [1 -2 1.13 -0.154]
p2 = polystab(p) = [1 -1.80909 0.958182 -0.127273]
Stabilisation of complex filters
Polynomial, p = [0.7+0i 3+0i -0.4+0i]
p2 = polystab(p) = [0.7+0i 0.067948+0i -0.0205197+0i]
Polynomial, p = [0.714017+0.178129i -0.638135-0.669155i 0.28555-0.0915693i 0.198004-0.310974i -0.0349565-0.166624i -0.190145-1.08748i 1.18706+0.721909i]
p2 = polystab(p) = [0.714017+0.178129i -0.260051+0.248677i 0.057957+0.0511906i 0.0641949+0.0923731i 0.128456+0.0895024i -0.373421-0.0730522i 0.230382+0.140106i]
Polynomial, p = [1+0i -2+0i 1.13+0i -0.154+0i]
p2 = polystab(p) = [1+0i -1.80909+0i 0.958182+0i -0.127273+0i]
a = [0.887142+0.707331i -0.79531+0.260976i 0.432532-0.0565171i -0.0718804+0.727733i]
b = [-0.975402-0.377971i 1.07181-0.0820541i 0.841014-0.0749022i 0.0729376-0.609413i -0.297478+0.210593i -0.20257+0.104277i]
freqz(b,a,32) = [-0.390293-0.418968i -0.376778-0.442316i -0.375058-0.500207i -0.392438-0.58878i -0.437173-0.705251i -0.519959-0.847477i -0.656567-1.01308i -0.872866-1.1967i -1.21413-1.38055i -1.75751-1.50211i -2.58745-1.35359i -3.48679-0.450656i -3.3609+1.15661i -2.14592+1.91141i -1.21084+1.72145i -0.766997+1.33333i -0.589406+1.00144i -0.53513+0.754372i -0.537235+0.577005i -0.564231+0.45178i -0.600343+0.364908i -0.637167+0.306094i -0.670059+0.267514i -0.696495+0.243036i -0.715261+0.227708i -0.726021+0.217449i -0.729054+0.208863i -0.725069+0.199115i -0.715061+0.185832i -0.700172+0.167008i -0.681574+0.140863i -0.660357+0.105634i]
Yulewalk filter design
f = [0 0.5 0.6 1]
m = [1 1 0 0]
filter_design_autocorrelation(32, f, m, R):
R = [0.532559 0.315821 -0.0319513 -0.0988215 0.0301935 0.0521175 -0.0274682 -0.0304724 0.0240488 0.0178926 -0.0202595 -0.0100257 0.0164289 0.00511788 -0.012847 -0.00222493 0.00973176 0.000712868 -0.00721071 -9.99167e-05 0.00531821 9.27421e-06 -0.00400823 -0.000158025 0.00317755 0.000353639 -0.0026942 -0.000486724 0.00242498 0.000516921 -0.00225726 -0.000453233 0.00211194 0.000332217 -0.00194694 -0.000197956 0.00175244 8.70039e-05 -0.00154072 -2.00003e-05 0.00133412 7.38934e-09 -0.00115423 -1.6124e-05 0.00101459 5.00768e-05 -0.000917723 -8.32878e-05 0.000856322 0.000102371 -0.000817091 -0.000101876 0.000785634 8.4105e-05 -0.000750681 -5.66668e-05 0.000706564 2.89561e-05 -0.000653554 -8.80372e-06 0.000596347 2.71063e-07 -0.000541524 -3.039e-06 0.000494924 1.32848e-05 -0.000459755 -2.55117e-05 0.000435883 3.4597e-05 -0.000420334 -3.73796e-05 0.000408632 3.3361e-05 -0.000396415 -2.44381e-05 0.00038078 1.39038e-05 -0.000360948 -5.14507e-06 0.000338155 5.03469e-07 -0.000314897 -6.43224e-07 0.000293886 4.56036e-06 -0.000277073 -1.01417e-05 0.000265069 1.50163e-05 -0.000257085 -1.73813e-05 0.000251352 1.65323e-05 -0.000245816 -1.29614e-05 0.000238842 8.0423e-06 -0.000229699 -3.45865e-06 0.00021868 5.95909e-07 -0.000206868 -1.045e-07 0.000195663 1.76098e-06 -0.000186261 -4.64034e-06 0.000179252 7.50707e-06 -0.000174477 -9.2695e-06 0.000171151 9.33717e-06 -0.00016819 -7.7704e-06 0.000164602 5.19278e-06 -0.000159813 -2.52019e-06 0.000153814 6.16824e-07 -0.000147104 -2.45942e-09 0.000140464 7.05945e-07 -0.000134654 -2.30102e-06 0.000130148 4.09521e-06 -0.000126995 -5.39308e-06 0.000124833 5.73668e-06 -0.000123048 -5.04286e-06 0.000121007 3.59914e-06 -0.000118273 -1.93203e-06 0.000114737 6.03786e-07 -0.000110629 -1.4056e-08 0.000106402 2.74184e-07 -0.000102558 -1.19181e-06 9.94645e-05 2.3621e-06 -9.72362e-05 -3.32546e-06 9.57126e-05 3.73199e-06 -9.45337e-05 -3.45489e-06 9.32788e-05 2.61766e-06 -9.16167e-05 -1.532e-06 8.94127e-05 5.74765e-07 -8.67604e-05 -5.13422e-08 8.39301e-05 9.3898e-08 -8.12606e-05 -6.27498e-07 7.90345e-05 1.41101e-06 -7.73817e-05 -2.13323e-06 7.62435e-05 2.52682e-06 -7.54057e-05 -2.45906e-06 7.45825e-05 1.96924e-06 -7.35218e-05 -1.24324e-06 7.20899e-05 5.3847e-07 -7.03101e-05 -8.86145e-08 6.83436e-05 2.29843e-08 -6.64231e-05 -3.27708e-07 6.47653e-05 8.59721e-07 -6.34955e-05 -1.40528e-06 6.26083e-05 1.75964e-06 -6.1977e-05 -1.79863e-06 6.14054e-05 1.51748e-06 -6.07007e-05 -1.02519e-06 5.97404e-05 4.9924e-07 -5.85114e-05 -1.18816e-07 5.71073e-05 1.45993e-09 -5.56889e-05 -1.65133e-07 5.44226e-05 5.2764e-07 -5.34216e-05 -9.41426e-07 5.2708e-05 1.24946e-06 -5.221e-05 -1.34128e-06 5.17923e-05 1.18945e-06 -5.13071e-05 -8.54645e-07 5.06467e-05 4.59276e-07 -4.97796e-05 -1.40791e-07 4.87566e-05 2.15083e-09 -4.76884e-05 -7.72108e-08 4.67029e-05 3.22406e-07 -4.58989e-05 -6.3629e-07 4.53117e-05 8.98541e-07 -4.49047e-05 -1.01361e-06 4.4585e-05 9.43324e-07 -4.42396e-05 -7.17459e-07 4.3776e-05 4.19702e-07 -4.31542e-05 -1.55197e-07 4.23976e-05 1.21269e-08 -4.15818e-05 -3.12379e-08 4.08044e-05 1.93723e-07 -4.01497e-05 -4.30839e-07 3.96588e-05 6.50784e-07 -3.93173e-05 -7.72446e-07]
arma_estimator(8, 8, R, b, a):
a = [1 0.674659 1.90375 0.910429 1.20285 0.356944 0.264436 0.0330877 0.00984953]
b = [0.0981912 0.427406 0.944264 1.36556 1.41977 1.11242 0.654916 0.27152 0.0625287]
R = [1 0.903713 0.642512 0.290564 -0.0549604 -0.304242 -0.401986 -0.342615 -0.168862 0.0451758 0.220277 0.298483 0.260759 0.130387 -0.0382984 -0.181211 -0.247891 -0.218682 -0.109979 0.0337286 0.157507 0.216539 0.192023 0.0968681 -0.0304545 -0.141182 -0.19469 -0.173208 -0.0875435 0.02797 0.129064 0.178348 0.15902 0.0804769 -0.0260044 -0.119609 -0.165531 -0.147829 -0.0748837 0.0244002 0.111968 0.155131 0.13871 0.0703143 -0.0230593 -0.105625 -0.146473 -0.131094 -0.0664904 0.021917 0.100251 0.139119 0.124608 0.0632292 -0.0209287 -0.0956214 -0.132772 -0.118998 -0.060405 0.0200628 0.0915791 0.127221 0.114084 0.0579281 -0.019296 -0.0880094 -0.122313 -0.109732 -0.0557329 0.0186108 0.0848271 0.117932 0.105843 0.0537696 -0.0179937 -0.0819667 -0.113991 -0.10234 -0.0520002 0.017434 0.0793774 0.11042 0.0991629 0.0503947 -0.0169235 -0.0770189 -0.107165 -0.0962646 -0.0489292 0.0164553 0.0748589 0.104181 0.0936065 0.0475845 -0.0160239 -0.072871 -0.101434 -0.0911571 -0.046345 0.0156247 0.0710334 0.0988932 0.0888904 0.0451975 -0.0152539 -0.0693282 -0.0965342 -0.0867848 -0.0441312 0.0149084 0.0677402 0.0943363 0.084822 0.043137 -0.0145852 -0.0662565 -0.0922821 -0.0829867 -0.042207 0.0142822 0.0648662 0.0903564 0.0812656 0.0413348 -0.0139973 -0.0635599 -0.0885465 -0.0796473 -0.0405144 0.0137288 0.0623295 0.0868411 0.0781219 0.0397411 -0.0134752 -0.0611679 -0.0852306 -0.076681 -0.0390104 0.0132352 0.0600689 0.0837066 0.075317 0.0383186 -0.0130075 -0.0590271 -0.0822614 -0.0740232 -0.0376623 0.0127911 0.0580376 0.0808886 0.072794 0.0370386 -0.0125853 -0.0570963 -0.0795824 -0.071624 -0.0364449 0.012389 0.0561994 0.0783374 0.0705086 0.0358789 -0.0122017 -0.0553435 -0.0771491 -0.0694438 -0.0353385 0.0120226 0.0545255 0.0760133 0.0684258 0.0348217 -0.0118511 -0.0537427 -0.0749262 -0.0674513 -0.034327 0.0116868 0.0529927 0.0738844 0.0665173 0.0338528 -0.0115291 -0.0522733 -0.0728849 -0.0656211 -0.0333977 0.0113777 0.0515824 0.071925 0.0647601 0.0329605 -0.011232 -0.0509181 -0.0710019 -0.0639321 -0.03254 0.0110919 0.0502789 0.0701136 0.0631352 0.0321352 -0.0109568 -0.0496632 -0.0692577 -0.0623673 -0.0317452 0.0108265 0.0490696 0.0684325 0.0616267 0.031369 -0.0107008 -0.0484967 -0.067636 -0.0609119 -0.0310059 0.0105794 0.0479435 0.0668668 0.0602214 0.0306551 -0.010462 -0.0474087 -0.0661232 -0.0595539 -0.0303159 0.0103484 0.0468915 0.0654039 0.0589081 0.0299878 -0.0102385 -0.0463908 -0.0647075 -0.0582829 -0.0296701 0.010132 0.0459058 0.0640329 0.0576772 0.0293623 -0.0100287 -0.0454358 -0.063379 -0.0570899 -0.0290638 0.00992854 0.0449799 0.0627448 0.0565203 0.0287743 -0.00983131 -0.0445374 -0.0621292]
arma_estimator(8, 8, R, b, a):
a = [1 -5.63992 14.8879 -23.7131 24.7885 -17.3553 7.93509 -2.16477 0.269989]
b = [0.117196 -0.457317 0.94702 -1.25853 1.15736 -0.723747 0.290453 -0.0621039 0.00460714]
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