{"id":193,"date":"2020-04-20T08:27:51","date_gmt":"2020-04-20T08:27:51","guid":{"rendered":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s20-teamb6\/?p=193"},"modified":"2020-04-20T08:27:51","modified_gmt":"2020-04-20T08:27:51","slug":"udits-status-report-4-19","status":"publish","type":"post","link":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s20-teamb6\/2020\/04\/20\/udits-status-report-4-19\/","title":{"rendered":"Udit&#8217;s Status Report 4\/19"},"content":{"rendered":"<p>So I had a pretty productive and interesting 2 weeks (missed last week&#8217;s update so will combine with this)<\/p>\n<ul>\n<li>By the end of last week, we came together and formalized our mathematical models. We are going to go with 3 approaches: System of Linear Eqs, System of Non-Lins, and an Extended Kalman Filter Smoothened model. I worked on implementing the former 2 options.\n<ul>\n<li><a href=\"https:\/\/1drv.ms\/u\/s!Aq6sdyvl6aqAoINq6sYTUcCRg3Vcmg?e=PBRUOJ\">Formalized linear math<\/a><\/li>\n<li><a href=\"https:\/\/1drv.ms\/u\/s!Aq6sdyvl6aqAoINribvklqa0Y6-0Ng?e=hGbPAU\">Formalized non-linear math<\/a><\/li>\n<\/ul>\n<\/li>\n<li>Then I implemented that math with a visualizer for each type where the TDoAs from the simulated tag will get passed to the algorithms and they will spit out a value that gets visualized back on the map\n<ul>\n<li>Rhea was able to add in the algorithm for Kalman filter as well<\/li>\n<\/ul>\n<\/li>\n<li><a href=\"https:\/\/1drv.ms\/u\/s!Aq6sdyvl6aqAoJJ12n1ySBov_YyfXg?e=njguAf\">Here is an image of what the live visualization.<\/a>\u00a0The black circle is the tag (real location), purple square is linear-algorithm calculated location,\u00a0green square is hyperbolic-algorithm calculated location, and blue square is the kalman filter algorithm calculated location.<\/li>\n<li>Then for this week I worked on adding in error-reporting so that we can calculate the Root Mean Squared Error for each algorithm<\/li>\n<li>Also have exported all values to CSVs so that we can store logs for each file and play back the data. I will be adding in a separate program that reads this CSV and visualizes the real path taken and the path modeled (for the paper).<\/li>\n<li>Here is <a href=\"https:\/\/1drv.ms\/u\/s!Aq6sdyvl6aqAoJJ0j9JvA4UIaDyxqA?e=bsQn07\">sample of results\u00a0<\/a>\u00a0for a basic movement of the tag around the lines of hte basketball court.\n<ul>\n<li>Our RMSE (in meters) for that sample run was the following:\n<ul>\n<li>Lin RMSE: 3.8608529220466794<br \/>\nnonLin RMSE: 0.14847987088052897<br \/>\nfilter RMSE: 0.11742101999581132<\/li>\n<li>This is really promising because it lines up with our general expectations that Linear&lt;Non-linear&lt;filtered\n<ul>\n<li>Right now this test is with 4 anchors which only leaves 2 equations for the linear. Hopefully Linear numbers become a lot more usable with 6 anchors<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<li>I am awaiting finalized hardware error numbers from Shiva to add in artificial error<\/li>\n<li>I will also be looking for a dataset of player movements for real NBA games so we can get realistic movement of players\/tags.<\/li>\n<li>I will try to run a BUNCH of variations of tests with multiple player paths (real-NBA and simulated), multiple anchor configurations, and multiple error simulations.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>So I had a pretty productive and interesting 2 weeks (missed last week&#8217;s update so will combine with this) By the end of last week, we came together and formalized our mathematical models. We are going to go with 3 approaches: System of Linear Eqs, System of Non-Lins, and an Extended Kalman Filter Smoothened model. &hellip; <\/p>\n<p class=\"link-more\"><a href=\"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s20-teamb6\/2020\/04\/20\/udits-status-report-4-19\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Udit&#8217;s Status Report 4\/19&#8221;<\/span><\/a><\/p>\n","protected":false},"author":268,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"_links":{"self":[{"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s20-teamb6\/wp-json\/wp\/v2\/posts\/193"}],"collection":[{"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s20-teamb6\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s20-teamb6\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s20-teamb6\/wp-json\/wp\/v2\/users\/268"}],"replies":[{"embeddable":true,"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s20-teamb6\/wp-json\/wp\/v2\/comments?post=193"}],"version-history":[{"count":1,"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s20-teamb6\/wp-json\/wp\/v2\/posts\/193\/revisions"}],"predecessor-version":[{"id":196,"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s20-teamb6\/wp-json\/wp\/v2\/posts\/193\/revisions\/196"}],"wp:attachment":[{"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s20-teamb6\/wp-json\/wp\/v2\/media?parent=193"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s20-teamb6\/wp-json\/wp\/v2\/categories?post=193"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s20-teamb6\/wp-json\/wp\/v2\/tags?post=193"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}