Heuristic Algorithm to Compute Geodetic Height (h) from Ellipse Equation
Authors: Eleiche Mohamed , Mansi Ahmed 
Published: March 2020

&lt;p&gt;The equation of the ellipse in a meridian plan is well known to be &lt;img src=&quot;data:image/jpeg;base64,%20/9j/4AAQSkZJRgABAQEAeAB4AAD/2wBDAAIBAQIBAQICAgICAgICAwUDAwMDAwYEBAMFBwYHBwcGBwcICQsJCAgKCAcHCg0KCgsMDAwMBwkODw0MDgsMDAz/2wBDAQICAgMDAwYDAwYMCAcIDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAz/wAARCABGAKADASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD9/KKKyfHXjO0+Hng/Udbv4dWuLPTIWnmi0zS7nU7yRR2itraOSeZvRY0Zj2FDdlcEm3ZGtXiP7Rn7cOm/AT4y+E/hzpngb4gfEvx/4xsbvVrPQ/CttZB7awtSizXdxcX91a2sUQkkijG6bezyIApzXmnhb/gsF4Y8bWfxFsNJ+FHxp1T4g/C3UZbDXPAVhpmm3viBEjtorlrlPJvntGiMcoCA3IllkRo443kAQv8A28f21fGfw+8O+APDXgvwf8UtB1X4l2/2rVPFVp8O9U8Ur8OrIRq0hlttPtrpJdSYt5UMLbolcNJKWjQJNLfwtbO3bXmTcbPa3VvXRNaPVPq49Vfztytc23Xsu7T1Wj9Z/Yv/AG0PC37cvwt1LxN4YsfEGiy6Brt94Z1vRtdto4NR0TUrOTZPbTCKSSIsMqwaKR0IcYbOQPXa8A/4Jxab4E8LfAW68PfDrwh8R/DHh7w/q00D33jfw/e6Nqviu8lSO5udVdL5I7qZppp3DzTRRlpI5Aq+WqE+/wBa1FZr0X5K9r6pPonqlo9TODun6v7ru17aXS0dtL7BRRRUFhRRXjf7Tn7Y9t+zh488DeE7PwD8QPiT4t+IT3p0zR/CcFiZYoLOJJLi5uJb26tbeCFfNiTe8oy80agEsBRfoNJs9Y8ReILTwn4fvtUv5fIsNNt5Lq5l2s3lxRqWdsKCTgAnABNfJvgH/gsl4P8AGF78KLvUvhl8YPB3gr436uui+CfGGuWGmR6VrVxKkj2uYYb6W+t0uVj3RNcWsYYOpO0HI6T9kD/gp7on7Ynjv4oaFpvwy+K3hj/hUEzWPiDUNUtdMvrA36qHk0+2m0y+vBdXcaHLxQ7jGSqttdlRvn74QftHxftV/tFeFfiZ8V/hn8f49W0fX1sfhz8O5fhN4gtrHwaLiYWja7ql9cWcVlJe+Szys5uDFaQM6QiSYtJI6S5qsYvVPlf/AG63+Lkrcj2Wspc0XpNXSEujV1/29bT5R+31eyae/wCjdFFFIYUUUUAFFFFABRRRQAUyeYW8DyEMQiliFUsxx6Ack+1Prj/jX+0L4B/Zr8K2+u/EXxx4P8AaJd3a2EGoeJNZttKtZrhkd1hWWd0QyFI5GCg5IjY4wppO7VkHqfLn/BDT4ReI/CX7NPjrx/438K6z4P8AG3xt+Iuv+NtT0zWbGWy1Gyimu2htYpoZQHjxBCjKvQCTI619p18//wDD2L9ln/o5b4Af+HD0j/5Io/4exfss/wDRy3wA/wDDh6R/8kVWloxjtFKK9IpRX4IHdylN7ycpP1k3J/iz6Aorx/4W/wDBQn4BfHHx3Y+FvBXxw+EHjDxNqnmfY9I0Txlp2oX935cbSyeXBFM0j7Y0dztBwqMTwCa9Z1TVLbQ9MuL29uILSztImnnnnkEcUEagszsxwFUAEkngAUgDVNUttD0y4vb24gtLO0iaeeeeQRxQRqCzOzHAVQASSeABVTwd4y0j4h+E9N17w/qum65oes20d7p+o6fcpc2l9BIoaOWKVCUkRlIKspIIIINfL/7Rn/BU79mLXP2fPHdlZftG/Ae7vLvw7qEEEEHj/SZJZ5GtpFVFUTksxJAAHJJrzX/glX/wUw/Zw+Hn/BM34AaD4g/aB+CWh65o3w90Oy1DTtQ8c6XbXdjPHYwrJFLE84eN1YEMrAEEEEUAfflfBn/BWL9qf4iXvjzRvgR4C0X4veENC8VWxn8bfFTw98PNf14eH9OOM2OlNp9nOH1Kddy+cfktkycmUrs+4fB3jLSPiH4T03XvD+q6brmh6zbR3un6jp9ylzaX0Eiho5YpUJSRGUgqykgggg15j8Uv+ChPwC+B3ju+8LeNfjh8IPB/ibS/L+2aRrfjLTtPv7TzI1lj8yCWZZE3RujjcBlXUjgg1EoczV9VfVdH5Py799no2XCbjdrfo+3n/Xqcf/wTIuvDOj/BXVvB3w++E3iT4UfDH4faoNC8Lrr+kX2j3/iSEW0E0+ovaXtvDcIHuJpU8yXc8zRPIxBbFfSFfP8A/wAPYv2Wf+jlvgB/4cPSP/kij/h7F+yz/wBHLfAD/wAOHpH/AMkVtUm5vme+l/N21fzevbsZRjyqyPoCiuP+Cn7QvgH9pTwrca78OvHHg/x/olpdtYT6h4b1m21W1huFRHaFpYHdBIEkjYqTkCRTjDCsH9rr4p/Ej4OfBm41v4U/Cr/hcvi+O6hii8Nf8JNa+HfOiZsSS/a7lWjXYOdpGW6CoKPTqK+AP+Hh/wC3X/0jq/8AM9+H/wD4zW58Mf28f20fFPxJ8PaZ4k/YJ/4RXw7qOp21rqmt/wDC7tCvv7HtXlVZrr7PHEHm8pCz+WpDPt2jk0AfcdZXjnx1ovwx8Hal4h8SavpmgaBo1u95qGpajdJa2ljCgy8ksrkKiKBksxAFatc347+EPhj4n6not34j0PTtcm8O3JvdNF7CJo7S4K7RMqNlfMAztYjK5OCMmgDzb9iz/goV8LP+Cg+meMNS+E+uyeJ9F8Fa0dAvNUW2eG0ubpY0kb7Oz4aSMB1/eBQrZypYc17bX5t/8G99rHZfE39uqGGNIoYv2kvFCRxooVUUSgAADgADtX6SUAFcf8a/2e/AP7SnhW30L4i+B/B/j/RLS7W/g0/xJo1tqtrDcKjosyxTo6CQJJIoYDIEjDOGNdhRQB8//wDDp39ln/o2n4Af+G80j/5Hrmfi3+wP+xP8APBc3iTx38Fv2WvBXh23kSGXVNe8IaDptlE7naitNNEqAseACck9Ku/Ez/gsL+zx8IoPEV5rXjjUf7B8J3b6fq/iSw8KazqXhuxu0QM1q2rW1pJY/aAWWMwicyeawi2+YQlaf7bniP4PeHfB/wAO/il8QPD83i3VfCetw3nw6023ilfUtT127geG1gs7Usiy3To7bRMNsO0ysYxEZEm94qcdVdLRX3tZK27a+FddLFWs+V72b102732SfxPoiD9kf9nT9kfXNQtfiP8AAfwJ+zleXWjXE1pbeKPAWi6NJJYzNEUmiS7s0JjcxTFWUOCUlwRhufobVNLttc0y4sr23gu7O7iaCeCeMSRTxsCrIynIZSCQQeCDXy9/wTx/Zuf4O/Fn4yeNvF+o+HLX4x/GbUbHxT4o8KaLqK3Nt4Ts1iktdPg6K8rlIZvMu2jRZ5lm2KFjr6orWcbWXWyv1s7K6utHZ3V1o7XRnFt39XbpdX0dt1dWdnrrrY+Tf2jP+CWP7MWh/s+eO72y/Zy+A9peWnh3UJ4J4PAGkxywSLbSMrqwgBVgQCCOQRXmv/BKv/gmf+zh8Q/+CZvwA17xB+z98Etc1zWfh7od7qGo6h4F0u5u76eSxhaSWWV4C8jsxJZmJJJJJr711TS7bXNMuLK9t4Luzu4mgngnjEkU8bAqyMpyGUgkEHgg1U8HeDtI+HnhPTdB8P6Vpuh6Ho1tHZafp2n2yW1pYwRqFjiiiQBI0VQAqqAAAABUFB4O8HaR8PPCem6D4f0rTdD0PRraOy0/TtPtktrSxgjULHFFEgCRoqgBVUAAAACvMfil/wAE9vgF8cfHd94p8a/A/wCEHjDxNqnl/bNX1vwbp2oX935caxR+ZPLC0j7Y0RBuJwqKBwAK9gr45/4LffHfx/8As9/sjaJqvwv8ean4D8ea5410Tw1pE1tY6ZdwX0l9drA8Vwt9bzKIxEZZA0exg0a5YruUq6TjF/acY/OTUV+LGle77Jv5JNv8Eejf8Onf2Wf+jafgB/4bzSP/AJHry34sfAP/AIJ2/ATxwnhjx14K/Yt8F+JZI45k0nXdH8M6dfMkhIRxDMiuVYg4OMHBxXtHwE/4KAfCT9oX4r3nw+8J+MpNZ8XaZo8eu+Tc6ReWC6vprSeSmpWU00EcF9aO4+W4tGlhbcpVtrKT86ftY/Brwd8YvEvxs/Z8+DXhrRbbxf8AGhI9Q+NHjG6Z7iz8JWlxbpCkku9m83UJLaMm1skKRx5a4k8tWHnk+aLVle6dul7O2/Rb3lra1ra6ELP4tLWv5X8ur1Vo6Xvo9k/sT4Jfs+eAP2bvCk+ifDjwR4P8BaHeXTX82n+G9GttLtJ7hkRGmaOBERpCkcalyMkRqM4ArC/a6+FnxI+MfwZuNE+FPxV/4U14vkuoZYvEv/CM2viLyYlbMkX2S5ZY23jjcTleore/Z70Dw14T+AvgrSvBmqRa34Q0vQrKz0TUY71b5L6yjgRIJROpKyho1U714bORwa7CtKsVGbjHZP0/DoZ03JwTmrM+AP8Ah3h+3X/0kV/8wJ4f/wDj1bnwx/YO/bR8LfEnw9qfiT9vb/hKvDunanbXWqaJ/wAKR0Kx/ti1SVWmtftEcpeHzUDJ5igsm7cORX3HRUFhUd3eRWFu0s8scMSfeeRgqr9Sakrj/jX+z34B/aU8K2+hfEXwP4P8f6JaXa38Gn+JNGttVtYbhUdFmWKdHQSBJJFDAZAkYZwxoA/PL/g3d+J3hzxL8bv259P0/XtIvb+4/aE8RatFbQXcbyy2c87CG4VQcmJzFJtccHY2Dwa/T2vC/D3/AAS+/Zo8Ja/Y6rpX7O/wM0zVNMuI7uzvLTwFpUNxaTRsHSWN1gDI6sAQwIIIBBr3SgAr51/4K5eJPGXhH/gmP8dNQ+H8epyeL7fwbqH9nnTSwvISYisksJX5vMjjLuu35soMc4r6KorDFUfbUZ0r25k1f1VjbD1fZVY1bX5Wn9x+MX7ZPxx+Cus/8Erv2ZPgl8N/E/hmf4Q6h4t8G6H408WW16qeH/Ddoji+uIru+yYYr2R7cvJC7b494aYR+dEX/U3xn8F/hJ+3B8PNBvvGXw+8I/EPQE3XmkxeLvCkdyLbd8pljgvYd0W5QMNtG5dpGVINeoUV21qqquo5L4pufzairfLluv6ZyUqXs1TjF/DBQ9bOTv8APms/RHA/A79lX4X/ALMUOox/DX4b+Avh7HrDRtfr4Z8P2mki+Me4IZRbxpvK7mxuzjccdTXfUUVk23uaJJbBRRRSGFfml/wWp+IHgPxN/wAFAf2QPh58Ub3QdN+F2l65qfj3xZeeIZY7fRLY2lo8WmLdzSERIsl1I6KJWVHbC/MSFr9LaKSuqkJ/yu/zW33StL5WejB2dOcP5ouP36P8Lr53PgL9l0zft2f8Ff8AX/2i/DMN5/wpz4eeAm+HnhnxA0DxWvjm+mvjcXl1ZOSBPZQeWIhOqmOWQkxs4QmvpTx7/wAE3f2d/ir4x1HxF4o+AnwX8SeINXmNxf6nqngjTLy8vZT1eWaSFndj6sSa9pop2ioxhFaRv6+9Jyf3ybf3LoDcnKU39q34RUV+CX4lHw14a07wX4dsNI0fT7LSdJ0u3jtLKys4Fgt7OGNQqRRxqAqIqgAKoAAAAFXqKKbbbuxJJKyCiiikMKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA//Z&quot; alt=&quot;&quot;&gt;which can be defined in its most generic form as &lt;img src=&quot;data:image/jpeg;base64,%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&quot; alt=&quot;&quot;&gt; where the variable (&lt;em&gt;s&lt;/em&gt;) is the indicator value of the ellipsoidal height. For (&lt;em&gt;h=0&lt;/em&gt;), the value of&amp;#160; (&lt;em&gt;s&lt;/em&gt;) equals (&lt;em&gt;1&lt;/em&gt;) , for negative&amp;#160; (&lt;em&gt;h&lt;/em&gt;), the value &amp;#160;is lower than (&lt;em&gt;1&lt;/em&gt;), and vice versa, for positive (&lt;em&gt;h&lt;/em&gt;) , the corresponding &amp;#160;value of (&lt;em&gt;s&lt;/em&gt;) is greater than (&lt;em&gt;1&lt;/em&gt;). Hence (&lt;em&gt;s&lt;/em&gt;) and (&lt;em&gt;h&lt;/em&gt;) are highly-correlated. The main goal of this work is to exploit the&amp;#160; (&lt;em&gt;s-h&lt;/em&gt;) correlation and to represent it both statistically and mathematically. Moreover, the essential role of (&lt;em&gt;s&lt;/em&gt;) in the transformation of the Cartesian coordinates (&lt;em&gt;x&lt;sub&gt;p&lt;/sub&gt;, y&lt;sub&gt;p&lt;/sub&gt;, z&lt;sub&gt;p&lt;/sub&gt;&lt;/em&gt;) of any generic point (&lt;em&gt;P&lt;/em&gt;) into its geodetic coordinates (&lt;em&gt;&amp;#966;, &amp;#955;, h&lt;/em&gt;) in reference to the geodetic ellipsoid is thoroughly studied.&lt;/p&gt;