Sensor Fusion and Object Tracking using an Extended Kalman Filter Algorithm — Part 1
<p>This algorithm is a recursive two-step process: <em>prediction</em>, and <em>update</em>. The <strong>prediction step</strong> produces estimates of the current variables along with their uncertainties. These estimates are based on the assumed model of how the estimates change over time. The<strong> update step</strong> is done when the next measurements <em>(subject to noise)</em> is observed. In this step, the estimates <em>(let’s call it </em><strong><em>state</em></strong><em> from here on) </em>are updated based on the weighted average of the predicted state and the state based on the current measurement. A lower weight is given to that with a higher uncertainty.</p>
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