Extended Kalman Filter Project

The goals / steps of this project are the following:

• Complete the Extended Kalman Filter algorithm in C++.
• Ensure that your project compiles.
• Test your Kalman Filter against the sample data. Ensure that the px, py, vx, and vy RMSE are below the values specified in the rubric.

Rubric Points

Here I will consider the rubric points individually and describe how I addressed each point in my implementation.

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Compiling

1. Your code should compile.

The code compiles without errors with cmake-3.7.2 and make-3.81 on macOS-10.12.4.

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Accuracy

1. The px, py, vx, vy output coordinates have an RMSE <= [0.08, 0.08, 0.60, 0.60] when using the file: "sample-laser-radar-measurement-data-1.txt".

The computationally stable RMSE on sample-laser-radar-measurement-data-1.txt is [0.0651653, 0.0605378, 0.543192, 0.544191].

2. The px, py, vx, vy output coordinates have an RMSE <= [0.20, 0.20, .50, .85] when using the file: "sample-laser-radar-measurement-data-2.txt".

The computationally stable RMSE on sample-laser-radar-measurement-data-2.txt is [0.185497, 0.190302, 0.476754, 0.804467].

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Follows the Correct Algorithm

1. Your Sensor Fusion algorithm follows the general processing flow as taught in the preceding lessons.

The implemented solution follows the Sensor Fusion method, including the Extended Kalman Filter, which is described in the lesson.

2. Your Kalman Filter algorithm handles the first measurements appropriately.

The first measurement is handled by EkfTracker::operator() defined in ekf_tracker.cpp.

3. Your Kalman Filter algorithm first predicts then updates.

The method EkfTracker::Predict is called before EkfTracker::LidarUpdate or EkfTracker::RadarUpdate in EkfTracker::operator().

4. Your Kalman Filter can handle radar and lidar measurements.

Method EkfTracker::LidarUpdate uses the constant measurement matrix kH, which is used along with the constant measurement covariance matrix kRlidar for estimating new state x_ and state covariance matrix P_.

Method EkfTracker::RadarUpdate uses the function ConvertCartesianToPolar to compute h(x') and CalculateJacobian to compute the Jacobian matrix Hj, which are used along with the constant measurement covariance matrix kRradar for estimating new state x_ and state covariance matrix P_.

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Code Efficiency

1. Your algorithm should avoid unnecessary calculations.

Most duplicating expensive computations (like floating point multiplications and divisions) are done only once and then reused, see functions CalculateJacobian, ConvertCartesianToPolar, EkfTracker::Predict, EkfTracker::LidarUpdate, EkfTracker::RadarUpdate in ekf_tracker.cpp. All constant matrices are moved to a nameless namespace of ekf_tracker.cpp.

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Notes

• The code is complying with the Google C++ Style Guide.
• The main Sensor Fusion class EkfTracker is made a functor, which is easy to plug into an algorithm generating estimations out from measurements, see the use of std::transform in function main of main.cpp
• The C++11 automatic type deduction is used wherever appropriate.
• All function/method comments are made Doxygen-friendly