Added support for a 4D state vector when using a range, bearing, range rate radar model #809
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If position_mapping and velocity_mapping are length 2, automatically fill the z components as 0.
…ent model TODO: test this! Also the plotter still seems to be having issues with these measurements
Added ReversibleModel type to CartesianToBearingRangeRate as inverse_function() now allows the xyz coordinates to be extracted from a measurement type. Rotation matrix is now sized correctly for 2D & 3D
Added both models to radar and measurement model
Returns CartesianToBearingRangeRate to how it was prior to modification - the 2D state space implementation is now captured in a new function, CartesianToBearingRangeRate2D, to maintain code seperation.
PACarniglia
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orosoman-dstl
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PACarniglia
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sdhiscocks
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Jun 1, 2023
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| out_vector[self.mapping, 0] = x, y | ||
| out_vector[self.velocity_mapping, 0] = x_rate, y_rate |
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From test failures, these mappings are 3D, but this is only assigning 2D values.
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This could be fixed by setting z and z_rate to 0 when mapping out_vector - is this supported by the theory, or is there a way to calculate the z parameters from measurements of range/bearing/range rate?
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Some conversation about this on PR #285 |
The mapping of the position and velocity required the z component, though this cannot be calculated directly from the measurements. To represent this fact, the z components of out_vector were set to NaN. This should be handled by any function that calls the inverse_function() method (ex. plotting)
inverse_function() of CartesianToBearingRangeRate should be implemented in another, separate PR. Commenting out for now to address the other more relevant addition of CartesianToBearingRangeRate2D.
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The RadarBearingRangeRate2D sensor and associated CartesianToBearingRangeRate2D measurement model were created to support tracking from measurements of bearing, range, and range rate to a 4D state space [x, vx, y, vy]. The current RadarBearingRangeRate/CartesianToBearingRangeRate class only supports a 6D state vector [x, vx, y, vy, z, vz] as a requirement for the creation of the rotation matrix.
Additionally, both measurement models were given inverse functions to support plotting measurements to a Cartesian plot space.