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I would like to consult the calculation method of noise measurement covariance matrix R (including distance variance, velocity variance, angle variance) .
Generally, observation noise covariance matrix is determined by the measurement accuracy of the sensor. But when I see in the cases of SRR, distance variance, velocity variance, angle variance are determined according to CRLB (Cramer Rao lower bound criterion , in function convertSNRdBToVar). That is Var (f) >=12/ ((2*pi) ^2*SNR*N (N^2-1)), while also considering the range resolution and other information, but this value is the minimum value of variance.
While in the IWR1642 routine, the distance variance is determined according to the range^2*SNR (in the function : RADARDEMO_clusteringDBscan_calcInfoFixed).
I can understand that the signal-to-noise ratio can reflect the measurement accuracy of the sensor, but the algorithm I do not particularly understand.
I would like to know, for the two calculation of the noise covariance matrix method, which is better? Are there any better algorithms to determine the covariance matrix of the observation noise? Are there any related materials for reference? Thanks!
Hi Cary,
Wanted to check if you were asking this questions in reference to specific implementation example that we have provided.
It would help us answer in the right context if you can point out which code example or processing chain you are referring to.
For example in context to SRR demo : short-range radar reference design
There is a similar thread that discusses this : Compiler/AWR1642: questions about "convertSNRdBToVar" algorithm (function) in AWR1642 SRR released C code
Thank you,
Vaibav