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CCS/AWR1843BOOST: The selection of process noise covariance matrix Q

Expert 1690 points

Replies: 5

Views: 108

Part Number: AWR1843BOOST

Tool/software: Code Composer Studio

Hi,

  In the MRR demo, there is a process noise covariance matrix Q  for kalman filtering algorithm. In the demo, the matrix is defined as follows:

QvecList[0]=halfatsq_x * halfatsq_x;

QvecList[1]=halfatsq_y * halfatsq_y;

QvecList[2]=at_x * at_x;

QvecList[3]=at_y * at_y;

 in the above array,halfatsq_x=1/2*ax*t^2,halfatsq_y=1/2*ay*t^2,at_x=ax*t,at_y=ay*t.

According to non-homogeneous state equation knowledge,there should be also other two elements, halfatsq_x*at_x and halfatsq_y*at_y for representing the process noise covariance matrix Q. Why  are the two elements discarded?

Thanks,

Regards,

Rata

  • Guru 54575 points

    Hi,

    We are checking with algorithm developer and will get back to you early next week

    thank you
    Cesar

  • In reply to Cesar:

    Thanks, I am looking forward for your reply.

  • In reply to Rata Zhang:

    Hi Rata, 

    We assume that there is no correlation between velocity and acceleration - the process noise matrix contains only diagonal elements. 

    There are other approaches - where correlations are assumed between velocity, acceleration (in both x, and y), and they have better performance in certain use-cases .

    Regards

    Anil

  • In reply to Anil Mani:

    Hi Anil,

        Thanks for your reply, if I add the other two elements to the the process noise covariance matrix Q,it is reasonable, is that right?

    Regards,

    Rata

  • In reply to Rata Zhang:

    Hi Rata,

    I am not sure how the process noise of acceleration relates to velocity.

    Also, You would have to overhaul the kalman filter (KF), since the KF assumes a diagonal process noise matrix. 

    Regards

    Anil 

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