Is it possible to sample multiple signals at different rates with the extendedKalmanFilter object?

I need to implement an extended Kalman filter in a MATLAB for loop and decided to use the extendedKalmanFilter object. This was not too difficult and I have it working on a non-linear system with 2 outputs. However, each output is sampled at a different rate, y(1) every minute and y(2) every ten minutes:

So my measurement data looks like this:

       t          y1        y2  
    ________    ______    ______

           0     740.4    526.58
    0.016667    741.23       NaN
    0.033333    738.07       NaN
        0.05    740.26       NaN
    0.066667    739.75       NaN
    0.083333    738.43       NaN
         0.1    738.98       NaN
     0.11667    739.46       NaN
     0.13333    741.76       NaN
        0.15    741.07       NaN
     0.16667    737.93    546.05
     0.18333     741.1       NaN
         0.2    739.31       NaN
     0.21667    738.65       NaN
     0.23333    739.16       NaN

When I search for anything on this topic I mostly see references to this capability in Simulink, but not in MATLAB code.

Here is the code I tried, naively, and the error that is raised:

EKF = extendedKalmanFilter(@arom3_StateFcnRodin, @arom3_MeasurementFcnRodin2, InitialState);
EKF.ProcessNoise = diag(sigma_w);
EKF.MeasurementNoise = diag(R);
EKF.StateCovariance = diag(P0);
     
for i = 1:nT

    % Process measurements from simulation
    yk_m = Y_m(i, :)';

    if isnan(yk_m(2))  % if 2nd measurement is missing
        EKF.MeasurementFcn = @arom3_MeasurementFcnRodin1;
        EKF.MeasurementNoise = diag(R(1));
        yk_m = yk_m(1);
    else
        EKF.MeasurementFcn = @arom3_MeasurementFcnRodin2;
        EKF.MeasurementNoise = diag(R);
    end

    % Update Kalman Filter
    [CorrectedState, CorrectedStateCovariance] = correct(EKF, yk_m);
    [PredictedState, PredictedStateCovariance] = predict(EKF, dt, params);

end

Where arom3_MeasurementFcnRodin1 and arom3_MeasurementFcnRodin2 are different measurement functions reflecting the 1 and 2 output system respectively:

function y = arom3_MeasurementFcnRodin1(xa)
    y = xa(1);
end

and

function y = arom3_MeasurementFcnRodin2(xa)
    y = [xa(1);
         xa(3)];
end

Error raised:

Error using ExtendedKalmanFilter

Expected z to be an array with number of elements equal to 1.

Error in matlabshared.tracking.internal.validateInputSizeAndType (line 14)

validateattributes(value, ...

Error in matlabshared.tracking.internal.ExtendedKalmanFilter/correct (line 629)

matlabshared.tracking.internal.validateInputSizeAndType...

Error in arom3_sim_EKF3 (line 193)

[CorrectedState, CorrectedStateCovariance] = correct(EKF, yk_m);

Note: the code works when the sample rates are both 1.

Is this even possible with extendedKalmanFilter? Are there any other options, or will I have to implement the EKF by hand?

[Bill Tubbs]

Further sleuthing has revealed that the error occurred at t=0.16667 when yk_m is [737.93; 546.05]. I.e. the first double sample after the first series of single samples.

Here is line 629 of the function [x_corr, P_corr] = correct(obj, z, varargin)

if coder.internal.is_defined(obj.pN)
    matlabshared.tracking.internal.validateInputSizeAndType...
        ('z', 'ExtendedKalmanFilter', z, obj.pN);
    ...

Looks like it is checking the size of z (which is yk_m) and comparing it to a private property obj.pN which is presumably still set to 2.