Function parameter estimation using Particle Filter

After reading the docs about "stateEstimatorPF" I get a little confused about how to create the StateTransitionFcn for my case. In my case I have 10 sensors measurments that decay exponentially and I want to find the best parameters for my function model.

The function model is x = exp(B*deltaT)*x_1, where x are the hypotheses, deltaT is the constant time delta in my measurments and x_1 is the true previous state. I would like to use the particle filter to estimate the parameter B. If I guess right, B should be the particles and the weighted mean of this particles should be what I'm looking for.

How can I write the StateTransitionFcn and use the "stateEstimatorPF" to solve this problem?

The code below is what I get so far (and it does not work):

pf = robotics.ParticleFilter
pf.StateTransitionFcn = @stateTransitionFcn
pf.StateEstimationMethod = 'mean';
pf.ResamplingMethod = 'systematic';
initialize(pf,5000,[0.9],1);

measu = [1.0, 0.9351, 0.8512, 0.9028, 0.7754, 0.7114, 0.6830, 0.6147, 0.5628, 0.7090]
states = []

for i=1:10
    [statePredicted,stateCov] = predict(pf);
    [stateCorrected,stateCov] = correct(pf,measu(i));
    states(i) = getStateEstimate(pf)
end


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function predictParticles = stateTransitionFcn(pf, prevParticles,x_1)
    predictParticles = exp(prevParticles)*x_1 %how to properly use x_1?%;
end