# Benchmark test

To see how the performance varies with the number of particles, we simulate several times. The following code simulates the system and performs filtering using the simulated measurements. We do this for varying number of time steps and varying number of particles.

Note

To run this code, see the bottom of src/example_lineargaussian.jl.

function run_test()
particle_count = [10, 20, 50, 100, 200, 500, 1000]
time_steps = [20, 100, 200]
RMSE = zeros(length(particle_count),length(time_steps)) # Store the RMS errors
propagated_particles = 0
t = @elapsed for (Ti,T) = enumerate(time_steps)
for (Ni,N) = enumerate(particle_count)
montecarlo_runs = 2*maximum(particle_count)*maximum(time_steps) ÷ T ÷ N
E = sum(1:montecarlo_runs) do mc_run
pf = ParticleFilter(N, dynamics, measurement, df, dg, d0) # Create filter
u = @SVector randn(2)
x = SVector{2,Float64}(rand(rng, d0))
y = SVector{2,Float64}(sample_measurement(pf,x,u,0,1))
error = 0.0
@inbounds for t = 1:T-1
pf(u, y) # Update the particle filter
x = dynamics(x,u,t) + SVector{2,Float64}(rand(rng, df)) # Simulate the true dynamics and add some noise
y = SVector{2,Float64}(sample_measurement(pf,x,u,0,t)) # Simulate a measuerment
u = @SVector randn(2) # draw a random control input
error += sum(abs2,x-weighted_mean(pf))
end # t
√(error/T)
end # MC
RMSE[Ni,Ti] = E/montecarlo_runs
propagated_particles += montecarlo_runs*N*T
@show N
end # N
@show T
end # T
println("Propagated $propagated_particles particles in$t seconds for an average of $(propagated_particles/t/1000) particles per millisecond") return RMSE end @time RMSE = run_test() Propagated 8400000 particles in 1.140468043 seconds for an average of 7365.397085484139 particles per millisecond We then plot the results time_steps = [20, 100, 200] particle_count = [10, 20, 50, 100, 200, 500, 1000] nT = length(time_steps) leg = reshape(["$(time_steps[i]) time steps" for i = 1:nT], 1,:)
plot(particle_count,RMSE,xscale=:log10, ylabel="RMS errors", xlabel=" Number of particles", lab=leg)