Spring mass damper set up for running kf, pf and cem. Not tested yet

src/fixedvel_aircraft.jl

@@ -11,7 +11,7 @@ using Statistics
 """
 Q = V measurement noise covariance
 R = W process noise covariance
-C = I because observation h is basically true state corrupted by noise
+C has to be the correct size to map state to observation
 """
 function kalman_filter(mu,sigma,u,z,A,B,C,R,Q)
 	mu_bar = A*mu + B*u

src/spring_mass_damper.jl

@@ -1,5 +1,7 @@
 # Damped spring mass oscillator as another case study
 
+# Idea: Plot true position, Kalman filter posterior mean, particle distribution, cem distribution
+
 using ParticleFilters
 using Distributions
 using StaticArrays
@@ -18,26 +20,61 @@ function run_exp()
 	A = [  0    1;
 	    -k/m -b/m]
 
-	f(x,rng) = (Matrix(1.0*Diagonal(I,2)) + dt*A)*x
+	f(x,u,rng) = (Matrix(1.0*Diagonal(I,2)) + dt*A)*x
 
 	# Initial state
 	x = [1.0, 0.0]
 	sigma_0 = [0.1 0;
 		   0  0.1]
 
-	positions = []
+	positions = []	# Store the location of the mass i.e. x location
 	plots = []
-	# Let's first run the system and see how it behaves
+
+		# Set up for kalman filter
+	C = [1.0 0.0] # Measurement matrix y=Cx
+	mu = [1.0 1.0]	# Mean of initial gaussian belief
+	sigma = sigma_0	# Covariance of initial gaussian belief
+
+		# Set up for particle filter and cem filter
+	model = ParticleFilterModel{Vector{Float64}}(f, g)
+	N = 100 # Number of particles
+
+	filter_sir = SIRParticleFilter(model, N) # Vanilla particle filter
+	filter_cem = CEMParticleFilter(model, N) # CEM filter
+
+	b = ParticleCollection([1.0*rand(2) for i in 1:N]) # Each particle is 2 element array
+
+		# Run iterations
 	for i in 1:100
-		x = f(x,rng)
+		u = [1.0, 1.0]	# Dummy variable as plays no role in state transition
+		x = f(x,u,rng)	# Propagate state forward by 1 step
+		y = h(x, rng)	# Generate a noisy observation of the state
+
+			# Kalman filtering estimate
+		mu,sigma = kalman_filter(mu,sigma,u,y,A,B,C,W,V)
+
+			# Particle filtering estimate
+		b = update(filter_sir, b, u, y)
+
+			# Cross entropy filtering estimate
+		b_cem = update(filter_cem,b,u,y)
 
-		plt = scatter([x[1]],[2.0],xlim=(-5,5), ylim=(-5,5))
+		plt = scatter([x[1]],[2.0],color=:black,label="true",xlim=(-5,5), ylim=(-5,5))
+		scatter!([mu[1]],[2.0],color=:blue,label="kf",xlim=(-5,5), ylim=(-5,5))
+		scatter!([p[1] for p in particles(b)],[2.0],color=:cyan,
+			label="sir",xlim=(-5,5), ylim=(-5,5))
+		scatter!([p[1] for p in particles(b_cem)],[2.0],color=:magenta,
+			label="cem",xlim=(-5,5), ylim=(-5,5))
+
 		push!(positions,x[1])
 		push!(plots,plt)
 	end
 	return positions,plots
 end
 
+"""
+gif making function
+"""
 function make_gif(plots,filename)
 display("Making gif")
 	frames = Frames(MIME("image/png"), fps=5)
@@ -48,6 +85,28 @@ display("Making gif")
 	return nothing
 end # End of the reel gif writing function
 
+"""
+Kalman filter function
+
+# Arguments:
+Q: Measurement noise covariance
+R: Process noise covariance
+
+# Understanding:
+The Kalman filter is the `optimal estimator` in that it yields the least possible
+mean value of sum of squared estimation error
+"""
+function kalman_filter(mu,sigma,u,z,A,B,C,R,Q)
+	mu_bar = A*mu + B*u
+	sigma_bar = A*sigma*A' + R
+
+	K = sigma_bar*C'*(inv(C*sigma_bar*C'+Q))
+
+	mu_new = mu_bar+K*(z-C*mu_bar)
+	sigma_new = (I-K*C)*sigma_bar
+	return mu_new,sigma_new
+end
+
 display("Running the spring mass damper system")
 positions,plots = run_exp()
 plot(positions)