A simulation of the smoke measurement system is implemented in MATLAB. A nonlinear programming solver, fminsearch, optimizes the ADC timing, amplifier time constants, and unconstrained filter weights. The result of the optimization for a 100 μs LED pulse and 20 μs ADC sampling rate is shown in Figure 3-5. The average and matched filter have initial parameters of τ₁ and τ₂ set to 0.31 times t_LED and the ADC samples centered on t_LED. Unconstrained filtering improves the SNR the most, by 6%, followed by matched filtering and average filtering at 4%. Each filter type also has a trend: as the number of ADC samples increases, the optimal τ₁ and τ₂ decreases, as shown in Figure 3-6. These trends are visible in the measurements as shown in Section 4.

tLED=100 µs

tSAMP=20 µs

Figure 3-5 Comparison of Simulated Digital Processing Methods

tLED=100 µs

tSAMP=20 µs

Figure 3-6 Simulated Optimal Averaging Filter Parameters