Engee documentation
Notebook

Realisation of FIR filter on basic elements

In this example we will understand the structure of the FIR filter and analyse the behaviour of the filter at different coefficients inside it.

A basic FIR filter consists of a delay line and coefficients by which the delayed signal is multiplied, after which the results are added up.

The figure below shows the FIR filter circuit containing 4 coefficients.

image.png

Next, let's add the auxiliary function of the model and determine the coefficients for several runs of the filter.

In [ ]: 
# Подключение вспомогательной функции запуска модели.
function run_model( name_model)
    
    Path = (@__DIR__) * "/" * name_model * ".engee"
    
    if name_model in [m.name for m in engee.get_all_models()] # Проверка условия загрузки модели в ядро
        model = engee.open( name_model ) # Открыть модель
        model_output = engee.run( model, verbose=true ); # Запустить модель
    else
        model = engee.load( Path, force=true ) # Загрузить модель
        model_output = engee.run( model, verbose=true ); # Запустить модель
        engee.close( name_model, force=true ); # Закрыть модель
    end
    sleep(5)
    return model_output
end
Out[0]:
run_model (generic function with 1 method)
In [ ]: 
К_arr = [0.1 0.2 0.3 0.4; 0.16 0.38 0.38 0.16; 1 2 3 4]
Out[0]:
3×4 Matrix{Float64}:
 0.1   0.2   0.3   0.4
 0.16  0.38  0.38  0.16
 1.0   2.0   3.0   4.0

Run the model in a cycle and analyse the results by plotting the results in time and frequency domains.

In [ ]: 
out_arr = zeros(1001,3)
K, K1, K2, K3, K4 = 0, 0, 0, 0, 0
for i in 1:3
    K = К_arr[i,:]
    K1, K2, K3, K4  = K[1], K[2], K[3], K[4]
    run_model("Basic_FIR_filter") # Запуск модели.
    out = collect(simout["Basic_FIR_filter/out"]);
    out_arr[:,i] = out.value
end 

plot(out_arr)

Building...
Progress 100%
Building...
Progress 100%
Building...
Progress 100%
Out[0]:
In [ ]: 
using FFTW

Comp_out = ComplexF64.(out_arr);
spec_out = fftshift(fft(Comp_out));

plot([10log10.(abs.((spec_out/3e6)))], label =["0.1 0.2 0.3 0.4" "0.16 0.38 0.38 0.16" "1 2 3 4"])
ylabel!("Мощность дБВт")
xlabel!("Частота МГц")
Out[0]:

Conclusion

As we can see from the resulting graphs, filter coefficients affect changes in both signal power and frequency characteristics.

Blocks used in example