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Commit9804b7e

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doa-using-just-eigenvectors
1 parent2402571 commit9804b7e

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# TLDR- works fine for 1 signal, but if there are more then the signals need to be at different amplitudes
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importnumpyasnp
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importmatplotlib.pyplotasplt
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sample_rate=1e6
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d=0.5# half wavelength spacing
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N=10000# number of samples to simulate
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t=np.arange(N)/sample_rate# time vector
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Nr=8# elements
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theta1=15/180*np.pi
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theta2=60/180*np.pi
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theta3=-50/180*np.pi
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tone1=1.0*np.exp(2j*np.pi*0.0173e6*t).reshape(1,-1)
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tone2=0.5*np.exp(2j*np.pi*0.0257e6*t).reshape(1,-1)
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tone3=0.25*np.exp(2j*np.pi*0.0312e6*t).reshape(1,-1)
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s1=np.exp(-2j*np.pi*d*np.arange(Nr)*np.sin(theta1)).reshape(-1,1)# 8x1
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s2=np.exp(-2j*np.pi*d*np.arange(Nr)*np.sin(theta2)).reshape(-1,1)
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s3=np.exp(-2j*np.pi*d*np.arange(Nr)*np.sin(theta3)).reshape(-1,1)
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# Simulate received signal
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r=s1 @tone1+s2 @tone2+s3 @tone3
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n=np.random.randn(Nr,N)+1j*np.random.randn(Nr,N)
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r=r+0.00001*n# 8xN
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# Compute covariance matrix
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R=r @r.conj().T/N
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# Eigenvalue decomposition
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w,V=np.linalg.eig(R)
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idx=np.argsort(np.abs(w))[::-1]
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w=w[idx]
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V=V[:,idx]
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ifFalse:
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# Plot eigenvalues
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plt.figure()
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plt.plot(np.sort(np.abs(w))[::-1],'k*')
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plt.xlabel('Index')
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plt.ylabel('Eigenvalue')
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plt.show()
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foriinrange(3):
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phases=np.angle(V[:,i])
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# find phase between adjacent elements
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phase_diffs= []
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foriinrange(len(phases)-1):
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phase_diffs.append(phases[i+1]-phases[i])
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phase_diffs=np.array(phase_diffs)
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phase_diffs=np.mod(phase_diffs+np.pi,2*np.pi)-np.pi# make them all between -np.pi and np.pi
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print(phase_diffs)
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phase_diff=np.mean(phase_diffs)
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# Convert to AoA
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result=np.arcsin(phase_diff/ (-2*np.pi*d))
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print(np.rad2deg(result))

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