Near-field non-circular information source parameter estimation method based on fourth-order cumulant
A fourth-order cumulant, source parameter technology, applied in measurement devices, radio wave measurement systems, direction finders using radio waves, etc. question
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example 1
[0110] Simulation example 1: The antenna array is a uniform symmetrical linear array with the number of array elements N=3 (M=1), the positions of the array elements are [-d, 0, d], the distance d between the array elements is λ / 4, and the wavelength of the λ signal is , and the corresponding near-field region is (0.22λ,0.5λ). The incident source is an equal power, statistically independent BPSK signal, and the noise is additive Gaussian noise. Assuming that there are 4 BPSK signals incident on the symmetrical uniform linear array, their position parameters are (-40°, 0.3λ), (-25°, 0.25λ), (-10°, 0.25λ) and (40° ,0.35λ). The number of snapshots and SNR were set to 4000 and 40dB, respectively, and a total of 100 experiments were carried out to obtain the average. The near-field non-circular signal parameter estimation algorithm based on GESPRIT can distinguish 2M=2 signal sources at most, and the method proposed by the present invention can distinguish 2×2M=4 signal sources a...
example 2
[0111] Simulation example 2: The antenna array is a uniform symmetrical linear array with the number of array elements N=7 (M=3), and the positions of the array elements are [-3d, -2d, -d, 0, d, 2d, 3d], and the array elements The distance d is λ / 4, and the corresponding near field area is (1.14λ, 4.5λ). Consider three BPSK signals with their location parameters (-30°, 2λ), (-10°, 3λ) and (20°, 4λ). The number of snapshots is 1000, and the SNR varies from 0dB to 10dB with a step size of 2dB. The simulation results under each SNR are evaluated by the root mean square error (RMSE) of 500 Monte Carlo experiments. Figure 4 and Figure 5 They are the results of the root mean square error of near-field non-circular source DOA estimation and distance estimation changing with the signal-to-noise ratio, where "◇" is the method proposed by the present invention, and "*" is the method proposed by Xie Jian et al. in 2015 The proposed near-field non-circular signal parameter estimation...
example 3
[0112] Simulation example 3: The SNR is 10dB, the number of snapshots changes from 100 to 1000, and the step size of the change is 100. Other parameter settings are the same as the simulation example 2. The simulation results under each snapshot are evaluated by the root mean square error (RMSE) of 500 Monte Carlo experiments. Figure 6 and Figure 7 are the results of the root mean square error (RMSE) of near-field non-circular source DOA estimation and distance estimation changing with the number of snapshots, where "◇" is the method proposed by the present invention, and "*" is added It is the near-field non-circular signal parameter estimation method based on GESPRIT proposed by Xie Jian et al. in 2015, and the "○" is the near-field signal parameter estimation method based on subspace in 2006 by Wu Yuntao et al. Depend on Figure 6 and Figure 7 It can be seen that as the number of snapshots increases, the method proposed by the present invention monotonically decrease...
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