Target tracking new algorithm based on fractional fuzzy function under stably distributed noise
A fractional fuzzy, stable distribution technology, applied in the direction of radio wave reflection/re-radiation, using re-radiation, measurement devices, etc., can solve problems such as performance degradation
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Embodiment 1
[0142] Embodiment 1: The characteristic index of the noise is set to α=1.4, and the generalized signal-to-noise ratio GSNR=12dB. figure 2 The estimated performance of the Doppler frequency is shown and compared to theoretical values. It can be seen from the figure that the algorithm in this paper not only effectively suppresses the interference of impulse noise, but also has better estimation performance.
[0143] Figure 3 shows the dynamic estimation results of the azimuth and elevation angles of the transceiver array at different times. It can be seen from the figure that the algorithm in this section has better estimation performance. It can be seen that the algorithm in this paper not only has a good ability to suppress impulse noise interference, but also has a good estimation accuracy, which lays the foundation for the subsequent location of interference sources.
Embodiment 2
[0144] Embodiment 2: In this experiment, the generalized signal-to-noise ratio is set as GSNR=12dB. Given a new definition, Indicates the root mean square error of each parameter estimate at different characteristic indices. Fig. 4 shows the RMSE of target parameter estimation as a function of noise characteristic index. It can be seen from the figure that the algorithm in this paper has good estimation performance.
Embodiment 3
[0145] Embodiment 3: In this experiment, the characteristic index of noise is α=1.4. Under different SNR environments, the RMSE calculation formula for target parameter estimation refers to the definition formula RMSE2 in Experiment 2. Figure 5 shows the RMSE of target parameters and localization estimates as a function of generalized SNR.
[0146] It can be seen from Fig. 5(a) that this algorithm has better performance in azimuth and elevation angle estimation, and the RMSE of parameter estimation becomes smaller as the generalized signal-to-noise ratio increases. Figure 5(b) shows the RMSE of Doppler frequency estimation. It can be seen from the figure that the RMSE limit of Doppler frequency estimation is relatively flat, because: the estimation of Doppler frequency in the algorithm in this section is through Estimating the Doppler frequency parameters is realized, and the estimation of the Doppler frequency parameters is realized by searching the peak of FLOS-FAF. Based o...
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