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Figure 8 MSE of local mean estimation versus averaging window length, v = 20 km h -1, X c = 10 m, shadowing variance σ s 2 = 6 dB, LOS scenario.

We present the variance and mean bias of frequency estimation versus number of iterations plots when estimating the randomly time-varying frequencies of two cisoids embedded in white noise (SNR1 = SNR2 = − 15 dB) for the proposed lattice method in Figures38 and39 respectively.

Figure 7 Root-mean-square error (RMSE) of power estimation versus different SNRs for ( θ 1,2 = 75°, 45°).

Figure 4 shows the plots of the mean estimation of f p using NLLS and OMP methods versus SNR. Figure 5 shows the plots of the MSE of the estimation of f p estimation for the two methods.

In Figure 17, we plot the normalized mean square error (NMSE) of the SNR estimation versus the true SNR for different M s.

Figure 1 NMSE of CFO estimation versus SNR.

Figure 2 NMSE of I/ Qimbalance estimation versus SNR.

Figure 3 NMSE of DCO estimation versus SNR.

Figure 4 RMSE curve of angle estimation versus SNR. Figure 5 RMSE curve of delay estimation versus SNR.

The results of comparison of the root mean square errors (RMSEs) of code tracking of the seven precedent waveforms are given in Figures 13 and 14. Figure 13 RMSE of the LOS signal delay estimation versus SNR.

Figure 8 Performance degradation in dB due to imperfect channel estimation versus ideal SNR.