## 5 Simulations and Performance Analyses
### Comparison with the Exiting Methods  
In the third experiment, ICM-CP and ICM-MP are considered, and we compare the performances of the proposed algorithm with those of the existing others, i.e., STAP, LSA, and SRA. In STAP, the existing STAP algorithm is used to suppress DP and MP clutter simultaneously with the ICM-CP mitigation. For a fair and reasonable comparison, LSA, SRA, and the proposed algorithm use the same STAP algorithm to remove DP clutter. In the existing LSA and SRA, we separate the temporal plane into \(11\) cells, and \(D=9\). For SRA, the forgetting factor is set to \(0.95\) and the constraint parameter is \(70\). For LSA, we set  
Figure 4: Gain performance in cost function versus external alternation number. Figure 3: SINR loss performance of the proposed approach versus the value of \(D\). the local threshold at 0.0001 and the iteration threshold at 0.1, which are the same as the local threshold and search threshold in the proposed algorithm, respectively. First, the distributions of optimal atoms selected by the proposed joint optimization algorithm and LSA are discussed. Table 2 gives the range-Doppler results of the selected atoms for different methods. The atoms corresponding to the real MP clutter component are called as real atoms, and the range-Doppler parameters of real atoms are also presented in Table 2. In the simulation, the number of iterative searches in LSA and the proposed algorithm are both six. Based on the simulated MDRs of three MP signals, the six real atoms are composed of three true atoms and three ghost atoms. Based on Equation (5), the range-Doppler parameters of a true atom and a ghost atom are denoted by \(\left[l_{p}-l_{\text{d}},\omega_{p}-\omega_{\text{d}}\right]\) and \(\left[l_{p}+l_{\overline{p}}-2l_{\text{d}},\overline{\omega}_{p}+\omega_{p}-2 \omega_{\text{d}}\right]\), respectively. As can be seen from Table 2, the selected atoms of LSA, especially the sixth selected atom, deviate far from the real atoms. This is because the presence of ICM-MP fluctuations affects the sparse model and disturbs the selection criteria. Compared with LSA, the selected atoms in the proposed algorithm are closer to the real atoms owing to the added ICM-MP error calibration step. This also indicates that the proposed algorithm can eliminate the impact of ICM-MP fluctuation on local search techniques, providing an accurate estimation for the MP clutter profile. Figure 5 presents the SINR loss performances of the proposed algorithm and three existing methods along the main beam direction. The normalized Doppler frequencies span from \(-0.5\) to \(0.5\). From the simulation results, we find that STAP has nulls both in the DP clutter area (normalized Doppler frequencies of \(-0.5\) and \(0.5\)) and the MP clutter area (normalized Doppler frequencies of \(-0.23\), \(0.08\), \(0.22\), \(-0.46\), \(-0.15\) and \(0.16\)), while the other methods exhibit nulls only in the DP clutter area. This is because SRA, LSA, and the proposed approach effectively cancel the MP clutter before DP clutter suppression. As a consequence, the SINR loss performance in the non-DP clutter region reflects the MP clutter suppression capability. Furthermore, it is also shown from Figure 5 that the proposed algorithm provides a better SINR loss performance than LSA and SRA. This is because the proposed algorithm adds the ICM-MP error calibration step to mitigate its effect on MP profile estimation compared with LSA and addresses the off-grid and ICM problems compared with SRA. Figure 6 presents the target detection results for different methods. Snapshots from the 992 th to 1008 th range cells are filtered in each method. It is found that STAP yields the worst target detection performance because the target located in the MP clutter region is also canceled. Moreover, SRA and LSA suffer from some performance loss in the presence of ICM-MP fluctuation when compared with the proposed algorithm.