# PBR Clutter Suppression Algorithm Based on Dilation Morphology of Non-Uniform Grid
## 4 Experiment result and Analysis
### Testing by Simulated Data
#### 4.1.4 Test the Performance Combining with Tracking Algorithm  
We combine the proposed suppression algorithm with two typical tracking algorithms to test the performance. One is traditional NN TO-MHT algorithm (Nearest Neighbor Track Oriented-Multiple Hypothesis Tracking) [26], abbreviated as NN-MHT in this paper. The other is SNN-Kalman tracking algorithm (Suboptimal Nearest Neighbor - Kalman) [27], which is proposed aiming at multi-target tracking in non-cooperative passive system. To explicitly name the proposed algorithm, we name its abbreviation as MCSNG (Multi-frame Clutter Suppression based on Non-uniform Grid). Combine the clutter suppression with two tracking algorithms mentioned above respectively. For each frame, the tracking process follows with the clutter suppression in pipeline operation. Based on the difference of the tracking process, we name these compound algorithms as MCSNG-NN and MCSNG-SNN-K respectively. To make comparison, we utilize the original data without clutter suppression for tracing. Choose four indexes (total number of traces, mean trace length, maximum trace length, time consuming) to evaluate the tracing performance. Set the maximum velocity and the maximum accelerated velocity as 1000 m/s and 200 m/s\({}^{2}\) respectively. In Kalman filtering process, the maximum time period of blind prediction is set as 15 s.  
Figure 11a,b illustrates the tracing result of NN-MHT and MCSNG-NN, respectively. In addition Table 4 lists the tracing result indexes of both. Utilize different colors to distinguish different tracks.  
Figure 10: The change of the performance indexes along with SCR.  
The number of false tracks produced from NN-MHT is 17, which is much more than MCSNG-NN. Besides, the time consuming of the MCSNG-NN is 76% less than the one of the NN-MHT.  
Figure 12a,b illustrates the valid tracing result of SNN-Kalman and MCSNG-SNN-K respectively. Comparing with five target tracks set before, we calculate the mean trace error of the valid tracing results from both algorithms, as Table 5 list. It is obvious that the tracking precision of MCSNG-SNN-K is higher than the one of SNN-Kalman. Since the time interval is not constant, the velocity estimation accuracy will be affected. However, the velocity estimation of MCSNG-SNN-K is closer to the true value than SNN-Kalman. Table 6 shows the index of the tracing result. It can be found that MCSNG-SNN-K is more efficiency and produces less false tracks than SNN-Kalman.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Total Number of Traces** & **Mean Trace Length** & **Max Trace Length** & **Time Consuming (s)** \\ \hline NN-MHT & 22 & 39.13 & 89 & 2.99 \\ MCSNG-NN & 6 & 78.83 & 89 & 0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The performance indexes of NN-MHT and MCSNG-NN.  
Figure 11: Tracing results comparison of both algorithms: (**a**) NN-MHT tracing result; (**b**) MCSNG-NN tracing result.  
Figure 12: Valid tracing results comparison of both algorithms: (**a**) SNN-Kalman tracing result; (**b**) MCSNG-SNN-K tracing result.