# PBR Clutter Suppression Algorithm Based on Dilation Morphology of Non-Uniform Grid
## 4 Experiment result and Analysis
### Testing by the Field Data  
In this part, we utilize the field data to test the performance of the proposed algorithm. The PBR field experiment is done with an uncooperative radar with frequency, PW and PRI agile, and it aims to detecting the air-flights. We make validations with the ADS-B (Automatic dependent surveillance-broadcast) dataset. The detection scope is from \(80^{\circ}\) to \(170^{\circ}\). The detection range is from 50 km to 200 km. Due to unknown parameters of illuminator, the performance of pulse compression among several pulses degrades. To increase the detection probability, we reduce the CFAR rate to \(10^{-2}\). After CFAR detection, we adopt the multi-beam amplitude comparison direction measurements. Figure 13a illustrates the final detection point map of the field data with 390 s duration. Two directions of jamming are located at \(144^{\circ}\) and \(148^{\circ}\). Suppress the jamming in two directions and adopt the proposed clutter suppression algorithm. The structural element size is \(5\times 5\). The half number of the reference frames is set as 2. The suppression result is shown in Figure 13b. It is obvious that most of points are filtered out. Instead, points in three suspected track areas are retained. Referring to the ADS-B dataset, we plot the real-time civil flight information in Figure 13c, which is selected with the same duration and detection scope as the field data. The line with different colors stands for different flight track. There are three flights in the detection scope. Comparing with the ADS-B data, we can find that the proposed algorithm can effectively suppress the clutters and retain most of the target information. Like the operations in Section 4.1.4, we test two compound tracking algorithms (MCSNG-NN and MCSNG-SNN-K) by the field data. The maximum velocity is set as 1200 m/s. The maximum accelerate velocity is set as 200 m/s\({}^{2}\). In Kalman filtering process, the maximum time period of blind prediction is set as 15 s. Each frame consists of the clustered point data with 0.5 s period. Figure 14 and Table 7 illustrates the tracing results and the performance indexes of NN-MHT and MCSNG-NN. Figure 15 and Table 8 illustrates the tracing results and the performance indexes of SNN-Kalman and MCSNG-SNN-K. Comparing with the ADS-B dataset, we marked the true tracks by the ellipses with dotted line. It is obvious that MCSNG-NN reduces the occurrence probability of false tracks relative to NN-MHT. In addition its time-consumption drops to 28.16% of the original NN-MHT time-consumption. Besides, similar conclusions are suitable to SNN-Kalman and MCSNG-SNN-K. We can find that MCSNG-SNN-K reduces the occurrence probability of false tracks and saves time relative to traditional 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 SNN-Kalman & 104 & 64.89 & 654 & 0.6715 \\ MCSNG-SNN-K & 23 & 124.47 & 474 & 0.6081 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The performance indexes of SNN-Kalman and MCSNG-SNN-K. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Mean Trace Error (m)**} & \multicolumn{2}{c}{**Velocity (m/s)**} \\ \hline
**Track NO.** & **SNN-Kalman** & **MCSNG-SNN-K** & **SNN-Kalman** & **MCSNG-SNN-K** & **True Value** \\ \hline \(1\) & 548.87 & 434.56 & 823.5 & 821.7 & 800 \\ \(2\) & 1596.55 & 389.63 & 873.6 & 809.4 & 750 \\ \(3\) & 644.06 & 141.85 & 614.7 & 612.0 & 600 \\ \(4\) & 1286.59 & 283.97 & 395.1 & 415.2 & 400 \\ \(5\) & 2721.07 & 1840.33 & 543.5 & 604.0 & 600 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The comparison of five targets tracing results between SNN-Kalman and MCSNG-SNN-K.  
Figure 14: Tracing results of NN-MHT and MCSNG-NN: (**a**) NN-MHT tracing result; (**b**) MCSNG-NN tracing result.