# Study on Active Tracking of Underwater Acoustic Target Based on Deep Convolution Neural Network
## 3 Simulation and Verification of Real World Signal
### Simulation  
In the simulation verification, the DCNN-KFAT method proposed in this paper is tested as follows: (a) the tracking effect after the azimuth of two moving targets crossed; (b) the tracking effect on a weak target. The test results of the new method are compared and analyzed with the KFAT method. In this article, we first test the distinguishing ability of the proposed method for different targets and target data association ability during active tracking, and set up a simulation environment containing two moving targets for verification. The two targets have different bright spot echo models, and the time-frequency image of the echo signal can reflect the characteristic information of the target. Figure 7 shows the simulation results of the KFAT method and the DCNN-KFAT method, the coordinate axis unit is km. In the Cartesian coordinate system, the initial position of target 1 is near (\(-\)4.5 km, 5 km), moving in the direction away from the active sonar, target 2 is near the initial position (\(-\)2 km, 11 km), moving in the direction close to the active sonar. Figure 16a,b are the simulation results of the KFAT method. We represent the simulated motion trajectory of the target, the observation result, and the result after Kalman filtering in Cartesian coordinates. The KFAT method loses a target after the azimuths overlap. Figure 16c,d are the results of using the DCNN-KFAT method. The tracking result is more accurate than the KFAT method. After the azimuth overlaps, the two targets are successfully identified and can continue to be tracked. Figure 15: Accuracy and loss after training 200 times. Figure 17a,b show the deviation of the DCNN-KFAT method for active tracking of two targets. The observation deviation of the DCNN-KFAT method is about 150-300 m, and it is stable at about 200 m. Among them, the deviation of target 1 increases to 300 m in the 400-500 s time period. Compared with their positions, in this time period, the two targets are very close, their azimuth angles are very close, and the distance from the receiving array is almost the same. When the position of the target is very close, since the echo signals of the active sonar are similar in the frequency domain, the deviation is likely to increase. Figure 16: Tracking two azimuth crossing targets. In order to accurately test the effect of the DCNN-KFAT method in tracking low SNR signals, we continue to set up simulated moving targets for simulation testing. Figure 18 shows the results of the simulation test. The coordinate axis represents the distance in km. The red solid line in Figure 9 represents the set trajectory of the simulated target, and the target moves away from the active sonar. Figure 17(a) shows the observation results and filtered results using the KFAT method. As the target moves, the intensity of the target echo signal received by the array gradually decreases; when the echo signal intensity is lower than the detection range, the active sonar cannot continue to track the moving target. Figure 17(b) shows the observation results and filtered results using the DCNN-KFAT method. When the KFAT method is lost, the DCNN-KFAT method can still identify the distance and azimuth of the tracked target from the echo signal, and continue tracking the goal. In the early stage of tracking, the deviation of KFAT method is relatively large, while the deviation of DCNN-KFAT method is relatively small. We specifically analyzed the observation bias of the two methods, and the results are shown in Figure 19.  
Figure 18: The target SNR gradually decreases and DCNN-KFAT tracks further than KFAT. Figure 17: Using the DCNN-KFAT method, the deviation is around 200 m.  
Figure 19a shows the comparison of the tracking deviation of the KFAT method before and after filtering.