# Signal Enhancement of Helicopter Rotor Aerodynamic Noise Based on Cyclic Wiener Filtering
## 5 Conclusions  
In this paper, a cyclic Wiener filter was proposed for its signal enhancement according to the cyclostationary characteristics of rotor aerodynamic noise. Additionally, how to achieve filtering by cyclic Wiener filter was theoretically investigated. Furthermore, the necessity of envelope spectrum analysis was analyzed. According to the harmonic characteristics of rotor aerodynamic noise, a detection function was constructed to realize helicopter detection. Based on the data of the helicopter acoustic target detection experiment, the main conclusions can be summarized as follows:  
1. The cyclic frequencies were first obtained at a close distance and then gradually detected farther away, until the limit distance where the cyclic frequency can be detected was found. This limit distance was revealed to be 4.114 km. Meanwhile, when comparing the cyclic frequencies at different distances, it is found that due to the influence of the Doppler frequency shift, the cyclic frequencies will be higher in the case of long distances than in the case of short distances. 2. The collected noise signal was subjected to cyclic Wiener filtering, and its filtering effect is analyzed combined with envelope spectrum. When cyclic Wiener filtering is performed at a close range, the frequency shift can be performed according to the detected cyclic frequency. However, For the case that the distance exceeds the limit distance of 4.114 km, it is necessary to appropriately increase the cyclic frequencies for correction, because in this case, the rotor aerodynamic noise with cyclostationarity has been submerged by the strong background noise, so the cyclic frequency detection is unable to detect its cyclic frequencies normally. Before filtering, the limit distance of the features of rotor aerodynamic noise through envelope spectrum analysis was 4.114 km, which is consistent with the limit distance at which the cyclic frequencies can be detected. The detection distance after cyclic Wiener filtering reaches 17.75 km, which is greatly improved compared to before filtering. Figure 16: False alarm rate of background noise signal. 3. According to the helicopter detection algorithm constructed in this paper, the collected helicopter noise signals are used for helicopter detection. The comparison before and after the cyclic Wiener filtering shows that the cyclic Wiener filter can effectively improve the detection rate of helicopters. For the time period when the helicopter flew from 18 km to 0 km, the detection rate before filtering was 44.2%, and the detection rate after filtering was 80.6%. Moreover, the cyclic Wiener filter can effectively improve the detection distance of the helicopter. In order to verify the correctness of the detection code, the background noise signal was collected separately during the experiment. The detection function is used to detect this background noise signal. The false alarm rate before and after cyclic Wiener filtering is very low, which is 12.4% before filtering and only 18.2% after filtering, which is consistent with the established fact. Due to the cyclostationarity of helicopter rotor aerodynamic noise, the application of other cyclostationarity analysis methods to helicopter rotor aerodynamic noise can certainly be further studied. In addition, regarding the use of helicopter acoustic signals to detect helicopters, the rest of the acoustic signal processing methods will also be further explored.