# The Use of Underwater Gliders as Acoustic Sensing Platforms
## 4 Application of Gliders to Underwater Acoustics
### Underwater Passive Surveillance
#### 4.2.2 Improvement of Signal Receiving Ability via ALE  
Although the glider is much quieter than AUVs, the self-noise of the glider, especially the roll motor noise, still has an impact on detection ability. As Figure 3 shows, the roll motor noise appears irregularly and frequently. If the roll motor noise can be eliminated, there will be more time for signal detection and the detection ability can be improved. Thus, to further reduce the influence of the self-noise, the following presents a method using an adaptive filter to improve the performance of signal receiving ability. The ALE is a spectrum estimation technique that is widely used for narrowband signal detection in additive broadband noise [16]. It can suppress broadband signals but enhances narrow band signals. Since the roll motor noise is a broadband signal, it is appropriate to extract the received narrowband signal by the ALE.  
As shown in Figure 9, the ALE can be implemented via a least mean squares (LMS) adaptive filter [14]. Different from other adaptive filters, it does not need to know the desired signal. It has two inputs, one of which is the received signal \(x(n)\), which is a combination of the desired narrow band signal \(s(n)\) and noise \(w(n)\). The other input is a delayed version of \(x(n)\), which can be written as \(x(n-\Delta)\), where \(\Delta\) denotes the delay time.  
Figure 9: The block diagram of the ALE.  
The first output of the ALE is \(y(n)\), which is the estimation of the desired signal. The second output \(e(n)\) is the difference between the \(x(n)\) and \(y(n)\). In every iteration, ALE separates the narrowband signal and adaptively adjusts its filter weight using the LMS algorithm. The mathematical formulas of ALE are as follows [16]:  
\[x(n)=s(n)+w(n) \tag{3}\]  
\[y(n)=\sum_{i=1}^{L}w_{i}(n)x(n-\Delta-i) \tag{4}\]  
\[e(n)=x(n)-y(n) \tag{5}\]  
\[w_{i}(n+1)=w_{i}(n)+2\mu e(n)x(n-\Delta-i), \tag{6}\]  
where \(\mu\) is the step size that controls the convergence rate, and \(L\) is the length of the filter.  
To illustrate the effectiveness of ALE in a real scenario, a 5 kHz PCW signal received from the experiment in August 2018 was chosen. The spectrogram of the signal is displayed in the left-hand panel of Figure 10. The 5 kHz PCW signal starts at about 30 s, but is corrupted by the roll motor noise. The right-hand panel of Figure 10 is the spectrogram of the ALE's output signal, where most of the roll motor noise is eliminated, but the PCW signal is enhanced, which makes it much easier to detect than the raw data. Figure 11 shows the spectrum of the raw signal and the ALE output signal. The SNR of the raw 5 kHz PCW signal is no more than 5 dB. However, after it was enhanced by the ALE, the output SNR was not less than 30 dB and the gain of ALE in this scenario was about 25 dB.  
Figure 11: The spectrum of raw data and the ALE filtered data, respectively.  
Figure 10: The spectrograms of raw data (**a**) and the ALE filtered data (**b**), respectively. The signal starts in about 30 s.  
Through this algorithm, the detection range of gliders, especially for narrowband signals, can be greatly improved.