# Design and Implementation of a Multi-Function Hydrophone for Underwater Acoustic Application
## 3 Results and Analysis
### Experimental Results in Mode 3  
Since the digital signal processing is performed in the MCU in mode 3, the parameters should be balanced between computational complexity and communication performance. To demonstrate the high reconfigurability, in this mode, the DSSS receiver with different center frequencies is implemented. The parameter setting is shown in Table 3. The sampling frequency was set at 64 kHz, the bandwidth was fixed at 4 kHz, and the length of the gold code is 127. As a result, the length of the DSSS symbol is 32 ms, and the DSSS receiver can handle multi-path that is less than 32 ms. The number of gold codes was set to 128, as a result, the raw data rate is 187.5 bps. The center frequency can be easily adjusted, for example, 9 kHz and 15.5 kHz in this experiment. To avoid the error bits, the convolutional coding technology with a coding rate of 1/2 was used, so the data rate after channel decoding was 93.75 bps.  
Figure 14 shows the channel estimation results using the signal in mode 3. The signal is obtained from the hydrophone near the surface. It can be seen that the channels exhibit rich multi-path, and the delay is about 80 ms.  
Figure 15 shows the corresponding Doppler estimation. From the two subfigures, we can see that the Doppler is insignificant. Since the transmitter and the hydrophones were fixed, the Doppler may be caused by current.  
The parameter BER is used to evaluate the communication performance of the DSSS receiver. Figure (a)a displays the average BER of the received DSSS signals when \(f_{c}=15.5\) kHz, the BER is obtained before channel decoding. The BER of seven multi-function hydrophones, numbered sequentially from NO. 1 to NO. 7, are counted, and each multi-function hydrophone contributed 33 frames. It can be found that except for NO. 1 and NO. 2 multi-function hydrophones, the other hydrophones achieved BER, free. Although the first two did not perform perfectly, the BER is still under \(10^{-3}\), and after convolutional  
Figure 14: Channel estimation results. (**a**) Channel estimation result when \(f_{c}=9\) kHz. (**b**) Channel estimation result when \(f_{c}=15.5\) kHz.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Notation** & **Value** \\ \hline Sampling frequency (kHz) & \(f_{s}\) & 64 \\ Center frequency (kHz) & \(f_{c}\) & 9/15.5 \\ Bandwidth (kHz) & B & 4 \\ Number of gold code & - & 128 \\ Length of gold code & \(L\) & 127 \\ Symbol length (ms) & \(T_{s}\) & 42.7 \\ Code rate of convolutional encoding & - & 1/2 \\ Data rate before channel decoding (bps) & \(R_{1}\) & 187.5 \\ Data rate after channel decoding (bps) & \(R_{2}\) & 93.75 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of DSSS receivers in mode 3.  
decoding, the BER is free. Figure 16b shows the BER of the first 19 frames from the first two multi-function hydrophones in detail. It should be noted that the last 14 frames have zero BER. In NO. 1 multi-function hydrophone, only 5 frames exist error bits, while in NO. 2 multi-function hydrophone, the number of frames that exist error bits is 4. It is worth noting that the BER of those nine frames is in the order of \(10^{-2}\). After channel decoding, the error bits are all corrected, which are error free.  
The BER obtained from the DSSS receiver whose center frequency is \(f_{c}=9\) kHz is 0 before and after channel decoding. The reason may be that the low-frequency acoustic signal has lower attenuation, which results in higher SNR.  
In conclusion, from the experiments, our proposed multi-function hydrophone has good acquisition and communication quality.