AUV: Underwater Acoustic Localization

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"Applying signal-processing algorithms on the sbRIO-9602 reduced the overall computation speed. Furthermore, the NI 9223 and sbRIO-9602 required minimal setup to work with LabVIEW. With the well-documented and straightforward Measurement & Automation Explorer GUI, installing the required drivers was a breeze."

- Radhakrishnan Vivek, National University of Singapore

The Challenge:
Determining the location of an underwater acoustic source autonomously.

The Solution:
Combining the benefits of NI 9223 with NI sbRIO-9602 to create a powerful, accurate system using NI LabVIEW function blocks for programming sophisticated digital signal-processing algorithms.

Author(s):
Radhakrishnan Vivek - National University of Singapore
Yongchang Huang - National University of Singapore
Soon Jin Tan - National University of Singapore

Localizing Acoustic Sources

It is important to localize acoustic sources to aid in search and rescue missions, such as those for sunken vessels, as reported in recent news.

Although there are several avenues for renting remote operated vehicles (ROV), these vehicles are expensive and require skilled pilots. To encourage more companies to adopt autonomous underwater vehicle (AUV) technology for missions, it is imperative to implement a commercially affordable AUV with passive acoustic capabilities.

Because passive acoustic systems are usually specialized and tailored to a particular vehicle, it would be beneficial to develop a modular system which can interface with various AUVs or ROVs. This modular approach would allow more companies to adopt the acoustic system into their underwater vehicles whenever needed.

System Specifications

In our system, the hydrophones receive the analog acoustic signals, which are simultaneously sampled by an NI 9223 at 250 kS/s. Using this digital waveform, NI sbRIO-9602 does further digital filtering before the signal is cropped to extract phase information via Fast Fourier Transform (FFT). This FFT information is broadcast to the AUV via user datagram protocol (UDP). Several palettes provided in NI LabVIEW are essential to system implementations.

The array manipulation palette is the most crucial toolkit used in our design because it extracts the signal of interest using the subarray function. And the Max/Min function extracts FFT peaks, which are necessary for obtaining the direction-of-arrival information.

Elliptic filters from the signal processing palette remove residual noise in the digital signal, while UDP from the network palette broadcasts FFT information to the AUV.

System Benefits

NI LabVIEW provides the connectivity needed for quick hardware interfacing between the hydrophones and data acquisition (DAQ) device. Different modes of communication between NI LabVIEW and other systems, including transmission control protocol and UDP, ensure accurate data transfer between the sbRIO-9602 and the AUV’s CPU.

With LabVIEW functional block-style programming, we can make incremental changes along the way. Readily available virtual instruments (VIs) from the example folder make it possible to add appropriate functional blocks to meet system requirements. The wide range of data manipulation and signal processing palettes help implement the acoustic localization algorithms, to translate them effortlessly from theoretical concepts into a practical system.

Applying signal-processing algorithms on the sbRIO-9602 reduced the overall computation speed. Furthermore, the NI 9223 and sbRIO-9602 required minimal setup to work with LabVIEW. With the well-documented and straightforward Measurement & Automation Explorer GUI, installing the required drivers was a breeze. Hence, there was minimal inconvenience during our interactions with NI products.

Readily available VI examples and other useful online materials found on the self-learning portal ensure that the system is implemented in a timely manner. These online training videos encourage experimentation by learning the nuances of NI products.

Right-clicking and using the choose indicator output provided quick debugging while increasing confidence in NI products. By reading off interprocess values, we could develop a deeper understanding of the effects and trade-offs of coercion, data manipulation, and storage.

While developing this system, we encountered an unexpected benefit. We chose NI products because NI offers a publisher/subscriber implementation in Robot Operating System (ROS). When ROS upgraded from ROS-Fuerte to ROS-Hydro Medusa, this implementation became incompatible. But because NI adopts the industry-standard communication protocol, LabVIEW could communicate with ROS, an AUV OS.

Conclusion

Using easy interfacing between NI products, the AUV, and hydrophones, we rapidly prototyped an effective system. The high NI 9223 data acquisition rate and the sbRIO-9602 real-time broadcasting helped offload acoustic signal processing from the CPU. This increases overall execution speed and responsiveness by tackling engineering problems associated with providing an affordable yet modular acoustic localizer. It is a blessing that Team Bumblebee chose to use NI products. Our success at SAUVC 2014 has increased our confidence in NI.

Author Information:
Radhakrishnan Vivek
National University of Singapore
EA-04-06, 9 Engineering Drive 1 Singapore
117576
Singapore
rvonliner@gmail.com

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