(A shorter version of this article appeared in the Nature Portfolio Communities blog )

The United Nations is giving massive emphasis to developing creative technological solutions to power Ocean exploration and sensing, boosted by a global thrust under the United Nations Decade of Ocean Sciences.

Technological improvements in Ocean sensing are vital to make the quantum jump needed during this Decade of development. To achieve this, we may have to tap into ideas from other research areas or even nature itself. Here is a story of how we drew inspiration from one of the most powerful sonars found in nature - dolphins - to build an improved imaging sonar.

Dolphins produce different kinds of sounds - often classified into whistles, barks and clicks. They scan their environment acoustically by transmitting sound pulses called echolocation clicks onto nearby objects. The echoes bouncing off the environment are received by the dolphin, providing it an acoustic sensing of its surroundings like a directional acoustic flashlight.

This biological acoustic sensing, i.e, Sonar, of dolphins is powerful, and superior to man-made imaging sonars of its size.

Are you listening or are you seeing ? Dolphins possess incredibly sophisticated acoustic imaging capabilities. Image credits:

Let the significance of that 'sink' in for a moment. Consider all those decades of manmade sonar technology development. And after all that, for its size, the dolphin still beats human technology in terms of acoustic imaging ! It's fascinating, and humbling. It also tells us that there is much we can still learn from nature, and gives another reason to preserve Earth's beautiful biodiversity.

Coming back to the sonar - wouldn’t it be great if we could create a small, effective imaging sonar with the size of the dolphin head, that can be mounted on underwater robots or small boats and used for seabed mapping/object detection ? Such a capability would be particularly important for ocean exploration, a task that is especially relevant in the light of the ongoing Decade.

This research problem has been studied since the 1950s, and lots of progress made. Researchers have replicated some features of dolphin sonar in man-made devices and shown that these can improve our sonars. We, too, are intrigued by this problem, and have been studying dolphin biological sonar with specific emphasis on the following question - scientists have been looking at the hardware aspects of the dolphin's sonar, but how much do we know about the 'software'? If millions of years of evolution has led to such an effective echolocation capability in the dolphins, they must have also developed capabilities to use (process) the echoes they receive efficiently to interpret their environment.

Unfortunately, this question is hard to answer fully without assessing the dolphin brain to understand what exactly it perceives. But strides have been made on this front. One study methodology developed for this involves experiments called echoic-to-visual matching-to-sample trials, where the dolphin acoustically scans an object underwater, and then picks the matching object from a set of alternatives provided in air. This experiment ensures the dolphin matches information from one sensory modality (acoustics) to another (visual), by perceiving the object shape or its features in some way.

We conducted these experiments and 'listened in' to what the dolphin was doing while it acoustically scanned the object, i.e. we recorded its acoustics. We recorded the echoes, and processed and visualised them. The idea behind this experient is: if the dolphin used those echoes to discern the object and discriminate it from other alternatives, those echoes must contain information about the object shape, right ? We also developed a biomimetic sonar inspired by the dolphin, to scan the same objects the dolphin was scanning.

Our bio-inspired sonar mounted underwater inside the pool.

Unfortunately, using conventional sonar processing, you can't see much of the object in the visualisation of the sound echoes. So, this processing is too rudimentary to capture the shape features, and fails where the dolphin succeeded. Can we do better with the data given to us ?

One way to improve the sonar is to use prior information. This is something humans do all the time - we turn our understanding of reality into expectations that can speed up our inferences and decisions. Eg., in the absence of other info, the human eye/brain assumes the light on an object is falling from ‘above’. Could the dolphin be using priors for its processing, and are there priors we can use to process the echoes better ? 

It turns out - yes, we do know something beforehand about the objects scanned in this study - they have well-defined boundaries (are not diffuse, like say, dust clouds), and occupy only a small fraction of the space being scanned, i.e there is ‘sparsity’ of the object. This information is useful, because we can now expect that our visualisation of echoes (from the dolphin and biomimetic sonar) should only contain a few parts that correspond to an object.

A schematic of the dolphin echolocating at the object during the trial, the sample objects used during the trials, and visualisations of these objects from our biomimetic sonar (with size similar to of a dolphin's head) with sparsity-based processing. The object features are clearly visible in the visualisations.

We incorporate this info into our sonar, and the new approach works better than a conventional approach, as you can see in the figure. Thus, we get one step closer to the size-performance tradeoff of the dolphins. 

The success of this opens up many exciting directions for us to explore. What other ‘priors’ can we use to push our sonar performance further ? And so on. Exciting research directions lay ahead.

Here is a link to our paper  [1].

[1] Hari Vishnu, Matthias Hoffmann-Kuhnt, Mandar Chitre, Abel Ho, Eszter Matrai, “A dolphin-inspired compact sonar for underwater acoustic imaging”, Communications Engineering, Apr 2022. [ http/pdf], DOI: https://dx.doi.org/10.1038/s44172-022-00010-x