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Underwater Acoustic Wave (UWAC) Communication technologies including Acoustic Modems enable Unmanned Underwater Vehicles (UUWs) missions

Approximately 70% of the Earth’s surface is covered by water, yet almost 95% of the underwater world remains unexplored. Nearly 4000 robots are swimming up and down in the world’s oceans, which allow scientists to measure and understand ocean dynamics, like the directions and speeds of currents, as well as physical characteristics like temperature and salinity, yet scientists can only recover the collected sensor data and track the position of the robots only when they rise to the surface or when the robots are retrieved at the end of a mission.

 

The large number of countries are developing submarines with enhanced stealth which is  driving Navies to enhance their Anti Submarine warefare capability.  Tracking submarines across large areas of ocean remains a key challenge for ASW. Manned platforms have limited ranges, and while the US Navy’s passive sonar system, SOSUS, is still in operation in parts, it is geographically bounded and requires substantial modernization to detect today’s quiet submarines. This gap has been partially filled by modern acoustic sensor arrays like the fixed reliable acoustic path, but in relative terms these cover very small areas of ocean.

 

Distributed remote sensing networks, however, which link interoperable manned and unmanned sensor platforms together as nodes in a larger system of systems, could be used to scale up persistent observation across wider areas. Networks such as the US Defense Advanced Research Projects Agency (DARPA) distributed agile submarine hunting program, which is developing ‘a scalable number of collaborative sensor platforms to detect and track submarines over large areas’, and PLUSNet (persistent littoral undersea surveillance network), which aims to create ‘a semi-autonomous controlled network of fixed bottom and mobile sensors, potentially mounted on intelligent [unmanned platforms]’ in littoral zones.

 

Developing and operating unmanned ASW systems introduces a new set of challenges in ASW sensing. The most significant challenge is the gathering, transfer, processing and analysis of sonar data, and the security of processing algorithms and data on an unprotected/unarmed ASW platform. Real-time data retrieval of frequent measurements, continuous tracking of underwater robots and increased spatial coverage and sensing from a network of submerged robots/sensors is hindered by the limited communication speed and absence of GPS underwater.

 

This hampers a wide range of activities, including real-time underwater sensing, sea-life monitoring, port surveillance, ocean mapping, subsea infrastructure inspection, wireless diver-to-diver communication, wireless diver/underwater vehicle communication, untethered sea exploration, subsea search-and-rescue operations, underwater wireless video feeds, and off- shore drilling monitoring.

 

One of the key challenges to overcome given the increased appetite for underwater missions supported by robotic systems is underwater communications. Traditional ASW sonar processing systems are located on a manned platform and operated by highly trained sonar operators. The trained eye/ear of a sonar operator and their ability to determine a legitimate threat amongst a complex operating environment ensures false reporting is minimized and the target classification confidence levels are maximized.

 

To achieve the same level of performance from an unmanned system, a high bandwidth data link is required to transfer all sensor data
from the platform to a remotely positioned operator. Such data links are either unavailable or extremely costly and power consuming. The data link also creates an electromagnetic signature, detectable by enemy forces thereby giving up the systems location and negating the
covertness desired from an unmanned system.

 

 

Underwater Acoustic communication (UWA)

It has long been recognised that any form of communications is difficult underwater  and there is a severe trade off between range and achievable data rates, primarily due to the transmission medium and its variability with time and location. Radio waves do not propagate well underwater due to the high attenuation. In fact, radio waves propagate at long distances through conductive salty water only at extra low frequencies (30− 300Hz), which require large antennae and high transmission power.

 

Acoustic communication is the most common medium choice, and is the only method for communicating over more than a couple of hundred metres. Most underwater nodes (whether they be static or mobile) will be outfitted with an acoustic modem, with some also receiving an RF or optical modem too. Recent efforts have led to the deployment of networks of these nodes, usually networks of seabed sensors communicating data to a surface station

 

Underwater acoustic communication is a technique of sending and receiving messages below water.  The acoustic waves are low frequency waves which offer small bandwidth but have long wavelengths. Thus, acoustic waves can travel long distances and are used for relaying information over kilometers. There are several ways of employing such communication but the most common is by using hydrophones.

Underwater Acoustic communication (UWA) is typically limited in range and coverage by the ocean conditions in which they exist. For this reason, while they are often utilised to support Autonomous Underwater Vehicles (AUVs) operations, they have been traditionally seen, from an autonomy perspective, as an additional mission constraint, often limiting the achievable objectives.

 

Underwater acoustic communication is relatively slow when compared to radio communication. This has to do largely with the speed of sound in water which is roughly 1500 meters/second. The result is a relatively low baud rate (typically 9600 baud). Not only is the medium slow but there are complications with the transmission due to signal absorption, geometric spreading losses, boundary effects, and multipath to name a few. The acoustic communications also suffer from the large propagation delays of acoustic waves and high bit error rates of the underwater acoustic channel, multi-path propagation and time variations of the channel. In addition, acoustic waves are affected by turbulence caused by tidal waves and suspended sediments, acoustic noise and pressure gradients.

 

Manufacturers have several techniques they employ to handle these challenges. The techniques come in the form of signal processing, data packaging, and coding schemes. These techniques, which are not the same for all manufacturers, help ensure reliable communication and possibly identify bit loss and/or repair these lost portions of data at the receiver end.

 

Recent developments in adaptive underwater communications, robust direction finding for GPS-less underwater localization, software-defined underwater acoustic modems, and soft robotics for low-cost macro/micro autonomous underwater vehicles (AUVs) are notable enabling technologies to achieve faster communication speeds, accurate positioning, and low-cost testbed deployments underwater. Further research is required to understand the physical environment and spatiotemporal characteristics of the underwater acoustic channel, application constraints and programmability, size, weight, and power of next-generation underwater wireless platforms to enable the design, development, and deployment of underwater wireless networks of robots/sensors at scale.

 

Acoustic modems

Two characteristics are required for acoustic communication. On the one hand, it is needed a modulation phase (in the transmitter) and a demodulation phase (in the receiver) using a carrier wave to optimize the quantity of information sent and to decrease the effects of noise and interference. It is also needed a medium to transport that carrier wave.

Underwater acoustic communications being improved solving many challenges for Navy and commercial customers. | International Defense Security & Technology Inc.

In essence, an underwater modem consists of:

1) A power unit, which has a battery and a set of DC/DC converters,

2) A processing unit, which usually consists of a small processor and memory (sometimes, it can be added as an external memory),

3) The physical hydrophone and loudspeaker,

4) Circuitry (used to adapt the digital signals to the processor) and the analog to digital converter and the digital to analog converter to adapt changes between the medium and the electric circuit.

Major components of an underwater acoustic modem: the analog front end... | Download Scientific Diagram

The water is a good medium to transmit the carrier wave with low noise rate. These facts make acoustic communication the most useful way to transfer data under the sea. However, acoustic modems have also some problems as transmission loss, propagation delay and Doppler Effect, refraction due to the variations of the temperature and pressure, multipath and even frequency attenuation. The current developed acoustic modems can only support point-to-point, low-data-rate and delay-tolerant applications.

 

There are several methods of transmitting data acoustically (i.e. modulation), but the most common method is the use of spread spectrum. Briefly, this is a method of sending data at several different frequencies (Multi-Frequency ­Shifted Key, MFSK) in order to increase data throughput. Another modulation scheme is the Phase Shifted Key, or PSK; this modulation scheme permits higher baud rates but is more susceptible to error sources.

 

The data are packed to ensure that a few errors will not corrupt the entire data message. This means that large amounts of data are sent as a series of these data packages. A typical data package is approximately 4 kb. A package contains the data plus additional bytes of data for identifying the package boundaries, modem identity, checksum, and error correction codes.

 

Some modems allow for a configuration where a retransmission request is sent from the receiver if errors are detected in a data package. The implication of lost data is that it must be retransmitted. This affects the effective baud rate if a modem is operating at a high acoustic baud rate. Apart from the modulation schemes and packaging techniques there are also techniques to minimize the effects of multipath. Multipath is the reception of the same signal several times, yet slightly delayed from one another. Since the signal is the same frequency and arrives at more or less the same time, it is challenging to separate the original signal from time delayed versions overlapping each other.

 

As the name suggests, multipath is the source of these “different” signals that are reflections of the original signal from boundaries that lie between the transmitter and receiver. Multipath is most prominent over long ranges and shallow water, whereby the original signal can bounce between the surface and bottom before arriving at the receiver. There are a few tricks in use to reduce the effects of multipath. These are convolutional coding, multipath guard period, and data redundancy.

 

Convolutional coding is data in a following frame that is capable of correcting up to one bit errors in the data frame previously sent. Multipath guard is a time delay inserted between data frames. Increasing the delay between frames reduces the interference from multipath. Data redundancy is simply the process by which data is retransmitted in the same data frame. All of these methods improve the reliability of a transmission, however they also reduce the data transmission rate. This means there is a trade off between reliability and data rate.

 

Underwater Acoustic Wave (UWAC) Communication systems

In 1996, Stojanovic et al. proposed a UWAC system at 40 kbps. In 2002, Zielinski et al. constructed an 8-kbps digital UWAC system over 13 km in length and 20 m in depth. In 2005, Ochi et al. preliminarily employed 32-quadrature amplitude modulation (QAM) to construct a 125-kbps UWAC system with a symbol error rate of 10−4 4. Zakharov et al. demonstrated the UWAC system with an orthogonal frequency division multiplexing (OFDM) data stream.

 

In addition, Li et al. proposed a UWAC system that applies the multiple-input-multiple-output technique. Moreover, Song et al. demonstrated a UWAC system with 60-kbps 32-QAM data covering a bandwidth of 32 kHz in a seawater environment more than 100 m deep with a distance of over 3-km. However, despite the aforementioned research, the transmission rate of the UWAC system is limited by its narrow modulated bandwidth.

 

Challenges and Opportunities of Underwater Cognitive Acoustic Networks

 

The underwater acoustic wave (UWAC) system can be applied only in low-noise environments for low-speed content. This is because of its strong attenuation in seawater, exhibiting inverse proportionality to the wavelength, as well as its significant propagation delay and the low signal-to-noise ratio (SNR) of data in the context of background ocean noise.

 

 

Breakthrough in Full-Duplex Acoustic Underwater Data Communications announced in 2017

Since the beginning of acoustic communications, the state of the art technology has been limited to half-duplex signals: transmit with the receiver off and then turn the transmitter off and receiver on and wait to receive, because the direct transmission at the source saturates the receiver electronics if they are enabled simultaneously.

 

QinetiQ North America (QNA) has announced that it has successfully demonstrated full-duplex underwater acoustic data communications, using its new, proprietary DOLPHIN technology.  It cancels the transmit signal at the receiver in real-time. This eliminates receiver self-signal saturation and enables simultaneous transmitting and receiving on the same frequency, with collocated transducers. The DOLPHIN technology is frequency and range independent – thus yielding flexible new approaches to underwater communications and sensors.

 

 

QNA and OSL have developed a unique way of using patented cancelation technology that will enable simultaneous transmit and receive (STAR) acoustic communications.  This technology will make it possible to create extensive undersea data and communication wireless networks, solving many challenges for Navy and commercial customers.

 

Dolphin benefits include:

  • Enables self-forming acoustic underwater networks to operate similarly to wireless land networks with nodes in motion
  • Multi-component control networks for fixed and mobile assets anywhere underwater
  • Frequency independence, allowing DOLPHIN Comms to be configurable on most systems and platforms (unmanned underwater vehicles, submarines, ships, etc.)
  • Full duplex communications greatly improves acoustic data transfer performance over other technology available today
  • Dolphin Comms STAR enables extremely low power communications

 

DOLPHIN Receive Rate vs. Conventional Receive Rates. Image: QinetiQ North America

Use of vector sensor receivers

A vector sensor is capable of measuring important non-scalar components of the acoustic field such as the wave velocity, which cannot be obtained by a single scalar pressure sensor. In recent decades, extensive research has been conducted on the theory and design of vector sensors. Many vector sensor signal processing algorithms have been designed. They have been mainly used for underwater target localization and sonar applications.

 

Earlier underwater acoustic communication systems have been relying on scalar sensors only, which measure the pressure of the acoustic field. Vector sensors measure the scalar and vector components of the acoustic field in a single point in space, therefore can serve as a compact multichannel receiver. This is different from the existing multichannel underwater receivers, which are composed of spatially separated pressure-only sensors, which may result in large-size arrays.

 

In general, there are two types of vector sensors: inertial and gradient. Inertial sensors truly measure the velocity or acceleration by responding to the acoustic medium motion, whereas gradient sensors employ a finite-difference approximation to estimate the gradients of the acoustic field such as velocity and acceleration.

 

The 1×3 single-input multiple-output (SIMO) vector sensor communications system, there is one transmitter pressure transducer, whereas for reception we use a vector sensor, which measures the pressure and the y and z components of the velocity. With more pressure transmitters, one can have a multiple-input multiple-output (MIMO) system also.

 

Adaptive modulation and coding in underwater acoustic communications by machine learning

Traditional UAC systems are generally equipped with a fixed set of physical layer (PHY) parameters, corresponding to a single modulation and coding scheme (MCS). However, underwater acoustic (UWA) channels are varying temporally and spatially. As a result, it is impossible for an UAC system to cope with a large variety of UWA channel dynamics well by only using one fixed MCS. To this end, the adaptive modulation and coding (AMC) technique has emerged to be an appealing avenue for UAC efficiency improvement through tracking channel dynamics and adaptively switching among a set of MCSs to achieve the most efficient transmission.

 

Unfortunately, in contrast to terrestrial wireless communications, UACs have to face several unique challenges caused by the undesirable UWA channel characteristics, such as the much more complex spatio-temporal channel variability, more severe multipath fading, and more limited bandwidth. As a result, the development of AMC in UACs is far behind its terrestrial-based counterpart.

 

So far, the underwater AMC researches have generally focused on the model-based methods. Unfortunately, although extensive efforts have been put on UWA channel modeling, there is not a general channel model yet that fits accurately in various practical scenarios  due to the high uncertainty and complexity of UWA channels. As such, those model-based AMC methods can be either insufficient or inaccurate in practical UAC scenarios. To address this problem, Researchers have  resorted to the data-driven machine learning (ML) technology to empower underwater AMC with intelligence, so as to offer immunity to channel modeling uncertainty and thus enabling flexible system optimization and sustainable performance improvement. The ML methods can make predictions or decisions from data observations without the aid of a specific model.

 

Software-Defined Underwater Acoustic Modems

Following developments in terrestrial radio systems, an upcoming concept for underwater acoustic modems is software-defined, possibly open-architecture, reconfigurable/user-programmable modems.

 

Historically, the development of reconfigurable, reprogrammable, and software-defined underwater acoustic modems has been driven by universities and research institutes, mainly because of the flexibility required by scientific experiments on underwater communications and networks. The first commercial modems to be available on the market basically served as serial link emulators, providing remote data telemetry via acoustic communications. Modem systems were mostly opaque to the end user, as their physical-layer (PHY) algorithms and bit-stream formats were hardcoded in the modem firmware.

 

More recently, lacking standardization of underwater communication protocols, the modem industry has been gradually opening their commercial off-the-shelf (COTS) systems to the (skilled) end user as well. This provides better opportunities for interoperability, among modems made by different manufacturers, and adaptivity, i.e., being able to switch protocols or parameter settings depending on an evolving environment or on changing application requirements.

 

More recent modems include at least some form of reconfigurability. A reconfigurable modem gives the user the chance to decide on some parameters by issuing commands that are recognized by the modem’s firmware. On-the-fly changes of the digital modulation’s bit rate and coding scheme at the PHY layer are a typical example of this reconfiguration capability.

 

However, the reconfiguration/reprogramming options for commercial modems today are typically limited, and any change requires a thorough knowledge of the specific hardware and software architecture. Therefore, an interesting next step would be the introduction of open-architecture/open-source modems, fostering the involvement and support of a global user community. This would boost the establishment of standards and, more importantly, their acceptance. Modem manufacturers could still make a profitable business, but their business model would change by focusing more on efficient hardware and proprietary high-performance algorithms.

 

Software-defined modems (sometimes briefly called softmodems) are a specific case of reprogrammable modems where the whole transmitter- and receiver-side processing chains are programmed in software. The recent development of open-source and open-architecture modems is a significant step toward making modem architectures more accessible to the end user.

 

Flexible/adaptive acoustic modems that are reprogrammable/reconfigurable at all layers of the communication stack, either by a user or by means of autonomous decisions, are considered as an important enabler for interoperability and cognitive networking in the underwater domain.

PDF] Design of A Software-defined Underwater Acoustic Modem with Real-time Physical Layer Adaptation Capabilities | Semantic Scholar

 

Smart Dust for Large Scale Underwater Wireless Sensing

Technologies exist for underwater communication using acoustic waves (sound) to carry data but this is a complex and demanding task requiring sophisticated processing. Hence these devices are expensive (£5-20k), bulky and power hungry which has generally limited their use to relatively small numbers and short duration. This has prevented the large scale deployment of sensor networks underwater despite huge demand for monitoring of subsea assets and the marine environment.

 

Pilot studies at Newcastle University have demonstrated the feasibility of producing underwater acoustic communication devices known as “nanomodems” which use novel approaches to signal processing to vastly reduce hardware complexity, size and cost. These have manufacturing cost as low as £50, very low receiver power consumption, to enable long life from small batteries, and tiny dimensions. However they can achieve data transfer and positioning capabilities found in much more expensive devices, over distances up to 1km through water.

 

Team at Newcastle University in the UK has developed ultra-low-cost acoustic ‘nanomodems’, which can send data via sound up to two kilometers for use in short-range underwater networks. Improving the ‘intelligence’ of each node in the network so it can discriminate useful data and minimize data packets would also increase the speed of transmission.

 

Novel contributions

  • Disruptive, low-cost technology enabling mass deployment with battery life of several years.
  • Large scale underwater monitoring (>100 devices) with high spatial resolution.
  • Rapid deployment and online data delivery (as opposed to data logging and collecting later).
  • Intelligent, adaptive sensing to maximise resource utilisation and fully exploit large scale.

 

With highly flexible sensor payload, the technology created may be applied to a wide range of monitoring tasks. However, the project will focus on three main demonstrator scenarios in close collaboration with industry & end users:

  • Subsea asset monitoring eg, condition of subsea cables, risers, seabed installations.
  • Marine environment / biodiversity monitoring – chemical or biological parameters.
  • Sensor nets for underwater security – detecting sound emitted or magnetic disturbances from underwater threats.

 

 

Acoustic communications market

AUVs for Ocean Data Collection Will Push Underwater Acoustic Communications to $2. 5 Bn by 2027. The projected growth in the market will be driven by continuous developments in wireless communication technologies that break through the water-air barrier. Underwater acoustic communication is a technique of sending and receiving messages below water. Underwater wireless communication has emerged to be important for marine military and commercial activities.

 

Underwater communication networks (UWCN) are rapidly growing to service submarine operations, AUVs, coastal surveillance systems, monitor autonomous oil-rigs, conduct ocean research & underwater exploration etc. AUVs are emerging into ubiquitous tools for ocean exploration and sampling. Acoustic transmission ranks primary among the technologies deployed given the ability of sound to travel far in water. Development in signal processing systems continues robustly with the newest being improvements in advanced frequency-hopping spread spectrum (FH-SS) for multimedia underwater acoustic communications. Carrying out communication underwater is a difficult task because of various factors such as strong signal attenuation manly over long ranges, multi-path propagation, small available bandwidth, and time variations of the channel.

 

Acoustic communication systems are intrinsically reliable, extremely sensitive, inherently rugged, and are competitively priced. Representing a vital technology, underwater acoustic communication is extensively employed for transmitting and receiving signals under water. While there are several commissioning methods for underwater acoustic communication, hydrophones are the most popular approach. Underwater communication involves various challenges like channel time variations, minimal present bandwidth, multi-path propagation and long-range signal attenuation, which can be mitigated using acoustic communication.

 

Over the years underwater wireless communication has emerged to be important for marine military and commercial activities. Underwater communication networks (UWCN) are rapidly growing to service submarine operations, coastal surveillance systems, monitor autonomous oil-rigs, conduct ocean research & underwater exploration etc. Acoustic transmission ranks primary among the technologies deployed given the ability of sound to travel far in water. Development in robust signal processing systems continues robustly with the newest being improvements in advanced frequency-hopping spread spectrum (FH-SS) for multimedia underwater acoustic communications. There is also growing R&D focus shed on resolving transmitted sound losses in underwater acoustic channels.

 

The increasing focus of companies on medium-range shallow water communication for scientific R&D and oil & gas exploration is expected to create a new market for these systems. The market growth is also favored by strong focus to gain insights into ocean depths for monitoring and controlling of commercial activities including underwater equipment dedicated to mineral and oil mineral extraction along with commercial fisheries and underwater pipelines. Increasing adoption of unmanned and autonomous underwater vehicles (UUVs and AUVs) with advanced sensors along with vehicles used for strategic applications related to surveillance and threat detection is another major driver. Another factor spurring growth is the increase in underwater exploration projects for environmental protection. These systems are witnessing growing popularity over traditional technologies for seabed mapping and data collection. Underwater acoustic communication is also finding increasing adoption as pre-warning system for underwater earthquakes or tsunamis and to monitor underwater pollution and habitat. There is a robust demand for high-capacity, reliable underwater acoustic networks to support R&D efforts intended to address issues related to transmission of data signals across shallow water regions.

 

 

References and Resources also include:

Wu, T.-C. et al. Blue Laser Diode Enables Underwater Communication at 12.4 Gbps. Sci. Rep. 7, 40480; doi: 10.1038/srep40480 (2017)

Underwater Acoustic Modems, Sandra Sendra, Member, IEEE, Jaime Lloret, Senior Member, IEEE, Jose Miguel Jimenez, and Lorena Parra, IEEE SENSORS JOURNAL, VOL. 16, NO. 11, JUNE 1, 2016 4063

https://www.york.ac.uk/electronic-engineering/research/communication-technologies/projects/smart-dust-underwater-wireless-sensing/

About Rajesh Uppal

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