Skip to main content

Understanding Directivity: AVS vs Hydrophone Arrays


Directivity Comparison Between AVS Arrays and Hydrophone Arrays

Underwater acoustic sensing plays an important role in applications such as marine exploration, underwater communication, sonar systems, and target tracking. To detect acoustic signals in water and determine their direction of arrival, engineers commonly use sensor arrays.

Two widely used sensing technologies are hydrophone arrays and Acoustic Vector Sensor (AVS) arrays. Although both can detect underwater sound, their ability to determine the direction of incoming signals differs significantly.

In this article, we explore how these two sensing approaches compare in terms of directivity, which is a key parameter describing how well a sensor focuses on signals coming from a particular direction while suppressing signals from other directions.

Understanding Directivity

The performance of an acoustic sensor or array is often evaluated using a metric called directivity. In simple terms, directivity indicates how selective a sensor is with respect to the direction of incoming sound waves.

A highly directive sensor concentrates its sensitivity toward a specific direction and reduces interference from other directions. This property is extremely useful in underwater environments where background noise and reflections are common.

To quantify this behavior, we use a parameter known as the Directivity Factor (DF). It represents the ratio of the acoustic power received from a particular direction to the average power received from all possible directions.

DF = 4Ï€ |B(θ, φ)|² / ∫∫ |B(θ, φ)|² sinθ dθ dφ

In this expression:

  • θ (theta) represents the elevation angle
  • φ (phi) represents the azimuth angle
  • B(θ, φ) describes the beam pattern of the sensor or array

The beam pattern essentially describes how sensitive the sensor is to signals arriving from different spatial directions.

Engineers often convert the directivity factor into decibel form using the Directivity Index (DI), which makes it easier to compare systems.

DI = 10 log10 (DF)

For a simple omnidirectional hydrophone, sound is received equally from every direction. Because of this uniform response, its directivity factor is 1.

DIp = 0 dB

This means a single hydrophone does not provide any directional gain.

Directivity of Acoustic Vector Sensors (AVS)

Unlike hydrophones that measure only acoustic pressure, an Acoustic Vector Sensor measures both pressure and the particle velocity components of the acoustic wave.

Because particle velocity contains directional information, vector sensors are able to estimate the direction of incoming sound much more effectively than conventional pressure sensors.

To understand this behavior, consider a simple uniaxial sensor that measures only the x-component of the particle velocity vector.

B(θ, φ) = (sinθ cosφ)²

Using this beam pattern, the directivity factor becomes:

DF = 3 sin²Î¸ cos²Ï†

If the sensor measures velocity along other axes, similar expressions are obtained.

DFy = 3 sin²Î¸ sin²Ï†
DFz = 3 cos²Î¸
These equations indicate that velocity sensors can achieve a directivity factor up to three times greater than a standard omnidirectional hydrophone. This corresponds to an improvement of roughly 4.8 dB in directivity index.

However, the exact directivity depends on the arrival angle of the acoustic signal. For certain directions, the directivity may become lower than that of a pressure sensor.

Beam Pattern of a General Vector Sensor

A complete vector sensor measures acoustic pressure along with particle velocity in three orthogonal directions (x, y, and z). These measurements can be combined using weighting coefficients to produce a directional beam pattern.

B(θ, φ) = (wp + a(θ,φ)wx + b(θ,φ)wy + c(θ,φ)wz)²

The directional coefficients are defined as:

  • a(θ, φ) = sinθ cosφ
  • b(θ, φ) = sinθ sinφ
  • c(θ, φ) = cosθ

The parameters wp, wx, wy, and wz represent weights applied to the pressure channel and the velocity channels respectively.

When calculating the directivity factor, cross-terms appearing in the denominator cancel out because they involve sinusoidal integrals evaluated over complete cycles.

DF = (1 + a wx + b wy + c wz)² / (1 + wx² + wy² + wz²)

To obtain the maximum directivity, we assume the pressure weight wp = 1 and solve the resulting equations.

a(θ, φ)(3 + wx² + wy² + wz²) = wx(1 + a wx + b wy + c wz)
b(θ, φ)(3 + wx² + wy² + wz²) = wy(1 + a wx + b wy + c wz)
c(θ, φ)(3 + wx² + wy² + wz²) = wz(1 + a wx + b wy + c wz)

Solving these equations provides the optimal weighting values:

wx = 3a(θ, φ)
wy = 3b(θ, φ)
wz = 3c(θ, φ)

Substituting these values back into the directivity expression yields:

DF = 1 + 3(a² + b² + c²)

Since the direction cosines satisfy the identity:

a² + b² + c² = 1

the directivity factor becomes:

DF = 4

Finally, converting this to the directivity index gives:

DI = 10 log10(4) = 6 dB
This result shows that a single acoustic vector sensor that measures both pressure and particle velocity can achieve a maximum directional gain of 6 dB. Compared with a simple hydrophone, vector sensors provide significantly better directional sensitivity, making them highly useful for applications such as underwater communication, target localization, and sonar signal processing.


Contact Us

Name

Email *

Message *

Popular Posts

Design of CMOS Flip-Flops (SR, D, JK)

Design of CMOS Flip-Flops (SR, D, JK) A flip-flop or latch is a circuit with two stable states, used to store state information. It is the basic storage element in sequential logic and a fundamental building block in digital electronics systems, including computers and communication devices. Flip-flops and latches act as data storage elements for states, pulse counting, and synchronization of variably-timed input signals to a reference clock. Flip-flops can be transparent/opaque (latches) or clocked (synchronous, edge-triggered). Latches are level-sensitive, while flip-flops are edge-sensitive. In sequential logic, the output depends on current inputs and previous states. Fig.1 shows a sequential circuit combining a combinational block and a memory element. ...

Q-function in BER vs SNR Calculation (with Simulation)

Q-function in BER vs. SNR Calculation In digital communications and signal processing, the Q-function plays a significant role in predicting system reliability. It allows engineers to quantify the probability that Gaussian noise will exceed a specific threshold, causing a bit error. What is the Q-function? The Q-function is a mathematical function representing the tail probability of the standard normal (Gaussian) distribution. It is the complementary cumulative distribution function (CCDF) of a standard Gaussian distribution. Q(x) = (1 / √(2Ï€)) ∫â‚“∞ e^(-t² / 2) dt Q-Function Interactive Simulator Move the slider to see how the "Tail Probability" (the area in red) changes. This area represents the Probability of Error (BER) . Threshold Distance ( x ) — (Simulates Increasing SNR) x = 1.0 Q(x) = 0.1587 ...

BER vs SNR for M-ary QAM, M-ary PSK, QPSK, BPSK, ...(MATLAB Code + Simulator)

Bit Error Rate (BER) & SNR Guide Analyze communication system performance with our interactive simulators and MATLAB tools. 📘 Theory 🧮 Simulators 💻 MATLAB Code 📚 Resources BER Definition SNR Formula BER Calculator MATLAB Comparison 📂 Explore M-ary QAM, PSK, and QPSK Topics ▼ 🧮 Constellation Simulator: M-ary QAM 🧮 Constellation Simulator: M-ary PSK 🧮 BER calculation for ASK, FSK, and PSK 🧮 Approaches to BER vs SNR What is Bit Error Rate (BER)? The BER indicates how many corrupted bits are received compared to the total number of bits sent. It is the primary figure of merit f...

Pulse Width Modulation (PWM)

Pulse-width modulation (PWM), or pulse-duration modulation (PDM), is a method of controlling the average power delivered by an electrical signal.   Fig: An example of PWM in an idealized inductor driven by a blue line voltage source modulated as a series of sawtooth pulses, resulting in a red line current in the inductor.    Generating a PWM Signal The simplest way to generate a PWM signal is the intersection method, which requires only a sawtooth or a triangle waveform (easily generated using a simple oscillator) and a comparator. When the value of the reference signal is more than the modulation waveform, the PWM signal (magenta) is in the high state; otherwise, it is in the low state.      Duty cycle A low duty cycle equates to low power because the power is off for most of the time; the word duty cycle reflects the ratio of "on" time to the regular interval or "period" of time. The duty cycle is measured in percent, with 100% representing full o...

FFT Butterfly Method Explained (with Example of 4-point DFT)

  FFT Using Butterfly Method Given: x[n] = {0, 1, 2, 3} Step 1: Split into Even & Odd Even indices: x e = {0, 2} Odd indices: x o = {1, 3} Step 2: 2-point DFT For any {a, b}: DFT = {a + b, a - b} Even Part: E = {0+2, 0-2} = {2, -2} Odd Part: O = {1+3, 1-3} = {4, -2} Step 3: Combine Using Butterfly X[k] = E[k] + W k O[k] X[k + N/2] = E[k] - W k O[k] For N = 4: W 0 = 1 W 1 = -j Final Calculations X[0] = 2 + 4 = 6 X[2] = 2 - 4 = -2 X[1] = -2 + (-j)(-2) = -2 + 2j X[3] = -2 - (-j)(-2) = -2 - 2j Final Answer: X[k] = {6, -2 + 2j, -2, -2 - 2j} Try Interactive Online Simulations Interactive FFT Online Simulator (For understanding Fundamentals)  Interactive FFT Online Simulator (Analyze .CSV, .MP3, .MP4, etc. Further Reading Fourier Transform OFDM Return to Fourier Transform Main Page →

Channel Impulse Response (CIR) (with MATLAB + Simulator)

📘 Overview & Theory 📘 How CIR Affects the Signal 🧮 Online Channel Impulse Response Simulator 🧮 MATLAB Codes 📚 Further Reading What is the Channel Impulse Response (CIR)? The Channel Impulse Response (CIR) is a concept primarily used in the field of telecommunications and signal processing. It provides information about how a communication channel responds to an impulse signal. It describes the behavior of a communication channel in response to an impulse signal. In signal processing, an impulse signal has zero amplitude at all other times and amplitude ∞ at time 0 for the signal. Using a Dirac Delta function, we can approximate this. Fig: Dirac Delta Function The result of this calculation is that all frequencies are responded to equally by δ(t) . This is crucial since we never know which frequenci...

AM Modulation Online Simulator

Amplitude Modulation Simulator s AM (t) = A c [1 + k a m(t)] cos(ω c t) where, ω = 2πf & k a = Amplitude Sensitivity Modulation index, μ = k a A m Message Frequency (fm): Carrier Frequency (fc): Carrier Amplitude (Ac): Modulation Index (m = Am / Ac):

Online Simulator for ASK, FSK, and PSK

Interactive Digital Signal Processing (DSP) Tutorial and Simulator for ASK, FSK, and BPSK modulation techniques. Try our new Digital Signal Processing Simulator!   •   Interactive ASK, FSK, and BPSK tools updated for 2025. Start Now Digital Modulation Visualizer: ASK, FSK, & BPSK Simulator Learn and visualize binary modulation techniques (ASK, FSK, BPSK) in real-time with adjustable carrier and sampling parameters. Perfect for DSP students and engineers. 📡 ASK Simulator 📶 FSK Simulator 🎚️ BPSK Simulator 📚 More Topics ASK Modulator FSK Modulator BPSK Modulator More Topics 1. ASK (Amplitude Shift Keying) Simulat...