Analysis of MEMS-based Acoustic Particle Velocity Sensor 

for Transient Localization 

by Latasha Solomon, Leng Sim, and Jelmer Wind 

ARL-TR-5686 September 2011 

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Army Research Laboratory 

Adelphi, MD 20783-1197 

ARL-TR-5686 September 2011 

Analysis of MEMS-based Acoustic Particle Velocity Sensor 

for Transient Localization 

Latasha Solomon and Leng Sim 

Sensors and Electron Devices Directorate, ARL 

and 

Jelmer Wind

Microflown Technologies

Arnhem, The Netherlands 

Approved for public release; distribution unlimited. 
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Analysis of MEMS-based Acoustic Particle Velocity Sensor for Transient 

Localization 

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Latasha Solomon, Leng Sim, and Jelmer Wind

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13. SUPPLEMENTARY NOTES 

14. ABSTRACT 

The U.S. Army Research Laboratory (ARL) has made tremendous progress in the area of acoustic target tracking. Current 

systems are reliable and provide actionable situational awareness, but they are often bulky. In an effort to reduce size, weight, 

and power, ARL has evaluated the feasibility of replacing our current acoustic sensor with a three-dimensional (3-D) acoustic 

particle velocity sensor based on micro-electro-mechanical systems (MEMS) technology. This sensor has the potential to 

simplify sensor setup and maintenance, as well as reduce the computational requirements of the signal processing software. 

This effort validates specified frequency response, identifies sensor limitations and capabilities, and quantifies sensor 

localization accuracy. Experimental results conducted in both ARL’s anechoic chamber as well as outdoor environments are 

analyzed. 

15. SUBJECT TERMS

Acoustics, localization, MEMS, particle velocity 

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Latasha Solomon 

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iii

Contents 

List of Figures iv

1. Introduction 1

2. Sensor Specifications 1

3. Experimental Procedures 2

4. Conclusion 6

5. References 8

Distribution List 9
iv

List of Figures 

Figure 1. Microflown USP (ultimate sound probe). ........................................................................2

Figure 2. Microflown azimuth estimates; data taken in the anechoic chamber. ..............................3

Figure 3. Microflown elevation estimates; data taken in the anechoic chamber. ............................3

Figure 4. Firing positions for propane canon. .................................................................................4

Figure 5. Unprocessed data of AVS. ...............................................................................................4

Figure 6. Microflown localization results associated with propane cannon and impact 

hammer. .....................................................................................................................................5

Figure 7. Microflown localization results M9 5-round bursts; shooter position 1. .........................6

Figure 8. Microflown localization results M9; shooter position 2. ..................................................6
1

1. Introduction 

In an ongoing effort to reduce size, weight, and power, of current acoustic target tracking 

systems, the U.S. Army Research Laboratory (ARL) has evaluated the feasibility of replacing 

acoustic sensors with a three-dimensional (3-D) acoustic particle velocity sensor based on microelectro-mechanical systems (MEMS) technology. This report presents a number of tests of the 

Microflown sensor, performed at ARL, which have focused on the sensor’s accuracy in 

determining the direction of small caliber fire. The focus of earlier articles by de Bree, Wind, and 

others (1) has been on outlining possible applications, with limited sidesteps to experimental 

results. This report presents a large amount of experimental data and attempts to evaluate the 

strengths and weaknesses of the sensor. 

This report is built up as follows. The specifications of the sensor are considered in section 2. 

Section 3 goes over the experiments and the experimental results. In section 4, the report is 

summarized and conclusions are drawn. 

2. Sensor Specifications 

Microphones respond to acoustic waves by generating an electrical signal when a diaphragm 

vibrates in response to fluctuating air pressure. In contrast, the Microflown sensor, developed by 

Microflown Technologies, measures the velocity of air across two tiny, resistive strips of 

platinum that are heated to 220 °C. In acoustics, this movement of air is termed particle velocity. 

When air flows across the strips, the first strip cools down a little, and due to heat transfer, the air 

picks up some heat. Hence, the second strip is cooled down with the heated air, but cools down 

less than the first wire. A temperature difference occurs in the wires, which causes a difference in 

their electrical resistance. This causes a voltage difference that is proportional to the particle 

velocity and the effect is directional: when the direction of the airflow reverses, the temperature 

difference will reverse too. In the case of a sound wave, the airflow across the strips alternates in 

conjunction with the waveform and thus the corresponding alternating voltage (2). 

The self-noise of the Microflown sensor is –10 dB√Hz at 1 kHz, where 0 dB√Hz is the threshold 

of hearing (3). Nonlinearity is estimated to occur around 135 dB (3). The elements have been 

tested successfully in a wide temperature range, from –78.5 to 300 °C. A weakness of the 

Microflown sensor is its sensitivity to wind. Without wind caps, the standard probes are limited 

to 2 m/s, which definitely is not sufficient for outdoor conditions. The basic wind protection used 

in this report extends the range to about 10 m/s. Improved wind protection for the Microflown 

sensor is a topic of ongoing research. 
2

The Microflown Ultimate Sound Probe (USP) (figure 1) is an acoustic vector sensor (AVS). It 

contains a pressure microphone as well as three Microflown elements that are sensitive in 

perpendicular directions. The sound intensity vector is obtained by multiplying the scalar 

pressure by the particle velocity vector. This vector points away from the acoustic source, such 

that the direction of the source can be determined for every sample of incoming data with very 

little processing. The direction of arrival can be determined from 10 Hz to 10 kHz (3), which is 

wider than most other systems.

Figure 1. Microflown USP. 

3. Experimental Procedures 

Initial evaluation of the Microflown sensor was conducted in ARL’s anechoic chamber and 

various outdoor environments (with and without windscreens). The overall objective was to 

verify the specified frequency response, identify sensor limitations and capabilities, and quantify 

sensor localization accuracy. 

Figure 2 corresponds to data acquired in the ARL anechoic chamber. The sensor was mounted on 

a pan-tilt unit and rotated clockwise 360° with increments of 10°. A stationary transient sound 

source was located at the 0th position of the pan-tilt unit approximately 6 in away. Outliers 

centered around 0° are believed to be a direct effect of the internal motor on the pan-tilt unit. 

Results for elevation estimates, seen in figure 3, proved to be inaccurate and less consistent. As a 

result, elevation estimates for the outdoor experiments were not presented. 
3

Figure 2. Microflown azimuth estimates; data taken in the anechoic chamber. 

Figure 3. Microflown elevation estimates; data taken in the anechoic chamber. 

The next experiment was conducted outdoors with a propane cannon and impact hammer as the 

transient targets of interest. The propane cannon was moved to six different locations, indicated 

by p1–p6, over the duration of the test, with the furtherst location approximately 1 km away. It 

should be noted that the transient sources were located in a building structure for scenarios 5 and 

6. Figure 4 is a cartoon illustration, not drawn to scale, of the various firing positions for this 

field experiment.
4

 

Figure 4. Firing positions for propane canon. 

Figure 5 contains a sample of unprocessed data of the Microflown AVS for one scenario 

mentioned above. 

2150 2200 2250 2300 2350 -0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

Pressure

Time (s)

2150 2200 2250 2300 2350 -0.5

0

0.5

1

Particle Velocity - x

Time (s)

2150 2200 2250 2300 2350 -0.5

0

0.5

1

Particle Velocity - y

Time (s)

2150 2200 2250 2300 2350 -0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

Particle Velocity - z

Time (s)

Figure 5. Unprocessed data of AVS. 

Figure 6 is a plot of the truth data associated with the target locations versus that of the 

estimated azimuth from the Microflown senor. The truth data and estimated results are 

represented by the squares and diamonds, respectively. 

AVS 

p1

p2

p3

p4

p6 p5
5

Figure 6. Microflown localization results associated with propane cannon and impact hammer. 

For scenarios 1, 3, 4, and 6, the sensor performed fairly well at localizing on the target of interest 

with an approximate standard deviation of 6°. In scenario 2, where the distance from the sensor 

to the target was 1 km, accurate target tracking was impossible due to terrain features. Finally, 

scenario 5 appears to have poor results; however, it should be noted that the system consistently 

points in the same general direction of p6, that of the nearest opening, which is more than likely 

the direction of the loudest transient sound. Spurious detections can be attributed to nearby sound 

sources such as the closing of car doors. 

The sensors are currently manually aligned toward some known aiming point. It is highly likely 

that a bias is introduced in the azimuth and elevation calculations each time the sensor is aligned. 

Ideally, the sensor should be equipped with an inertial sensor to provide a more accurate heading 

reading. 

Figures 7 and 8 contain data from a live-fire experiment in a highly reverberant environment as 

the shooter fires the same weapon from two different locations. Analysis of both figures 

indicates a bias of approximately 2° and 7°, respectively, again thought to be related to 

alignment, as well as reverberation from a nearby structure. The shooter’s actual position is 

identified by the square and the estimated position with a diamond. The standard deviation of the 

estimated shooter location is approximately 0.3°. 

p1

p2

p3

p4

p5

p6
6

Figure 7. Microflown localization results M9 5-round bursts; shooter position 1. 

Figure 8. Microflown localization results M9; shooter position 2. 

4. Conclusion 

The Microflown sensor has verifiable application to both transient detection and broadband 

tracking applications. This technology will allow for fewer, lightweight, low-power deployed 

systems. These sensors can also potentially eliminate the need for computationally intensive 

signal processing techniques. One limitation is the sensor is sensitive to environmental effects 

such as moisture and dust. As a result, additional engineering to the packaging of the sensor is 

Shooter 

Position 

Estimated Position 

Reverberation 

Shooter 

Position 

Estimated Position 

Reverberation 
7

necessary prior to prolonged fielding of sensor. Other areas of future work include improving the 

elevation estimation either by modifying existing hardware or compensating for errors via 

software corrections, and developing intelligent signal processing techniques to eliminate 

reverberation. Wind noise may also pose a threat by degrading a signal of interest, particularly 

when it comes to signatures within the infrasonic range. Finally, an interesting and important 

topic of future research is to determine if the Microflown sensor can be used to resolve multiple 

simultaneous targets. 
8

5. References 

1. de Bree, H. E.; Wind, J. W.; Druyvesteyn, W. F.; te Kulve, H. Multi-purpose Acoustic 

Vector Sensors for battlefield acoustics. In Proceedings of the DAMA Conference, 2010. 

2. Microflown Technologies. Charting sound fields V1.0 2009-03 Manual: USP regular, April 

27, 2001. 

http://www.microflown.com/files/media/library/Manuals/manual_uspregular_v10.pdf 

(accessed July 2011). 

3. Yntema, D. R. An Integrated Three-Dimensional Sound-Intensity Probe. Ph.D. Thesis, 

University of Twente, 2008. 

www.microflown.com/files/media/library/books/phd_thesis_yntema.pdf (accessed July 

2011). 
9

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