SBIR-STTR Award

The project extends previous work which constructed a triaxial acoustic vector sensor prototype from a piezoelectric 3-d accelerometer combined with a microphone. The net density of the sensor approaches that of air by embedding the accelerometer in lightweight rigid foam, which also improves its resistance to wind noise and environmental effects. The APV+P sensor integrates acceleration to derive particle velocity, has well behaved low frequency phase response, and is not dependent on in-situ calibration even in reactive intensity fields. The portable sensor package is suitable for installation in airborne or land-based autonomous platforms. Phase 1 lays a foundation for low SWaP+C productization efforts by creating a parametric APV+P sensor model based on new and highly sensitive 3-d MEMS accelerometers. An associated acoustic source model will facilitate study of the expected sensor performance envelope for both continuous and impulsive stationary monopole point sources. A range of targeted MEMS devices will be investigated encompassing application dependent noise floors and frequency bandwidths. Performance of an APV+P sensor subject to wind noise will be investigated using the sensor model and archived measurement data. Phase 2 focuses on the development of prototypes integrated with Nanotok’s low SWaP+C sensor processing node, and linked to environmental monitoring.

The flow of acoustic energy is described by intensity, equal to the product of acoustic pressure and the velocity of the air moved by the acoustic field. A new triaxial acoustic particle velocity sensor measures the motion of a lightweight rigid volume, within which a MEMS accelerometer is embedded. Pressure is measured by a complementary MEMS microphone. Together, these MEMS sensors facilitate accurate measurement of acoustic intensity spanning the infrasonic (<1 Hz) to mid (>1.5 kHz) frequency bands, with 100 dB of dynamic range. Benchtop calibration of the sensors is accomplished by an acoustic wave tube calibrator. The new Accelerometer-based Intensity Vector Sensor (AIVS, pronounced /?vs/) is combined with a battery operated, low SWaP sampling and processing system based on an IoT system-on-chip, and packaged in a 10 cm cube for use in the field. Each of these wireless nodes can be synchronized by wire or GNSS, such that a collection of AIVS in diverse positions can self-organize into a collaborating sensor network for applications related to sound field mapping, target detection and tracking, acoustic source location, and sound power estimation. To minimize power dissipation (averaging less than 20 mW), the processing architecture facilitates local detection based on time and frequency dependent intensity signatures, which limits use of the wireless subsystem to periods when acoustic activity exceeds the detection criteria. In lieu of real-time wireless transfer, local storage can be employed to capture events for later export or analysis. The AIVS node interfaces to commercial environmental and weather sensors, is weatherproof, wind protected, and sustains operation over a wide temperature range. AIVS nodes are delivered with software demonstrating how the system is used for multi-copter (drone) tracking. Upon detection of a passing drone, the triaxial measurements are combined to estimate the azimuth and elevation angles to the target. When operating in a small network and other nodes observe the same target, a network controller computes the 3-d position and direction of the detected target. The demonstration software is intended to facilitate adoption of the AIVS in other such multi-node detection and tracking scenarios. For very low frequency infrasound measurements, the AIVS occupies little space and for some applications a small AIVS network could replace large aperture microphone arrays. Because the accelerometer is embedded within a protective lightweight solid substrate, the sensor is much more resistant to wind noise than microphone systems. MEMS accelerometers are sensitive to DC, facilitating very good phase match between axes as well as between physically separated AIVS nodes.