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An acoustic wireless sensing network affronts environmental reverberations engendering spurious “image sources,” which can confuse the sensors. Against such “image sources,” this paper advances an acoustic source-localization algorithm adaptively constricting the warranted spatial domain in search for the physical source, thereby simplifying real-time computation. Furthermore, acoustic wireless sensing is complicated by sound's arbitrarily ultrawide frequency bands over arbitrarily transient durations, and the sound source could be positioned anywhere near or far from any sensing node. Such difficult-to-handle time–frequency unknown irregularities are mitigated (with computational simplicity) by this proposed scheme, which populates each sensing node with a tri-axial velocity sensor, whose array manifold is advantageously invariant of time, frequency, and range. This proposed scheme needs no prior information on the reverberant environment's space–time impulse response, no prior information on the physical source's frequency spectrum nor time–frequency signal structure. This computationally efficient scheme also bypasses any computationally burdensome pre- or post-processing procedures, such as frequency sub-banding, or temporal correlation filtering.

Conventional antenna-array beamforming along a selected direction cannot discriminate between different far-field subranges. To select a particular subrange in the far-field, a distance-specific multiantenna transmission architecture known as the frequency diverse array along with a matching receiver architecture known as equivalent transmit beamforming (ETB) has been recently proposed. However, these breakthroughs have not explicitly addressed the effects of target motion, which is ubiquitous in radar applications. These effects are analyzed in this article, leading to the conception of a range-direction-Doppler version of the “direction cosine” which is used to develop analytical formulas for the ETB beampattern characteristics in terms of the ETB system parameters along with insights for system design.

A cardioid sensor has a remarkably directional gain pattern of $[ \alpha + (1 - \alpha) \cos (\beta) ] \cos ^{k}(\beta)$, where $\alpha$ represents the sensor’s cardioidicity index, $k$ symbolizes the sensor’s directivity order, and $\beta$ refers to the incident angle with respect to the cardioid sensor’s axis. Cardioid microphones/hydrophones are commercially available through numerous manufacturers and widely adopted in workaday acoustics. Nonetheless, this letter is first in the open literature to consider any array comprising cardioid sensors of (possibly) different cardioidicity indices/orders. That is, this analysis pioneers the versatile possibility of $(k, \alpha)$ being different between the two cardioids that constitute the pair. These two foresaid cardioids are perpendicularly oriented, but spatially colocated in this article. First, their dissimilar orientations facilitate the estimation of an incident signal’s 2-D direction-of-arrival over the azimuth and the elevation, despite the deployment of simply two sensors. Second, their spatial colocation decouples the incident signal’s time/frequency dimensions from the signal’s azimuth/elevation directional dimensions, thus simplifying any subsequent signal-processing computations. This article will analytically explore such a pair’s azimuth/elevation direction-of-arrival estimation precision, as measured by the Cramér–Rao lower bound, producing action-able insights for the system engineer to choose what combination of $(k_{x}, \alpha _{x})$ and $(k_{y}, \alpha _{y})$ for the two cardioids forming the pair.