The acoustic vector-sensor is a practical and versatile sound-measurement system, for applications in-room, open-air, or underwater. Its far-field measurement model has been introduced into signal processing over a decade ago; and many direction-finding algorithms have since been developed for acoustic vector-sensors, but only for far-field sources. Missing in the literature is a near-field measurement model for the acoustic vector-sensor. This correspondence fills this literature gap.

A new scheme is herein proposed to localize an acoustic source. This new method blends the "received signal strength indication" (RSSI) approach of geolocation, and the acoustic vector-sensor (AVS) (a.k.a., vector-hydrophone) based direction-finding (DF). Unlike customary RSSI-based source-localization, this proposed approach needs only two (not three or more) passive anchor-nodes: 1) one pressure-sensor, and 2) one physically compact triad of three (collocating, but orthogonally oriented) acoustic velocity-sensors. The latter can estimate the direction-of-arrival (DOA) of an emitter, regardless of that emitter's arbitrary/unknown center-frequency, bandwidth, spectrum, and near-field/far-field location. This triad's DOA estimates can be "distributed processed," locally, apart from the pressure-sensor's measured power, to estimate the emitter's radial distance. This proposed algorithm is noniterative, requires no initial estimate, is closed form, and can accommodate any prior known propagation-loss exponent.

Proposed in this paper is a wireless sensor network source localization algorithm in the three-dimensional space. By using the measurements of the received signal strength (RSS), two closed-form none-ambiguous estimators are proposed to geometrically locate the source position under the shadowing noise in the wireless channel. The dynamic adjustment scheme has been applied in the localization algorithm to combat the measurements error in the ranging. With multiple measurements of the RSS, a lower bound of the source localization error is also derived. The numerical simulations show that the proposed estimators outperform the conventional least squares estimator, especially under the adverse scenario when the reference sensor nodes are close to be coplanar. Comparing with the maximum likelihood estimator that approaches the localization lower bound, the proposed estimators result in significantly lower computational burden, but without showing much degradation of localization accuracy.

¿Geometric modeling¿ idealizes the spatial geometric relationships among the transmitter, the scatterers, and the receiver in a wireless propagation channel - to produce closed-form formulas of various channel-fading metrics (e.g., the distribution of the azimuth angle-of-arrival of the arriving multipaths). Scattered in the open literature are numerous such ¿geometric models,¿ each advancing its own closed-form formula of a fading metric, each based on a different idealization of the spatial geometry of the scatterers. Lacking in the open literature is a comprehensive and critical comparison among all such single-cluster geometric-model-based formulas of the arriving multipaths' azimuth direction-of-arrival distribution. This paper fills this literature gap. The comparison here uses all empirical data legibly available in the open literature for landmobile wireless radiowave propagation. No one geometric model is best by all criteria and for all environments. However, a safe choice is the model with a Gaussian density of scatterers centered at the transmitter. Despite this model's simplicity of having only one degree of freedom, it is always either the best fitting model or offers an LSE within one third of an order-of-magnitude as the best fitting model for all empirical dataset of all environments.

Herein investigated are computationally simple microphone-array beamformers that are independent of the frequency-spectra of all signals, all interference, and all noises. These beamformers allow the listener to tune the desired azimuth-elevation “look direction.” No prior information is needed of the interference. These beamformers deploy a physically compact triad of three collocated but orthogonally oriented velocity sensors. These proposed schemes’ efficacy is verified by a jury test, using simulated data constructed with Mandarin Chinese (a.k.a. Putonghua) speech samples. For example, a desired speech signal, originally at a very adverse signal-to-interference-and-noise power ratio (SINR) of −30 dB, may be processed to become fully intelligible to the jury.

Energy harvesting rates of sensors in renewable (e.g., solar energy) wireless sensor networks are not only lower than their energy consumption rates, but also temporally-varying. Existing studies exploited spatial data correlations among sensors to reduce their energy consumptions, where the data correlations mean that the sensing data of nearby sensors have high similarities. They assumed that the sensing data of nearby sensors are very likely to highly correlated. They adopted a coarse-grained spatial-correlation model, in which sensors are partitioned into different clusters, such that the sensors in the same cluster have high data similarities with each other. Then, only the sensor with the maximum residual energy in each cluster sends its sensing data, while the other sensors do not. We however notice that the data similarities among nearby sensors in real sensor networks may vary significantly, i.e., ranging from very similar to not similar at all. Since existing algorithms require that the sensors in the same cluster have high data similarities with each other, the sensors in a network may be partitioned into many clusters and each cluster consists of only a few sensors, where two nearby sensors belong to two different clusters if the sensing data of the two sensors are not highly correlated. Therefore, in the existing studies, many sensors have to send all their data as there are many clusters. Unlike the existing studies, in this paper we first propose a fine-grained spatial correlation model, in which sensors are partitioned into only a few clusters and each cluster consists of many sensors. Then, each cluster master sensor sends all its data to the sink, while the majority of other sensors in the cluster transmit only their nonredundant data, thereby significantly saving sensor energy consumptions. We formulate a novel sensor clustering problem under the proposed model, which is to partition sensors into different clusters and choose a representative sensor for each cluster, such that the amount of suppressed redundant data transmissions is maximized. We propose a randomized (0.5-)-approximation algorithm for the clustering problem, where is a given constant with 0<≤0.5. To further reduce sensor energy consumption, we consider temporal data correlations, where the sensing data by a sensor in a short period are likely to be highly correlated. We investigate a data utility maximization problem that allocates sensor data rates and routing, so that the accumulative utility of both spatially and temporally correlated data received by the sink is maximized. We devise a near-optimal algorithm for the problem. We finally evaluate the performance of the proposed algorithms through experiments. Experimental results show that the proposed algorithms are very promising.