Experimental results on the estimation of both geometric and geoacoustic parameters in shallow water are presented. Genetic algorithms are used for estimation of the forward model parameters; the estimated parameters are then used by a standard Bartlett processor for source localization. A stationary source at a range of 5.6 km and a moving source at ranges from 5.8–7.7 km were successfully localized in range and depth using a single frequency Bartlett processor. The results indicate that global estimation of the forward model parameters significantly improves source localization performance.
The estimation of all forward model parameters-geometric, geoacoustic, and ocean sound speedby the inversion of acoustic field observations is considered. The data was taken at a mildly range-dependent shallow water site in the Mediterranean Sea. The inversion is based on data from a vertical array and carried out using information at multiple frequencies. Global optimization using a directed Monte Carlo search based on genetic algorithms and the Bartlett objective function is used. All geometric parameters are well determined, a range-dependent geoacoustic model is determined, and the ocean sound speed is estimated. Comparisons of the observed pressure field as a function of depth and the predicted field show good agreement. The use of observations at multiple frequencies provides considerable stability for the estimated parameters. Optimization of only geometric and geoacoustic parameters in a range-independent environment is found to be satisfactory at the lower frequencies ͑165-175 Hz͒, but for the higher frequencies ͑325-335 Hz͒ optimization of additional parameters by inclusion of either a range-dependent forward model or the ocean-sound-speed profile seems essential for successful inversion.
In coastal regions the presence of the marine boundary layer can significantly affect RF propagation. The relatively high specific humidity of the underlying "marine layer" creates elevated trapping layers in the radio refractivity structure. While direct sensing techniques provide good data, they are limited in their temporal and spatial scope. There is a need for assessing the three-dimensional (3-D) time-varying refractivity structure. Recently published results (Gingras et al.[1]) indicate that matched-field processing methods hold promise for remotely sensing the refractive profile structure between an emitter and receive array. This paper is aimed at precisely quantifying the performance one can expect with matched-field processing methods for remote sensing of the refractivity structure using signal strength measurements from a single emitter to an array of radio receivers. The performance is determined via simulation and is evaluated as a function of: 1) the aperture of the receive array; 2) the refractivity profile model; and 3) the objective function used in the optimization. Refractivity profile estimation results are provided for a surface-based duct example, an elevated duct example, and a sequence of time-varying refractivity profiles. The refractivity profiles used were based on radiosonde measurements collected off the coast of southern California.
Recent waveguide array processing methods have incorporated the physics of wave propagation as an integral part of the processing. Matched-field processing (MFP) refers to signal and array processing techniques in which, rather than a planewave arrival model, complex-valued (amplitude and phase) field predictions for propagating signals are used. Matchedfield processing has been successfully applied in ocean acoustics. In this paper, the extension of MFP to the electromagnetic domain, i.e., electromagnetic (EM) MFP (EM-MFP) is described. Simulations of EM-MFP in the tropospheric setting suggest that under suitable conditions, EM-MFP methods can enable EM sources to be both detected/localized and used as sources of opportunity for estimating the environmental parameters that determine EM propagation.
Most array processing schemes rely on the use of a signal replica correlated with the sensor observations to detect and localize targets of interest. Matched-field processors make use of signal replicas that are accurately tuned to available environmental knowledge. When knowledge about the array system, such as sensor positions, or environmental parameters, such as sound speed, is imprecise, this causes a "mismatch" between the replica and the ß observations. The performance of the processor may be seriously degraded by this mismatch. Analytic methods for predicting the sensitivity of the output power level to replica mismatch are developed, and bounds on the reduction in power level are developed. The use of these methods is illustrated through discussion of an example. Matched-field array processing methods can, in many situations, significantly improve target detection and localization performance. This article provides analytical tools which can be used to assess the performance of such processors in the context of real world system limitations.
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