Methods for predicting the sensitivity of matched-field processors to mismatch

Abstract

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.

Adaptive matched field processing in an uncertain propagation environment

“…The sensitivity of various adaptive and non-adaptive processors to model mismatch has been analyzed extensively [13,14,15].…”

Performance bounds on matched-field methods for source localization and estimation of ocean environmental parameters

“…There have been numerous studies in analyzing the mismatch effects [31,70,34,30,65] as well as reducing the sensitivity of matched-field methods to mismatch [46,67,28]. 2 In general, the effects of mismatch include two aspects.…”

“…Second, it shifts the peak away from the correct position, leading to biased estimates. Most current research [31,70,34,30] investigates, through simulations or some quantitative measures, how the peak output of a source localization algorithm is deteriorated in the presence of mismatch in individual environmental/system parameters. However, few results are reported regarding how a performance measure in parameter estimation (e.g., bias or mean square error) can be determined given the size of mismatch.…”

VLF source localization with a freely drifting acoustic sensor array

“…A "mismatch" occurs between the measured data and the modelled pressure field, and the performance of the MFP is degraded and leads to errors in the estimation of the source location [ToWstoy, 1989;Feuillade et. al., 1989;Hamson and Heitmeyer, 1989;Gingras, 1989;Daugherty and Lynch, 1990].…”