2003Unpublished venueRequires access

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

Donald F. Gingras

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Abstract

Summary form only given, as follows. Most array processing schemes rely on the use of a signal replica correlated with the 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, which are used to form the matched field signal replica, is imprecise, this causes a mismatch between the replica and the actual signal and the performance of the processor may be seriously degraded. Analytic methods for predicting the sensitivity of matched field processors to replica mismatch are developed. Bounds on the overall effect of mismatch are also 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 work provides one of the only analytical tools that can be used to assess the performance of such processors in the context of real-world system limitations.

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Summary form only given, as follows. Most array processing schemes rely on the use of a signal replica correlated with the 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, which are used to form the matched field signal replica, is imprecise, this causes a mismatch between the replica and the actual signal and the performance of the processor may be seriously degraded. Analytic methods for predicting the sensitivity of matched field processors to replica mismatch are developed. Bounds on the overall effect of mismatch are also 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 work provides one of the only analytical tools that can be used to assess the performance of such processors in the context of real-world system limitations.

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Available abstract

Summary form only given, as follows. Most array processing schemes rely on the use of a signal replica correlated with the 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, which are used to form the matched field signal replica, is imprecise, this causes a mismatch between the replica and the actual signal and the performance of the processor may be seriously degraded. Analytic methods for predicting the sensitivity of matched field processors to replica mismatch are developed. Bounds on the overall effect of mismatch are also 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 work provides one of the only analytical tools that can be used to assess the performance of such processors in the context of real-world system limitations.

Key concepts: Replica, Computer science, Context (archaeology), Sensitivity (control systems), SIGNAL (programming language), Field (mathematics), Signal processing, Electronic engineering

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