Detection Rate Optimization for Swerling Targets in Gaussian Noise
In this paper, we consider a pulse radar and study the tradeoff between integration time and scan rate for diverse target scattering models. At the design stage, we optimize the available degrees of freedom (namely, pulse train length and detection threshold) so as to maximize the detection rate, de...
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Published in | IEEE transactions on aerospace and electronic systems Vol. 55; no. 4; pp. 2054 - 2065 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
New York
IEEE
01.08.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
ISSN | 0018-9251 1557-9603 |
DOI | 10.1109/TAES.2018.2882938 |
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Abstract | In this paper, we consider a pulse radar and study the tradeoff between integration time and scan rate for diverse target scattering models. At the design stage, we optimize the available degrees of freedom (namely, pulse train length and detection threshold) so as to maximize the detection rate, defined as the average number of detections from the target per unit of time, subject to a constraint on the false alarm rate, which is the average number of false alarms from the monitored area per unit of time. This objective function allows to carefully balance the contrasting needs for a large probability of detection (achievable through a large dwell time) and a short scan time. Closed-form solutions are provided for Swerling's Cases 1 and 3 target fluctuation and for the Marcum nonfluctuating model, while, for the Swerling's Cases 2 and 4, the solution is found with the aid of computer simulation. A thorough performance analysis is given to show the achievable tradeoffs among the principal system parameters under the different target models. |
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AbstractList | In this paper, we consider a pulse radar and study the tradeoff between integration time and scan rate for diverse target scattering models. At the design stage, we optimize the available degrees of freedom (namely, pulse train length and detection threshold) so as to maximize the detection rate, defined as the average number of detections from the target per unit of time, subject to a constraint on the false alarm rate, which is the average number of false alarms from the monitored area per unit of time. This objective function allows to carefully balance the contrasting needs for a large probability of detection (achievable through a large dwell time) and a short scan time. Closed-form solutions are provided for Swerling's Cases 1 and 3 target fluctuation and for the Marcum nonfluctuating model, while, for the Swerling's Cases 2 and 4, the solution is found with the aid of computer simulation. A thorough performance analysis is given to show the achievable tradeoffs among the principal system parameters under the different target models. In this paper, we consider a pulse radar and study the tradeoff between integration time and scan rate for diverse target scattering models. At the design stage, we optimize the available degrees of freedom (namely, pulse train length and detection threshold) so as to maximize the detection rate, defined as the average number of detections from the target per unit of time, subject to a constraint on the false alarm rate, which is the average number of false alarms from the monitored area per unit of time. This objective function allows to carefully balance the contrasting needs for a large probability of detection (achievable through a large dwell time) and a short scan time. Closed-form solutions are provided for Swerling's Cases 1 and 3 target fluctuation and for the Marcum nonfluctuating model, while, for the Swerling's Cases 2 and 4, the solution is found with the aid of computer simulation. A thorough performance analysis is given to show the achievable tradeoffs among the principal system parameters under the different target models. |
Author | Grossi, Emanuele Lops, Marco Venturino, Luca |
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SubjectTerms | Azimuth Computer simulation Design optimization Detection rate (DR) Dwell time false alarm rate (FAR) False alarms Monitoring Optimization Performance analysis Pulse radar Radar detection Random noise Random variables scan time surveillance Swerling targets Tradeoffs Variations |
Title | Detection Rate Optimization for Swerling Targets in Gaussian Noise |
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