TY - GEN
T1 - Robust decision making method for adaptive ordered-statistics CFAR technique using information elasticity
AU - Liu, Andrew Z.
AU - Narayanan, Ram M.
AU - Rangaswamy, Muralidhar
N1 - Funding Information:
This work was supported by the US Air Force Office of Scientific Research Grant (POC: R.D. Riecken).
Publisher Copyright:
© 2109 SPIE.
PY - 2019
Y1 - 2019
N2 - Some performance characteristics of ordered-statistics CFAR (OS-CFAR), such as probability of false alarm (PFA) and probability of detection (PD), are controlled by many parameters. Some of these parameters can be considered decision parameters, since they are user defined to achieve a fixed PFA, and an optimized PD. However, other parameters that control these probabilities have the tendency to fluctuate based on the radar environment and operating conditions. These environmental variables can sometimes be difficult to predict and may affect performance. In this paper, a robust decision making method is presented, which selects decision parameters that provide robust performance even in the presence of these variations. The relevant environmental variables investigated in this paper are the number of interfering targets within the detection window and the signal-to-noise ratio (SNR). The Forward Automatic Order Selection Ordered Statistics Detector (FAOSOSD) is used to provide an estimate for the number of interfering targets, and the accuracy of this estimate is observed as a function of SNR. The proposed method defines a performance metric and observes its mean and variance over the uncertain parameter SNR. A trade-off behavior is shown between this mean and variance, and using information elasticity analysis, a decision is selected.
AB - Some performance characteristics of ordered-statistics CFAR (OS-CFAR), such as probability of false alarm (PFA) and probability of detection (PD), are controlled by many parameters. Some of these parameters can be considered decision parameters, since they are user defined to achieve a fixed PFA, and an optimized PD. However, other parameters that control these probabilities have the tendency to fluctuate based on the radar environment and operating conditions. These environmental variables can sometimes be difficult to predict and may affect performance. In this paper, a robust decision making method is presented, which selects decision parameters that provide robust performance even in the presence of these variations. The relevant environmental variables investigated in this paper are the number of interfering targets within the detection window and the signal-to-noise ratio (SNR). The Forward Automatic Order Selection Ordered Statistics Detector (FAOSOSD) is used to provide an estimate for the number of interfering targets, and the accuracy of this estimate is observed as a function of SNR. The proposed method defines a performance metric and observes its mean and variance over the uncertain parameter SNR. A trade-off behavior is shown between this mean and variance, and using information elasticity analysis, a decision is selected.
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U2 - 10.1117/12.2519677
DO - 10.1117/12.2519677
M3 - Conference contribution
AN - SCOPUS:85072614200
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Radar Sensor Technology XXIII
A2 - Ranney, Kenneth I.
A2 - Doerry, Armin
PB - SPIE
T2 - Radar Sensor Technology XXIII 2019
Y2 - 15 April 2019 through 17 April 2019
ER -