Mr. Andrew Z. Liu Profile

Andrew Z. Liu

at Pennsylvania State Univ

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Proceedings Article | 3 May 2019 Paper

Robust decision making method for adaptive ordered-statistics CFAR technique using information elasticity

Andrew Liu, Ram Narayanan, Muralidhar Rangaswamy

Proceedings Volume 11003, 1100308 (2019) https://doi.org/10.1117/12.2519677

KEYWORDS: Signal to noise ratio, Monte Carlo methods, Target detection, Statistical analysis, Computer simulations, Sensors, Detection and tracking algorithms, Radar

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Some performance characteristics of ordered-statistics CFAR (OS-CFAR), such as probability of false alarm (P_FA) and probability of detection (P_D), are controlled by many parameters. Some of these parameters can be considered decision parameters, since they are user defined to achieve a fixed P_FA, and an optimized P_D. 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.

Proceedings Article | 4 May 2018 Paper

Information elasticity in pseudorandom code pulse compression

Andrew Liu, Ram Narayanan, Muralidhar Rangaswamy

Proceedings Volume 10633, 106330F (2018) https://doi.org/10.1117/12.2305853

KEYWORDS: Radar, Modulation, Data modeling, Phase shift keying, Sensors, Data processing, Target detection, Systems modeling, Performance modeling

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Information elasticity is a new concept which characterizes the role of information in making effective decisions in sensor processing. Information elasticity is defined as the ratio of the fractional increase in decision effectiveness to the fractional increase in information. Increasing the quantity of information used in radar processing has the ability to decrease the performance of the radar in certain contexts, depending on what constraints and objectives exist. Because of this phenomenon (known as information overload), it is advantageous to find the optimal amount of information tailored to the specific context the radar is used in. This paper analyzes the process of finding the point of information overload using the information elasticity model. This model is used in observation of different contexts in pseudorandom code pulse compression. In this model, the length of the pseudorandom code represents the amount of information. Increasing this quantity affects both the quality of pulse compression and constraints of the system. We observe this relationship between the constraints and information quantity by developing constraint functions. In this paper, two decision metrics are created for pseudorandom pulse compression, the first based on the peak to side-lobe ratio and the second based on the detection region of the radar.

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