SAM-1.1

Probability of False Alarm Estimation in Oversampled Active Sonar Systems
Douglas A Abraham (University of Connecticut)

The probability of false alarm (Pfa) in active sonar systems is an important system performance measure. This measure is typically estimated by the proportion of alarms to opportunities over some finite window, essentially forming the sample exceedance distribution function (EDF). It is common for sonar systems to be `over-sampled'; that is, to have a sampling rate higher than the minimum required for representing the bandwidth of the received signal, resulting in reverberation data that are correlated from sample to sample. The performance of the sample EDF in Pfa estimation under such conditions is of interest. It is easily shown that the estimator remains unbiased with correlated data. However, it is shown in this paper that the variance of the estimator may be reduced from that for independent data by oversampling. Further, the variance is seen to fall between the Cramer-Rao lower bound based on independent thresholded (binary) data and that based on the complex matched filter output data.

SAM-1.2

Data Adaptive Constant False Alarm Rate Normalizer Design for Active Sonar and Radar
Donald W Tufts (University of Rhode Island), Edward C Real (Sanders, A Lockheed Martin Company)

We present a method for estimating threshold values for signal detection and classification systems in which a prescribed value of false alarm probability is needed. The threshold values are determined directly from observed test statistic data without knowledge of the probability distribution of the data. Our method uses the concept of tolerance intervals from nonparametric statistics.

SAM-1.3

Active Source Detection in a Dispersive Multiple Reflection Environment
Zoi-Heleni Michalopoulou (Department of Mathematical Sciences, New Jersey Institute of Technology, Newark, NJ 07102)

A signal propagating in a shallow water waveguide is subjected to (a) multiple reflections off the ocean boundaries and (b) distortion because of the dispersive properties of the propagation medium. Because of these corruptions, the received signal differs substantially from the transmitted signal. Although the transmission is sometimes exactly known, the received signal cannot be described in detail because of inadequate knowledge of the ocean impulse response. Ignoring the effects of the ocean on the signal, or representing them inaccurately, can lead to deterioration of the detection statistics. This paper compares the performance of methods designed for distortion-free, multiple-reflection transmission in realistic, dispersive environments. Two existing methods, the RCI processor and the simple source-receiver matched-filter, and a new detector are evaluated. The impact of distortion on signal transmission is assessed by comparing the distortion-free methods to the optimal processor, which models the effects of the propagation medium on the signal.

SAM-1.4

Transient Detection Using a Homogeneity Test
Biao Chen, Peter K Willett (University of Connecticut), Roy L Streit (Naval Undersea Warfare Center, Division Newport)

A simple yet effective statistic is proposed for detecting transient buried in partially unknown ambient noise. The transient model is the frequency scattered increased variance observations. We pose the transient detection problem as homogeneity test and the statistic is derived as the (generalized) likelihood ratio test of overdispersion when the underlying observation sequence follows a double exponential distribution. Numerical testing focuses on the comparison of this scheme with the CFAR power-law detector.

SAM-1.5

Estimating Range, Velocity, and Direction with a Radar Array
Aleksandar Dogandzic, Arye Nehorai (EECS Department, University of Illinois at Chicago)

We present maximum likelihood (ML) methods for active estimation of range (time delay), velocity (Doppler shift), and direction of a point target with a radar array in spatially correlated noise with unknown covariance. We consider structured and unstructured array response models and compute the Cramer-Rao bound (CRB) for the time delay, Doppler shift, and direction of arrival. We derive ambiguity functions for the above models and discuss the relationship between identifiability, ambiguity, and the Fisher information matrix.

SAM-1.6

The Effects of Signal-to-Noise Ratio Mismatch on Bayesian Matched-Field Source Localization Performance
Stacy L Tantum, Loren W Nolte (Department of Electrical Engineering, Duke University)

The signal-to-noise ratio of real data is rarely known with complete certainty. However, Bayesian matched-field processing techniques for ocean acoustic source localization often require the signal-to-noise ratio (SNR) to be known a priori. In this paper, the effects of SNR mismatch on the performance of a Bayesian matched-field source localization method, the optimum uncertain field processor [A. M. Richardson and L. W. Nolte, J. Acoust. Soc. Am. 89(5), 2280-2284 (1991)], are investigated. Theoretical and empirical analyses show that when the maximum a posteriori (MAP) estimate is utilized as the source location estimate, the localization performance is unaffected by the uncertainty regarding the SNR, provided that the assumed SNR is sufficiently high.

SAM-1.7

Cross-Product Algorithms for Source Tracking Using an EM Vector Sensor
Arye Nehorai (EECS Department, University of Illinois at Chicago), Petr Tichavsky (Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic)

We present two adaptive cross-product algorithms for tracking the direction to a moving source using an electromagnetic vector sensor. The first is a cross-product algorithm with a forgetting factor, for which we analyze the performance and derive an asymptotic expression of the variance of angular estimation error. We find the optimal forgetting factor that minimizes this variance. The second is a Kalman filter combined with the cross-product algorithm, which is applicable when the angular acceleration of the source is approximately constant.

SAM-2 >

Last Update: February 4, 1999 Ingo Höntsch