ICASSP99 Detection, Classification, Localization, and Tracking

Detection, Classification, Localization, and Tracking
Home Full List of Titles 1: Speech Processing CELP Coding Large Vocabulary Recognition Speech Analysis and Enhancement Acoustic Modeling I ASR Systems and Applications Topics in Speech Coding Speech Analysis Low Bit Rate Speech Coding I Robust Speech Recognition in Noisy Environments Speaker Recognition Acoustic Modeling II Speech Production and Synthesis Feature Extraction Robust Speech Recognition and Adaptation Low Bit Rate Speech Coding II Speech Understanding Language Modeling I 2: Speech Processing, Audio and Electroacoustics, and Neural Networks Acoustic Modeling III Lexical Issues/Search Speech Understanding and Systems Speech Analysis and Quantization Utterance Verification/Acoustic Modeling Language Modeling II Adaptation /Normalization Speech Enhancement Topics in Speaker and Language Recognition Echo Cancellation and Noise Control Coding Auditory Modeling, Hearing Aids and Applications of Signal Processing to Audio and Acoustics Spatial Audio Music Applications Application - Pattern Recognition & Speech Processing Theory & Neural Architecture Signal Separation Application - Image & Nonlinear Signal Processing 3: Signal Processing Theory & Methods I Filter Design and Structures Detection Wavelets Adaptive Filtering: Applications and Implementation Nonlinear Signals and Systems Time/Frequency and Time/Scale Analysis Signal Modeling and Representation Filterbank and Wavelet Applications Source and Signal Separation Filterbanks Emerging Applications and Fast Algorithms Frequency and Phase Estimation Spectral Analysis and Higher Order Statistics Signal Reconstruction Adaptive Filter Analysis Transforms and Statistical Estimation Markov and Bayesian Estimation and Classification 4: Signal Processing Theory & Methods II, Design and Implementation of Signal Processing Systems, Special Sessions, and Industry Technology Tracks System Identification, Equalization, and Noise Suppression Parameter Estimation Adaptive Filters: Algorithms and Performance DSP Development Tools VLSI Building Blocks DSP Architectures DSP System Design Education Recent Advances in Sampling Theory and Applications Steganography: Information Embedding, Digital Watermarking, and Data Hiding Speech Under Stress Physics-Based Signal Processing DSP Chips, Architectures and Implementations DSP Tools and Rapid Prototyping Communication Technologies Image and Video Technologies Automotive Applications / Industrial Signal Processing Speech and Audio Technologies Defense and Security Applications Biomedical Applications Voice and Media Processing Adaptive Interference Cancellation 5: Communications, Sensor Array and Multichannel Source Coding and Compression Compression and Modulation Channel Estimation and Equalization Blind Multiuser Communications Signal Processing for Communications I CDMA and Space-Time Processing Time-Varying Channels and Self-Recovering Receivers Signal Processing for Communications II Blind CDMA and Multi-Channel Equalization Multicarrier Communications Detection, Classification, Localization, and Tracking Radar and Sonar Signal Processing Array Processing: Direction Finding Array Processing Applications I Blind Identification, Separation, and Equalization Antenna Arrays for Communications Array Processing Applications II 6: Multimedia Signal Processing, Image and Multidimensional Signal Processing, Digital Signal Processing Education Multimedia Analysis and Retrieval Audio and Video Processing for Multimedia Applications Advanced Techniques in Multimedia Video Compression and Processing Image Coding Transform Techniques Restoration and Estimation Image Analysis Object Identification and Tracking Motion Estimation Medical Imaging Image and Multidimensional Signal Processing Applications I Segmentation Image and Multidimensional Signal Processing Applications II Facial Recognition and Analysis Digital Signal Processing Education Author Index A B C D E F G H I J K L M N O P Q R S T U V W X Y Z	Probability of False Alarm Estimation in Oversampled Active Sonar Systems Authors: Douglas A Abraham, Page (NA) Paper number 1237 Abstract: 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. IC991237.PDF (From Author) IC991237.PDF (Rasterized) TOP Data Adaptive Constant False Alarm Rate Normalizer Design for Active Sonar and Radar Authors: Donald W Tufts, Edward C Real, Page (NA) Paper number 2243 Abstract: 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. IC992243.PDF (From Author) IC992243.PDF (Rasterized) TOP Active Source Detection in a Dispersive Multiple Reflection Environment Authors: Zoi-Heleni Michalopoulou, Page (NA) Paper number 1773 Abstract: 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. IC991773.PDF (From Author) IC991773.PDF (Rasterized) TOP Transient Detection Using a Homogeneity Test Authors: Biao Chen, Peter K Willett, Roy L Streit, Page (NA) Paper number 1715 Abstract: 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. IC991715.PDF (From Author) IC991715.PDF (Rasterized) TOP Estimating Range, Velocity, and Direction with a Radar Array Authors: Aleksandar Dogandzić, Arye Nehorai, Page (NA) Paper number 1090 Abstract: 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. IC991090.PDF (From Author) IC991090.PDF (Rasterized) TOP The Effects of Signal-to-Noise Ratio Mismatch on Bayesian Matched-Field Source Localization Performance Authors: Stacy L Tantum, Department of Electrical Engineering, Duke University (U.K.) Loren W Nolte, Department of Electrical Engineering, Duke University (U.K.) Page (NA) Paper number 1805 Abstract: 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. IC991805.PDF (From Author) IC991805.PDF (Rasterized) TOP Cross-Product Algorithms for Source Tracking Using an EM Vector Sensor Authors: Arye Nehorai, Petr Tichavsky, Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic (Czech Republic) Page (NA) Paper number 1401 Abstract: 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. IC991401.PDF (From Author) IC991401.PDF (Rasterized) TOP