Serdar Tugac

Radar Target Detection Using Hidden Markov Models

Standard radar detection process requires that the sensor output is compared to a predetermined threshold. The threshold is selected based on a-priori knowledge available and/or certain assumptions. However, any knowledge and/or assumptions become inadequate due to the presence of multiple targets with varying signal return and usually non stationary background. Thus, any fixed predefined threshold may result in either increased false alarm rate or increased track loss. Even approaches where the threshold is adaptively varied will not perform well in situations when the signal return from the target of interest is too low compared to the average level of the background. Track-before-detect techniques eliminate the need for a detection threshold and provide detecting and tracking targets with lower signal-to-noise ratios than standard methods. However, although track-before-detect techniques eliminate the need for detection threshold at sensor’s signal processing stage, they often use tuning thresholds at the output of the filtering stage. This paper presents a Hidden Markov Model based target detection method that avoids any thresholding at any stage of the detection process. Moreover, since the proposed Hidden Markov Model method is based on the target motion models, the output of the detection process can easily be employed for maneuvering target tracking.

Hidden Markov Model based target detection

Standard tracking filters perform target detection process by comparing the sensor output signal with a predefined threshold. However, selecting the detection threshold is of great importance and a wrongly selected threshold causes two major problems. The first problem occurs when the selected threshold is too low which results in increased false alarm rate. The second problem arises when the selected threshold too high resulting in missed detection. Track-before-detect (TBD) techniques eliminate the need for a detection threshold and provide detecting and tracking targets with lower signal-to-noise ratios than standard methods. Although TBD techniques eliminate the need for detection threshold at sensors signal processing stage, they often use tuning thresholds at the output of the filtering stage. This paper presents a Hidden Markov Model (HMM) based target detection method for employing with TBD techniques which does not employ any thresholding.

Channel and source considerations of a bit-rate reduction technique for a possible wireless communications system's performance enhancement

In wireless commercial and military communications systems, where bandwidth is at a premium, robust low-bit-rate speech coders are essential. They operate at fix bit rates and those bit rates cannot be altered without major modifications in the vocoder design. A novel approach to vocoders, in order to reduce the bit rate required to transmit speech signal, is proposed. While traditional low-bit-rate vocoders code original input speech, the proposed procedure operates on the time-scale modified signal. The proposed method offers any bit rate from 2400 b/s to downwards without modifying the principle vocoder structure, which is the new NATO standard, Stanag 4591, Mixed Excitation Linear Prediction (MELP) vocoder. We consider the application of transmitting MELP-encoded speech over noisy communication channels by applying different modulation techniques, after time-scale compression is applied. Three different time-scale modification algorithms have been evaluated and waveform similarity overlap and add (WSOLA) algorithm has been selected for time-scale modification purposes. Computer simulation results, both source and channel, are presented in terms of objective speech quality metrics and informal subjective listening tests. Design parameters such as codec complexity and delay are also investigated. Simulation results lead to a possible wireless communications system, whose performance might be enhanced by using the spared bits offered by the procedure.

Multi-channel uniform line array processing for submarine sonar applications

Detection, classification and localization (DCL) are the essential functions of modern sonar systems. Once the bearing of a source is estimated, it is tracked continuously in bearing and its spectral information is used for classification purposes. A continuous and reliable DCL method is an important component of the underwater operations. In this paper; data of a multi-channel uniform line flank arrays broadband, narrowband and demon analysis are made. Localization is made possible by broadband analysis. The data needed for the classification is collected by narrowband and demon analysis. The method proposed in this method consists of four steps: Array interface, signal processing, analysis and pixellate. The proposed method is tested successfully with a simulator scenario.

Hidden Markov based target detection for track-before-detect

A tracking filter using conventional methods perform target detectios by setting a threshold at the output of the sensors signal processor. The result of using thresholding brings out that, targets with low signal-to-noise ratio (SNR) may not be detected if the signal power is below the required threshold. Track-before-detect (TBD) techniques avoid the usage of a detection threshold and provide detecting and tracking targets with lower signal-to-noise ratios. Although TBD techniques eliminate the need for detection threshold at sensors signal processing stage, they often use tuning thresholds at the output of the filtering stage. This paper presents a Hidden Markov Model (HMM) based target detection method for employing with TBD techniques which does not use any of the thresholding method.

Bit stream based wireless speech recognition using mixed excitation linear prediction (MELP) vocoder

Wireless speech recognition techniques have been the focal point of considerable research activity in the last decade. Most of the previous research activity assumes that the communication channel is only large enough to transmit either speech coding or speech recognition parameters. A bit-stream based front-end for a wireless speech recognition system that operates on a mixed excitation linear prediction (MELP) vocoder is presented. Speaker dependent, isolated word recognition accuracies obtained from conventional and bit-stream based front-end systems are obtained and their statistical significance is discussed. The recognition accuracies prove that the bit-stream based front end gives comparable performance to that obtained with the original input speech and is superior to the recognizer obtained from low bit rate decoded speech. The proposed systems performance in wireless channel conditions is also evaluated, via simulations, in order to observe adverse channel effects; these simulation results prove that the bitstream based front-end is useful for wireless systems.

Channel and source considerations of a MELP derived vocoder operating at reduced bit rates

Abstract Robust low bit rate speech coders are essential in commercial and military communication systems. They operate at fixed bit rates and those bit rates cannot be altered without major modifications in the vocoder design. In this paper we introduce a scaled speech coder, which operates on time-scale modified input speech. The proposed method offers any bit rate from 2400 b/s to downwards without modifying the principle vocoder structure, which is the mixed excitation linear prediction (MELP) vocoder. We consider the application of transmitting MELP-encoded speech over noisy communication channels after time scale compression is applied. Computer simulation results, both source and channel, are presented in terms of objective speech quality metrics and informal subjective listening tests. A statistical tool called bootstrap is also used to determine the accuracy of these test results. Design parameters such as codec complexity and delay are also investigated.