Blind equalization schemes are used to cancel the effects of a channel on the received signal when the transmission of a training sequence in a predefined time slot is not possible. In the absence of a training sequence, blind equalization schemes can also increase the throughput of the overall system. A general problem with blind adaptation techniques is that they have poor convergence properties compared to the traditional techniques using training sequences. Having a multi-modal cost surface, blind adaptation techniques may force the equalizer to converge to a false minimum, depending on the initialization. The most commonly used blind adaptation algorithm is the Constant Modulus Algorithm (CMA). It is shown by simulation that a logarithmic error equation can make CMA converge to a global minimum, if a differential encoding scheme is used. The performance of CMA with different error equations is also investigated for different channel conditions. For a time varying channel, the performance of an equalizer not only depends on the convergence behavior but also on the tracking property, which indicates the ability of an equalizer to track changes in the channel. The tracking property of a blind equalizer with CMA has been investigated under different channel conditions. It is also shown that the tracking property of a blind equalizer can be improved by using a recursive linear predictor at the output of the equalizer to predict the amplitude of the equalizer output. The predicted value of the amplitude is then used to adjust the instantaneous gain of the overall system. A recursive linear predictor is designed to predict a colored signal without having a priori knowledge about the correlation function of the input sequence. The performance of the designed predictor is also investigated by predicting the envelope of a flat fading channel under constant mobile velocity and constant acceleration conditions. / Master of Science

This work is concerned with gunshot detection and recognition. Contains overview of published works and methods used in this field. Furthermore, it lists several commercial gunshot detectors. Binary gunshot detection and recognition algorithm is proposed which discriminates between gunshot and non-gunshot sounds occuring in nature. Algorithm is tested in Matlab. Proposed algorithm is implemented in TMS320C6713 digital signal processor, achieved results are compared.

ITC/USA 2015 Conference Proceedings / The Fifty-First Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2015 / Bally's Hotel & Convention Center, Las Vegas, NV / Over the past several years DoD imposed constraints on test deliverables, requiring objective measures of test results, i.e., statistically defensible test and evaluation (SDT&E) methods and results. These constraints force testers to employ statistical hypotheses, analyses and modeling to assess test results objectively, i.e., based on statistical metrics, analytical methods, probability of confidence complemented by, rather than solely on expertise, which is too subjective. In this and companion papers we discuss methods of objectifying testing. We employ an earth coordinate model and statistical modeling of telemetry (TM) tracking antenna employing time-space position information (TSPI) and derived statistical measures for tracking-error and auto-tracking mode. Test data were statistically analyzed via analysis of covariance (ANCOVA) which revealed that the antenna control unit (ACU) under test (AUT) does not track statistically identically, nor as practically or efficiently in C-band while receiving data carriers in both S- and C-bands. The conclusions of this paper add support to that hypothesis. In this third of three papers we use data from a range test, but make no reference to the systems under test as the purpose of this paper is to present an example of tools useful for employing a SDT&E methodology.