Current Issue : July - September Volume : 2016 Issue Number : 3 Articles : 4 Articles
An important characteristic of a cognitive radar is the capability to adjust its transmitted waveform to adapt to the\nradar environment. The adaptation of the transmit waveform requires an effective framework to synthesize\nwave forms sharing a desired ambiguity function (AF). With the volume-invariant property of AF, the integrated\nsidelobe level (ISL) can only be minimized in a certain area on the time delay and Doppler frequency shift plane. In this\npaper, we propose a new algorithm for unimodular sequence to minimize the ISL of an AF in a certain area based on\nthe phase-only conjugate gradient and phase-only Newton�s method. For improving detection performance of a\nmoving target detecting (MTD) radar system, slow-time ambiguity function (STAF) is defined, and the proposed\nalgorithm is presented to optimize the range-Doppler response. We also devise a cognitive approach for a MTD radar\nby adaptively altering its sidelobe distribution of STAF. At the simulation stage, the performance of the proposed\nalgorithm is assessed to show their capability to properly shape the AF and STAF of the transmitted waveform....
Traditional multiple kernel dimensionality reduction models are generally based on graph embedding and manifold assumption.\nBut such assumption might be invalid for some high-dimensional or sparse data due to the curse of dimensionality, which has\na negative influence on the performance of multiple kernel learning. In addition, some models might be ill-posed if the rank of\nmatrices in their objective functions was not high enough. To address these issues, we extend the traditional graph embedding\nframework and propose a novel regularized embedded multiple kernel dimensionality reduction method. Different from the\nconventional convex relaxation technique, the proposed algorithm directly takes advantage of a binary search and an alternative\noptimization scheme to obtain optimal solutions efficiently. The experimental results demonstrate the effectiveness of the proposed\nmethod for supervised, unsupervised, and semisupervised scenarios....
Microphones integrated on a seat belt are an interesting alternative to conventional sensor positions used for handsfree\ntelephony or speech dialog systems in automobile environments. In the setup presented in this contribution, the\nseat belt consists of three microphones which usually lay around the shoulder and chest of a sitting passenger. The\nmain benefit of belt microphones is the small distance from the talker�s mouth to the sensor. As a consequence, an\nimproved signal quality in terms of a better signal-to-noise ratio (SNR) compared to other sensor positions, e.g., at the\nrear view mirror, the steering wheel, or the center console, can be achieved. However, the belt microphone\narrangement varies considerably due to movements of the passenger and depends on the size of the passenger.\nFurthermore, additional noise sources arise for seat belt microphones: they can easily be touched, e.g., by clothes, or\nmight be in the path of an air-stream from the automotive ventilation system. This contribution presents several robust\nsignal enhancement algorithms designed for belt microphones in multi-seat scenarios. The belt microphone with the\nhighest SNR (usually closest to the speaker�s mouth) is selected for speech signal enhancement. Further improvements\ncan be achieved if all belt microphone signals are combined to a single output signal. The proposed signal\nenhancement system for belt microphones includes a robust echo cancelation scheme, three different microphone\ncombining approaches, a sophisticated noise estimation scheme to track stationary as well as non-stationary noise,\nand a speech mixer to combine the signals from each seat belt to a single channel output in a multi-seat scenario....
This paper presents the analytical derivation of joint probability density functions (pdfs) of the maximum likelihood\n(ML) estimates of a real and complex persymmetric correlation matrices (PCM) of multivariate Gaussian processes. It\nis oriented at the modifications of the classical Wishartââ?¬â?¢sââ?¬â??Goodmanââ?¬â?¢s pdfs adapted to the ML estimates of the data\nCMs in a wide class of signal processing (SP) problems in systems with centrally symmetric (CS) receive channels.\nThe importance of the derived modified pdfs for such CS systems could be as great as that of the classical\nWishartââ?¬â?¢sââ?¬â??Goodmanââ?¬â?¢s pdfs for systems with arbitrary receive channels. Some properties of the new obtained joint\npdfs are featured....
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