Current Issue : April - June Volume : 2015 Issue Number : 2 Articles : 6 Articles
In the past decade, many researchers have dedicated their efforts to exploring brain computer interface (BCI)\ntechnology. With a growing number of investigations into the BCI and its related areas, BCI systems nowadays are not\nonly developed for the disabled but also for the normal and healthy people. In this paper, a signal-processing-based\ntechnique with its applications into the development of automated character recognition is introduced. The task of the\npattern recognition to such a BCI design problem was mainly accomplished based on the detection of P300 evoked\npotentials. We approached this detection problem by employing a template-matching-based method to extract the\nmorphological information from EEG signals first, and then applying a linear discriminant function (LDF) to the features\nselected for pattern classification. The entire detection process was further implemented on an existing BCI system\nplatform, called the BCI2000 system. The algorithm performance was evaluated using an existing reliable database\nprovided by BCI Competition 2003. Numerical experimental results produced by the database indicated that the\nproposed algorithm actually achieved 100% character recognition accuracy....
Synthetic aperture imaging is a high-resolution imaging technique employed in radar and sonar applications, which\nconstruct a large aperture by constantly transmitting pulses while moving along a scene of interest. In order to avoid\nazimuth image ambiguities, spatial sampling requirements have to be fulfilled along the aperture trajectory. The latter,\nhowever, limits the maximum speed and, therefore, the coverage rate of the imaging system. This paper addresses\nthe emerging field of compressive sensing for stripmap synthetic aperture imaging using transceiver as well as\nsingle-transmitter and multi-receiver systems so as to overcome the spatial Nyquist criterion. As a consequence,\nfuture imaging systems will be able to significantly reduce their mission time due to an increase in coverage rate. We\ndemonstrate the capability of our proposed compressive sensing approach to at least double the maximum sensor\nspeed based on synthetic data and real data examples. Simultaneously, azimuth image ambiguities are successfully\nsuppressed. The real acoustical measurements are obtained by a small-scale ultrasonic synthetic aperture laboratory\nsystem....
This paper presents the design and performance evaluation of a reduced complexity algorithm for timing\nsynchronization. The complexity reduction is obtained via the introduction of approximate computing, which\nlightens the computational load of the algorithm with a minimal loss in precision. Timing synchronization for\nwideband-code division multiple access (W-CDMA) systems is utilized as the case study and experimental results\nshow that the proposed approach is able to deliver performance similar to traditional approaches. At the same time,\nthe proposed algorithm is able to cut the computational complexity of the traditional algorithm by a 20% factor.\nFurthermore, the estimation of power consumption on a reference architecture, showed that a 20% complexity\nreduction, corresponds to a total power saving of 45%....
A general echo model is derived for the synthetic aperture radar (SAR) imaging with high resolution based on the\nscalar form of Maxwell�s equations. After analyzing the relationship between the general echo model in frequency\ndomain and the existing model in time domain, a compressive sensing (CS) matrix is constructed from random partial\nFourier matrices for processing the range CS SAR imaging. Simulations validate the orthogonality of the proposed CS\nmatrix and the feasibility of CS SAR imaging based on the general echo model....
Multi-static passive radar (MPR) systems typically use narrowband signals and operate under weak signal conditions,\nmaking them difficult to reliably estimate motion parameters of ground moving targets. On the other hand, the\navailability of multiple spatially separated illuminators of opportunity provides a means to achieve multi-static\ndiversity and overall signal enhancement. In this paper, we consider the problem of estimating motion parameters,\nincluding velocity and acceleration, of multiple closely located ground moving targets in a typical MPR platform with\nfocus on weak signal conditions, where traditional time-frequency analysis-based methods become unreliable or\ninfeasible. The underlying problem is reformulated as a sparse signal reconstruction problem in a discretized\nparameter search space. While the different bistatic links have distinct Doppler signatures, they share the same set\nof motion parameters of the ground moving targets. Therefore, such motion parameters act as a common sparse\nsupport to enable the exploitation of group sparsity-based methods for robust motion parameter estimation. This\nprovides a means of combining signal energy from all available illuminators of opportunity and, thereby, obtaining\na reliable estimation even when each individual signal is weak. Because the maximum likelihood (ML) estimation of\nmotion parameters involves a multi-dimensional search and its performance is sensitive to target position errors,\nwe also propose a technique that decouples the target motion parameters, yielding a two-step process that\nsequentially estimates the acceleration and velocity vectors with a reduced dimensionality of the parameter search\nspace. We compare the performance of the sequential method against the ML estimation with the consideration\nof imperfect knowledge of the initial target positions. The Cram�©r-Rao bound (CRB) of the underlying parameter\nestimation problem is derived for a general multiple-target scenario in an MPR system. Simulation results are\nprovided to compare the performance of the sparse signal reconstruction-based methods against the traditional\ntime-frequency-based methods as well as the CRB....
Ship detection in heavy sea clutter is a big challenge for over-the-horizon (OTH) radar. Wideband signal is helpful for\nimproving range resolution and the signal-to-clutter ratio. In this paper, to support OTH radar employing wideband in\ncochannel interference, we propose environmental sensing-based waveform (ESBW) strategy, by considering transmit\nwaveform design as an active approach and cognitive loop for the time-varying environment. In ESBW strategy, OTH\nradar monitors the environment in real time, estimates interference characteristics, designs transmit waveform\nadaptively, and employs traditional signal processing structure to detect targets in the presence of interference. ESBW\noptimization problem employs the criteria of maximizing the output signal-to-interference-plus-noise ratio (SINR) of\nmatched filter and similarity constraint for reasonable range resolution and sidelobe levels. The analytic solution to\nthis constrained problem is developed, so that ESBW design algorithm�s efficiency is guaranteed, with adjustable SINR\nand autocorrelation function. A simulated scenario with strong interference and colored noise has been introduced.\nSimulation results demonstrate that OTH radar with ESBW strategy detects the target successfully in the background\nof cochannel interference...
Loading....