Many system and signal related problems involve matrix inversion of some kind. For example, in estimation and signal recovery applications, inversion of the channel response matrix is often required in order to estima...
详细信息
ISBN:
(纸本)0819450782
Many system and signal related problems involve matrix inversion of some kind. For example, in estimation and signal recovery applications, inversion of the channel response matrix is often required in order to estimate the source signals. In the control of multivariable systems, inverting a process gain matrix may be called for in order to deliver appropriate control actions. There are situations where these matrices should be considered as uncertain (or random): for example, when the process/channel environments vary randomly, or when significant uncertainties are involved in estimating these matrices. Based on a unified approach, this paper considers both the right inversion (for control) and the left inversion (for estimation) of random matrices. In both cases, minimizing a statistical error function leads to the determination of optimal or linear optimal inversion. Connections with related techniques, such as the total least squares (TLS), the ridge regression, the Levenberg-Marquardt algorithm and the regularization theory are discussed. A variant Kalman filtering problem with randomly varying measurement gain matrix is among the applications addressed. Monte Carlo simulation results show substantial benefits by taking process/model uncertainty into consideration.
Concatenation of space-time (ST) coding with orthogonal frequency-division multiplexing (OFDM) has gained much interest recently. In this work, we derive the exact pairwise error probability (PEP) of space-frequency (...
详细信息
ISBN:
(纸本)0819450782
Concatenation of space-time (ST) coding with orthogonal frequency-division multiplexing (OFDM) has gained much interest recently. In this work, we derive the exact pairwise error probability (PEP) of space-frequency (SF) codes for MIMO OFDM Systems. Based on the exact PEP, we derive the tighter upper and lower bounds for the PEP. For asymptotically high SNRs, the design criteria for SF codes differ significantly from those for ST codes over flat fading channels. In this paper, by drawing an analogy between SF and ST codes, we show that when the number of receive antennas is large, the minimum Euclidean distance among code words dominates the performance of SF codes. Therefore, SF codes can be optimized by using the Euclidean-distance criterion valid for AWGN channels. Simulation results are given to show that the results valid for a number of receive antennas tending to infinity still provide correct indications when the number of antennas is small.
Highly sophisticated methods for detection and classification of signals and images are available. However, most of these methods are not robust to nonstationary variations such as imposed by Doppler effects or other ...
详细信息
ISBN:
(纸本)0819450782
Highly sophisticated methods for detection and classification of signals and images are available. However, most of these methods are not robust to nonstationary variations such as imposed by Doppler effects or other forms of warping. Fourier methods handle time-shift or frequency shift variations in signals or spatial shifts in images. A number of methods have been developed to overcome these problems. In this paper we discuss some specific approaches that have been motivated by time-frequency analysis. Methodologies developed for images can often be profitably used for fime-frequency analysis as well, since these representations are essentially images. The scale transform introduced by Cohen can join Fourier transforms in providing robust representations. Scale changes are common in many signal and image scenarios. We call the representation which results from appropriate transformations of the object of interest the Scale and Translation Invariant Representation or STIR. The STIR method is summarized and results from machine diagnosis, radar, marine mammal sounds, TMJ sounds, speech and word spotting are discussed. Some of the limitations and variations of the method are discussed to provide a rationale for selection of particular elements of the method.
Moving average filters are commonly used in industries for real-time processing of noisy data. Though they perform well in filtering out the noise, they introduce significant lag in the signal. The resulting peak valu...
详细信息
ISBN:
(纸本)0819450782
Moving average filters are commonly used in industries for real-time processing of noisy data. Though they perform well in filtering out the noise, they introduce significant lag in the signal. The resulting peak value of the filtered signal at the operating point is likely to be lower due to averaging of higher and lower peak signals in the averaging interval. The generalized moving average smoothing filter by Golay-Savitzky preserves the higher moments and does not suffer from the limitations imposed by the conventional moving average filter. The smoothing strategy is derived from least squares fitting of a lower order polynomial to a number of consecutive points. Due to polynomial curve fitting as opposed to a line fitting in the case of conventional moving average filter, this filter preserves the higher frequency components of the signal and their line width. This paper presents a generalized causal moving average filter deduced using the concepts in Golay-Savitzky smoothing filter for real-time applications. Golay-Savitzky filter is non-causal, relies on the future data that is not available, hence not suitable for real-time applications. Further, the designed causal filter makes use of the filtered data as opposed to the original data in the case of Golay-Savitzky. This approach allows us to conduct frequency response studies to evaluate the quality and the applicability of the filter for various signals in the aircraft engines and other engineering applications. Frequency response studies cannot carried out using the Golay-Savitzky filter. This paper also investigates the performance of various polynomial orders in-reproducing the signal from a noisy data. Some of the performance measures used are bandwidth, overshoots, and lags introduced by the filter. The mathematical technique to extract the signal and deduce the coefficients in off-line is also presented.
Despite the enhanced time-frequency analysis (TFA) detailing capability of quadratic TFAs like the Wigner and Cohen representations, their performance with signals of large dynamic range (DNR in excess of 40 dB) is no...
详细信息
Despite the enhanced time-frequency analysis (TFA) detailing capability of quadratic TFAs like the Wigner and Cohen representations, their performance with signals of large dynamic range (DNR in excess of 40 dB) is not acceptable due to the inability to totally suppress the cross-term artifacts which typically are much stronger than the weakest signal components that they obscure. AMTI and GMTI radar targets exhibit such high dynamic range when microDoppler is present, with the aspects of interest being the weakest components. This paper presents one of two modifications of linear TFA to provide the enhanced detailing behavior of quadratic TFAs without introducing cross terms, making it possible to see the time-frequency detail of extremely weak signal components. The technique described here is based on subspace-enhanced linear predictive extrapolation of the data within each analysis window to create a longer data sequence for conventional STFT TFA. The other technique, based on formation of a special two-dimensional transformed data matrix analyzed by high-definition two-dimensional spectral analysis methods such as 2-D AR or 2-D minimum variance, is compared to the new technique using actual AMTI and GMTI radar data.
In this paper, we present implementations of a pattern recognition algorithm which uses a RBF (Radial Basis Function) neural network. Our aim is to elaborate a quite efficient system which realizes real time faces tra...
详细信息
ISBN:
(纸本)0819445584
In this paper, we present implementations of a pattern recognition algorithm which uses a RBF (Radial Basis Function) neural network. Our aim is to elaborate a quite efficient system which realizes real time faces tracking and identity verification in natural video sequences. Hardware implementations have been realized on an embedded system developed by our laboratory. This system is based on a DSP (Digital signal Processor) TMS320C6x. The optimization of implementations allow us to obtain a processing speed of 4.8 images (240 x 320 pixels) per second with a correct rate of 95% of faces tracking and identity verification.
The Sensor-Angle Distribution (SAD) is a recently introduced tool representing the power arriving at each sensor as a function of angle (or spatial frequency). It can be used to characterize near-field scatter environ...
详细信息
ISBN:
(纸本)0819445584
The Sensor-Angle Distribution (SAD) is a recently introduced tool representing the power arriving at each sensor as a function of angle (or spatial frequency). It can be used to characterize near-field scatter environments. The SAD, as originally introduced, under-sampled the spatial correlation of the received signal (measured at each sensor) causing the SAD to be aliased for common source location cases. In this paper we indicate how this may be overcome. Additional results are provided showing that the SAD may be implemented as a multiple weighted subarray beamformer.
Multiple input multiple output (MIMO) communications have been a hot research area in recent years. Most literature makes the assumption that the channel information is not known at the transmitter but known perfectly...
详细信息
ISBN:
(纸本)0819445584
Multiple input multiple output (MIMO) communications have been a hot research area in recent years. Most literature makes the assumption that the channel information is not known at the transmitter but known perfectly at the receiver. We focus on the situation where both the transmitter and the receiver know the channel information. We consider a transmit diversity scheme that maximizes the signal to noise ratio at the receiver. We analyze its performance in terms of capacity, duality and asymptotic behavior. By simulation, we compare this scheme with Alamouti's transmit diversity to show the advantage of utilizing the channel side information to improve the performance of the wireless systems.
A new feature set and decision function are proposed for classifying transient wandering-tone signals. signals are partitioned in time and modeled as having piecewise-linear instantaneous frequency and piecewise-const...
详细信息
ISBN:
(纸本)0819445584
A new feature set and decision function are proposed for classifying transient wandering-tone signals. signals are partitioned in time and modeled as having piecewise-linear instantaneous frequency and piecewise-constant amplitude. The initial frequency, chirp rate, and amplitude are estimated in each segment. The resulting sequences of estimates are used as features for classification. The decision function employs a linear Gaussian dynamical model, or hidden Gauss-Markov model (HGMM). The parameters that characterize the HGMM for each class are estimated from labeled training sequences, and the trained models are used to evaluate the class-conditional likelihoods of an unlabeled signal. The signal is assigned to the-class whose model gives the maximum conditional likelihood. Simulation experiments demonstrate perfect classification performance in a three-class forced-choice problem.
暂无评论