Signals In Additive White Gaussian Noise Research Articles

Current-to-Frequency Converter Based Photometer Circuit

In this article, the design of a photometer circuit based on a current-to-frequency converter is presented. This circuit is a piecewise linear circuit that makes the most of feedback to ensure a linear relationship between the input photocurrent and the output frequency. Here, Proteus simulations were used to verify the performance of the proposed circuit, and the electronic simulations and the experimental results were shown to be in total agreement. The experimental results showed that the proposed circuit rejected better additive white Gaussian noise signals than the classic photometer circuit based on the transimpedance amplifier. In addition, despite that the proposed circuit is more complex than the classic one, its high linearity, noise rejection, and easy implementation make it suitable for applications where measurement precision and noise rejection are of paramount importance.

Determination of Cramer-Rao Lower Bound (CRLB) and Minimum Variance Unbiased Estimator of a DC Signal in AWGN Using Laplace Transform

This paper presents an alternative approach for the determination of Cramer-Rao Lower Bound (CRLB) and Minimum Variance Unbiased Estimator (MVUE) using Laplace transformation. In this work, a DC signal in Additive White Gaussian Noise (AWGN) was considered. During the investigation, a number of experiments were conducted to analyze different possible outputs under different conditions, and then the patterns of the outcomes were studied. Finally closed-form expressions for the CRLB and MVUE were deduced employing the Laplace transformation. The resulting expressions showed that the proposed method has almost the same number of steps as the existing method. However, the latter requires only the knowledge of algebra to arrive at the CRLB expressions contrary to the existing approach where a strong mathematical background is required and hence making it superior over the existing method, in that sense.
 Keywords: Additive White Gaussian Noise, Cramer-Rao Lower Bound, DC-Value, Laplace transform, and Minimum Variance Unbiased Estimator (MVUE).

Determination of CRLB and Minimum Variance Unbiased Estimator of a DC Signal in AWGN Using Laplace Transform

This paper presents an alternative approach for the determination of Cramer-Rao Lower Bound (CRLB) and Minimum Variance Unbiased Estimator (MVUE) using Laplace transformation. In this work, a DC signal in Additive White Gaussian Noise (AWGN) was considered. During the investigation, a number of experiments were conducted to analyze different possible outputs under different conditions, and then the patterns of the outcomes were studied. Finally closed-form expressions for the CRLB and MVUE were deduced employing the Laplace transformation. The resulting expressions show that the proposed method has almost the same number of steps as the existing method. However, the later requires only the knowledge of algebra to arrive at the CRLB expressions contrary to the existing approach where a strong mathematical background is required and hence making it superior over the existing method, in that sense.

A Low-Complexity Iterative Phase Noise Tracker for Bit-Interleaved Coded CPM Signals in AWGN

This paper considers iterative detection of bit-interleaved coded continuous phase modulation in the presence of both phase noise (PN) and additive white Gaussian noise (AWGN). The proposed receiver iterates between a detection module and an estimation module. The detection module operates according to the sum-product algorithm and the factor graph framework in order to perform coherent maximum a posteriori bit detection in AWGN, using a PN estimate provided by the estimation module. The latter module, which results from the expectation-maximization algorithm for maximum likelihood estimation of the unknown PN samples, is implemented as a smoothing phase-locked loop that uses soft decisions provided by the detector. The separation between the detection and the estimation modules allows the use of an off-the-shelf (coherent) bit detector. The technique is further characterized by a very low computational complexity, a small error performance degradation and a small number of overhead symbols.

대역 확장을 통한 MP3 오디오의 음질 향상

In this paper, we investigate methods to enhance the perceptual quality of MP3-coded audios. Based on the high frequency reconstruction method by Liu, in the proposed method, we determine adaptively the starting point of high frequency reconstruction. We also present an improved linear estimation method. For high frequency component generation, we compare two methods. One is a replication of low-frequency components and the other is an insertion of additive white Gaussian noise signals. Through subjective tests, we shall show that the proposed method can improve the perceptual quality of MP3-coded audio.

Invariant tests for rapid-fluctuating radar signal detection with unknown arrival time

We study the rapid-fluctuating radar signal detection, where the arrival time of the received signal, i.e., the range cell index of the target, as well as the complex amplitude of the signal and the noise are unknown. We show that even in additive white Gaussian noise (AWGN) environment, uniformly most powerful invariant (UMPI) test does not exist. Instead, the UMPI detector in known SNR is used as an upper performance bound for performance evaluation of any invariant detector performance. In addition, we derive generalized likelihood ratio (GLR) detectors for this signal in AWGN with unknown noise variance and also in clutter with unknown covariance matrix. The GLR test statistic for AWGN represents the ratio of the maximum power over all cells to the total power. The GLRT for a clutter environment is also a power ratio which is the maximum over all range cells of the Euclidean norms of the spatially whitened observed sequence. Simulation results demonstrate that the performance of the proposed GLR test in AWGN is very close to the upper bound performance, i.e., to that of the UMPI test in known SNR.

Near-optimal signal detection for finite-state Markov signals with application to magnetic resonance force microscopy

Detection of a finite-state Markov signal in additive white Gaussian noise (AWGN) can be done in an intuitive manner by applying an appropriate filter and using an energy detector. One might not expect this heuristic approach to signal detection to be optimal. However, in this paper, we show that for a certain type of finite-state Markov signal, namely, the discrete-time (DT) random telegraph, this filtered energy detector is approximately optimal under the following conditions of: symmetric transition probabilities, low signal-to-noise ratio (SNR), long observation time, and small probability of transition between two consecutive time instances. When these last three conditions hold, but the transition probabilities are not symmetric, we show that a variant of the filtered energy detector is approximately optimal. It is also shown, under low SNR conditions, that the likelihood ratio test (LRT) for a finite-state DT Markov signal in AWGN reduces to the matched filter statistic with the minimum mean-squared-error (MMSE) predictor signal values used in place of the known signal values. Using this result, we propose a general methodology for obtaining an approximation to the LRT of a finite-state DT Markov signal in AWGN. Specifically, instead of the conditional mean (also MMSE) estimators, affine estimators with lowest mean squared error are used. This work is relevant to magnetic resonance force microscopy, an emerging technology that uses ultrasensitive force sensing to detect magnetic resonance. Sensitivity down to the single spin level was demonstrated in a recent experiment.