Low Signal-to-noise Ratio Scenarios Research Articles

Direction of Arrival estimation by modified Orthogonal Propagator Method with linear prediction in low SNR scenarios

Abstract In propagator based methods for Direction of Arrival (DoA) finding, conventional approaches for eliminating the noise impact are based on signal enhancement or noise power estimation. In this paper, a new method is introduced from another point of view. When the noise is an additive spatially and temporally white Gaussian noise, theoretically, it only impacts the diagonal elements of the data covariance matrix. Firstly, the proposed method utilizes the principle of Linear Prediction (LP) to rebuild the denoised diagonal elements of the data covariance matrix. Then, the Orthogonal Propagator Method (OPM) is directly applied for DoA estimation. Compared with the conventional OPM-based methods, the proposed method is more robust to the noise, especially in low Signal to Noise Ratio (SNR) scenarios. Simulation examples are provided to demonstrate the performance of the proposed method.

Partial intersection sphere decoding with weighted voting for sparse rotated V-OFDM systems

Vector orthogonal frequency division multiplexing (V-OFDM) is a general system that builds a bridge between OFDM and single-carrier frequency domain equalization in terms of intersymbol interference level and receiver complexity. In this paper, we focus on a rotated V-OFDM system over a broadband sparse channel owing to its large time delay spread and fewer nonzero taps. In order to collect the multipath diversity, a simple rotation matrix is designed, independent of the number of subchannels at the transmitter. For the rotated V-OFDM receiver, a partial intersection sphere decoding with weighted voting method is proposed by exploiting the sparse nature of the multipath channel. The proposed receiver chooses the transmitted vector from the set with the maximum likelihood estimation generated using the partial intersection sphere decoding method. For an extreme case, such as when a candidate set is empty, which usually occurs at a low signal-to-noise ratio (SNR), an efficient weighted voting system is used to estimate the transmitted vectors symbol-by-symbol. Simulation results indicate that the proposed receiver improves the symbol error rate performance with reduced complexity, especially for low SNR scenarios.

Noise robust target identification based on the wave-coefficients-2dimension case

To correctly identify a remote target in a noisy environment is very challenging. Usually, the accuracy of target recognition degrades under the condition of a low signal-to-noise ratio (SNR). Radar target identification based on wave-coefficients (WCs) is proposed and seems to be promising. We introduce the WCs of two-dimensional (2D) targets in this paper. The main problem of WC based target identification is that the extraction of wave-coefficients is an ill-posed problem. Thus the recognition results yielded in the presence of noise is not reliable. The regularization algorithm is exploited to extract the wave-coefficients, the regularization parameter is determined by the L-curve method. Simulation results using 4 2D targets show that the proposed scheme is effective and high target recognition rate is achieved in cases of low SNR scenarios.

Joint optimal power allocation and relay location for decode‐and‐forward multi‐hop relaying over log‐normal channel

A joint optimisation of power allocation (PA) and relay location is proposed for decode-and-forward relaying over multi-hop, independent, log-normal fading channels. Optimisation is carried out at high signal-to-noise ratio (SNR) and low SNR scenarios to minimise the outage probability with a constraint on total transmit power and end-to-end relaying distance. End-to-end average bit error rate is also minimised assuming the same constraints. Optimisation is carried out to find optimal PA (OPA) for a given relay location, optimal relay location (ORL) for a given PA and joint OPA and relay location. All the optimisation problems reformulated from the original problems are shown to be convex except relay location optimisation minimising outage probability at low SNR scenario. It is then solved using properties of concave minimisation. Optimal allocation factors do not depend on the instantaneous channel state information. It also does not depend on total available power in the system except allocation factors evaluated by optimising outage at high SNR. It is observed that OPA allocates more power to weaker links and ORL allocates less distance to weaker links. It is also observed that performance improvement increases with increase in link unbalance and distance-based allocation is most effective.

Iterative Sparse Asymptotic Minimum Variance Based Approaches for Array Processing

This paper presents a series of user parameter-free iterative Sparse Asymptotic Minimum Variance (SAMV) approaches for array processing applications based on the asymptotically minimum variance (AMV) criterion. With the assumption of abundant snapshots in the direction-of-arrival (DOA) estimation problem, the signal powers and noise variance are jointly estimated by the proposed iterative AMV approach, which is later proved to coincide with the Maximum Likelihood (ML) estimator. We then propose a series of power-based iterative SAMV approaches, which are robust against insufficient snapshots, coherent sources and arbitrary array geometries. Moreover, to overcome the direction grid limitation on the estimation accuracy, the SAMV-Stochastic ML (SAMV-SML) approaches are derived by explicitly minimizing a closed form stochastic ML cost function with respect to one scalar paramter, eliminating the need of any additional grid refinement techniques. To assist the performance evaluation, approximate solutions to the SAMV approaches are also provided for high signal-to-noise ratio (SNR) and low SNR scenarios. Finally, numerical examples are generated to compare the performances of the proposed approaches with those of the existing ones.

A HIGH RESOLUTION DOA ESTIMATING METHOD WITHOUT ESTIMATING THE NUMBER OF SOURCES

The performance of high resolution subspace-based algorithms are particularly sensitive to the prior information of the source number, the Signal-to-Noise Ratio (SNR) and the snapshot. Although the existing direction-of-arrival (DOA) estimation methods without estimating the source number could eliminate the awful impact brought by incorrect source number estimation, yet its performance would get deteriorated by small snapshots and low SNR. Methods which exploit noise and signal subspaces information simultaneously, such as SSMUSIC, could provide a high resolution performance in such nonideal circumstances. However, its performance would degrade severely when the prior information of the source number is incorrect. To provide a DOA estimation method without estimating the number of source, which has a high resolution performance in small sample and low SNR scenario, using all information spreads in eigenvalues and eigenvectors, this paper reconstructs a new spatial spectrum which is very similar to the SSMUSIC algorithm. In order to enhance the robustness of the new method, we provide an empirical method to modify the eigenvalues to prohibit the spreading of noise eigenvalues caused by snapshot deflcient and low SNR. To verify the validity of the new method, comparisons with other algorithms are made in computer simulations and the measured data test.

Robust forward backward based beamformer for a general-rank signal model with real-valued implementation

A robust forward backward (FB) beamformer for an incoherently scattered general-rank signal model with real-valued implementation is proposed based on the uniform linear array (ULA) structure by introducing a preprocessing transformation matrix. With the preprocessing, the computational complexity is reduced significantly due to a real-valued close-form solution for the optimum weight vector; furthermore, the proposed algorithm can achieve a higher output SINR (signal-to-interference-plus-noise ratio) and has lower sensitivity to the diagonal-loading parameters especially for low input SNR (signal-to-noise ratio) scenarios.

Relay Scheduling in the Half-Duplex Gaussian Parallel Relay Channel

This study investigates the problem of communication for a network composed of two half-duplex parallel relays with additive white Gaussian noise (AWGN). There is no direct link between the source and the destination. However, the relays can communicate with each other through the channel between them. Two protocols, i.e., simultaneous and successive relaying, associated with two possible relay schedulings are proposed. The simultaneous relaying protocol is based on the Broadcast-Multiaccess with Common Message (BCM) scheme considered in . For the successive relaying protocol: (i) a non-cooperative scheme based on the Dirty Paper Coding (DPC) and (ii) a cooperative scheme based on the Block Markov Encoding (BME) are considered. The composite scheme of employing BME in at most one relay and DPC in at least another one is also proposed. It is proved that this scheme achieves at least the same rate when compared to the cooperative and non-cooperative schemes for the Gaussian case. A Simultaneous-Successive Relaying based on Dirty Paper Coding scheme (SSRD) is also proposed. The optimum scheduling of the relays, and hence the capacity of the half-duplex Gaussian parallel relay channel in the low and high signal-to-noise ratio (SNR) scenarios, is derived. In the low SNR scenario, it is revealed that under certain conditions for the channel coefficients the ratio of the achievable rate of the simultaneous relaying based on BCM to the cut-set bound tends to be 1. On the other hand, as SNR goes to infinity it is proved that successive relaying, based on the DPC, asymptotically achieves the capacity of the network.

차량에서의 음성인식율 향상을 위한 전처리 기법

본 논문에서는 차량에서의 자동 음성인식 시스템과 같이 신호대잡음비가 낮은 잡음 환경에서의 음성인식에 적합한 변형된 스펙트럼 차감법을 제안한다. 기존의 스펙트럼 차감법은 스펙트럼에서 낮은 신호대 잡음비(SNR)를 갖는 부분은 감쇄되고, 신호대잡음비가 높은 부분은 강조되는 신호대잡음비에 의존한다. 그러나 이러한 구성은 높은 신호대잡음비를 갖는 환경에서는 적절하나 차량 환경과 같이 낮은 신호대잡음비를 나타내는 환경에서는 매우 부적절하다. 제안하는 방법은 낮은 신호대잡음비를 갖는 잡음 환경을 위해 음성우세영역을 강조하여 불필요하게 음성영역이 과차감되지 않도록 방지한다. 차량용 음성명령어 어휘를 대상으로 한 실험 결과에서 제안하는 방법이 기존의 방법에 비해 우수한 것을 확인하였다. This paper addresses a modified spectral subtraction schemes which is suitable to speech recognition under low signal-to-noise ratio (SNR) noisy environment such as the automatic speech recognition (ASR) system in car. The conventional spectral subtraction schemes rely on the SNR such that attenuation is imposed on that part of the spectrum that appears to have low SNR, and accentuation is made on that part of high SNR. However, such postulation is adequate for high SNR environment, it is grossly inadequate for low SNR scenarios such as that of car environment. Proposed methods focused specifically to low SNR noisy environment by using weighting function for enhancing speech dominant region in speech spectrum. Experimental results by using voice commands for car show the superior performance of the proposed method over conventional methods.

Multiscale MSE-minimizing filters for gradient-based motion estimation

Gradient-based algorithms play a vital role in motion estimation. In this paper, a motion estimation algorithm based on gradient methods for the low signal-to-noise ratio (SNR) scenarios was presented using statistical performance of the estimator. The cost function model of mean square error (MSE) was developed based on the Cramer-Rao lower bound by considering the influence of the noises on motion estimation. The optimal gradient filters for motion estimation were obtained by minimizing the MSE cost function. In combination with the multiscale pyramid approach, the accuracy of such a motion estimation algorithm can be further improved. Experimental simulations show that the proposed method improves the estimator performance for low SNR scenarios.

Efficiency Evaluation of the Unconditional Maximum Likelihood Estimator for Near-Field DOA Estimation

In this paper, we address the problem of closely spaced source localization using sensor array processing. In particular, the performance efficiency (measured in terms of the root mean square error) of the unconditional maximum likelihood (UML) algorithm for estimating the direction of arrival (DOA) of near-field sources is evaluated. Four parameters are considered in this evaluation: angular separation among sources, signal-to-noise ratio (SNR), number of snapshots, and number of sources (multiple sources). Simulations are conducted to illustrate the UML performance to compute the DOA of sources in the near-field. Finally, results are also presented that compare the performance of the UML DOA estimator with the existing multiple signal classification approach. The results show the capability of the UML estimator for estimating the DOA when the angular separation is taken into account as a critical parameter. These results are consistent in both low SNR and multiple-source scenarios.

Quantitative ambiguity analysis for matched-field parameter estimation

Matched-field methods concern the estimation of source location and/or ocean environmental parameters by matching the measured signal field with the modeled signal field. The resulted ambiguity output is often characterized by a multimodal structure since the typical signal field is a highly nonlinear function of the embedded parameters. In the high signal-to-noise ratio (SNR) region, the peak of the true parameter protrudes prominently above the noise and can be located accurately; below some SNR, the true peak often falls below the noise level and is obscured by other ambiguous peaks, leading to a larger estimation error and the well-known threshold phenomenon. Therefore, issues on the output ambiguity structure are very important for the development of any matched-field algorithm operated in low SNR scenarios (e.g., passive source localization). To understand some fundamental ambiguity behaviors, a quantitative approach for error analysis is developed in the context of the maximum likelihood estimate. The derived probabilistic error at each scanning parameter point is determined by the signal ambiguity level, the distance to the true parameter point, and the SNR. Accordingly, the threshold SNR can be located by comparing the relative size of the main lobe and side lobe error contributions.