A multi-rate multiple model track-before-detect particle filter

Current particle filter track-before-detect (PF-TBD) algorithms assume constant velocity (CV) motion and the filter updates at a full-rate (i.e. at every measurement scan). Previous work in multi-rate processing, via a discrete wavelet transform (DWT), has shown that multi-rate tracking algorithms can provide comparable performance at a lower computational cost. To date, these multi-rate approaches have not yet been applied to low signal-to-noise ratio (SNR) targets. This paper presents a full-rate multiple model particle filter for track-before-detect (MMPF-TBD) and a multi-rate multiple model track-before-detect particle filter (MRMMPF-TBD) that extends the areas mentioned above and tracks low SNR targets which perform small maneuvers. The MRMMPF-TBD and MMPF-TBD both use a combined probabilistic data association (PDA) and maximum likelihood (ML) approach. The MRMMPF-TBD provides equivalent root-mean-square error (RMSE) performance at substantially lower particle counts than a full-rate MMPF-TBD. A performance analysis for various SNR and particle count scenarios is presented.     

Blind SNR estimation for limited time series

A blind SNR estimation method for discrete data is presented. The original noise-free data is assumed to follow a known signal model with an unknown attenuation. The SNR in noisy data is estimated using a polynomial fit obtained from the correlative characteristics between SNR and variance fractal dimension values. The bias and the “standard error” (root mean square error) of the estimator are used as performance measures. The simulated performance of the estimator for a specific signal model with real additive white Gaussian noise assumption is compared to that of a published decision-aided (nonblind) SNR estimator.     

Array-enhanced stochastic resonance in finite systems

A finite system of three coupled stochastic resonators is investigated with respect to the coupling constant. The consideration allows the analytical calculation of the spectral power amplification and of the signal-to-noise ratio. Whereas the local signal-to-noise ratio (SNR) of a single spin exhibits a maximum with respect to the coupling, the SNR of the global summed output of all spins decreases monotonically.     

Stochastic resonance in an interacting-agent model of stock market

On the specific example of an interacting-agent model of speculative activity we have demonstrated that stochastic resonance (SR), where an increase in the noise (market volatility) increases the signal-to-noise ratio (SNR) describing the response to global periodic investment bias, can occur in the stock market. This phenomenon may be in principle utilized by market traders.     

Signal-to-noise ratio gain by stochastic resonance in a bistable system

It was shown recently that the signal-to-noise ratio (SNR) could be improved by stochastic resonance (SR) in certain monostable systems and certain systems with monotonous nonlinearity working in the nonlinear response (NLR) regime. Here we demonstrate that a simple bistable system driven by periodic pulse train and band-limited Gaussian white noise can also provide an SNR gain. We show the reasons why SNR gain had not been found in double well potential systems, despite strong efforts made during the last 15 years.     

实验设计法在电缆芯线优化中的应用

对设计因子的每个配置 ,随着均值的变大 ,方差也趋于增加 .田口的新噪比 [2 ] 是方差与均值平方成正比 .但实际上方差比均值的平方增加速度更快或更慢 .本文用稳定方差的变换去减少质量特性的变异 ,给出计算信噪比的新公式 ,并用在汽车点火儿电缆芯线生产过程 ,找到了对电缆芯线拉伸强度有显著影响的因子 ,确定了最佳工程条件 ,实践证明本方法计算方便 ,明显减少质量特性的变异 ,比文献 [1 ]、[2 ]给出的方法优越 .     

Chaos time-domain reflectometry for fault location on live wires

We propose a chaos time-domain reflectometry (CTDR) for locating faults on live wires. This method uses a chaotic output of an improved Colpitts oscillator as probe signal, and detects wire faults by correlating a duplicate with the echo of the probe signal. Benefiting from the anti-jamming of the correlation function of the wideband chaos, fault location on live wires can be achieved. We experimentally demonstrate the detection for live wires in a digital communication system, in which a type of digital signal named high density bipolar of order 3 (HDB3) is transmitted. The effects of the chaotic probe signal on the bit error rate (BER) of the transmitted HDB3 at different rates are analyzed. Meanwhile, the influences of the backward HDB3 reflected by wiring faults on the signal-noise-ratio (SNR) of CTDR measurement are examined experimentally. The results show that fault detection on live wires is achieved when the power of the chaotic probe signal is about from -24.8 dB to -13.5 dB lower than that of the transmitted digital signal. In this case, the BER is kept less than 3E-10, and the SNR of CTDR is higher than 3 dB. Besides, the auto-correlation properties of the improved Colpitts oscillator at different states are investigated experimentally to explore the suitable chaotic states for the CTDR.     

High-order total variation-based Poissonian image deconvolution with spatially adapted regularization parameter

Images captured by image acquisition systems using photon-counting devices such as astronomical imaging, positron emission tomography and confocal microscopy imaging, are often contaminated by Poisson noise. Total variation (TV) regularization, which is a classic regularization technique in image restoration, is well-known for recovering sharp edges of an image. Since the regularization parameter is important for a good recovery, Chen and Cheng (2012) proposed an effective TV-based Poissonian image deblurring model with a spatially adapted regularization parameter. However, it has drawbacks since the TV regularization produces staircase artifacts. In this paper, in order to remedy the shortcoming of TV of their model, we introduce an extra high-order total variation (HTV) regularization term. Furthermore, to balance the trade-off between edges and the smooth regions in the images, we also incorporate a weighting parameter to discriminate the TV and the HTV penalty. The proposed model is solved by an iterative algorithm under the framework of the well-known alternating direction method of multipliers. Our numerical results demonstrate the effectiveness and efficiency of the proposed method, in terms of signal-to-noise ratio (SNR) and relative error (RelRrr).     

Stochastic resonance in a linear static system driven by correlated multiplicative and additive noises

In this work, we investigate the signal transmission in a linear static system driven by correlated multiplicative and additive noises. When the input signal is periodic, we depict the stochastic resonance (SR) phenomenon by employing the signal-to-noise ratio (SNR) theory; while the input signal is aperiodic, we describe the SR phenomenon by using the input–output cross correlation theory. And the exact analytic expressions of the output SNR and the normalized time averaged cross covariance between input and output are obtained. The results show: under the condition of negative correlated noises, SR arises; while with positive correlated or uncorrelated noises, there is no SR. This result may extend the SR theory to a common linear static system.