Speech Signal Detection Based on Bayesian Estimation by Observing Air-Conducted Speech under Existence of Surrounding Noise with the Aid of Bone-Conducted Speech

In order to apply speech recognition systems to actual circumstances such as inspection and maintenance operations in industrial factories to recording and reporting routines at construction sites, etc. where hand-writing is difficult, some countermeasure methods for surrounding noise are indispensable. In this study, a signal detection method to remove the noise for actual speech signals is proposed by using Bayesian estimation with the aid of bone-conducted speech. More specifically, by introducing Bayes’ theorem based on the observation of air-conducted speech contaminated by surrounding background noise, a new type of algorithm for noise removal is theoretically derived. In the proposed speech detection method, bone-conducted speech is utilized in order to obtain precise estimation for speech signals. The effectiveness of the proposed method is experimentally confirmed by applying it to air- and bone-conducted speeches measured in real environment under the existence of surrounding background noise.

An innovative detection method of high frequency BPSK signal with low signal-to-noise ratio

Based on chaotic oscillator system, this paper proposes a novel method on high frequency low signal-to-noise ratio BPSK(Binary Phase Shift Keying) signal detection. Chaotic oscillator system is a typical non-linear system which is sensitive to periodic signals and immune to noise at the same time. Those properties make it possible to detect low signal-to-noise ratio signals. The BPSK signal is a common signal type which is widely used in modern communication. Starting from the analysis of advantages of chaotic oscillator system and signal features of the BPSK signal, we put forward a unique method that can detect low signal-to-noise ratio BPSK signals with high frequency. The simulation results show that the novel method can detect low signal-to-noise ratio BPSK signals with frequency in an order of magnitude of 108 Hz, and the input Signal-to-Noise Ratio threshold can be -20 dB.

Low-Noise Amplification, Detection and Spectroscopy of Ultra-Cold Systems in RF Cavities

The design and development of a cryogenic Ultra-Low-Noise Signal Amplification (ULNA) and detection system for spectroscopy of ultra-cold systems are reported here for the operation in the 0.5 - 4 GHz spectrum of frequencies (the “L” and “S” microwave bands). The design is suitable for weak RF signal detection and spectroscopy from ultra-cold systems confined in cryogenic RF cavities, as entailed in a number of physics, physical chemistry and analytical chemistry applications, such as NMR/NQR/EPR and microwave spectroscopy, Paul traps, Bose-Einstein Condensates (BEC’s) and cavity Quantum Electrodynamics (cQED). Using a generic Low-Noise Amplifier (LNA) architecture for a GaAs enhancement mode High-Electron Mobility FET device, our design has especially been devised for scientific applications where ultra-low-noise amplification systems are sought to amplify and detect weak RF signals under various conditions and environments, including cryogenic temperatures, with the least possible noise susceptibility. The amplifier offers a 16 dB gain and a 0.8 dB noise figure at 2.5 GHz, while operating at room temperature, which can improve significantly at low temperatures. Both dc and RF outputs are provided by the amplifier to integrate it in a closed-loop or continuous-wave spectroscopy system or connect it to a variety of instruments, a factor which is lacking in commercial LNA devices. Following the amplification stage, the RF signal detection is carried out with the help of a post-amplifier and detection system based upon a set of Zero-Bias Schottky Barrier Diodes (ZBD’s) and a high-precision ultra-low noise jFET operational amplifier. The scheme offers unique benefits of sensitive detection and very-low noise amplification for measuring extremely weak on-resonance signals with substantial low- noise response and excellent stability while eliminating complicated and expensive heterodyne schemes. The LNA stage is fully capable to be a part of low-temperature experiments while being operated in cryogenic conditions down to about 500 mK.

Wigner-Hough transform based on slice's entropy and its application to multi-LFM signal detection

To enhance the capacity of the radar-reconnaissance interception receiver recognizing linear frequency modulated (LFM) at a low signal-noise ratio, this paper presents WignerHough transform (WHT) of the LFM signal and its corresponding characteristics, derives the probability density functions of the LFM signal and Gaussian white noise within WHT based on entropy (WHTE), dimension under different assumptions and puts forward a WHT algorithm based on entropy of slice to improve the capacity of detecting the LFM signal. Entropy of the WHT domain slice is adopted to assess the information size of polar radius or angle slice, which is converted into the weight factor to weight every slice. Double-deck weight is used to weaken the influences of noise and disturbance terms and WHTE treatment and signal detection procedure are also summarized. The rationality of the algorithm is demonstrated through theoretical analysis and formula derivation, the efficiency of the algorithm is verified by simulation comparison between WHT, fractional Fourier transform and periodic WHT, and it is highlighted that the WHTE algorithm has better detection accuracy and range of application against strong noise background.

Combining Radon-ambiguity transform with second-order difference to improve detection probability of LFM signals in low SNR

The Radon-ambiguity transform （RAT）, although efficient for detecting the linear frequency modulated signals （LFMs）, is troubled by the energy accumulation of noise in low signal-to-noise ratio （SNR）. A secondorder difference （SOD） method is proposed to treat with this problem. In the SOD method, the optimal search step and difference step are derived from the LFM rate resolution formula. The sharpness of the peaks of RAT is measured by curvature, and the sharpness, but not the magnitude of the peaks, is used to detect the LFMs. The SOD method removes the noise energy accumulation and reserves the drastically changing components integrally; thus, it improves the detection probability of LFMs in low SNR. The expected performance of the new method is verified by 100 Monte Carlo simulations.

Photoelectric detection technology of laser seeker signals

The measurement of the rolling angle of the projectile is one of the key technologies for the terminal correction projectile.To improve the resolution accuracy of the rolling angle in the laser seeker weapon system, the imaging model of the detector, calculation model of the position and the signal-to-noise ratio(SNR) model of the circuit are built to derive both the correlation between the resolution error of the rolling angle and the spot position, and the relation between the position resolution error and the SNR. Then the influence of each parameter on the SNR is analyzed at large,and the parameters of the circuit are determined. Meanwhile, the SNR and noise voltage of the circuit are calculated according to the SNR model and the decay model of the laser energy. Finally,the actual photoelectric detection circuit is built, whose SNR is measured to be up to 53 d B. It can fully meet the requirement of0.5° for the resolution error of the rolling angle, thereby realizing the analysis of critical technology for photoelectric detection of laser seeker signals.

Implication of two-coupled tri-stable stochastic resonance in weak signal detection

Stochastic resonance （SR） has been proved to be an effective approach to extract weak signals overwhelmed in noise. However, the detection effect of current SR models is still unsatisfactory. Here, a coupled tri-stable stochastic resonance （CTSSR） model is proposed to further increase the output signal-to-noise ratio （SNR） and improve the detection effect of SR. The effects of parameters a, b, c, and r in the proposed resonance system on the SNR are studied, by which we determine a set of parameters that is relatively optimal to implement a comparison with other classical SR models. Numerical experiment results indicate that this proposed model performs better in weak signal detection applications than the classical ones with merits of higher output SNR and better anti-noise capability.

Single Epoch GPS Deformation Signals Extraction and Gross Error Detection Technique Based on Wavelet Transform

Wavelet theory is efficient as an adequate tool for analyzing single epoch GPS deformation signal. Wavelet analysis technique on gross error detection and recovery is advanced. Criteria of wavelet function choosing and Mallat decomposition levels decision are discussed. An effective deformation signal extracting method is proposed, that is wavelet noise reduction technique considering gross error recovery, which combines wavelet multi-resolution gross error detection results. Time position recognizing of gross errors and their repairing performance are realized. In the experiment, compactly supported orthogonal wavelet with short support block is more efficient than the longer one when discerning gross errors, which can obtain more finely analyses. And the shape of discerned gross error of short support wavelet is simpler than that of the longer one. Meanwhile, the time scale is easier to identify.

New low noise interface circuit for detecting weak current of micro-sensors

A new low noise interface circuit for detecting weak current of micro-sensors is designed.By using the transimpedance amplifier to substitute the charge amplifier,the closed-loop circuit can avoid the phase error of the charge amplifier.Therefore,the phase compensation devices will be cancelled,because there is no phase transformation through the transimpedance amplifier.As well as,by using CCCII devices to implement the high value feedback resistor of the impedance amplifier,the noise of the I-V transformation devices is reduced,comparing with the passive resistor.The floating resistor is easy to be integrated into chips,making the integration of the interface circuit of the intelligent sensors increase.Through the simulation,the phase error of the charge amplifier is almost 9°at 2 kHz and it changes with the working frequency of the micro-sensors making the phase compensation not easy.The value of the floating resistor is 250 kΩ where the bias current is 50 μA.The noise of the active resistor is 0.037 fV2/Hz,comparing with the noise of the passive resistor,which is 4.14 fV2/Hz.

Application of local polynomial estimation in suppressing strong chaotic noise

In this paper, we propose a new method that combines chaotic series phase space reconstruction and local polynomial estimation to solve the problem of suppressing strong chaotic noise. First, chaotic noise time series are reconstructed to obtain multivariate time series according to Takens delay embedding theorem. Then the chaotic noise is estimated accurately using local polynomial estimation method. After chaotic noise is separated from observation signal, we can get the estimation of the useful signal. This local polynomial estimation method can combine the advantages of local and global law. Finally, it makes the estimation more exactly and we can calculate the formula of mean square error theoretically. The simulation results show that the method is effective for the suppression of strong chaotic noise when the signal to interference ratio is low.

Asymmetric stochastic resonance under non-Gaussian colored noise and time-delayed feedback

Based on adiabatic approximation theory,in this paper we study the asymmetric stochastic resonance system with time-delayed feedback driven by non-Gaussian colored noise.The analytical expressions of the mean first-passage time(MFPT)and output signal-to-noise ratio(SNR)are derived by using a path integral approach,unified colored-noise approximation(UCNA),and small delay approximation.The effects of time-delayed feedback and non-Gaussian colored noise on the output SNR are analyzed.Moreover,three types of asymmetric potential function characteristics are thoroughly discussed.And they are well-depth asymmetry(DASR),well-width asymmetry(WASR),and synchronous action of welldepth and well-width asymmetry(DWASR),respectively.The conclusion of this paper is that the time-delayed feedback can suppress SR,however,the non-Gaussian noise deviation parameter has the opposite effect.Moreover,the correlation time plays a significant role in improving SNR,and the SNR of asymmetric stochastic resonance is higher than that of symmetric stochastic resonance.Our experiments demonstrate that the appropriate parameters can make the asymmetric stochastic resonance perform better to detect weak signals than the symmetric stochastic resonance,in which no matter whether these signals have low frequency or high frequency,accompanied by strong or weak noise.

DETECTING TECHNIQUE OF WEAK PERIODIC PULSE SIGNAL VIA SYNTHESIS OF CROSS-CORRELATION AND CHAOTIC SYSTEM

In this letter, with the synthesis of usual cross-correlation detecting method andchaotic detecting method, a new detecting system for the weak periodic pulse signal is constituted,in which the two methods can play respective preponderance. Theoretical analyses and simulationstudies have shown that the detecting system is very sensitive to the periodic pulse signal understrong noise background and has exceedingly powerful capability of suppressing complex noise.

Digital coherent detection research on Brillouin optical time domain reflectometry with simplex pulse codes

The digital coherent detection technique has been investigated without any frequency-scanning device in the Brillouin optical time domain reflectometry （BOTDR）, where the simplex pulse codes are applied in the sensing system. The time domain signal of every code sequence is collected by the data acquisition card （DAQ）. A shift-averaging technique is applied in the frequency domain for the reason that the local oscillator （LO） in the coherent detection is fix-frequency deviated from the primary source. With the 31-bit simplex code, the signal-to-noise ratio （SNR） has 3.5-dB enhancement with the same single pulse traces, accordant with the theoretical analysis. The frequency fluctuation for simplex codes is 14.01 MHz less than that for a single pulse as to 4-m spatial resolution. The results are believed to be beneficial for the BOTDR performance improvement.

Extracting hidden weak sinusoidal signal with short duration from noisy data: Analytical theory and computational realization

Signal detection is both a fundamental topic of data science and a great challenge for practical engineering. One of the canonical tasks widely investigated is detecting a sinusoidal signal of known frequency ω with time duration T：I（t）=Acos ω t＋Γ（t）, embedded within a stationary noisy data. The most direct, and also believed to be the most efficient, method is to compute the Fourier spectral power at ω：B=｜2/T∫0^T I（t）e^iωtdt｜. Whether one can out-perform the linear Fourier approach by any other nonlinear processing has attracted great interests but so far without a consensus. Neither a rigorous analytic theory has been offered. We revisit the problem of weak signal, strong noise, and finite data length T=O（1）, and propose a signal detection method based on resonant filtering. While we show that the linear approach of resonant filters yield a same signal detection efficiency in the limit of T→∞, for finite time length T=O（1）, our method can improve the signal detection due to the highly nonlinear interactions between various characteristics of a resonant filter in finite time with respect to transient evolution. At the optimal match between the input I（t）, the control parameters, and the initial preparation of the filter state, its performance exceeds the above threshold B considerably. Our results are based on a rigorous analysis of Gaussian processes and the conclusions are supported by numerical computations.

On the Small SNR Processing Ability of IR Point Target Detection

Three-dimensional (3-D) matched filtering has been suggested as a powerful processing technique for detecting weak, moving IR point target immersed in a noisy field. Based on the theory of the 3-D matched filtering and the optimal linear processing, the optimal point target detector is being analyzed in this paper. The performance of the detector is introduced in detail. The results provide a standard reference to evaluate the performance of any other point target detection algorithms.

Digital Cross-Correlation Detection of Multi-Laser Beams Measuring System for Wind Field Detection

A cross-correlation detection method to process backscatter signals of multi-laser beams measuring （MLBM） is presented, which can be firstly filtered by the digital filter composed of average median filter and finite impulse response （FIR） digital filter. The processing of backscatter signals using single-pulse and three-pulse cross-correlation detection methods is depicted in detail. From calculation results, the multi-pulse cross-correlation detection could effectively improve signal-to-noise ratio （SNR）. Finally, both wind velocity and direction are determined by the peak-delay method based on the correlation function which shows high measuring precision and high SNR of the MLBM system with the assistance of the digital cross- correlation detection.

Characteristic Analysis of White Gaussian Noise in S-Transformation Domain

The characteristic property of white Gaussian noise (WGN) is derived in S-transformation domain. The results show that the distribution of normalized S-spectrum of WGN follows X2?distribution with two degrees of freedom. The conclusion has been confirmed through both theoretical derivations and numerical simulations. Combined with different criteria, an effective signal detection in S-transformation can be realized.

Research on the Strategy of Underwater United Detection Fusion and Communication Using Multi-sensor

In order to solve the distributed detection fusion problem of underwater target detection, when the signal to noise ratio (SNR) of the acoustic channel is low, a new strategy for united detection fusion and communication using multiple sensors was proposed. The performance of detection fusion was studied and compared based on the Neyman-Pearson principle when the binary phase shift keying (BPSK) and on-off keying (OOK) modes were used by the local sensors. The comparative simulation and analysis between the optimal likelihood ratio test and the proposed strategy was completed, and both the theoretical analysis and simulation indicate that using the proposed new strategy could improve the detection performance effectively. In theory, the proposed strategy of united detection fusion and communication is of great significance to the establishment of an underwater target detection system.