Random Detection Research Articles

Multipath Detection and Mitigation of Random Noise Signals Propagated through Naturally Lossy Dispersive Media for Radar Applications

This paper describes a methodological analysis of the Brillouin precursor formation to understand the impairments undergone by like-noise and random noise waveforms propagating through naturally dispersive media commonly found in radar applications. By means of a frequency-domain methodology based on considering the frequency response of the medium under study, the effect of these dispersive media on the evolution of an input signal can be seen as frequency filtering. The simulations were performed at a center frequency of 1.5 GHz and for a signal bandwidth of 3 GHz. Four random noise signals were considered: Barker codes, PRBS codes, Frank codes, Costas codes and additive white Gaussian noise. The experienced impairments were assessed in terms of cross-correlation function (CCF) degradation. The differences in the behavior of each type of phase and frequency coded signal to face the dispersive propagation have been demonstrated in terms of parameters used for information retrieval: peak amplitude decay, CCF secondary sidelobe level and multipath detectability. Finally, a frequency filtering approach is proposed to mitigate the impairments due to dispersive propagation under multipath conditions.

FBMC-Based Random Access Signal Design and Detection for LEO Base Stations

The integration of non-terrestrial networks into the 5G ecosystem is mainly driven by the possibility of provisioning service in remote areas. In this context, the advent of flying base stations at the low Earth orbit (LEO) will enable anywhere and anytime connectivity. To materialize this vision, it is of utmost importance to improve radio protocols with the aim of allowing direct satellite access. Bearing this aspect in mind, we present a new random access signal, which is based on the filter bank multicarrier (FBMC) waveform, and a computationally efficient detection scheme. The proposed solution outperforms the standardized access scheme based on single-carrier frequency division multiplexing (SC-FDM), by reducing out-of-band (OOB) emissions and reducing the missed detection probability in presence of very high carrier frequency offset (CFO), which is inherent to LEO satellite systems. The improvement is related to the fine frequency resolution of the detector and the use of pulse shaping techniques. Interestingly, the FBMC-based random access signal achieves a high level of commonality with 5G new radio, as the preamble generation method and the time-frequency allocation pattern can be kept unchanged. Concerning the practical implementation aspects, the complexity of the detector is similar in both SC-FDM and FBMC.

Automatic Detection and Tracking of Random Frequency Signals Using Magnitude and Phase Information

This paper presents an approach to the problems of detection and frequency estimation of a frequency modulated narrow-band signal in additive complex Gaussian noise. The signal is assumed to have an unknown amplitude, initial phase and frequency trajectory over time, while a <i>priori</i> information regarding random frequency variability is taken to be available. The proposed approach operates in the frequency domain and uses the magnitude and phase of the discrete-time Fourier transforms computed over nonoverlapping signal segments. Robustness at low signal-to-noise ratios is achieved by suppressing the segment-related likelihoods for which the phase estimation error is large. The approach utilises unthresholded transform data and thus works in a track-before-detect manner, and the frequency trajectory is estimated by applying a search over the frequency-time bins. The results of a simulation study involving two signal types with different frequency variability are described.

Bayesian random projection-based signal detection for Gaussian scale space random fields

Bayesian random projection-based signal detection for Gaussian scale space random fields

Joint Collaboration and Compression Design for Random Signal Detection in Wireless Sensor Networks

In this work, we propose a joint collaboration-compression framework for sequential estimation of a random vector parameter in a resource constrained wireless sensor network (WSN). Specifically, we propose a framework where the local sensors first collaborate (via a collaboration matrix) with each other. Then a subset of sensors selected to communicate with the FC linearly compress their observations before transmission. We design near-optimal collaboration and linear compression strategies under power constraints via alternating minimization of the sequential minimum mean square error. We show that the objective function for collaboration design can be non-convex depending on the network topology. We reformulate and solve the collaboration design problem using quadratically constrained quadratic program (QCQP). Moreover, the compression design problem is also formulated as a QCQP. We propose two versions of compression design, one centralized where the compression strategies are derived at the FC and the other decentralized, where the local sensors compute their individual compression matrices independently. It is noted that the design of decentralized compression strategy is a non-convex problem. We obtain a near-optimal solution by using the bisection method. In contrast to the one-shot estimator, our proposed algorithm is capable of handling dynamic system parameters such as channel gains and energy constraints. Importantly, we show that the proposed methods can also be used for estimating time-varying random vector parameters. Finally, numerical results are provided to demonstrate the effectiveness of the proposed framework.

RTS Noise Detection and Voltage Effect on RTS in HgCdTe Focal-Plane Arrays

An automated random telegraph signal (RTS) detection and characterization method and a blinking noise and slow drift separation method adapted to focal-plane arrays have been developed and applied to study the evolution of the number of RTS pixels and the amplitude of the blinking signal as functions of the reverse bias voltage and temperature. The results show that the physical characteristics of the RTS follow Arrhenius laws and increase with bias. The origin of this increase in the amplitude as a function of the reverse bias is discussed.

Statistical Properties of Kendall's Tau Under Contaminated Gaussian Model With Applications in Random Signal Detection

This letter investigated the statistical properties of Kendall's tau (KT) under a specific contaminated Gaussian model (CGM) emulating impulsive noise frequently encountered in practice. The major contributions of this work include 1) deriving the closed-form expressions concerning the mean and variance of KT under the CGM; 2) establishing the analytic forms of false alarm and detection probabilities when applying KT to the problem of bi-channel random (Gaussian) signal detection. Monte Carlo simulations not only validated our theoretical findings, but also revealed the robustness of KT against impulsive noise (modeled by CGM) in the aspect of random signal detection.

Optimal Detection and Error Exponents for Hidden Semi-Markov Models

We study detection of random signals corrupted by noise that over time switch their values (states) between a finite set of possible values, where the switchings occur at unknown points in time. We model such signals as hidden semi-Markov signals, which generalize classical Markov chains by introducing explicit (possibly nongeometric) distribution for the time spent in each state. Assuming two possible signal states and Gaussian noise, we derive optimal likelihood ratio test and show that it has a computationally tractable form of a matrix product, with the number of matrices involved in the product being the number of process observations. The product matrices are independent and identically distributed, constructed by a simple measurement modulation of the sparse semi-Markov model transition matrix that we define in the paper. Using this result, we show that the Neyman–Pearson error exponent is equal to the top Lyapunov exponent for the corresponding random matrices. Using theory of large deviations, we derive a lower bound on the error exponent. Finally, we show that this bound is tight by means of numerical simulations.

Detection of Unknown Signals Under Complex Elliptically Symmetric Distributions

Detection of unknown signals is considered under complex elliptically symmetric (CES) distributions, a wide family that includes several well-known distributions as special cases. We study the detection problem for unknown deterministic signals and random CES signals, both corrupted by CES noise. For detection of unknown deterministic signals, we form the generalized likelihood ratio test and reduce it to a sufficient test statistic, which is either the norm of the received vector or a function of it. Performance analysis is provided under both known and estimated noise scatter matrix. For detection of random CES signals, we form the likelihood ratio test, provide performance analysis, and establish conditions under which the sufficient test statistic is given by the norm of the received vector. Interestingly, this norm test statistic is proven to be the uniformly most powerful test for the detection of all CES distributed signals.

Regression Algorithms for Detection and Recognition of Non-Centered Non-Stationary Random Signals in the Short-Range Autonomous Information Systems

The article forms the rationale for using the regression algorithms to detect and recognize the non-stationary non-centered signals and noise taking into account the specifics of the short-range autonomous information systems (SRAIS) under conditions of unknown mathematical expectations of informative parameters of signals. When representing each sample realization of the non-stationary processes, subordinated to the normal law of distribution of probabilities, in discrete time, based on the approximate Kotelnikov theorem to solve the problems of signal detection and recognition on the background of band white noise were obtained the expressions for the coefficients of private plausibility of the respective hypotheses. The resulting algorithms require computation of quadratic forms and knowledge of the expectations of selected informative parameters. It is shown that taking into consideration the specific SRAIS – equality relations of mathematical expectation to the RMS values in the samples and high correlation coefficients of the parameter estimates of informative parameters – it is possible to proceed from calculating the quadratic forms in the signal processing algorithms to calculating the modules of errors of multiple initial regression representations for linear correlation. The article justifies the regression algorithms to form the areas of decision-making in which relative distances from the initial regression line are restricted, that is, the algorithms have a clear geometric meaning. Such algorithms can be applied regardless of the probability distribution of estimated random parameters of the signals (for unimodal distributions), since a priori information about the coefficients of the initial regression is obtained when investigating the correlations curves of the random parameters of the signal in the entire area of their change in linear correlation. In non-linear correlation in the SRAIS, using the information on the application conditions it is possible to make alterations of parameters of decision-making algorithms. The article investigates the performance and decision-making regression algorithm to detect the correlated two-dimensional non-centered random vectors for the normal distributions of signal and noise with different primary regression specifications. The considered regression algorithms can be applied in SRAIS to improve noise immunity when solving the tasks of detection and recognition of signals and noise.

The Geometry of Signal Detection with Applications to Radar Signal Processing

The problem of hypothesis testing in the Neyman–Pearson formulation is considered from a geometric viewpoint. In particular, a concise geometric interpretation of deterministic and random signal detection in the philosophy of information geometry is presented. In such a framework, both hypotheses and detectors can be treated as geometrical objects on the statistical manifold of a parameterized family of probability distributions. Both the detector and detection performance are geometrically elucidated in terms of the Kullback–Leibler divergence. Compared to the likelihood ratio test, the geometric interpretation provides a consistent but more comprehensive means to understand and deal with signal detection problems in a rather convenient manner. Example of the geometry based detector in radar constant false alarm rate (CFAR) detection is presented, which shows its advantage over the classical processing method.

The Impairing Effect of Mental Fatigue on Visual Sustained Attention under Monotonous Multi-Object Visual Attention Task in Long Durations: An Event-Related Potential Based Study.

The impairing effects of mental fatigue on visual sustained attention were assessed by event-related potentials (ERPs). Subjects performed a dual visual task, which includes a continuous tracking task (primary task) and a random signal detection task (secondary task), for 63 minutes nonstop in order to elicit ERPs. In this period, the data such as subjective levels of mental fatigue, behavioral performance measures, and electroencephalograms were recorded for each subject. Comparing data from the first interval (0–25 min) to that of the second, the following phenomena were observed: the subjective fatigue ratings increased with time, which indicates that performing the tasks leads to increase in mental fatigue levels; reaction times prolonged and accuracy rates decreased in the second interval, which indicates that subjects’ sustained attention decreased.; In the ERP data, the P3 amplitudes elicited by the random signals decreased, while the P3 latencies increased in the second interval. These results suggest that mental fatigue can modulate the higher-level cognitive processes, in terms of less attentional resources allocated to the random stimuli, which leads to decreased speed in information evaluating and decision making against the stimuli. These findings provide new insights into the question that how mental fatigue affects visual sustained attention and, therefore, can help to design countermeasures to prevent accidents caused by low visual sustained attention.

Compressive Detection of Random Subspace Signals

The problem of compressive detection of random subspace signals is studied. We consider signals modeled as $\mathbf{s} = \mathbf{H} \mathbf{x}$ where $\mathbf{H}$ is an $N \times K$ matrix with $K \le N$ and $\mathbf{x} \sim \mathcal{N}(\mathbf{0}_{K,1},\sigma_x^2 \mathbf{I}_K)$. We say that signal $\mathbf{s}$ lies in or leans toward a subspace if the largest eigenvalue of $\mathbf{H} \mathbf{H}^T$ is strictly greater than its smallest eigenvalue. We first design a measurement matrix $\mathbf{\Phi}=[\mathbf{\Phi}_s^T,\mathbf{\Phi}_o^T]^T$ comprising of two sub-matrices $\mathbf{\Phi}_s$ and $\mathbf{\Phi}_o$ where $\mathbf{\Phi}_s$ projects the signals to the strongest left-singular vectors, i.e., the left-singular vectors corresponding to the largest singular values, of subspace matrix $\mathbf{H}$ and $\mathbf{\Phi}_o$ projects it to the weakest left-singular vectors. We then propose two detectors which work based on the difference in energies of the samples measured by two sub-matrices $\mathbf{\Phi}_s$ and $\mathbf{\Phi}_o$ and prove their optimality. Simplified versions of the proposed detectors for the case when the variance of noise is known are also provided. Furthermore, we study the performance of the detector when measurements are imprecise and show how imprecision can be compensated by employing more measurement devices. The problem is then re-formulated for the case when the signal lies in the union of a finite number of linear subspaces instead of a single linear subspace. Finally, we study the performance of the proposed methods by simulation examples.

The Simulation Research of Classic Spectrum Estimation Periodogram Method Based on Matlab

The spectrum estimation is a main content of modern signal processing, it is very important for random signal detection and analysis. This paper researched the classical spectrum estimation method, simulated the periodogram method, Barlett method, and Welch method of power spectrum estimation, then mainly discussed the spectral resolution about different length of data, also discussed the resolution of the spectral estimation and the performance of variance in the different way.

Observation of spontaneous quantum fluctuations in photon absorption by atoms

Fluctuations in light absorption by atoms are observed by applying laser light on rubidium atoms and measuring the transmitted light intensity fluctuations. These fluctuations are spontaneous noise, which are generic to photon atom interactions. By making use of the sub-shot noise random signal detection technology, we have measured the spectra at sub-shot noise levels to reveal their rich nature, which had previously been unobserved. The effects of atoms transiting the laser beam, Rabi flopping in the optical transitions and Larmor precession of the magnetic moment are observed in the spectra. The properties of the fluctuations reflect not only the quantum behavior of atoms, but also that of light.

Random Phase Weak Sine Signal Detection Based on Autocorrelation and Chaos Theory

The paper expounds the basic principle of the weak signal detection on chaotic system at first , according to the actual situation that the signal phase is unknown Received in the communication and radar systems, a new chaos detection model is presented based on traditional chaos system, combined with autocorrelation detection technology , completed the random phase weak sinusoidal signal detection. The simulation analysis indicates that the proposed method succeeds in detecting Random Phase weak sinusoidal signal, and furthermore the signal-to-noise threshold is lower than either autocorrelation or chaotic oscillator.

A 1.62 Gb/s–2.7 Gb/s Referenceless Transceiver for DisplayPort v1.1a With Weighted Phase and Frequency Detection

This paper proposes a 2.7 Gb/s referenceless transceiver with weighted PFD for frequency detection of random signals. A single loop referenceless CDR is also proposed to overcome the disadvantages of a dual loop CDR. The ANSI 8b/10b encoder & decoder with the scrambler, the serializer & de-serializer, and the output driver with pre-emphasis are included in the proposed transceiver architecture for DisplayPort v1.1a. The jitter of the generated clock at the Tx PLL is 3.28 psrms at 2.7 Gb/s with 1.2 V supply. The eye opening of the transmitter output with 3 m cable is 0.54 UI. The measured jitter of the recovered clock at the CDR is 1.57 psrms, and BER is less than 10-12. The receiver consumes 23 mW at 2.7 Gb/s with 1.2 V supply. The CDR core and transceiver occupy 0.07 mm2 and 0.94 mm2, respectively, in a 0.13 μm 1P8M CMOS process.

Life support techniques related to survival after out-of-hospital cardiac arrest in infants

Aim of the studyThe incidence rate of out-of-hospital cardiac arrest (OHCA) among infants is high; however, little is known about effective life-support for this group. Thus, we examined factors related to 1-month survival after OHCA among infants. MethodsAll infant OHCA cases occurring between 2005 and 2008 in Japan were extracted from the National Utstein Registry. Split-half random sampling and signal detection analysis were used to identify the effective factors on 1-month survival after OHCA. ResultsThe mutual interaction of life support techniques and other factors were identified and used to divide the study population into five subgroups. A witness to the cardiac arrest, rescue breathing administered by a bystander, and less than 18min to hospital arrival or a witness to the arrest, no rescue breathing and less than 7min for the ambulance to arrive at the scene were found to be related to higher survival after OHCA in infants. The survival proportions for these subgroups were 44.83% (95% CI: 25.58–64.08) and 19.18% (95% CI: 14.64–23.72), respectively. ConclusionThe probability of survival after OHCA in infants may be improved by a bystander witnessing the arrest and providing the rescue breathing at the first sight of arrest.

Copula-Based Fusion of Correlated Decisions

Detection of random signals under a distributed setting is considered. Due to the random nature of the spatial phenomenon being observed, the sensor decisions collected at the fusion center are correlated. Assuming that local detectors are single threshold binary quantizers, a novel approach for the fusion of correlated decisions is proposed using the theory of copulas. The proposed approach assumes only the knowledge of the marginal distribution of sensor observations but no prior knowledge of their joint distribution. Using a Neyman-Pearson (NP) framework for detection at the fusion center, the optimal fusion rule is derived. An example involving the detection of nuclear radiation is presented to illustrate the proposed approach, and results demonstrating the efficiency of the copula-based fusion rule are shown.

Digital detection, grouping and classification of partial discharge signals at DC voltage

In this paper, a digital dc PD pulse detection system with bandwidth of 10 kHz - 40 MHz is introduced, which was developed using some artificial intelligence methodologies. Focus is made on digital detection, grouping and classification of random pulse signals generated by PD phenomena at dc voltage. Digital detection is developed only resorting to a band-pass filter, a high-speed digitizer (100 MS/s) and a PC with data processing software. Grouping is realized with feature extraction of pulse waveshapes using equivalent time-frequency method (ETFM), making the 2D parameters plane or 3D parameters space, then using the unsupervised clustering Fuzzy C-Means (FCM) method to achieve fast separation for pulse sequence. And classification resorts to least square support vector machine (LS-SVM) based on a fingerprint, which is derivate form 2D histograms of basic parameters, the discharge magnitude q and the time between discharges Deltat of each sub-group. Field application is made for typical defects of oil-paper insulation under dc voltage. At last, several methods to improve separability of the grouping technique are also given for some special cases, including threshold value grouping, marginal coordinates grouping based on 2D parameters plane and grouping using ETFM preprocessed by wavelet denosing. Experimental results show that the dc PD detection system developed with artificial intelligence methodologies is practical and effective.