Background Of White Noise Research Articles

Recognition and classification of noise signals by dolphins under conditions of noise interference and spatial uncertainty of their simultaneous presentation

The ability of dolphin`s auditory system to recognize and classify noise signals according to certain invariant features under the influence of noise interference and spatial uncertainty of their simultaneous presentation was studied. The bottlenose dolphins trained to differentiate such signals had to solve this problem under conditions simulating real marine ones, when the perception of a useful signal occurs against the background of similar signals and noise interference. First, the noise signals were sequentially presented to the animal against the background of white masking noise. Further the dolphin had to identify a signal of a positive class from several simultaneously sounding sound sources. The efficiency of the animal was evaluated at several given levels of noise interference. In this case, the actual noise interference was both white noise and simultaneously sounding negative signals. It`s shown that the efficiency and noise immunity of dolphin`s auditory system depend on the degree of alternativeness of the spatial uncertainty of the simultaneous presentation of signals.

Quasi-likelihood estimation of the arrival time of an ultra-wideband quasi-radio signal with unknown amplitude and initial phase

The problem of estimating the time of arrival of a signal is relevant in the fields of radio and hydrolocation, radio astronomy, remote observation and sensing, etc. The useful signal is often described by a quasi-deterministic function of time, which can be regular or discontinuous. In recent years, problems of processing ultra-wideband signals have been of significant interest. Among the many UWB signals, it is customary to distinguish a separate class of ultra-wideband quasi-radio signals, the structure of which is similar to the structure of narrowband radio signals, but the condition of relative narrowband for which may not be met. Algorithms for estimating the arrival time of regular ultra-wideband quasi-radio signals have been studied in the existing literature. In this case, the synthesis and analysis of an algorithm for quasi-plausible estimation of the arrival time of a discontinuous signal of finite duration and arbitrary shape is of practical interest. The purpose of the work is to synthesize and analyze a quasi-likelihood algorithm for estimating the time of arrival of a discontinuous ultra-wideband quasi-radio signal with an unknown amplitude and initial phase against a background of white Gaussian noise. In this work, closed asymptotically exact expressions for the bias and scattering of the estimate are obtained, which make it possible to study the effectiveness of the synthesized algorithm, as well as compare its effectiveness with special cases of signal models - narrowband and wideband radio and video signals. The results of the study make it possible to determine losses in the accuracy of estimation the signal arrival time due to a priori ignorance of the initial phase and shape of the modulating function of the signal. Depending on the required accuracy of estimate the time of arrival of an ultra-wideband quasi-radio signal, it is possible to optimize the parameters of the transmitted signals and determine the limits of applicability of the designed radio system.

Design and Analysis of a Contact Piezo Microphone for Recording Tracheal Breathing Sounds.

Analysis of tracheal breathing sounds (TBS) is a significant area of study in medical diagnostics and monitoring for respiratory diseases and obstructive sleep apnea (OSA). Recorded at the suprasternal notch, TBS can provide detailed insights into the respiratory system's functioning and health. This method has been particularly useful for non-invasive assessments and is used in various clinical settings, such as OSA, asthma, respiratory infectious diseases, lung function, and detection during either wakefulness or sleep. One of the challenges and limitations of TBS recording is the background noise, including speech sound, movement, and even non-tracheal breathing sounds propagating in the air. The breathing sounds captured from the nose or mouth can interfere with the tracheal breathing sounds, making it difficult to isolate the sounds necessary for accurate diagnostics. In this study, two surface microphones are proposed to accurately record TBS acquired solely from the trachea. The frequency response of each microphone is compared with a reference microphone. Additionally, this study evaluates the tracheal and lung breathing sounds of six participants recorded using the proposed microphones versus a commercial omnidirectional microphone, both in environments with and without background white noise. The proposed microphones demonstrated reduced susceptibility to background noise particularly in the frequency ranges (1800-2199) Hz and (2200-2599) Hz with maximum deviation of 2 dB and 2.1 dB, respectively, compared to 9 dB observed in the commercial microphone. The findings of this study have potential implications for improving the accuracy and reliability of respiratory diagnostics in clinical practice.

Detection of phase modulation disorder when processing a sequence of ultra-wideband quasi-radio signals against a background of white Gaussian noise

In conditions of a complex electromagnetic environment in communication and data transmission systems, a change in the type of modulation occurs when one or more signal parameters change abruptly, therefore the task of determining the type of signal modulation can be reduced to the task of detecting a disorder, that is, detecting the fact of a change in the type of modulation of the signal and estimation of the disorder time. The purpose of the work is to synthesize an algorithm for detecting phase modulation disorder when processing a sequence of ultra-wideband quasi-radio signals with unknown amplitude and initial phase against a background of white Gaussian noise and to analyze the effectiveness of the resulting algorithm based on the results of statistical modeling. The work synthesizes an algorithm for detecting a phase modulation disorder of a received sequence of ultra-wideband quasi-radio signals against a background of white Gaussian noise. To analyze the effectiveness of the resulting algorithm, statistical modeling of algorithm for detecting a phase modulation disorder is carried out. The dependences of the probability of detection of a disorder on the signal-to-noise ratio and the length of the sequence of received signals at a fixed moment of disorder are obtained. A comparison of the detection characteristics of phase modulation disorder of ultra-wideband quasi-radio signals and narrowband radio signals is made. It is shown that when a low-order phase modulation disorder is detected, the required values of the probability of detection are achieved at low signal-to-noise ratios, while with an increase in the modulation order and the use of its modifications, the efficiency indicators of the algorithm will deteriorate. The introduction of algorithms for detecting a disorder in radio engineering applications will allow optimizing synchronization algorithms and adaptation to interference conditions with a concomitant change in the type of modulation, as well as reducing the amount of transmitted service information in telecommunication systems.

Adaptive stochastic resonance under Poisson white noise background and its application for bolt looseness detection

Adaptive stochastic resonance under Poisson white noise background and its application for bolt looseness detection

Optimization of the Weight Processing Algorithm in Multichannel Doppler Filtering

Introduction. The use of non-equidistant pulse sequences as probing radar signals makes it possible to eliminate blind spots in speed and range. However, the implementation of multi-channel Doppler filtering (MDF) based on the classical fast Fourier transform (FFT) algorithm of non-equidistant signal samples in the signal detection problem is associated with energy losses. The use of modified FFT algorithms increases the efficiency of MDF against the background of white Gaussian noise, while reducing the efficiency of signal accumulation in the part of signal processing channels blocked by the narrow-band clutter. To eliminate this drawback, the authors previously proposed using combined classical and modified FFT algorithms. However, the use of the combined method does not lead to an optimal solution in terms of MDF efficiency.Aim. Optimization of weight processing of non-equidistant signals to improve the efficiency of MDF.Materials and methods. An MDF synthesis was carried out using optimization procedures, and the effectiveness of the algorithms was assessed using computer calculations.Results. The results show that the Kaiser Bessel window with a window parameter of 4.42 provides the highest signal-(clutter+noise) ratio improvement coefficient averaged over frequency channels equal to 30.06 dB and the highest probability of correct signal detection averaged over MDF channels equal to 0.5 at processing of non-equidistant pulse sequences. Optimization of the weight processing of MDF under the specified conditions increased the average efficiency characteristics used of up to 53.18 dB and 0.92, respectively.Conclusion. Separate optimization of weighting processing for each frequency channel can significantly improve the average efficiency characteristics of a multichannel Doppler filter and eliminate all the shortcomings of the classical and modified FFT algorithms when processing non-equidistant pulse sequences. However, these advantages are achieved at the cost of not using the FFT, i.e., implemented within the framework of the discrete Fourier transform (DFT) algorithm.

Effects of Different Types of Music on Students’ Study Efficiency

Music has been omnipresent in people’s daily lives; therefore, it is crucial to evaluate how it exerts influence as people depend more on it. Many studies suggest that listening to background music has a positive impact and raises study efficiency, while others suggest that it is counterproductive because it negatively impacts attention levels. These equivocal results occur because people focus on different tasks while testing the effect of background music. This paper explores various effects on students’ study efficiency of three genres of background music, classical music, white noise or environmental sounds, and lyrical pop music. Classical music had both beneficial and harmful impacts depending on different cognitive tasks. White noise, surprisingly, has some positive impact on students, especially for ADHD students. Lyrical music has a generally distracting effect on study tasks. Certainly, more research about how three types of music impact students is needed. Since music has become more and more entrenched in people’s life, this paper hopes to provide insight into how three types of music can be incorporated into tasks that helps performance and avoid using those that might hurt students’ performance.

Effects of urban traffic noise on the early growth and transcription of Arabidopsis thaliana

Man-made noise in cities has been recognized as one of the major contributors to a range of adverse health outcomes, including heart disease. Besides the detrimental effects on human health, noise can act as a potential threat to other organisms in the ecosystem. Despite the growing concern, the biological effects of noise pollution on plants, in particular, remain largely obscure. Therefore, this study aimed to investigate the impacts of urban traffic noise on the growth of Arabidopsis thaliana (A. thaliana). We hypothesized that traffic noise could stimulate plant growth (seed germination and seedling growth) since noise treatment has been reported to improve immunity and drought tolerance in A. thaliana (1, 2). Seeds in the experimental group were exposed to 48 hours of traffic noise recorded in a highly congested area of Seoul, while the control group was exposed to background white noise. Compared with background white noise, traffic noise exposure had no effects on seed germination but significantly increased the growth of seedlings by 39%. We further investigated the traffic noise-induced molecular changes in the A. thaliana seedlings by employing RNA sequencing. We detected aberrant expression of 690 genes in traffic noise-exposed seedlings. The differentially expressed genes were enriched for various biological pathways, such as stress response and auxin signaling. The observed molecular changes suggest profound impacts of traffic noise on the physiology of A. thaliana. The results of our research can help shed light on the fundamental principles underlying the noise-plant interaction as well as provide a further basis for conserving our ecosystem.

A Combined Noise Reduction Method for Floodgate Vibration Signals Based on Adaptive Singular Value Decomposition and Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise

To address the issue of the vibration characteristic signals of floodgates being affected by background white noise and low-frequency water flow noise, a noise reduction method combining the improved adaptive singular value decomposition algorithm (ASVD) and the improved complete ensemble EMD with adaptive noise (ICEEMDAN) is proposed. Firstly, a Hankel matrix is constructed based on the collected discrete time signals. After performing SVD on the Hankel matrix, the ASVD algorithm is used to automatically select the effective singular values to filter out most of the background white noise and retain the useful frequency components with similar energy in the signal. Then, ICEEMDAN combined with the Spearman correlation coefficient method is used to further filter out residual white noise and low-frequency water flows. The noise reduction performance of this combined method is verified through simulation experiments. Filtered by the ASVD-ICEEMDAN method, the signal-to-noise ratio of the simulation signal (50% noise level) is increased from 4.417 to 16.237, and the root mean square error is reduced from 2.286 to 0.586. Based on the practically measured vibration signals of a floodgate at a large hydropower station, the result shows that the ASVD-ICEEMDAN method exhibits good noise reduction performance and feature information extraction abilities for floodgate vibration signals, and can provide support for operational mode analysis and damage identification of practical structures under complex interference conditions.

Effects of target-amplitude and background-contrast uncertainty predicted by a normalized template-matching observer.

When detecting targets under natural conditions, the visual system almost always faces multiple, simultaneous, dimensions of extrinsic uncertainty. This study focused on the simultaneous uncertainty about target amplitude and background contrast. These dimensions have a large effect on detection and vary greatly in natural scenes. We measured the human performance for detecting a sine-wave target in white noise and natural-scene backgrounds for two levels of prior probability of the target being present. We derived and tested the ideal observer for white-noise backgrounds, a special case of a template-matching observer that dynamically moves its criterion with the background contrast (the DTM observer) and two simpler models with a fixed criterion: the template-matching (TM) observer and the normalized template-matching (NTM) observer that normalizes template response by background contrast. Simulations show that, when the target prior is low, the performance of the NTM observer is near optimal and the TM observer is near chance, suggesting that manipulating the target prior is valuable for distinguishing among models. Surprisingly, we found that the NTM and DTM observers better explain human performance than the TM observer for both target priors in both background types. We argue that the visual system most likely exploits contrast normalization, rather than dynamic criterion adjustment, to deal with simultaneous background contrast and target amplitude uncertainty. Finally, our findings show that the data collected under high levels of uncertainty have a rich structure capable of discriminating between models, providing an alternative approach for studying high dimensions of uncertainty.

Association Between Bilingualism, Vocal Effort, and Background Noise With Voice Fundamental Frequency and LTAS Among Spanish-English Bilingual Teachers

As the world becomes increasingly interconnected, the demand for bilingual teachers has grown exponentially. However, the unique combination of being a bilingual occupational voice user establishes challenges that warrant careful examination. To investigate the association between bilingualism, vocal effort, and background noise with fundamental frequency and long-term spectrum average among Spanish-English bilingual teachers. Exploratory cross-sectional correlational study with the participation of eight Spanish-English proficient bilingual teachers (native Spanish speakers) who were teaching online classes during the quarantines established to mitigate the propagation of COVID-19. Participants were asked to read two standardized texts (one in English and one in Spanish) under two background noise conditions (with and without background white noise). There was a significant interaction between language and vocal effort considering that the association of vocal effort with fo was different for Spanish and English. There was also a significant main effect of background noise on fo. There were significant differences in voice spectral characteristics between productions in Spanish and English and between productions with and without background noise. The alpha ratio was significantly higher in productions in English and with background noise compared with productions in Spanish without background noise. The mid to high spectral energy ratio (1-5K/5-8K) was significantly lower in productions in English and with higher perceived vocal effort compared with productions in Spanish and with lower scores on the Borg Scale. Our findings introduce a need perspective by emphasizing the impact of speaking a second language on the work-relatedness of voice disorders among teachers. These findings stress the necessity to factor in language and environmental conditions for the comprehensive evaluation and management of work-related voice disorders.

A comparison of background noise reduction algorithms for improving acoustic beamforming output

This work presents a comparison of different background noise reduction algorithms available in literature at a range of signal-to-noise ratios (SNRs) for improving the conventional beamforming (CB) source maps. The algorithms include the classical background noise removal (BNR), eigenvalue identification organization and subtraction (EIOS), subspace-based background subtraction (SBS), and ensemble empirical mode decomposition (EEMD), amongst others. To assess the performance of different algorithms, three test-cases were considered: (1) localizing a loudspeaker placed in an anechoic environment in the presence of background white-noise, (2) diagnosing machine-tool noise source(s) during machining, i.e., when the workpiece and tool piece are in contact and (3) localizing airfoil trailing-edge (TE) noise sources in the presence of background tunnel noise. The analysis showed that the suitability of an algorithm depends upon the given situation – for the loudspeaker source, all algorithms deliver comparable results while for machine-tool application, the relatively simple BNR algorithm delivered the desired improvements in CB maps. For the experimental airfoil TE noise test-case, the EIOS and EEMD algorithms demonstrated substantial enhancements as compared to other methods. This investigation, therefore, suggests that for a given application, it is desirable to customize the background noise reduction algorithm to obtain optimal results.

Orbitofrontal cortex conveys stimulus and task information to the auditory cortex

Auditory cortical neurons modify their response profiles in response to numerous external factors. During task performance, changes in primary auditory cortex (A1) responses are thought to be driven by top-down inputs from the orbitofrontal cortex (OFC), which may lead to response modification on a trial-by-trial basis. While OFC neurons respond to auditory stimuli and project to A1, the function of OFC projections to A1 during auditory tasks is unknown. Here, we observed the activity of putative OFC terminals in A1 in mice by using invivo two-photon calcium imaging of OFC terminals under passive conditions and during a tone detection task. We found that behavioral activity modulates but is not necessary to evoke OFC terminal responses in A1. OFC terminals in A1 form distinct populations that exclusively respond to either the tone, reward, or error. Using tones against a background of white noise, we found that OFC terminal activity was modulated by the signal-to-noise ratio (SNR) in both the passive and active conditions and that OFC terminal activity varied with SNR, and thus task difficulty in the active condition. Therefore, OFC projections in A1 are heterogeneous in their modulation of auditory encoding and likely contribute to auditory processing under various auditory conditions.

Оценка длительности сверхширокополосного квазирадиосигнала при неуверенности в его наличии

We synthesize a quasi-likelihood algorithm for estimation of the duration of an arbitrary shape ultra-wideband quasi-radio signal with unknown amplitude and initial phase, which observed against the background of additive Gaussian white noise. Among the ultra-wideband signals, a separate class is distinguished – ultra-wideband quasi-radio signals, which structure is similar to narrowband radio signals, but the condition of relative narrowband is not satisfied. We suggest that its duration can be equal to several periods or a fraction of the period of a harmonic oscillation. We consider the model of a signal which may be absent in the received realization, while the structure of the receiver does not take into account the disappearance of the signal. Statistical characteristics of the synthesized duration estimates are conditional bias and dispersions. The obtained results can be used for estimation of the decrease in efficiency in evaluation of the duration of a signal, when such a signal may disappear in the received realization.

Does Task-Irrelevant Brightness Modulation Affect Auditory Contrast Processing? Exploring the Interplay Between Temporal Synchrony and Stimulus Salience

Abstract Stimulus factors such as timing, spatial location, and stimulus effectiveness affect whether and how information across the senses is integrated. Extending recent work highlighting interactions between stimulus factors, here we investigated the influence of visual information on auditory processing, complementing previous studies on the influence of auditory information on visual processing. We hypothesized that task-irrelevant and spatially non-informative visual information would enhance auditory contrast processing, when visual information was at an optimal salience level and changed synchronously with the sound. We asked human observers to indicate the location of an amplitude-modulated white-noise sound, while its loudness against a constant white-noise background varied across trials. To test for the influence of task-irrelevant visual information, we modulated screen brightness smoothly (Experiment 1) or transiently (Experiment 2) in phase or out of phase with the amplitude modulation of the target sound. In addition, to test for the interaction between temporal synchrony and stimulus salience, maximum brightness varied systematically across trials. Auditory contrast thresholds were compared across conditions. Results showed that task-irrelevant visual information did not alter auditory contrast thresholds regardless of the nature of modulation of brightness, contrary to our expectations. Nonetheless, task-irrelevant visual information modulated in phase with the target sound reduced auditory contrast thresholds if we accounted for individual differences in the optimal salience required for the largest multisensory effects. Our results are discussed in light of several stimulus factors that might be critical in modulating multisensory enhancement.

Spatial multifractal spectrum distribution method for breast ultrasonic image classification

The point-wise multifractal spectrum (MFS) of the ultrasonic image in the time-frequency domain is studied in this paper. With the theoretical derivation, the spatial multifractal spectrum (SMFS) distribution, as an extension of the conventional MFS, is proposed to analyze and characterize the spatial multiscale multifractal feature. Furthermore, the homogeneous region statistical MFS (HRS-MFS) with respect to different physical regions and the singularity-exponent-domain statistical multi-resolution (SSMR) feature image can be extracted from the renormalized SMFS data cube. The SMFS is rigorously derived from the two-dimensional multifractal spectrum and the Pseudo Wigner-Ville distribution (PWVD). In addition, both the SSMR feature images based on the SMFS and the singularity exponent of the 2D-PWVD is proved to be the SNR independence in the Gaussian white noise (GWN) background, which ensures the robust estimation of SMFS features of images under low SNR conditions. Furthermore, the HRS-MFS and the SSMR feature images of the breast ultrasound images (BUSI) are extracted to discriminate the specificity of the tumor regions. Then, a novel BUSI classification method based on the SMFS and deep learning network is proposed and tested on the public BUSI datasets. The experiment results indicate that the SMFS method can significantly reduce the risk of the ‘intermediate effects’. In fact, the classification accuracy of the original deep network is considerately improved by 9.3 % with the SMFS method, and 94.8 % classification accuracy is achieved, which is superior by 3.3 % to the state-of-art method.

Background noise responding neurons in the inferior colliculus of the CF-FM bat, Hipposideros pratti

The Lombard effect, referring to an involuntary rise in vocal intensity, is a widespread vertebrate mechanism that aims to maintain signal efficiency in response to ambient noise. Previous studies showed that the Lombard effect could be sufficiently implemented at subcortical levels and operated by continuously monitoring background noise, requiring some subcortical auditory sensitive neurons to have continuous responses to background noise. However, such neurons have not been well characterized. The inferior colliculus (IC) is a major auditory integration center under the auditory cortex and provides projections to the putative vocal pattern generator in the brainstem. Thus, it is reasonable to speculate that the IC is a likely auditory nucleus candidate having background noise responding neurons (BNR neurons). In the present study, we isolated 183 sound-sensitive IC neurons in a constant frequency-frequency modulation bat, Hipposideros pratti, and found that around 19% of these IC neurons are BNR neurons when stimulated with 70 dB SPL background white noise. Their firing rates in response to noise increased with increasing noise intensity and could be suppressed by sound stimulation. Furthermore, compared to neurons with similar best frequencies, the BNR neurons had smaller Q10-dB values and lower noise-induced minimal threshold change, indicating that BNR neurons received fewer inhibitory inputs. These results suggested that the BNR neurons are ideal candidates for collecting information about background noise. We proposed that the BNR neurons synapsed with neurons in vocal-pattern-generating networks in the brainstem and initiated the Lombard effect by a feed-forward loop.

IDBD-Based Beamforming Algorithm for Improving the Performance of Phased Array Radar in Nonstationary Environments

Adaptive array processing technology for a phased array radar is usually based on the assumption of a stationary environment; however, in real-world scenarios, nonstationary interference and noise deteriorate the performance of the traditional gradient descent algorithm, in which the learning rate of the tap weights is fixed, leading to errors in the beam pattern and a reduced output signal-to-noise ratio (SNR). In this paper, we use the incremental delta-bar-delta (IDBD) algorithm, which has been widely used for system identification problems in nonstationary environments, to control the time-varying learning rates of the tap weights. The designed iteration formula for the learning rate ensures that the tap weights adaptively track the Wiener solution. The results of numerical simulations show that in a nonstationary environment, the traditional gradient descent algorithm with a fixed learning rate has a distorted beam pattern and reduced output SNR; however, the IDBD-based beamforming algorithm, in which a secondary control mechanism is used to adaptively update the learning rates, showed a similar beam pattern and output SNR to a traditional beamformer in a Gaussian white noise background; that is, the main beam and null satisfied the pointing constraints, and the optimal output SNR was obtained. Although the proposed algorithm contains a matrix inversion operation, which has considerable computational complexity, this operation could be replaced by the Levinson-Durbin iteration due to the Toeplitz characteristic of the matrix; therefore, the computational complexity could be decreased to O(n), so additional computing resources are not required. Moreover, according to some intuitive interpretations, the reliability and stability of the algorithm are guaranteed.

A Single-Hydrophone Coherent-Processing Method for Line-Spectrum Enhancement

Improving the line-spectrum detection capability of a single hydrophone is of great significance for the passive detection of small underwater platforms. In this paper, we propose a single-hydrophone cross-power spectrum (SHCS) method based on time-domain coherence. This method uses the coherence of the line spectrum and the non-coherence of the continuous spectrum noise to obtain coherent gain and improve the signal-to-noise ratio (SNR) of the line spectrum. The effects of the input SNR, number of averaging operations, and overlap ratio on the performance of the SHCS method under a background of Gaussian white noise are simulated and analyzed. The results show that when the overlap ratio is 0 and the number of averaging operations reaches saturation, the SHCS method can achieve the best performance and about 15 dB coherence gain is obtained. The performance of the SHCS method was verified by sea experiments. Under the extremely low input SNR, in which the line spectrum was almost completely submerged in the marine environmental noise, the SHCS method can obtain about 10 dB coherence gain. Under the conventional input SNR, in which the line spectrum could be observed, the SHCS method can obtain about 13 dB coherence gain. The results of processing the radiated noise from an actual cargo ship also demonstrate the effectiveness of the SHCS method.

INTERFERENCE RESISTANCE OF A CORRELATION RECEIVER AT A LIMITED OBSERVATION INTERVAL

The article considers the possibility of increasing the capability of detecting a useful radio signal against the background of noise and simultaneously reducing the possibility of mistaking noise fluctuations for a signal. Moreover, if it is absent then the Neumann-Pearson statistical test should be used. This criterion is a partial case from the class of criteria consisting in the calculation of the likelihood ratio and does not require knowledge of a priori probabilities of the presence or absence a useful signal while constructing an optimal receiver and Bayesian methods are used.
 The effectiveness of the detection procedure according to the Neumann-Pearson test is characterized by the probability of correct detection at a fixed false alarm probability. These probabilities depend on the signal-to-noise ratio at the output of the detection device. So, an important preparatory stage of the reception immunity analysis is the study of this parameter which is very important in the section of statistical radio engineering.
 It is known that the optimal detection of a signal against a background of white noise is reduced to the correlation integral calculation. This calculation can be done directly by using a multiplier and integrator (correlation method), by using a filter that is optimal for this signal (filter method) or by using a correlation-filter method.
 The presence of a priori uncertainty during signal reception makes it impossible to create an optimal (consistent) receiving path and therefore makes it impossible to obtain the required value of the signal-to-noise ratio at the output of the detection device. In this case, an autocorrelation algorithm is used which is resistant to a priori uncertainty of signal parameters and is “optimal” while detecting signals of unknown shape. The most general character for determining the signal/noise ratio has an expression for the case of an incoherent single-channel autocorrelation algorithm for detecting a noisy signal against a background of stationary white noise (interference).
 For example, quite a large number of scientific works are devoted to the interference immunity issue of the correlation reception method. They mainly consider the interference immunity of correlation receivers when a mixture of harmonic signal and noise passes through them. The circuits with the same average frequencies of multiplying processes and etc. are analyzed. There are also known works in which formulas relating the value of the signal-to-noise ratio at the output of the receiving path for a complex signal to the input signal-to-noise ratio are given.
 In the above works, idealized cases are considered, namely, when the observation is conducted on a fairly long observation interval but the real observation interval is quite limited. In this case, the averaging is carried out over a limited time interval and it is necessary to take into account the intercorrelation relations between the signal and noise, therefore, along with the signal-signal, noise-noise, signal-noise and noise-signal components, we will have the following mutual correlation moments between the combinations of signal and noise component.
 The issue of an autocorrelation receiver when passing an additive mixture of signal and white noise at a limited observation interval is investigated in which the immunity index is considered and a general solution of the signal/noise ratio based on the deviation criterion is obtained. Therefore, obtaining an analytical expression that establishes the relationship between the signal-to-noise ratio at the input and output of the receiver is very important and relevant.
 The purpose of the article is to obtain an analytical expression of the signal-to-noise ratio value at the threshold device input of an incoherent autocorrelation receiver at a limited observation interval.