Active Target Detection Research Articles

Bio-inspired target detection method of active sonar based on multi-ping perception

Abstract The performance of the active sonar may degrade severely in real ocean environments, influenced by strong reverberation and multiple clutters. Inspired by the observation that dolphins always utilize a train of clicks when detecting underwater, we proposed an active target detection method by making full use of the multi-ping echo information which encompasses the differences between targets and reverberation/clutters. The detection performance of the click trains with different parameters were analysed first to give an insight on signal choice. Then we achieved apparent reverberation/clutter suppression result by utilizing the correlations and differences between multi-ping echoes. Further, the relative motion status between the target and the sonar platform could be perceived within multiple echoes, which strengthened the detection ability of weak target. We proposed two weighting strategies for multi-ping co-utilization. Either equal weights or unequal weights based on the different envelopes of dolphin echolocation click trains were exerted on multiple echoes, which can achieve different perception results for the relative target motion status. Both simulation and lake trials were conducted to verify the performance of the proposed method. The proposed method effectively suppress reveberaton and noise background by around 2-3 dB and achieve bionic detection of weak targets compared to traditional active sonar signal processing method.

Cortical Generators and Connections Underlying Phoneme Perception: A Mismatch Negativity and P300 Investigation.

The cortical generators of the pure tone MMN and P300 have been thoroughly studied. Their nature and interaction with respect to phoneme perception, however, is poorly understood. Accordingly, the cortical sources and functional connections that underlie the MMN and P300 in relation to passive and active speech sound perception were identified.An inattentive and attentive phonemic oddball paradigm, eliciting a MMN and P300 respectively, were administered in 60 healthy adults during simultaneous high-density EEG recording. For both the MMN and P300, eLORETA source reconstruction was performed. The maximal cross-correlation was calculated between ROI-pairs to investigate inter-regional functional connectivity specific to passive and active deviant processing.MMN activation clusters were identified in the temporal (insula, superior temporal gyrus and temporal pole), frontal (rostral middle frontal and pars opercularis) and parietal (postcentral and supramarginal gyrus) cortex. Passive discrimination of deviant phonemes was aided by a network connecting right temporoparietal cortices to left frontal areas. For the P300, clusters with significantly higher activity were found in the frontal (caudal middle frontal and precentral), parietal (precuneus) and cingulate (posterior and isthmus) cortex. Significant intra- and interhemispheric connections between parietal, cingulate and occipital regions constituted the network governing active phonemic target detection. A predominantly bilateral network was found to underly both the MMN and P300.While passive phoneme discrimination is aided by a fronto-temporo-parietal network, active categorization calls on a network entailing fronto-parieto-cingulate cortices. Neural processing of phonemic contrasts, as reflected by the MMN and P300, does not appear to show pronounced lateralization to the language-dominant hemisphere.

Analysis of Bottom Reverberation Intensity Under Beam-Controlled Emission Conditions in Deep Water

A comprehensive understanding of the characteristics and the formation mechanism of reverberation is the key to improving the performance of the active target detection. In response to the challenge of analyzing the intensity of bottom reverberation in typical deep-sea environments, this study proposes a prediction method for the bottom reverberation intensity under beam-controlled emission conditions. It explains the variation law of bottom reverberation intensity under beam-controlled emission conditions in typical deep-sea environments of the South China Sea through theoretical and simulation analyses. Reverberation intensity of the deep-sea bottom under beam-controlled emission conditions exhibits significant fluctuations during the duration of reverberations in the direct sound zone of the seabed. This phenomenon is closely related to the directionality of the source emission, leading to intermittent reverberation masking and detectable areas in the active sonar detection. In addition, the duration of the high-reverberation zone near the cutoff distance of the direct sound from the seabed is longer under the beam-controlled emission conditions of the emission array located within the surface waveguide layer of the deep sea during winter.

Extraction of comprehensive feature for active target detection using two hand-crafted acoustic features

We propose a model for creating a new feature suitable for classifying targets and non-targets using a small active sonar data. The proposed model extracts a comprehensive feature from two different hand-crafted features using deep-learning (DL). To extract comprehensive feature, we contrived a complementary learning module (CLM), which consists of two stages. In the first stage, a transformer encoder was adopted to reinforce the input features and then complementarily learned through attention mechanism in the following stage. The output features from the CLM are passed through a shallow CNN to classify targets and non-targets. In addition, an uncertainty quantification method is proposed using two CLMs to quickly add new samples acquired in the real ocean dataset. Using the decisions from two CLMs, we calculate the variance and determine which samples from the entire dataset are worthy of prioritized addition. Two real-ocean datasets were used to examine the model generalization and compare the classification results with conventional DL models. The generalization performance of the proposed model was comparable or superior to other DL models and uncertainty quantificaion method improved the generalization performance. [Work supported by Korea Research Institute for defense Technology planning and advancement(KRIT)—Grant funded by the Korea government (DAPA—Defense Acquisition Program Administration) (No. KRIT-CT-23-026, Integrated Underwater Surveillance Research Center for Adapting Future Technologies, 2024)]

Deep learning based effective feature extraction for active sonar target detection

We propose a deep learning (DL) model suitable for classifying target and non-target using a small amount of active sonar data. The proposed model uses two hand-crafted features (STFT and CQT) extracted from the same raw active sonar data, to complement each other and enhance the generalization of DL under insufficient data. The attention-based complementary learning module in proposed model reinforces one feature by referring to the other feature. The comprehensive feature from the complementary learning module pass through a shallow layer CNN and classify targets and non-targets. To verify the performance of the proposed model, we compared it with prevalent deep learning models including ResNet and ViT in terms of generalization performance and learning stability using two real-ocean datasets. The generalization performance of the proposed model having much smaller number of parameters was similar to or superior to the existing deep learning models depending on the training dataset, while the proposed model had the best learning stability for the two datasets.

Effective feature fusion via analysis of quantitative similarity matrices among various acoustic features for underwater active target detection

For underwater active target detection, using conventional machine learning technique is limited and unsuitable because of small training data samples and diversity of environment. Therefore, to apply conventional machine learning for underwater acoustics target detection, three methodologies can be manipulated (1) feature, (2) architecture, and (3) learning strategy. In this paper, we implement various acoustic features in terms of feature similarity and feature fusion. From numerous studies in field of acoustics, various acoustic feature extraction methods have been proposed such as Mel-frequency cepstral coefficient, Gammatone-frequency cepstral coefficient, cepstral coefficient, short-time Fourier transform, constant Q transform, and wavelet packet decomposition. In this paper, we calculate a quantitative similarity between acoustic features by interpreting their data distributions with the corresponding probability densities in a reduced dimension. Furthermore, we fuse the acoustic features by simple concatenation. Fusion of a strongly correlated two-dimensional features tends to follow the performance of poor one, whereas the fusion of weakly correlated features improves performance remarkably. The performance improvement by the fusion of weakly correlated features is attributable to complementing acoustic information each other.

Active target classification using a shallow neural network with dimension reduction

Techniques for automatically identifying sonar targets using machine learning algorithms are being actively developed, and modern algorithms based on deep learning are often considered first. However, deep learning based algorithms require a lot of data to train a neural network, and if there is not enough data, overfitting easily occurs and the performance on real data can be degraded. In fact, in the case of active target detection where there is not enough data, it has been reported that using a shallow neural network after extracting appropriately designed features from the raw data showed better detection performance than applying deep learning directly to the raw data. With regard to this, we investigate the performance of a shallow neural network combined with dimensionality reduction techniques for active sonar target classification. In particular, several linear and nonlinear dimension reduction techniques are compared in terms of target classification performance, and the effects of the characteristics of background noise and the presence of reverberation on the target classification performance are discussed. [This work was supported by the research project PES4380 funded by KRISO.]

Sex differences in automatic emotion regulation in adolescents with autism spectrum disorder.

Autism may be underdiagnosed in females because their social difficulties are often less noticeable. This study explored sex differences in automatic facial emotion processing in 45 adolescents with autism spectrum disorder (22 female, 23 male), age 10-16 years, performing active target detection task and Go/NoGo tasks where faces with positive and negative emotional expressions served as irrelevant distractors. The combined sample demonstrated more accurate performance on the target detection (response initiation) than the Go/NoGo task (response inhibition), replicating findings previously reported in typical participants. Females exhibited greater difficulty than males with response initiation in the target detection task, especially in the context of angry faces, while males found withholding a response in the Go/NoGo block with happy faces more challenging. Electrophysiological data revealed no sex differences or emotion discrimination effects during the early perceptual processing of faces indexed by the occipitotemporal N170. Autistic males demonstrated increased frontal N2 and parietal P3 amplitudes compared to females, suggesting greater neural resource allocation to automatic emotion regulation processes. The associations between standardized behavioral measures (autism severity, theory of mind skills) and brain responses also varied by sex: more adaptive social functioning was related to the speed of perceptual processing (N170 latency) in females and the extent of deliberate attention allocation (P3 amplitudes) in males. Together, these findings suggest that males and females with autism may rely on different strategies for social functioning and highlight the importance of considering sex differences in autism. LAY SUMMARY: Females with autism may exhibit less noticeable social difficulties than males. This study demonstrates that autistic females are more successful than males at inhibiting behavioral responses in emotional contexts, while males are more likely to initiate a response. At the neural level, social functioning in females is related to the speed of automatic perceptual processing of facial cues, and in males, to the extent of active attention allocation to the stimuli. These findings highlight the importance of considering sex differences in autism diagnosis and treatment selection.

Target-to-Anomaly Conversion for Hyperspectral Anomaly Detection

A known target detection assumes that the target to be detected is provided <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> , while an anomaly detection is an unknown target detection without any prior knowledge. As a result, the known target detection generally performs search-before-detect detection in an active mode, referred to as active target detection as opposed to anomaly detection which performs throw-before-detect detection in a passive mode, referred to as passive target detection. Accordingly, techniques designed for these two types of detection are completely different. This paper shows that there is indeed a mechanism, called target-to-anomaly conversion which can convert hyperspectral target detection (HTD) to hyperspectral anomaly detection (HAD) via a novel idea, called dummy variable trick (DVT). By virtue of such target-to-anomaly conversion many well-known target detection techniques, such as likelihood ratio test (LRT), constrained energy minimization (CEM) and orthogonal subspace projection (OSP), spectral angle mapper (SAM) and adaptive cosine estimator (ACE) can be converted to their corresponding anomaly detector, referred to as target-to-anomaly conversion-derived anomaly detector (TAC-AD). Since a target detector requires target knowledge while TAC-AD does not, a direct use of RAC-AD is not effective. To make TAC-AD work, a newly developed approach to effective anomaly space (EAS) is implemented in conjunction with TAC-AD so that anomalies can be retained in EAS and interference and noise including background (BKG) can be removed from EAS. The experiments demonstrate that TAC-AD operating in EAS performs better than many existing anomaly detection approaches including model-based methods.

Underwater reverberation suppression based on non-negative matrix factorisation

Reverberation is the main background interference in underwater active sonar target detection that seriously interferes with the extraction of the target highlight echo. In consideration of the difference in the time-frequency distributions of the target highlight echo and reverberation, an underwater reverberation suppression method based on non-negative matrix factorisation is proposed in this study. The Wigner-Ville time-frequency matrix of the underwater target echo is used as the input, and non-negative matrix factorisation is applied to express the time-frequency matrix as a low-rank matrix. The influences of rank selection on the reverberation suppression effect and signal expression of the target highlight echo are analysed. Given that the time-frequency matrix of the target highlight echo cannot be expressed in the low-rank form when transmitting linear frequency modulation signal, this matrix is processed with low-rank preprocessing through matrix rotation. The components of the target highlight echo, which can be expressed in the form of a low-rank matrix, are preserved. By contrast, the components of the reverberation interference, which cannot be represented in such a form, are removed in the underwater target echoes. The simulation and experimental results show that the algorithm can effectively improve the signal-to-reverberation ratio of underwater target echoes.

Radar Detector in Uncoordinated Communication Interference Plus Partially Homogeneous Clutter

Many literatures regarding the coexisting radar and communications assume that some information is exchanged mutually, i.e., they work in a cooperative or coordinated manner. In this letter, we consider an uncoordinated scenario to examine the influence of the unknown communication interference on the active radar target detection, just provided with a known power of the transmitted communication signal. To this end, a numerical generalized likelihood ratio test (GLRT) in the partially homogeneous clutter is derived to handle this problem, with the maximum likelihood estimations (MLEs) approximately solved. Simulations illustrate the superiority of the addressed detector, principally in the strong interference environment.

Deficits in attentional modulation of auditory N100 in first-episode schizophrenia.

Reductions of the auditory N100 are present in schizophrenia, even at the first episode (FESz). Because most studies examine auditory N100 on active target detection oddball tasks, it remains unclear if the abnormality in FESz results from sensory deficits or impaired enhancement of N100 by selective attention, or both. N100 was recorded from 21 FESz and 22 matched healthy controls (HC) on a single-tone task and a two-tone oddball task. Overall, N100 was smaller in FESz (p=.036). Attention enhanced N100 amplitude (p<.001), but this differed between groups, with FESz impaired in N100 modulation (group x attention, p=.012). The oddball task showed greater N100 enhancement than the single-tone task (p<.001) in both groups. Group differences in N100 enhancement in the oddball task were large (Cohen's d=0.85). Exploratory correlations showed that better N100 enhancement on the oddball task in FESz was associated with better MATRICS Overall Composite scores (cognitive tasks highly sensitive to psychosis), lower PANNS Negative factor and SANS scores, and better interpersonal (social) and role functioning in the last year. N100 during ignore conditions showed no significant difference between groups, albeit smaller in FESz, with small to medium effect sizes. Although sensory deficits in N100 are likely present, they are compounded by a failure to enhance N100 with attention. The failure of N100 enhancement by attentional gain control in FESz suggests functional dysconnection between cognitive control areas and the sensory cortex. N100 amplitude on active attention tasks may be a useful outcome biomarker for targeted enhancement of the cognitive control system.

Multiple vertical depression-based HMS active target detection using GSFM pulse

Multiple vertical depression-based HMS active target detection using GSFM pulse

Underwater Acoustic Research Trends with Machine Learning: Active SONAR Applications

Underwater acoustics, which is the study of phenomena related to sound waves in water, has been applied mainly in research on the use of sound navigation and range (SONAR) systems for communication, target detection, investigation of marine resources and environments, and noise measurement and analysis. The main objective of underwater acoustic remote sensing is to obtain information on a target object indirectly by using acoustic data. Presently, various types of machine learning techniques are being widely used to extract information from acoustic data. The machine learning techniques typically used in underwater acoustics and their applications in passive SONAR systems were reviewed in the first two parts of this work (Yang et al., 2020a; Yang et al., 2020b). As a follow-up, this paper reviews machine learning applications in SONAR signal processing with a focus on active target detection and classification.

激光近距探测装置抗云雾干扰研究

Aerosol-interference is a serious interference source for active optical target-detection device (AOTD) when it is used in lower sky or super low sky. When AOTD in a vehicle passes through aerosol, it may be wrong armed, even give out execution signal before right time. In order to increase the ability in aerosol environment, how the AOTD worked and how the aerosol interfered the AOTD were analyzed. It also showed out the techniques of anti-aerosol-interference used in engineering. The general techniques for anti-aerosol-interference were settings of cut-off distance method, weaken methods by optical parts, optical field restraining method, cancellation by symmetric views, setting reference view field, body recognition method, composite with Ka detectors, decreasing the width of transmitted pulse, image recognition method et al. Among these methods, the multimode detection method was a valuable way. Effects of different anti-interference methods were listed, the results shows integrated application of all the methods can increase the ability of AOTD.

Iterative matching-based parameter estimation for time-scale underwater acoustic multipath echo

In this paper, a wideband underwater acoustic multipath channel is investigated with a sonar-target relative movement. The underwater environment causes time delay, amplitude and Doppler factor changes on the transmitted signals, as a result, the signal components from the respective paths may be differentiated from these parameters. The Doppler factor of a specific path is analyzed in a moving condition, and the impulse responses of the propagation paths are illustrated by the wideband ambiguity function. Since different Doppler factors should be taken into consideration, an effective iterative method is proposed for parameter estimation of multipath echo. The proposed method retrieves a signal component by searching for the optimal scale factor from the resampling matching outputs, and eliminates the matching output of the estimated signal to analyze the next component. The effectiveness of the method is confirmed by some simulation results. It is also effective for various wideband signals with a broad range of frequencies, because the method prevents the time-domain subtractions of the received signals for providing a higher robustness. Therefore, it can be applied in many fields including active target detection and underwater communication.

Generation and analysis of pseudo-random sequence laser guidance signal

In view of the current state of the technology of the laser-guided weapon system that is vulnerable to fraudulent interference, a new idea using random sequence coding is proposed to improve its anti-jamming performance. By using the characteristics of better anti-interference performance of pseudo-random sequence, the laser active detection target system can not only achieve long-distance active target detection, but also effectively prevent external interference and improve the reliability of the system. The signal generation system is designed and implemented by combining Arduino IDE, Arduino UNO R3 microcontrollers, with oscilloscopes and YAG lasers, with good anti-interference performance. The system can be used for the study of new laser target indicators.

A Method for Active Marine Target Detection Based on Complex Interferometric Dissimilarity in Dual-Channel ATI-SAR Systems

Synthetic aperture radar (SAR) operating in an along-track interferometric (ATI) mode has the advantage of minimum detectable velocity (MDV) in active marine target detection. However, most of the conventional ATI detectors fail to identify the marine targets cruising with blind speeds, in which case the interferometric phases of these targets are closely distributed around those of the sea background due to phase wrapping and aliasing through ATI processing. To address this issue, we propose a method to detect the active marine targets based on complex interferometric dissimilarity for dual-channel ATI-SAR systems. First, an interferometric bilateral filter is designed to smooth the random noises and locally adapt to the spatial structure of the interferogram for measuring the interferometric magnitude and phase. Then, the target detection metric is constructed based on the complex interferometric dissimilarity between the marine targets and the sea background. By adaptively regressing into a magnitude-based test toward the blind-speed targets, the target detection metric can mitigate the blind-speed detection problem and thus yield a satisfactory detection result. Furthermore, this metric is of a constant false-alarm rate (CFAR), and its probability density function (pdf) is derived to facilitate the detection threshold computation. Finally, both the simulated and real-data processing results are given to validate the superiorities of the proposed method.

Functional MRI evidence of the cortico-olivary efferent pathway during active auditory target processing in humans

Auditory target detection has been explored by a number of studies, but none have demonstrated activity in the auditory subcortical centers evoked by the top-down attentional mechanism related to target detection in humans. We applied functional magnetic resonance imaging (fMRI) with sparse sampling to explore activity in the auditory centers, particularly in the subcortex, during an active auditory target detection task. Fourteen healthy subjects with normal hearing tapped the left index finger in response to target tonal stimuli presented among other (non-target) stimuli during continuous white noise stimulation. General linear model, region-of-interest, and connectivity analyses were performed. In the cortex, bilateral auditory cortices as well as the cingulate gyrus, thalamus, and supramarginal gyrus were activated to target stimuli and functionally connected to each other. In the subcortex, the superior olivary complex (SOC) and locus coeruleus were activated to the target but not to the non-target or background noise stimuli. The SOC was the only auditory subcortical center that displayed connectivity to the auditory cortical areas as well as the cingulate and supramarginal gyri during target presentation but not during other conditions. SOC activation appears to be the first fMRI evidence of direct cortico-olivary projections in the human brain as well as SOC participation in auditory target detection. Our results may be an initial step towards developing a noninvasive methodology to evaluate the functional integrity of the auditory efferent system in humans.

The spatio-temporal dynamics of deviance and target detection in the passive and active auditory oddball paradigm: a sLORETA study

BackgroundNumerous studies have investigated the neural underpinnings of passive and active deviance and target detection in the well-known auditory oddball paradigm by means of event-related potentials (ERPs) or functional magnetic resonance imaging (fMRI). The present auditory oddball study investigates the spatio-temporal dynamics of passive versus active deviance and target detection by analyzing amplitude modulations of early and late ERPs while at the same time exploring the neural sources underling this modulation with standardized low-resolution brain electromagnetic tomography (sLORETA).MethodsA 64-channel EEG was recorded from twelve healthy right-handed participants while listening to ‘standards’ and ‘deviants’ (500 vs. 1000 Hz pure tones) during a passive (block 1) and an active (block 2) listening condition. During passive listening, participants had to simply listen to the tones. During active listening they had to attend and press a key in response to the deviant tones.ResultsPassive and active listening elicited an N1 component, a mismatch negativity (MMN) as difference potential (whose amplitudes were temporally overlapping with the N1) and a P3 component. N1/MMN and P3 amplitudes were significantly more pronounced for deviants as compared to standards during both listening conditions. Active listening augmented P3 modulation to deviants significantly compared to passive listening, whereas deviance detection as indexed by N1/MMN modulation was unaffected by the task. During passive listening, sLORETA contrasts (deviants > standards) revealed significant activations in the right superior temporal gyrus (STG) and the lingual gyri bilaterally (N1/MMN) as well as in the left and right insulae (P3). During active listening, significant activations were found for the N1/MMN in the right inferior parietal lobule (IPL) and for the P3 in multiple cortical regions (e.g., precuneus).DiscussionThe results provide evidence for the hypothesis that passive as well as active deviance and target detection elicit cortical activations in spatially distributed brain regions and neural networks including the ventral attention network (VAN), dorsal attention network (DAN) and salience network (SN). Based on the temporal activation of the neural sources underlying ERP modulations, a neurophysiological model of passive and active deviance and target detection is proposed which can be tested in future studies.