Direct sequence spread spectrum (DSSS) signals are now widely used in air and underwater acoustic communications. A receiver which does not know the pseudo-random (PN) sequence cannot demodulate the DSSS signal. In this paper, firstly, the principle of principal component analysis (PCA) for PN sequence estimation of the DSSS signal is analyzed, then a modified online unsupervised learning machine (LEAP) is introduced for PCA. Compared with the original LEAP, the modified LEAP has the following improvements: (1) By normalizing the system state transition matrices, the modified LEAP can obtain better robustness when the training errors occur; (2) with using variable learning steps instead of a fixed one, the modified LEAP not only converges faster but also has excellent estimation performance. When the modified LEAP is converging, we can utilize the network connection weights which are the eigenvectors of the autocorrelation matrix of the DSSS signal to estimate the PN sequence. Due to the phase ambiguity of the eigenvectors, a novel approach which is based on the properties of the PN sequence is proposed here to exclude the wrong estimated PN sequences. Simulation results showed that the methods mentioned above can estimate the PN sequence rapidly and robustly, even when the DSSS signal is far below the noise level.

In active sonar systems, the target echoes are usually equivalent to a superposition of the Doppler-scaled reflections from multiple highlights. The reflections overlap with each other both in the time and frequency domain, which results in a decreased velocity estimation performance. Recently, the hyperbolic-frequency modulated signal has been widely employed in sonar systems for moving targets due to its Doppler tolerance, while the precise velocity estimation becomes a great challenge under such conditions. In this paper, the echo c is modeled onsidering a target with a constant velocity and multi-highlights. The velocity estimation performance is analyzed though the signal’s matched filter and the wideband ambiguity function. An improved method based on the sliding window matching algorithm is proposed to improve the performance. The method controls the energy of environmental noise and interference by focusing on the dominant target highlight, and applying a designed window which utilizes the Doppler characteristics of hyperbolic-frequency modulated signals. Simulations and lake experiment allow us to compare between the improved method and the conventional matched filter method. The results verify the influence of the multi-highlights in velocity estimation and indicate that the improved method has more effective performance.

For detecting the target echoes undergoing distortion in frequency and time dispersive channels, a novel robust active sonar detector is proposed. The proposed active detector firstly extracts the matching envelope (ME) of the multi-pulse linear frequency modulation (LFM) signals, and then the peaks of the matching envelope spectrum (MES) contained in the designed grid searching windows are detected. The grid searching windows are designed based on the theoretical analysis of the features of the MES. Lastly, the detector utilizes the detected peaks to make a judgement for determining whether there are target echoes existing in the received signal. In addition, the proposed detector can be applied to detect the multi-pulse single frequency target echoes. The proposed active detector shows a more stable performance in the frequency and time dispersive channels, compared to several classical methods, including the replica correlation (RC), the segmented RC (SRC), and the RC integration (RCI) detectors. Simulation results validate the improved performance of the active detector, showing a gain of 0.5-4 dB compared with more standard state-of-the-art detectors. Experimental results based on sea trial data have also verified the validity and feasibility of the proposed active detector.

The characterization of wideband underwater acoustic (UWA) multi-path signals is investigated in this paper. The UWA multi-path channel can be considered as a sparse model and it causes changes of the transmitted signal on Doppler factor, time delay, and amplitude, thus the signal component from each path can be differentiated from the set of parameters. This paper proposes an FrFT-based method to estimate the channel parameters, which can decompose and retrieve the multi-path signals from the UWA channels. The proposed algorithm proceeds in an iterative manner and returns a set of estimated parameters at each turn. The estimated time delay is obtained directly without any extra correlation computation. Then, the estimated component is extracted by eliminating its FrFT result from the fractional domain. Compared to the existing methods, the most improvement is that the method can be applied to the wideband LFM signals with a wider frequency range in low signal-to-noise environment. Because the indirect subtractions avoid the bad chain effect caused by the low resolution or estimation error. The effectiveness of the method is confirmed by several simulations with low and high frequency. The performance is compared with a matching pursuit-based algorithm and an FrFT-based algorithm. It is shown that the proposed method outperforms the existing algorithms in low-frequency multi-component separation as well as estimation accuracy.

Attentional bias to threatening information may play a vital role in the onset and maintenance of obsessive-compulsive disorder (OCD). This study aimed to explore whether adolescents with OCD exhibited attentional bias toward faces that express disgust or fear. Participants were 27 adolescents with a first-time primary diagnosis of OCD and 27 healthy controls. To assess OCD, depression, and anxiety symptoms, all participants completed the Yale–Brown Obsessive Compulsive Scale, the Hamilton Depression Scale, and the Hamilton Anxiety Scale, respectively, followed by the modified dot probe task. Repeated measures ANOVA revealed a main effect of validity type and a significant group × validity type interaction effect. The results of one sample t-tests showed that participants in the OCD group had an attentional bias toward both disgusted and fearful faces. Further analysis indicated that adolescents in the OCD group showed facilitated attention toward the fearful faces and difficulty disengaging from disgusted faces. Adolescents with OCD exhibited facilitated attention toward threat stimuli, and when they allocated attention to threat, they experienced difficulty disengaging from it. Treatment procedures to modify the attentional bias may be effective.

To address the multipath delay estimation problem in distributed hydrophone passive localization systems, a global energy minimization-based method is proposed in this paper. In this method, correlation pulses are treated as tracking targets, and their trajectories are estimated from correlograms formed by multiple frames. Specifically, an energy function is designed to jointly encode pulse similarity, motion continuity, trajectory persistence, data fidelity, and regularization, thereby reformulating multipath delay estimation as a global optimization problem. In order to balance the discreteness of observations and the continuity of trajectories, the optimization process is implemented alternating between discrete association (solved via $\alpha$-expansion) and continuous trajectory fitting (using weighted cubic splines). Furthermore, a dynamic hypothesis space expansion strategy based on trajectory merging and splitting is introduced to improve robustness while accelerating convergence. By exploiting both the intrinsic characteristics of correlation pulses in multi-frame processing and the physical properties of motion trajectories, the proposed method achieves higher tracking accuracy without requiring prior knowledge of the number of delay trajectories in a noisy environment. Numerical simulations under various noise conditions and sea trial results validate the superiorities of the proposed multipath delay estimation method.