Passive Detection Technology Research Articles

Simulation research of underwater passive direction finding technology

As one of the indispensable technologies for underwater target detecting, underwater acoustic passive detection technology has always been a hot spot in marine system research. A vector sensor can detect underwater targets by combination with the pressure and vibration velocity information; vector sensor array can obtain higher gain than pressure array with the same number hydrophones. Vector sensor can reduce the length of the underwater sensor array and can detect underwater targets in the case of low signal to noise ratio. This is the goal that many researchers strive to solve for many years; therefore, vector sensor passive detection technology has far-reaching scientific research value.Through the verification of the simulation experiment, the azimuth estimation technique and the adaptive frequency estimation technique are feasible. By estimating the azimuth sequence and frequency sequence of the target radiation signal, the relevant information of the target can be obtained, and so it is of great significance in the research of underwater acoustic detection technology.

Network health monitoring method based on multimodal spatiotemporal correlation fuzzy inference

By using the traditional active E2E (end-to-end) detection method to locate the congested link in IP network, we can only judge whether the network is congestion or not, and the congested cause cannot be effectively known. However, the passive detection method needs to acquire the syslog of all routers in the managed network, which requires a large amount of data. In this paper, a kind of multimodal spatiotemporal correlation fuzzy inference method for network health monitoring method was proposed through combining with the method of active E2E detection and passive detection. First, the set of network-congested links is obtained based on the Bayesian theorem through E2E path performance detection; then, the router syslog associated with the congested links will be acquired for a period of time, and the abnormal events in the syslog are detected based on the principle of mutual information (MI), so as to find the causes of network congestion. Based on the multimodal spatiotemporal correlation fuzzy paradigms combining with the active end-to-end detection and passive detection technology, the amount of data analysis will be greatly reduced and the operation speed will be improved. Simulation experiments and actual experiments verified the accuracy of the method this paper proposed.

The research of passive detection parameter measurement based on vector sensor

As one of the indispensable technologies for underwater target detecting, underwater acoustic passive detection technology has always been a hot spot in marine system research. A vector sensor can detect underwater targets by combination with the pressure and vibration velocity information; vector sensor array can obtain higher gain than pressure array with the same number hydrophones. Vector sensor can reduce the length of the underwater sensor array and can detect underwater targets in the case of low signal to noise ratio. This is the goal that many researchers strive to solve for many years; therefore, vector sensor passive detection technology has far-reaching scientific research value. Through the verification of the simulation experiment, the azimuth estimation technique and the adaptive frequency estimation technique are feasible. By estimating the azimuth sequence and frequency sequence of the target radiation signal, the relevant information of the target can be obtained, so it is of great significance in the research of underwater acoustic detection technology.

Unsupervised Classification of Hydrophone Signals With an Improved Mel-Frequency Cepstral Coefficient Based on Measured Data Analysis

Recently, passive detection technology has developed the ability to detect surface ships based on the noise emissions recorded by hydrophones, making it possible in some cases to classify surface ships. One of the most concerning issues with ships and underwater targets is the current lack of reliable features for unsupervised classification. To solve this problem, this paper proposes an improved Mel-frequency cepstral coefficients (IMFCC) feature for unsupervised clustering of marine targets. As the feature extraction of hydrophone signals rely on preprocessing, the IMFCC adds cyclic modulation spectrum (CMS) and cross-correlation bispectrum (CCB) in the preprocessing module before traditional the Mel-frequency cepstral coefficient process, and the principal component analysis (PCA) is added as the backend processing module. There are four contributions as follows: First, for IMFCC extraction, it combines the advantages of the CMS, CCB, MFCC, and PCA. Second, the two common unsupervised clusters, Gaussian mixture model (GMM) and fuzzy C-means are used to evaluate the CMS, CCB, and several MFCCs—MFCC-vector quantization (MFCC-VQ), MFCC-Gaussian mixture model (MFCC-GMM), TEO-MFCC (Teager Energy Operator based MFCC), and IMFCC. Third, the performances of traditional MFCC, Teager-energy operator (TEO)-MFCC, IMFCC, MFCC-VQ, and MFCC-GMM are discussed under different dimensions, signal-to-noise ratios, distances, and depths. Finally, the experimental results prove that the IMFCC method has a strong anti-interference ability and is robust and has a high-success rate for clustering multitarget or different depth targets, which enables the IMFCC to be a reliable feature for unsupervised classification.