Correlation Detection Algorithm Research Articles

Time Synchronization and Signal Detection in Non-Orthogonal Unicast and Broadcast Networks

The next generation digital TV (DTV) broadcast, namely Advanced Television System Committee (ATSC) 3.0, relies on substantial single frequency networks (SFN), each of which requires a studio to transmitter link (STL) to transmit data from the broadcast gateway (BG), leading to significant construction costs. Therefore, the wireless in-band distribution link (IDL) technology is proposed, which uses layered-division multiplexing (LDM) to transmit the STL data via wireless links from the BG to the SFN transmitters within the same spectrum as the broadcast services. In this paper, we propose the time synchronization and signal detection algorithm for wireless IDL systems with both unicast and broadcast signals. We introduce two multiplexing schemes, N-LDM and 3-LDM, and demonstrate their superiority over traditional frequency division multiplexing (FDM). To overcome the challenge of multi-access interference and synchronization errors caused by multipath, we develop a unique word (UW) design algorithm for transmitted signals and a double threshold correlation detection algorithm (DTCDA) that outperforms traditional ones. We employ a second threshold to enhance detection performance at low signal-to-noise ratios, and utilizes successive interference cancellation (SIC) to mitigate multiple-access interference. More importantly, our approach requires no knowledge of the unicast user number or channel state information. Simulation results validate the effectiveness of the proposed scheme.

Robust Burst Detection Algorithm for Distributed Unique Word TDMA Signal

In recent years, distributed unique word (DUW) has been widely used in satellite single carrier TDMA signals, such as very small aperture terminal (VSAT) satellite systems. Different from the centralized structure of traditional unique word, DUW is uniformly dispersed in a burst signal, where the traditional unique word detection methods are not applicable anymore. For this, we propose a robust burst detection algorithm based on DUW. Firstly, we allocated the sliding detection windows with the same structures as DUW in order to effectively detect it. Secondly, we adopt the method of time delay conjugate multiplication to eliminate the influence of frequency offset on detection performance. Due to the uniform dispersion of DUW, it naturally has two different kinds of time delays, namely the delay within the group and the delay between the two groups. So, we divide the traditional dual correlation formula into two parts to calculate them separately and obtain a dual correlation detection algorithm, which is suitable for DUW. Simulation and experimental results demonstrate that when the distribution structure of DUW changes, detection probability of the proposed algorithm fluctuates little, and its variance is 1.56×10−5, which is 99.83% lower than the existing DUW detection algorithms. In addition, its signal to noise ratio (SNR) threshold is about 1 dB lower than the existing algorithms under the same circumstance of the missed detection probability.

Semantic‐Based Sports Music Information Fusion and Retrieval in Wireless Sensor Networks

The wireless sensor network has developed rapidly in recent years. It is formed by the intersection of multiple disciplines. It integrates embedded technology, sensor technology, distributed technology, wireless communication technology, and modern networks. It is a brand new information acquisition platform. The characteristics of sensor networks determine that information fusion technology is a hot spot in the research of wireless sensor networks. Information fusion can achieve high performance and low cost in terms of energy and communication, which is of great significance to the research of sensor networks. This paper is aimed at studying the semantic‐based sports music information fusion and retrieval research in wireless sensor networks. WSNs may face various attacks including eavesdropping attacks, replay attacks, Sybil attacks, and DOS attacks. Therefore, they are designing sensor network solutions. It is necessary to consider the network security issues. This article summarizes and analyzes the existing WSN security data fusion solutions for this issue and compares them by classification. This paper proposes methods and theories such as the spatial correlation detection algorithm, CBA algorithm, FTD algorithm, and DFWD algorithm, which enriches the research of information fusion and retrieval in wireless sensor networks, which is of exploratory significance, and it also establishes this problem. The model was studied, and reliable data was obtained. The experimental results of this paper show that when using these methods to diagnose faults in WSN, the correct rate of model diagnosis is higher than 77%.

Weak signal detection algorithm for colored noise in laser guidance

Correlation detection can be used to extract weak signals such as those weakened by noise and attenuation. However, complex background noise contains both white and colored noise. The white noise is independent in the time domain, whereas the colored noise has a certain dependency, and the correlation detection algorithm cannot extract useful signals from the background mixed with colored noise. An improved correlation detection algorithm for whitening colored noise is discussed. Simulation experiments show that for laser signal amplitude similar to the noise amplitude, the correlation detection algorithm can extract the pulse signal, and the white noise background has a significantly improved signal-to-noise ratio of 2.3 times higher than the colored noise background. The experiment also proves that the whitening algorithm discussed can extract weak laser signals very well.

A Correlation Detection Method of Low SNR Based on Multi-Channelization

This letter proposes a correlation detection algorithm based on multi-channelization. The existing method has poor performance for detecting a time-varying signal with a low signal-to-noise ratio(SNR). To overcome this problem, we use the method of correlation summation after multi-channelization. According to the difference between the correlation between signals and noise in the received data, the single-channel data is converted into multi-channel data by a multi-channelization method without changing the signals. Then the noise is filtered by correlation calculation of multi-channel data, which can improve the detection probability of signals under low SNR. Based on the theory of multi-channelization, we propose two different methods to detect signals. To better reflect the performance of the algorithm, we compared several classical signal detection methods, such as energy detection (ED), correlation detection (CD), and cyclic spectrum detection (CSD). Simulation analysis and experimental results show that the multi-channelization correlation detection algorithm has better detection performance when the SNR is low.

Multi Feature Selection based Network Traffic Anomaly Detection Method

In this paper, a method is proposed to solve the difficult problem of the training model and the dynamic variability of the deployment environment. Firstly, the network traffic data is converted into numerical value and projected onto histograms of different dimensions to construct detection vectors. Based on the detection vector, some kinds of classifiers are compared. SVDD, which can handle high-dimensional data and has strong generalization ability, is chosen for anomaly detection. Secondly, in order to improve the true positive rate of detection and reduce training time, the classifier is trained continuously and trying various different combinations of features. Finally, a multi-step correlation detection algorithm is adopted to optimize the detection accuracy, and obvious abnormal samples are eliminated from the newly added samples, reducing the training cost and improving the classification accuracy. Through experiments based on a large amount of real network traffic data, the result demonstrate that the proposed method has higher accuracy and lower false alarm rate, and can effectively reduce the training cost.

A Direct Current Measurement Method Based on Terbium Gallium Garnet Crystal and a Double Correlation Detection Algorithm.

When applying an optical current transformer (OCT) to direct current measurement, output signals exhibit a low signal-to-noise ratio and signal-to-noise band overlap. Sinusoidal wave modulation is used to solve this problem. A double correlation detection algorithm is used to extract the direct current (DC) signal, remove white noise and improve the signal-to-noise ratio. Our sensing unit uses a terbium gallium garnet crystal in order to increase the output signal-to-noise ratio and measurement sensitivity. Measurement errors of single correlation and double correlation detection algorithms are compared, and experimental results showed that this measurement method can control measurement error to about 0.3%, thus verifying its feasibility.

One-step singular correlation and most relevant vector filtering for color images

Concerning the fuzzy and unclear details after color image denoising,the correlation properties of the adjacent pixels as well as the correlation among the color channels were analyzed.First,one-step singular correlation detection algorithm was used to detect the noises of each layer of the pre-treatment color image,and then the most relevant vector median was used to fill value of the noises.Finally,the color image filtering processing was realized.The experimental results show that this method not only accurately detects the salt-pepper noises,but also well restores and protects the original information such as the edge details.The color image filtering accuracy and performance criterion such as Peak-Signal-to Noise Ratio(PSNR) are further improved.

Automatic Detection of a Subsurface Wire Using an Electromagnetic Gradiometer

A model-based correlation detection scheme is presented with the aim of detecting and localizing subsurface tunnel infrastructure in an automated fashion. Our goal is to develop a comprehensive detection technology that can be fielded and successfully used by nonexperts, while simultaneously being sufficiently robust as to be effective. Our correlation detection algorithm relies on a library of model signals that are generated using an analytical model of a thin subsurface wire in a homogeneous half-space. The wire is illuminated using an active transmitter source (12, 20, or 200 kHz), and its response is sensed using a man-portable electromagnetic gradiometer (EMG) system. The performance of the detector is assessed using synthetic data and receiver operating characteristic (ROC) analysis as well as experimental data collected during a field test. Preliminary ROC results indicate that at sufficient signal-to-noise ratio, the detector can achieve detection probabilities greater than 0.9 with corresponding false alarm rates of less than one every 1000 m. Results from the field tests revealed that the responses from the EMG can be used to detect and localize (to within 0.5 m in the horizontal) a wire target down to a depth of at least 7 m. We believe the EMG system and correlation detector combine to form a promising technology for detecting tunnel infrastructure that can be used by experts and, more importantly, nonexperts as well.

MG-join: detecting phenomena and their correlation in high dimensional data streams

A phenomenon appears in a sensor network when a group of sensors continuously produces similar readings (i.e., data streams) over a period of time. This involves the processing of hundreds and maybe thousands of data streams in real-time. This paper focuses on detecting environmental phenomena and determining possible correlation between such phenomena. This paper proposes an efficient scheme for a detecting and tracking phenomena, e.g., air pollution and oil spills. To achieve fast online response, the proposed algorithms use a Discrete Fourier Transformation (DFT) to reduce the dimensionality of the streams. Each stream is represented by a point in a multidimensional grid in the frequency domain. The algorithm uses an improved unsupervised grid-based clustering technique to detect similar streams and to form clusters. The paper also proposes an efficient algorithm for detecting correlation among phenomena. The proposed algorithm calculates the correlation coefficient in the frequency domain. It makes use of the DFT coefficients that are calculated for detecting the phenomena. The proposed correlation detection algorithm uses only few DFT coefficients in the frequency domain. Experiments on synthetic data streams show that the proposed algorithm for detecting and tracking phenomena is much faster than the DBSCAN clustering technique, which is based on the R-tree index. At the same time, the proposed phenomena detection algorithm produces the same quality as that of the DBSCAN by only using two DFT coefficients in most of the cases. The experimental results also showed that the proposed technique for detecting the correlation among phenomena performs as good as the traditional Pearson correlation formula but it is much faster.