Yongsheng Yan

Efficient convex optimization method for underwater passive source localization based on RSS with WSN

The widespread applications of wireless sensor network (WSN) and the advancement of the micro-electro-mechanical systems (MEMS) technology, and wireless communication develop the underwater wireless sensor network (UWSN). The passive source localization application which is an important application in underwater signal processing is focused on in this paper. We model that the underwater source radiates acoustic noise or energy isotropically and utilize the receiving signal energy (RSS) taken at individual sensors of the UWSN to estimate passively the location of the source. Firstly, we choose the node whose energy is maximal as the reference node to decrease the computational complexity compared to our previous work which use division between pairs of sensor energy output. Secondly, the one-step least-square (OS) method is reviewed and another description based on maximum likelihood source location estimator (MLE) is given. Thirdly, a semidefinite programming (SDP) method is developed to covert the nonconvex problem into the convex optimization problem (CVX). Compared to the least-square (LS) method, this CVX-SDP method based on RSS achieves more accurate results and has better robustness with less number of sensor nodes and with lower SNR.

TDOA-Based source collaborative localization via semidefinite relaxation in sensor networks

The time delay of arrival- (TDOA-) based source localization using a wireless sensor network has been considered in this paper. The maximum likelihood estimate (MLE) is formulated by taking the correlated TDOA noise into account, which is caused by the difference with the TOA of the reference sensor. The global optimal solution is difficult to obtain due to the nonconvex nature of the ML function. We propose an alternative semidefinite programming method, which transforms the original ML problem into a convex one by relaxing nonconvex equalities into convex matrix inequalities. In addition, the source localization algorithm in the presence of sensor location errors and non-line-of-sight (NLOS) observations is developed. Our simulation results demonstrate the potential advantages of the proposed method. Furthermore, the proposed source localization algorithm by taking the NLOS TOA measurements as the constraints of the convex problem can provide a good estimate.

Deep-water riser fatigue monitoring systems based on acoustic telemetry

Marine risers play a key role in the deep and ultra-deep water oil and gas production. The vortex-induced vibration (VIV) of marine risers constitutes an important problem in deep water oil exploration and production. VIV will result in high rates of structural failure of marine riser due to fatigue damage accumulation and diminishes the riser fatigue life. In-service monitoring or full scale testing is essential to improve our understanding of VIV response and enhance our ability to predict fatigue damage. One marine riser fatigue acoustic telemetry scheme is proposed and an engineering prototype machine has been developed to monitor deep and ultra-deep water risers’ fatigue and failure that can diminish the riser fatigue life and lead to economic losses and eco-catastrophe. Many breakthroughs and innovation have been achieved in the process of developing an engineering prototype machine. Sea trials were done on the 6th generation deep-water drilling platform HYSY-981 in the South China Sea. The inclination monitoring results show that the marine riser fatigue acoustic telemetry scheme is feasible and reliable and the engineering prototype machine meets the design criterion and can match the requirements of deep and ultra-deep water riser fatigue monitoring. The rich experience and field data gained in the sea trial which provide much technical support for optimization in the engineering prototype machine in the future.

Decision Fusion with Channel Errors in Distributed Decode-Then-Fuse Sensor Networks

Decision fusion for distributed detection in sensor networks under non-ideal channels is investigated in this paper. Usually, the local decisions are transmitted to the fusion center (FC) and decoded, and a fusion rule is then applied to achieve a global decision. We propose an optimal likelihood ratio test (LRT)-based fusion rule to take the uncertainty of the decoded binary data due to modulation, reception mode and communication channel into account. The average bit error rate (BER) is employed to characterize such an uncertainty. Further, the detection performance is analyzed under both non-identical and identical local detection performance indices. In addition, the performance of the proposed method is compared with the existing optimal and suboptimal LRT fusion rules. The results show that the proposed fusion rule is more robust compared to these existing ones.

A calibration method for position, gain and phase uncertainty of nonplanar array with arbitrary geometry

In this paper, a calibration method is proposed for a nonplanar array with arbitrary geometry. This method can estimate the errors of array sensor position, gain and phase simultaneously using known calibration sources. Note that the technique does not use iterative calculation in estimating the array parameters and hence it has no convergent problem. However, it requires that four directions of arrival (DOAs) of signal sources to be known to calibrate the array. Some representative computer simulations are presented to illustrate the improved performance in resolution and accuracy for multiple source localization problems using this technique.

Efficient Convex Optimization for Energy-Based Acoustic Sensor Self-Localization and Source Localization in Sensor Networks

The energy reading has been an efficient and attractive measure for collaborative acoustic source localization in practical application due to its cost saving in both energy and computation capability. The maximum likelihood problems by fusing received acoustic energy readings transmitted from local sensors are derived. Aiming to efficiently solve the nonconvex objective of the optimization problem, we present an approximate estimator of the original problem. Then, a direct norm relaxation and semidefinite relaxation, respectively, are utilized to derive the second-order cone programming, semidefinite programming or mixture of them for both cases of sensor self-location and source localization. Furthermore, by taking the colored energy reading noise into account, several minimax optimization problems are formulated, which are also relaxed via the direct norm relaxation and semidefinite relaxation respectively into convex optimization problems. Performance comparison with the existing acoustic energy-based source localization methods is given, where the results show the validity of our proposed methods.

Functional Link Based Adaptive Filter for underwater acoustic detection signal

The communication and detection signal become hard to recognize after they go through the underwater channel. To enhance the underwater signal, the functional link theory is introduced. According to the characteristic of the underwater signals, some improvements are made based on the split functional link adaptive filter. Experiment shows that the split functional link adaptive filter is fit for the lake channel through signal.

Feature extraction of underwater low-velocity targets based on 1 1 over 2-spectrum

The feature extraction and target recognition of low-velocity targets near the port is difficult in signal processing. After the spectral characteristics of low-velocity targets are analyzed, the paper uses 1 1 over 2-spectrum as the method of feature extraction. The advantages are as follow. 1 1 over 2-spectrum can suppress the Gaussian white noise. It can enhance weak fundamental frequency components of harmonic signal and extract quadratic phase coupling components. In view of the advantages mentioned above, the paper applies 1 1 over 2-spectrum based on higher order statistics to the feature extraction of low-velocity targets for the first time. A 20-dimensional feature vector is extracted by using 1 1 over 2-spectrum sub-band energy method. High and low velocity targets are classified with the use of BP neutral network. The results show that the average recognition rate is more than 85%. It is concluded that the method of 1 1 over 2-spectrum is effective to distinguish high-velocity targets with low-velocity ones.

Research on doppler frequency shift estimation in low Echo-to-Reverberation Ratio condition

Using the relationship between the number of zero-crossings and signal frequency, A Variable Bandwidth Band-pass Filter-Zero-Crossing Detection is proposed. This method can be used to estimate the echo frequency in Doppler Velocity Measurement. According to computer simulation, the estimation error of echo is 2Hz approximately (Echo-to-Reverberation Ratio is −15dB, echo frequency is 10360Hz); according to experiment, the estimation error of echo is 4Hz approximately (Echo-to-Reverberation Ratio is −3dB, echo frequency is 8080Hz). The amount of calculation of this method is lower than FFT. Therefore, this method has a good application prospects.