Underwater acoustic localization (UWAL) is a useful technique, and is applied in finding the acoustical sources, such as the black box. The existing UWAL methods are mainly based on the time of arrival (TOA), the time difference of arrival (TDOA), and the direction of arrival (DOA), which need the time synchronization, the knowledge of signal period, and the high precision angular measurement, respectively. However, these conditions are difficult to be satisfied in the scene of locating the black box utilizing single autonomous underwater vehicle. In this article, we propose the definition of second-order time difference of arrival (STDOA) and establish the localization model based on the STDOA. The proposed STDOA-based method eliminates the unknown signal period and overcomes the restriction of the traditional TDOA-based method. Combined with the particle filter, target tracking algorithm based on the STDOA is also presented. Precision analysis and the simulation results validate the effectiveness of the STDOA-based method and prove that the proposed method achieves a comparable localization precision compared with the TOA-based method and the TDOA-based method, and a higher precision compared to the DOA-based method.

Underwater acoustic localization (UWAL) of the black box is a significant technique for transportation safety. The traditional UWAL algorithm based on the time difference of arrival (TDOA) suffers from large localization error due to the signal period drift in the scenario of high pressure and low temperature under sea. To solve this problem, an improved TDOA algorithm is proposed in this letter. We define the second-order time difference of arrival, which eliminates the drifted signal period. Utilizing the second-order time difference of arrival information, we establish the equations to solve the position of the black box. Precision analysis is presented to theoretically calculate the localization precision of the proposed algorithm and test the robustness with respect to the input errors. Experiments in different scenes validate the effectiveness of the algorithm.

Underwater acoustic localization (UWAL) of the black box for a sunken airplane utilizing an autonomous underwater vehicle (AUV) is a useful technique in ensuring the traffic safety. Aiming at improving the localization precision, this paper proposes a new path planning algorithm based on the improved artificial potential field (APF). Compared with the conventional APF, we modify the conventional gravitation force and introduce a new localization precision force. Therefore, a balance between the localization precision and the obstacle avoidance is achieved, and the localization precision is significantly improved. Lake trail result validates the effectiveness of the proposed method.

As the installed capacity of wind power generation is increasing continuously, its impacts on system voltage stability have been intensively studied in recent days. In this paper, only the wind farms connected to distribution network are treated. Wind gusts will produce output power spikes and cause poor feeder voltage regulation, which may lead to voltage collapse when there is no sufficient reactive power support. An accurate voltage stability index (VSI) is needed to quantify the voltage stability margin in real time. This paper addresses such an index for distribution systems, where the required measurements are available only at feeder roots. The proposed VSI has been implemented in simulations with wind farms integrated. Simulation results are presented to examine the performance of both fixed-speed wind turbine and variable speed wind turbine. STATCOM is also included to demonstrate its capability for voltage stability improvement.

Azimuth angle estimation using a single vector hydrophone is a well-known problem in underwater acoustics. In the presence of multiple sources, a conventional complex acoustic intensity estimator (CAIE) cannot distinguish the azimuth angle of each source. In this paper, we propose a steering acoustic intensity estimator (SAIE) for azimuth angle estimation in the presence of interference. The azimuth angle of the interference is known in advance from the global positioning system (GPS) and compass data. By constructing the steering acoustic energy fluxes in the x and y channels of the acoustic vector hydrophone, the azimuth angle of interest can be obtained when the steering azimuth angle is directed toward the interference. Simulation results show that the SAIE outperforms the CAIE and is insensitive to the signal-to-noise ratio (SNR) and signal-to-interference ratio (SIR). A sea trial is presented that verifies the validity of the proposed method.

This paper considers an autonomous underwater vehicle (AUV) equipped with a sensor array to detect the presence of moving targets and estimate their relative locations. We propose a track-before-detect labeled multi-Bernoulli filter, which directly utilizes the signals received from the sensor array as the measurement in the form of a sample-covariance matrix (SCM). Using the unthresholded sensor data avoids creating bearing measurements and thus alleviates the challenges in the presence of multiple targets and high levels of background noise. The SCM measurement is affected by multiple targets in an additive fashion, which causes multi-dimensional integrals in measurement updating. We employ a change of variables and complex inverse-Wish art approximations to derive the closed form solutions, which ensures the filter is computationally tractable. Moreover, sequential Monte Carlo implementation is provided, complemented by an adaptive birth procedure based on the current SCM measurement. Simulation results demonstrate the improved performance of the proposed filter in challenging scenarios of low signal-to-noise ratio, small number of snapshots, trajectory crossing, and fluctuating received signal power.