Underwater acoustic localization (UWAL) of the black box for a sunken airplane utilizing an autonomous underwater vehicle (AUV) is a useful technique in ensuring traffic safety. Aiming at improving localization precision, this article 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 localization precision and obstacle avoidance is achieved, and the localization precision is significantly improved. The lake trial result validates the effectiveness of the proposed method.

For the target with 3-D rotation, the 3-D synthetic aperture radar (SAR) imaging is very important for the feature extraction and classification. To solve this issue, a novel 3-D imaging algorithm through the airborne SAR system with a single antenna is proposed in this article, which has great advantages for the simplification of system structure compared with the traditional interferometric system. The proposed 3-D airborne SAR imaging algorithm can be implemented with three steps: 1) the azimuth signal is modeled as multicomponent linear frequency modulation (LFM) signal due to the relative movement between the target and radar. 2) The scatterer height position can be obtained by estimating the frequency modulation rate (FMR) for the LFM signal. 3) The 3-D airborne SAR image is obtained via the range-Doppler (RD) algorithm. Furthermore, the reconstruction performance under different rotation patterns, including roll, pitch, and yaw, is analyzed. The availability of the presented novel technique is demonstrated by the results of simulated and real experimental data.

Underwater acoustic localization (UWAL) is widely applied in ocean exploration, and a key issue for the high-rate time-of-arrival (TOA)-based UWAL method is to select the direct signal in the multipath acoustic channel. However, the existing direct signal selection methods suffer from a deterioration of the selection accuracy since the received signals from adjacent periods are mixed with each other. Addressing the problem, we propose a high-rate direct signal selection algorithm in this article. First, we choose the amplitude, TOA, bandwidth, and Doppler frequency of the detected pulses as the input features. Second, a direct signal classifier is established utilizing the decision tree. Finally, the localization model is built based on the TDOA information of the direct signal, and the localization precision analysis evaluates the performance of the UWAL method. Sufficient experiments, including simulation, tank experiment, and sea trial, are implemented to verify the effectiveness of the proposed 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.

The black box plays an important role in transportation security, and the underwater acoustic localization (UWAL) methods are the most effective way to find the black box sunken undersea. The traditional UWAL methods suffer from a significant decrease in the localization precision due to the signal period drift of the black box in the cold and high-pressure undersea environment. In our former work, we carried out the second-order time-difference-of-arrival (STDOA) algorithm to address the problem. However, for a real maneuvering ship, the localization precision has the potential to be further improved with the help of the uniformly distributed localization nodes. In this article, a generalized STDOA (GSTDOA) algorithm is proposed. First, we establish the general form of the STDOA information, where the localization nodes can be selected arbitrarily. Second, we analyze the localization precision of the GSTDOA algorithm, and it shows that the localization precision is closely related to the selected nodes. Third, we optimize the localization nodes for maximum localization precision. Finally, experiments in different scenes validate the effectiveness of the proposed method.