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.

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.

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.

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.

For targets with complex motion, the echo of inverse synthetic aperture radar (ISAR) is a time-varying frequency signal in azimuth. Hence, the traditional range-Doppler (R-D) algorithm based on a constant frequency is invalid. In this letter, a novel ISAR imaging method for targets with complex motion is presented. The echo in azimuth is characterized as an amplitude-modulated (AM)-cubic phase signal, which is closer to the real ISAR scene, and Radon transform cubic chirplet decomposition (RTCCD) algorithm is proposed to process the signal. By introducing Radon transform and improved cubic chirplet function (CPF), the proposed algorithm estimates the chirp rate and the cubic chirp rate simultaneously to avoid error accumulation. Therefore, the parameter estimation precision is improved, and a high quality ISAR image can be obtained. Simulations and real data experiment validate the effectiveness of the proposed method.