High Accuracy AUV-Aided Underwater Localization: Far-Field Information Fusion Perspective

An autonomous underwater vehicle (AUV) can be employed to estimate an underwater target's position using the Doppler shift measurement extracted from received signals. Conventionally, the received signals have to be divided into several short frames so that the Doppler shift is constant in each one. When the AUV is far away from the target, the signal to noise ratio (SNR) is quite low. An intuitive solution is to increase the frame length to suppress noise and boost SNR. However, it is worth noting that the Doppler shift is actually changing over time, and the prolonged frame length will inevitably induce modeling error. This is a fundamental dilemma in the Doppler-shift-based underwater localization. In this paper, we reveal that the assumption of constant Doppler shift comes from the zero-th order Taylor expansion of the real-time model. To increase the frame length without jeopardizing the model accuracy, we modify the model by taking the first-order Taylor expansion. By doing so, the frame length can be prolonged by one order of magnitude without introducing non-negligible modeling error. What this new model says is that when the target broadcasts a single-tone signal, the AUV will receive a linear frequency modulated (LFM) signal parameterized by a Doppler shift and a corresponding changing rate, i.e., Doppler rate. Although the Doppler rate is very small, it helps us improve the estimation accuracy of Doppler shift. Besides, the Doppler rate also contains target's posit...
Source: IEEE Transactions on Signal Processing - Category: Biomedical Engineering Source Type: research