We present Acoustic Inertial Measurement (AIM), a one-of-a-kind technique for indoor drone localization and tracking. Indoor drone localization and tracking are arguably a crucial, yet unsolved challenge: in GPS-denied environments, existing approaches enjoy limited applicability, especially in Non-Line of Sight (NLoS), require extensive environment instrumentation, or demand considerable hardware/software changes on drones. In contrast, AIM exploits the acoustic characteristics of the drones to estimate their location and derive their motion, even in NLoS settings. We tame location estimation errors using a dedicated Kalman filter and the Interquartile Range rule (IQR) and demonstrate that AIM can support indoor spaces with arbitrary ranges and layouts. We implement AIM using an off-the-shelf microphone array and evaluate its performance with a commercial drone under varied settings. Results indicate that the mean localization error of AIM is 46% lower than that of commercial UWB-based systems in a complex 10m×10m indoor scenario, where state-of-the-art infrared systems would not even work because of NLoS situations. When distributed microphone arrays are deployed, the mean error can be reduced to less than 0.5m in a 20m range, and even support spaces with arbitrary ranges and layouts.

In this paper, we propose a scheme of quantum information splitting arbitrary three-qubit state by using seven-qubit entangled as quantum channel. The sender Alice first performs Bell-state measurements (BSMs) on her qubits pairs respectively and tells her measurement outcome to authorizers Bob to reconstruct the original state, then Charlie should carries out single-qubit measurement (SQM) on his qubits. According to the results from Alice and Charlie, Bob can reconstruct the original state by applying an appropriate unitary operation. After analyzing, the method achieved the desired effect of quantum information splitting (QIS). We also realize the QIS of arbitrary three-qubit state in cavity quantum electrodynamics (QED).