We presentMicNest: an acoustic localization system enabling precise drone landing. InMicNest, multiple microphones are deployed on a landing platform in carefully devised configurations. The drone carries a speaker transmitting purposefully-designed acoustic pulses. The drone may be localized as long as the pulses are correctly detected. Doing so is challenging:i)because of limited transmission power, propagation attenuation, background noise, and propeller interference, the Signal-to-Noise Ratio (SNR) of received pulses is intrinsically low;ii)the pulses experience non-linear Doppler distortion due to the physical drone dynamics;iii)as location information is used during landing, the processing latency must be reduced to effectively feed the flight control loop. To tackle these issues, we design a novel pulse detector, Matched Filter Tree (MFT), whose idea is to convert pulse detection to a tree search problem. We further present three practical methods to accelerate tree search jointly. Our experiments show thatMicNestcan localize a drone 120 m away with 0.53% relative localization error at 20 Hz location update frequency. For navigating drone landing,MicNestcan achieve a success rate of 94%. The average landing error (distance between landing point and target point) is only 4.3 cm.

We present MicNest: an acoustic localization system enabling precise landing of aerial drones. Drone landing is a crucial step in a drone's operation, especially as high-bandwidth wireless networks, such as 5G, enable beyond-line-of-sight operation in a shared airspace and applications such as instant asset delivery with drones gain traction. In MicNest, multiple microphones are deployed on a landing platform in carefully devised configurations. The drone carries a speaker transmitting purposefully-designed acoustic pulses. The drone may be localized as long as the pulses are correctly detected. Doing so is challenging: i) because of limited transmission power, propagation attenuation, background noise, and propeller interference, the Signal-to-Noise Ratio (SNR) of received pulses is intrinsically low; ii) the pulses experience non-linear Doppler distortion due to the physical drone dynamics while airborne; iii) as location information is to be used during landing, the processing latency must be reduced to effectively feed the flight control loop. To tackle these issues, we design a novel pulse detector, Matched Filter Tree (MFT), whose idea is to convert pulse detection to a tree search problem. We further present three practical methods to accelerate tree search jointly. Our real-world experiments show that MicNest is able to localize a drone 120 m away with 0.53% relative localization error at 20 Hz location update frequency.

Frequent and wide changes in operation conditions are quite common in real process industry, resulting in typical wide-range nonstationary and transient characteristics along time direction. The considerable challenge is, thus, how to solve the conflict between the learning model accuracy and change complexity for analysis and monitoring of nonstationary and transient continuous processes. In this work, a novel condition-driven data analytics method is developed to handle this problem. A condition-driven data reorganization strategy is designed which can neatly restore the time-wise nonstationary and transient process into different condition slices, revealing similar process characteristics within the same condition slice. Process analytics can then be conducted for the new analysis unit. On the one hand, coarse-grained automatic condition-mode division is implemented with slow feature analysis to track the changing operation characteristics along condition dimension. On the other hand, fine-grained distribution evaluation is performed for each condition mode with Gaussian mixture model. Bayesian inference-based distance (BID) monitoring indices are defined which can clearly indicate the fault effects and distinguish different operation scenarios with meaningful physical interpretation. A case study on a real industrial process shows the feasibility of the proposed method which, thus, can be generalized to other continuous processes with typical wide-range nonstationary and transient characteristics along time direction.Note to Practitioners—Industrial processes in general have nonstationary characteristics which are ubiquitous in real world data, often reflected by a time-variant mean, a time-variant autocovariance, or both resulting from various factors. The focus of this study is to develop a universal analytics and monitoring method for wide-range nonstationary and transient continuous processes. Condition-driven concept takes the place of time-driven thought. For the first time, it is recognized that there are similar process characteristics within the same condition slice and changes in the process correlations may relate to its condition modes. Besides, the proposed method can provide enhanced physical interpretation for the monitoring results with concurrent analysis of the static and dynamic information which carry different information, analogous to the concepts of “position” and “velocity” in physics, respectively. The static information can tell the current operation condition, while the dynamic information can clarify whether the process status is switching between different steady states. It is noted that the condition-driven concept is universal and can be extended to other applications for industrial manufacturing applications.

A low power source-synchronous source-series- terminated (SST) transmitter (Tx) in 65 nm CMOS technology is presented. The Tx, comprised of nine data/control channels, a forwarded-clock channel and one PLL, merely dissipates 26.2 mW/channel while exhibiting a 750 mV differential eye height at 6.4 Gbps. The SST drivers can save ¾ output stage power of CML ones, and moreover, the proposed novel topology can independently control impedance self-calibration and equalization. To implement half-rate architecture, the PVT- tolerant PLL provides a pair of quadrature clocks with 2.5 ps rms cycle to cycle jitters running at 3.2 GHz.