This work presents a study of the extended counting technique for a 1.2-V micropower voice-band A/D converter. This extended counting technique is a blend of 61 modulation with its high resolution but relatively low speed and algorithmic conversion with its higher speed but lower accuracy. To achieve this, the converter successively operates first as a first-order 61 modulator to convert the most significant bits, and then the same hardware is used as an algorithmic converter to convert the remaining least significant bits. An experimental prototype was designed in 0.8-m CMOS. With a 1.2-V power supply, it consumes 150 W of power at a 16-kHz Nyquist sampling frequency. The measured peak S (N + THD) was 80 dB and the dynamic range 82 dB. The converter core including the controller and all reconstruction logic occupies about 1 3 1 mm 2 of chip area. This is considerably less than a complete 61 modulation A/D converter where the digital decimation filter would occupy a significant amount of chip area.
The vulnerability of the Global Navigation Satellite System (GNSS) open service signals to spoofing and meaconing poses a risk to the users of safety-of-life applications. This risk consists of using manipulated GNSS data for generating a position-velocity-timing solution without the user’s system being aware, resulting in presented hazardous misleading information and signal integrity deterioration without an alarm being triggered. Among the number of proposed spoofing detection and mitigation techniques applied at different stages of the signal processing, we present a method for the cross-correlation monitoring of multiple and statistically significant GNSS observables and measurements that serve as an input for the supervised machine learning detection of potentially spoofed or meaconed GNSS signals. The results of two experiments are presented, in which laboratory-generated spoofing signals are used for training and verification within itself, while two different real-world spoofing and meaconing datasets were used for the validation of the supervised machine learning algorithms for the detection of the GNSS spoofing and meaconing.
Precision Agriculture (PA) refers to applications asking for reliable and highly available precise positions, at centimeter level, in most of operational scenarios. Machinery guidance, automatic steering and controlled traffic farming enable machinery to move along repeatable tracks on the field, minimizing pass-to-pass errors and overlaps. In the recent years, satellite-based navigation has also opened the door to (semi) autonomous machineries for some specific farming scenarios and operations. Farming industry is now looking to use small robots to bring efficiencies and benefits to farms, capable of complex tasks that have not been possible with traditional large-scale agricultural machinery. Even though the state-of-the-art Real Time Kinematic (RTK) Global Navigation Satellite System (GNSS) receivers usually match the requirements posed by PA applications in open fields, propagation effects degrade the performance under foliage or with surrounding obstacles. This paper presents an experimental testbed and methodology suitable to assess the real performance of RTK GNSS-based devices in operational environments. Such testbed and methodology were effective to compare different devices, which resulted to be equivalent in open-sky conditions, but with significant differences in other types of environments. The paper also discusses opportunities and current limits of GNSS for emerging PA applications based on small robots and artificial intelligence. INDEX TERMS Global navigation satellite system (GNSS), real time kinematic (RTK), horizontal position accuracy, positions availability.
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