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Autonomous car is an important tool for transportation and military in the future, and its precise positioning is the basis of autonomous navigation. Most positioning algorithms based on map-matching for autonomous car make little use of environmental perception information. To solve this problem, a positioning algorithm is proposed in this paper, which is based on map-matching and environmental perception for autonomous car. The algorithm includes macroscopic road matching and microscopic precise positioning. As for macroscopic road matching, the algorithm makes use of computational geometry to match the position of autonomous car to the corresponding road, based on GPS point and map information of the road network. As for microscopic precise positioning, the algorithm makes use of the environmental perception, which is detected by the autonomous car to make precise positioning. Macroscopic road matching provides matching road to microscopic precise positioning, and microscopic precise positioning eliminates gross error produced in macroscopic road matching. Through real car tests, the algorithm can match map quickly, improving the positioning precision with strong real-time.

The digitized partial discharge (PD) signal recorded at one power cable end consists of a sequence of pulses whose separation time contains information on the relative location of the PD sites. However, noisy PD signals can lead to a large PD site estimation error. The most annoying noise encountered during PD measurements consists primarily of amplitude modulated (AM) radio signals. The performance and the resolution of the wide band PD detection system are critically affected by this noise. This paper describes an adaptive noise mitigating system (ANMS) which is aimed at effectively reducing this noise.

This paper describes a partial discharge propagation model and a location estimate which uses digital signal processing techniques. The model and the estimate are developed for an instrument which is capable of locating partial discharge sites in power cables non-destructively and in situ. Since the model utilizes actual cable response to partial discharge signals, it provides an excellent waveform match between simulations and measurements. The estimator can locate partial discharge sites in noisy environments with an error not exceeding 2 m. Applications of this model to field test data are also discussed. >

The resolution and robustness of a cable PD locating instrument, based on time-domain reflectrometry (TDR), are critically affected by noise present in a field environment. The permanent background noise encountered during field tests has been shown to be mainly due to amplitude modulation (AM) radio signals. This paper describes a self-learning digital filter aimed at mitigating this noise. The filter, in conjunction with a signal averaging routine, has been successfully used to reduce noise and enhance the PD signal obtained from a cable in a field environment. Examples are provided to demonstrate the ability of the filter to significantly improve the signal-to-noise ratio (SNR) and effectively extract PD signals from field recordings. >