Direction of Arrival Estimation Based on Support Vector Regression: Experimental Validation and Comparison with MUSIC

In this letter, the problem of estimating the directions of arrival (DOAs) of coherent electromagnetic waves impinging upon a uniform linear array (ULA) is considered. In particular, an efficient DOA estimation approach based on the support vector regression is assessed using experimental measurements. Moreover, the obtained results are compared with those yielded by the multiple signal classification (MUSIC) algorithm

Improving performance of pedestrian positioning by using vehicular communication signals

Pedestrian-to-vehicle communications, where pedestrian devices transmit their position information to nearby vehicles to indicate their presence, help to reduce pedestrian accidents. Satellite-based systems are widely used for pedestrian positioning, but have much degraded performance in urban canyon, where satellite signals are often obstructed by roadside buildings. In this paper, we propose a pedestrian positioning method, which leverages vehicular communication signals and uses vehicles as anchors. The performance of pedestrian positioning is improved from three aspects: (i) Channel state information instead of RSSI is used to estimate pedestrian-vehicle distance with higher precision. (ii) Only signals with line-of-sight path are used, and the property of distance error is considered. (iii) Fast mobility of vehicles is used to get diverse measurements, and Kalman filter is applied to smooth positioning results. Extensive evaluations, via trace-based simulation, confirm that (i) Fixing rate of positions can be much improved. (ii) Horizontal positioning error can be greatly reduced, nearly by one order compared with off-the-shelf receivers, by almost half compared with RSSI-based method, and can be reduced further to about 80cm when vehicle transmission period is 100ms and Kalman filter is applied. Generally, positioning performance increases with the number of available vehicles and their transmission frequency

Different Motion Cues Are Used to Estimate Time-to-arrival for Frontoparallel and Loming Trajectories

Estimation of time-to-arrival for moving objects is critical to obstacle interception and avoidance, as well as to timing actions such as reaching and grasping moving objects. The source of motion information that conveys arrival time varies with the trajectory of the object raising the question of whether multiple context-dependent mechanisms are involved in this computation. To address this question we conducted a series of psychophysical studies to measure observers’ performance on time-to-arrival estimation when object trajectory was specified by angular motion (“gap closure” trajectories in the frontoparallel plane), looming (colliding trajectories, TTC) or both (passage courses, TTP). We measured performance of time-to-arrival judgments in the presence of irrelevant motion, in which a perpendicular motion vector was added to the object trajectory. Data were compared to models of expected performance based on the use of different components of optical information. Our results demonstrate that for gap closure, performance depended only on the angular motion, whereas for TTC and TTP, both angular and looming motion affected performance. This dissociation of inputs suggests that gap closures are mediated by a separate mechanism than that used for the detection of time-to-collision and time-to-passage. We show that existing models of TTC and TTP estimation make systematic errors in predicting subject performance, and suggest that a model which weights motion cues by their relative time-to-arrival provides a better account of performance