The Experts below are selected from a list of 306 Experts worldwide ranked by ideXlab platform
D. Hatzinakos - One of the best experts on this subject based on the ideXlab platform.
-
An adaptive Gaussian sum algorithm for Radar Tracking
Signal Processing, 1999Co-Authors: Ip Tam, Kostas N. Plataniotis, D. HatzinakosAbstract:Abstract In this paper, we propose a new Radar Tracking algorithm based on the Gaussian sum filter. To alleviate the computational burden associated with the Gaussian sum filter, we have developed a new systematic and efficient way to approximate a non-Gaussian and measurement-dependent function by a weighted sum of Gaussian density functions and we have also suggested a way to alleviate the growing memory problem inherited in the Gaussian sum filter. Our method is compared with the extended Kalman filter (EKF) and the converted measurement Kalman filter (CMKF) and it is shown to be more accurate in term of position and velocity errors.
-
ICC (3) - An adaptive Gaussian sum algorithm for Radar Tracking
Proceedings of ICC'97 - International Conference on Communications, 1Co-Authors: Ip Tam, D. HatzinakosAbstract:In this paper, we propose a new Radar Tracking algorithm based on the Gaussian sum filter. We have developed a new systematic and efficient way to approximate a non-Gaussian and measurement-dependent function by a weighted sum of Gaussian density functions. We have derived the formula for updating the weights involved in the bank of Kalman-type filters and also suggested a way to alleviate the growing memory problem inherent in the Gaussian sum filter. Our method is compared with the extended Kalman filter (EKF) and the converted measurement Kalman filter (CMKF) and it is shown to be more accurate in term of position and velocity errors.
Maria V Kulikova - One of the best experts on this subject based on the ideXlab platform.
-
accurate continuous discrete unscented kalman filtering for estimation of nonlinear continuous time stochastic models in Radar Tracking
Signal Processing, 2017Co-Authors: Yu G Kulikov, Maria V KulikovaAbstract:Abstract This paper presents a new state estimation technology grounded in the unscented Kalman filtering for nonlinear continuous-time stochastic systems. The resulting accurate continuous–discrete unscented Kalman filter is based on adaptive solvers with automatic global error control for treating numerically the moment differential equations arising in the mean and covariance calculation of propagated Gaussian density. It is intended for an accurate and robust state estimation in nonlinear continuous–discrete stochastic systems of various sorts, including in Radar Tracking models. This new filter is examined in severe conditions of tackling a seven-dimensional Radar Tracking problem, where an aircraft executes a coordinated turn. The latter is considered to be a challenging one for testing nonlinear filtering algorithms. For comparison, we also examine such efficient state estimators as the accurate continuous–discrete extended Kalman filter, the continuous–discrete unscented Kalman filter and the mixed-type accurate continuous–discrete extended-unscented Kalman filter designed earlier, but further modified in the present study. The comparison is fulfilled in terms of accuracy and efficiency of estimating the state in the mentioned air traffic control scenario.
-
the accurate continuous discrete extended kalman filter for Radar Tracking
IEEE Transactions on Signal Processing, 2016Co-Authors: Gennady Yu Kulikov, Maria V KulikovaAbstract:This paper elaborates the Accurate Continuous-Discrete Extended Kalman Filter grounded in an ODE solver with global error control and its comparison to the Continuous-Discrete Cubature and Unscented Kalman Filters. All these state estimators are examined in severe conditions of tackling a seven-dimensional Radar Tracking problem, where an aircraft executes a coordinated turn. The latter is considered to be a challenging one for testing nonlinear filtering algorithms. Our numerical results show that all the methods can be used for practical target Tracking, but the Accurate Continuous-Discrete Extended Kalman Filter is more flexible and robust. It treats successfully (and without any manual tuning) the air traffic control scenario for various initial data and for a range of sampling times.
Weixian Liu - One of the best experts on this subject based on the ideXlab platform.
-
An approach to identification of variances for Radar Tracking systems
Signal Processing, 2002Co-Authors: Jianping Yao, Leonard Chin, Weixian LiuAbstract:An approach for the identification of the variances of the process noise W(k) and the measurement noise V(k) for Radar Tracking systems is presented. In the proposed algorithm, the variances are expressed as a linear combination of the covariances of the third-order difference of the measurement data. The variances can then be estimated by estimating the covariances of the third-order difference and solving the matrix equation. The two cases for the measurement noise being white and color are considered in this algorithm. Simulation results show that for both cases a steady estimation is reached after 20 samples.
Lars Chittka - One of the best experts on this subject based on the ideXlab platform.
-
Life-long Radar Tracking of bumblebees
PLoS ONE, 2016Co-Authors: Joseph L. Woodgate, James C. Makinson, Ka S. Lim, Andrew M Reynolds, Lars ChittkaAbstract:Insect pollinators such as bumblebees play a vital role in many ecosystems, so it is impor- tant to understand their foraging movements on a landscape scale. We used harmonic Radar to record the natural foraging behaviour of Bombus terrestris audax workers over their entire foraging career. Every flight ever made outside the nest by four foragers was recorded. Our data reveal where the bees flew and how their behaviour changed with expe- rience, at an unprecedented level of detail. We identified how each bee’s flights fit into two categories—which we named exploration and exploitation flights—examining the differ- ences between the two types of flight and how their occurrence changed over the course of the bees’ foraging careers. Exploitation of learned resources takes place during efficient, straight trips, usually to a single foraging location, and is seldom combined with exploration of other areas. Exploration of the landscape typically occurs in the first few flights made by each bee, but our data show that further exploration flights can bemade throughout the bee’s foraging career. Bees showed striking levels of variation in how they explored their environment, their fidelity to particular patches, ratio of exploration to exploitation, duration and frequency of their foraging bouts. One bee developed a straight route to a forage patch within four flights and followed this route exclusively for six days before abandoning it entirely for a closer location; this second location had not been visited since her first explor- atory flight nine days prior. Another bee made only rare exploitation flights and continued to explore widely throughout its life; two other bees showedmore frequent switches between exploration and exploitation. Our data shed light on the way bumblebees balance explora- tion of the environment with exploitation of resources and reveal extreme levels of variation between individuals. Introduction
-
Radar Tracking and Motion-Sensitive Cameras on Flowers Reveal the Development of Pollinator Multi-Destination Routes over Large Spatial Scales
PLoS Biology, 2012Co-Authors: Mathieu Lihoreau, Andrew M Reynolds, Nigel E. Raine, Ralph Stelzer, Ka Lim, Allan Smith, Juliet L. Osborne, Lars ChittkaAbstract:Central place foragers, such as pollinating bees, typically develop circuits (traplines) to visit multiple foraging sites in a manner that minimizes overall travel distance. Despite being taxonomically widespread, these routing behaviours remain poorly understood due to the difficulty of Tracking the foraging history of animals in the wild. Here we examine how bumblebees (Bombus terrestris) develop and optimise traplines over large spatial scales by setting up an array of five artificial flowers arranged in a regular pentagon (50 m side length) and fitted with motion-sensitive video cameras to determine the sequence of visitation. Stable traplines that linked together all the flowers in an optimal sequence were typically established after a bee made 26 foraging bouts, during which time only about 20 of the 120 possible routes were tried. Radar Tracking of selected flights revealed a dramatic decrease by 80% (ca. 1500 m) of the total travel distance between the first and the last foraging bout. When a flower was removed and replaced by a more distant one, bees engaged in localised search flights, a strategy that can facilitate the discovery of a new flower and its integration into a novel optimal trapline. Based on these observations, we developed and tested an iterative improvement heuristic to capture how bees could learn and refine their routes each time a shorter route is found. Our findings suggest that complex dynamic routing problems can be solved by small-brained animals using simple learning heuristics, without the need for a cognitive map.
Jianping Yao - One of the best experts on this subject based on the ideXlab platform.
-
An approach to identification of variances for Radar Tracking systems
Signal Processing, 2002Co-Authors: Jianping Yao, Leonard Chin, Weixian LiuAbstract:An approach for the identification of the variances of the process noise W(k) and the measurement noise V(k) for Radar Tracking systems is presented. In the proposed algorithm, the variances are expressed as a linear combination of the covariances of the third-order difference of the measurement data. The variances can then be estimated by estimating the covariances of the third-order difference and solving the matrix equation. The two cases for the measurement noise being white and color are considered in this algorithm. Simulation results show that for both cases a steady estimation is reached after 20 samples.
-
Variance estimation for a Radar Tracking system
1999 Information Decision and Control. Data and Information Fusion Symposium Signal Processing and Communications Symposium and Decision and Control S, 1999Co-Authors: Jianping Yao, Leonard ChinAbstract:In this paper, an approach for the estimation of the variance Q and R of the process noise W(k) and the measurement noise V(k) for Radar Tracking systems is presented. In the proposed algorithm, the variance Q and R are expressed as a linear combination of the covariances of the third-order difference of the measurement data. The variances can then be calculated by simply solving the matrix equation. The two cases for the measurement noise being white and color are considered in this algorithm. Simulation results showed that for both cases the steady estimation is reached after 20 samples.
