This paper presents a source localization algorithm based on the source signal's time-difference-of-arrival(TDOA) for asynchronous wireless sensor network.To obtain synchronization among anchors,all anchors broadc...This paper presents a source localization algorithm based on the source signal's time-difference-of-arrival(TDOA) for asynchronous wireless sensor network.To obtain synchronization among anchors,all anchors broadcast signals periodically,the clock offsets and skews of anchor pairs can be estimated using broadcasting signal's time-of-arrivals(TOA) at anchors.A kalman filter is adopted to improve the accuracy of clock offsets and track the clock drifts due to random fluctuations.Once the source transmits signal,the TOAs at anchors are stamped respectively and source's TDOA error due to clock offset and skew of anchor pair can be mitigated by a compensation operation.Based on a Gaussian noise model,maximum likelihood estimation(MLE) for the source position is obtained.Performance issues are addressed by evaluating the Cramer-Rao lower bound and the selection of broadcasting period.The proposed algorithm is simple and effective,which has close performance with synchronous TDOA algorithm.展开更多
In Electronic Warfare, and more specifically in the domain of passive localization, accurate time synchronization between platforms is decisive, especially on systems relying on TDOA （time difference of arrival） and...In Electronic Warfare, and more specifically in the domain of passive localization, accurate time synchronization between platforms is decisive, especially on systems relying on TDOA （time difference of arrival） and FDOA （frequency difference of arrival）. This paper investigates this issue by presenting an analysis in terms of final localization performance of an experimental passive localization system based on off-the-shelf components. This system is detailed, as well as the methodology used to carry out the acquisition of real data. This experiment has been realized with two different kinds of clock. The results are analyzed by calculating the Allan deviation and time deviation. The choice of these metrics is explained and their properties are discussed in the scope of an airborne bi-platform passive localization context. Conclusions are drawn regarding the overall localization performance of the system.展开更多
A real-time pedestrian detection and tracking system using a single video camera was developed to monitor pedestrians. This system contained six modules: video flow capture, pre-processing, movement detection, shadow ...A real-time pedestrian detection and tracking system using a single video camera was developed to monitor pedestrians. This system contained six modules: video flow capture, pre-processing, movement detection, shadow removal, tracking, and object classification. The Gaussian mixture model was utilized to extract the moving object from an image sequence segmented by the mean-shift technique in the pre-processing module. Shadow removal was used to alleviate the negative impact of the shadow to the detected objects. A model-free method was adopted to identify pedestrians. The maximum and minimum integration methods were developed to integrate multiple cues into the mean-shift algorithm and the initial tracking iteration with the competent integrated probability distribution map for object tracking. A simple but effective algorithm was proposed to handle full occlusion cases. The system was tested using real traffic videos from different sites. The results of the test confirm that the system is reliable and has an overall accuracy of over 85%.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.61571452 and No.61201331
文摘This paper presents a source localization algorithm based on the source signal's time-difference-of-arrival(TDOA) for asynchronous wireless sensor network.To obtain synchronization among anchors,all anchors broadcast signals periodically,the clock offsets and skews of anchor pairs can be estimated using broadcasting signal's time-of-arrivals(TOA) at anchors.A kalman filter is adopted to improve the accuracy of clock offsets and track the clock drifts due to random fluctuations.Once the source transmits signal,the TOAs at anchors are stamped respectively and source's TDOA error due to clock offset and skew of anchor pair can be mitigated by a compensation operation.Based on a Gaussian noise model,maximum likelihood estimation(MLE) for the source position is obtained.Performance issues are addressed by evaluating the Cramer-Rao lower bound and the selection of broadcasting period.The proposed algorithm is simple and effective,which has close performance with synchronous TDOA algorithm.
文摘In Electronic Warfare, and more specifically in the domain of passive localization, accurate time synchronization between platforms is decisive, especially on systems relying on TDOA （time difference of arrival） and FDOA （frequency difference of arrival）. This paper investigates this issue by presenting an analysis in terms of final localization performance of an experimental passive localization system based on off-the-shelf components. This system is detailed, as well as the methodology used to carry out the acquisition of real data. This experiment has been realized with two different kinds of clock. The results are analyzed by calculating the Allan deviation and time deviation. The choice of these metrics is explained and their properties are discussed in the scope of an airborne bi-platform passive localization context. Conclusions are drawn regarding the overall localization performance of the system.
基金Project(50778015)supported by the National Natural Science Foundation of ChinaProject(2012CB725403)supported by the Major State Basic Research Development Program of China
文摘A real-time pedestrian detection and tracking system using a single video camera was developed to monitor pedestrians. This system contained six modules: video flow capture, pre-processing, movement detection, shadow removal, tracking, and object classification. The Gaussian mixture model was utilized to extract the moving object from an image sequence segmented by the mean-shift technique in the pre-processing module. Shadow removal was used to alleviate the negative impact of the shadow to the detected objects. A model-free method was adopted to identify pedestrians. The maximum and minimum integration methods were developed to integrate multiple cues into the mean-shift algorithm and the initial tracking iteration with the competent integrated probability distribution map for object tracking. A simple but effective algorithm was proposed to handle full occlusion cases. The system was tested using real traffic videos from different sites. The results of the test confirm that the system is reliable and has an overall accuracy of over 85%.
