R. Niu

Syracuse University, Syracuse, New York, United States

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Publications (7)9.59 Total impact

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    ABSTRACT: In this paper, we study the source localization problem in wireless sensor networks. Sensors transmit their quantized signal amplitude measurements to the fusion center and source location is estimated based on these quantized measurements. In this paper, we propose an energy efficient iterative localization scheme, where the algorithm starts with a coarse location estimate obtained from a set of anchor sensors. At each consecutive iteration, some of the non anchor sensors are activated which minimize the Posterior Cramer Rao Lower Bound (PCRLB). Then, using the available information received at previous iterations as side information, the quantized data of each activated sensor is further compressed to conserve energy using distributed data compression techniques prior to transmission to the fusion center. Simulation results show that the proposed iterative method achieves the same estimation performance as when all the sensors transmit their quantized data to the fusion center within a few iterations, while at the same time significantly reducing the communication requirements resulting in energy savings.
    Information Sciences and Systems, 2009. CISS 2009. 43rd Annual Conference on; 04/2009
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    ABSTRACT: In this paper, we propose a new maximum-likelihood (ML) target localization approach which uses quantized sensor data as well as wireless channel statistics in a wireless sensor network. The novelty of our approach comes from the fact that statistics of imperfect wireless channels between sensors and the fusion center along with some physical layer design parameters are incorporated in the localization algorithm. We call this approach ldquochannel-aware target localization.rdquo ML target location estimators are derived for different wireless channel models and receiver architectures. Furthermore, we derive the Cramer-Rao lower bounds (CRLBs) for our proposed channel-aware ML location estimators. Simulation results are presented to show that the performance of the channel-aware ML location estimators are quite close to their theoretical performance bounds even with relatively small number of sensors and their performance is superior compared to that of the channel-unaware ML estimators.
    IEEE Transactions on Signal Processing 04/2009; DOI:10.1109/TSP.2008.2009893 · 3.20 Impact Factor
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    Ruixin Niu, P.K. Varshney
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    ABSTRACT: For a wireless sensor network (WSN) with randomly deployed sensors, the performance of the counting rule, where the fusion center employs the total number of detections reported by local sensors for hypothesis testing, is investigated. It is assumed that the signal power decays as a function of the distance from the target. For both the case where the total number of sensors is known and the wireless channels are lossless, and the case where the number of sensors is random and the wireless channels have nonnegligible error rates, the exact system level probability of detection is derived analytically. Some approximation methods are also proposed to attain an accurate estimate of the probability of detection, while at the same time to reduce the computation load significantly. To obtain a better system level detection performance, the local sensor level decision threshold is determined such that it maximizes the system level deflection coefficient.
    IEEE Transactions on Signal Processing 02/2008; DOI:10.1109/TSP.2007.906770 · 3.20 Impact Factor
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    Ruixin Niu, P.K. Varshney
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    ABSTRACT: A signal intensity based maximum-likelihood (ML) target location estimator that uses quantized data is proposed for wireless sensor networks (WSNs). The signal intensity received at local sensors is assumed to be inversely proportional to the square of the distance from the target. The ML estimator and its corresponding Crameacuter-Rao lower bound (CRLB) are derived. Simulation results show that this estimator is much more accurate than the heuristic weighted average methods, and it can reach the CRLB even with a relatively small amount of data. In addition, the optimal design method for quantization thresholds, as well as two heuristic design methods, are presented. The heuristic design methods, which require minimum prior information about the system, prove to be very robust under various situations
    IEEE Transactions on Signal Processing 01/2007; 54(12-54):4519 - 4528. DOI:10.1109/TSP.2006.882082 · 3.20 Impact Factor
  • M. Xu, R. Niu, P.K. Varshney
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    ABSTRACT: Change detection is known to be a significant and difficult research problem in automated surveillance systems. In this paper, we propose a new change detection approach based on the least squares method, which is robust to changes in illumination and shadow conditions. This new approach is employed to design our detection and tracking system that is shown to successfully detect a moving object in a complex outdoor environment.
    Image Processing, 2004. ICIP '04. 2004 International Conference on; 11/2004
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    ABSTRACT: In this paper, a multisensor data fusion system for object tracking is presented. It is able to track in real-time multiple targets in outdoor environments. The system can take advantage of the redundant information coming from different sensors monitoring the same scene. The measurements (positions of the targets) obtained from the available sources are fused together to obtain a more accurate estimate. Data fusion is performed considering sensor reliability at every time instant. A confidence measure has been employed to weight sensor data in the fusion process. Compared to single camera systems, the adopted approach has produced more accurate and continuous trajectories, reducing calibration and segmentation errors.
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    ABSTRACT: An outdoor multi-camera video surveillance system operating under changing weather conditions is presented. A new confidence measure, appearance ratio (AR), is defined to evaluate automatically the sensors' performance for each time instant. By comparing their ARs, the system can select the most appropriate cameras to perform specific tasks. When redundant measurements are available for a target, the AR measures are used to perform a weighted fusion of them. Experimental results are presented on outdoor scenes under different weather conditions.
    Proceedings. IEEE Conference on Advanced Video and Signal Based Surveillance, 2003.; 08/2003