Decision Fusion in a Wireless Sensor Network with a Large Number of Sensors

For a wireless sensor network (WSN) with a large number of sensors, a decision fusion rule using the total number of detections reported by local sensors for hypothesis testing, is proposed and studied. Based on a signal attenuation model where the received signal power decays as the distance from the target increases, the system level detection performance, namely probabilities of detection and false alarms, are derived and calculated. Without the knowledge of local sensors\u27 performances and at low signal to noise ratio (SNR), this fusion rule can still achieve very good system level detection performance if the number of sensors is sufficiently large. The problem of designing an optimum local sensor level threshold is investigated. For various system parameters, the optimal thresholds are found numerically. Guidelines on selecting the optimal local threshold have been presented

Fusing Dependent Decisions for Hypothesis Testing with Heterogeneous Sensors

In this paper, we consider a binary decentralized detection problem where the local sensor observations are quantized before their transmission to the fusion center. Sensor observations, and hence their quantized versions, may be heterogeneous as well as statistically dependent. A composite binary hypothesis testing problem is formulated, and a copula-based generalized likelihood ratio test (GLRT) based fusion rule is derived given that the local sensors are uniform multi-level quantizers. An alternative computationally efficient fusion rule is also designed which involves injecting a deliberate random disturbance to the local sensor decisions before fusion. Although the introduction of external noise causes a reduction in the received signal to noise ratio, it is shown that the proposed approach can result in a detection performance comparable to the GLRT detector without external noise, especially when the number of quantization levels is larg

Sensor Fusion for Video Surveillance

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

Target Localization and Tracking in Non-Coherent Multiple-Input Multiple-Output Radar Systems

For a non-coherent MIMO radar system, the maximum likelihood estimator (MLE) of the target location and velocity, as well as the corresponding CRLB matrix, is derived. MIMO radar’s potential in localization and tracking performance is demonstrated by adopting simple Gaussian pulse waveforms. Due to the short duration of the Gaussian pulses, a very high localization performance can be achieved, even when the matched filter ignores the Doppler effect by matching to zero Doppler shift. This leads to significantly reduced complexities for the matched filter and the MLE. Further, two interactive signal processing and tracking algorithms, based on the Kalman filter and the particle filter respectively, are proposed for non-coherent MIMO radar target tracking. For a system with a large number of transmit/receive elements and a high SNR value, the Kalman filter (KF) is a good choice; while for a system with a small number of elements and a low SNR value, the particle filter outperforms the KF significantly. In both methods, the tracker provides predictive information regarding the target location, so that the matched filter can match to the most probable target locations, reducing the complexity of the matched filter and improving the tracking performance. Since tracking is performed without detection, the presented approach can be deemed as a track-before-detect approach. It is demonstrated through simulations that the non-coherent MIMO radar provides significant tracking performance improvement over a monostatic phased array radar with high range and azimuth resolutions. Further, the effects of coherent integration of pulses are investigated for both the phased array radar and a hybrid MIMO radar, where only the pulses transmitted and received by co-located transceivers are coherently integrated and the other pulses are combined non-coherently. It is shown that the hybrid MIMO radar achieves significant tracking performance improvement when compared to the phased array radar, by using the extra Doppler information obtained through coherent pulse integration

Tracking in Wireless Sensor Networks Using Particle Filtering: Physical Layer Considerations

Abstract-In this paper, a new framework for target tracking in a wireless sensor network using particle filters is proposed. Under this framework, the imperfect nature of the wireless communication channels between sensors and the fusion center along with some physical layer design parameters of the network are incorporated in the tracking algorithm based on particle filters. We call this approach "channel-aware particle filtering." Channel-aware particle filtering schemes are derived for different wireless channel models and receiver architectures. Furthermore, we derive the posterior Cramér-Rao lower bounds (PCRLBs) for our proposed channel-aware particle filters. Simulation results are presented to demonstrate that the tracking performance of the channel-aware particle filters can reach their theoretical performance bounds even with relatively small number of sensors and they have superior performance compared to channel-unaware particle filters. Index Terms-Channel-aware signal processing, particle filters, posterior Cramér-Rao lower bound, wireless communication channels, wireless sensor networks (WSNs)