Tsang-Yi Wang

A tracking algorithm for maneuvering targets

A methodology for the tracking of maneuvering targets is presented. A quickest-detection scheme based on the innovation sequence is developed for a prompt detection of target maneuvers. The optimal length of a sliding window that minimizes the maneuver detection delay for a given false-alarm rate is determined. After maneuver detection, the system model is modified by adding a maneuver term. A recursive algorithm is proposed to estimate the maneuver magnitude. With this estimate, a modified Kalman filter is used for tracking. Simulation results demonstrate the superior performance of the algorithm, especially during target maneuvers. >

A combined decision fusion and channel coding scheme for fault-tolerant classification in wireless sensor networks

In this paper, we consider the distributed classification problem in wireless sensor networks. Local decisions made by local sensors, possibly in the presence of faults, are transmitted to the fusion center through fading channels. We integrate channel coding with the distributed fault-tolerant classification fusion approach, i.e., the DCFECC approach. We obtain a new fusion rule that combines both soft-decision decoding and local decision rules without introducing any additional redundancy. The soft decoding scheme is utilized to combat channel fading, while the DCFECC fusion structure provides excellent fault-tolerance capability.

Fault-tolerant distributed classification based on non-binary codes in wireless sensor networks

In this letter, we consider the distributed classification problem in wireless sensor networks. The DCSD approach employing the binary code matrix has recently been proposed to cope with the errors caused by both sensor faults and the effect of fading channels. However, the performance of the system employing the binary code matrix could be degraded if the distance between different hypotheses can not be kept large. In this letter, we design the DCSD approach employing the D-ary code matrix when log/sub 2/D bits local decision information is used, where D>2. Simulation results show that the performance of the DCSD approach employing the D-ary code matrix is better than that of the DCSD approach employing the binary code matrix.

Distributed fault-tolerant classification fusion using error correcting codes

In this paper we propose a new approach for distributed multiclass classification using a hierarchical fusion architecture. Binary decisions from local sensors, possibly in the presence of faults, are fused locally. Locally fused results are forwarded to the global fusion center that determines the final classification result. Classification fusion in our approach is implemented via error correcting codes to incorporate fault-tolerance capability. This new approach not only provides an improved fault-tolerance capability but also reduces bandwidth requirements as well as computation time and memory requirements at the fusion center. Numerical examples are provided to illustrate the performance of this new approach.

Fault-tolerant classification in multisensor networks using coding theory

A fault-tolerant distributed classification system employing the fault-tolerant fusion rule ap- proach was proposed in (I). Code mat& design is essential for the design of such systems. Two efi- cient code mat& design algorithms are proposed in this paper. The relative merits of both algorithms are also studied. Perfonnance evaluation of the DCFECC (distributed classification fusion using error correcting codes) approach with faults due to hardware/software damage OT drained batteries in sensors are provided. These results have shown significant improvement of fault-tolerance capability as compared vith conven- tional parallel fusion networks. A generalization of the above approach that may handle channel transition er- TOTS is also pmvided.

A Measurement Preprocessing Approach for Target Tracking

Abstract In this paper, we present a novel tracking algorithm which explicitly employs the knowledge available about target motion and its trajectory. Received measurements are preprocessed and are provided to the Kalman filter for further processing. For the cluttered environment, weighted measurements are employed in the preprocessing algorithm. Simulation results are presented to demonstrate the superior performance of our algorithm

Combining channel estimation and sensor fault protection in wireless sensor networks

The ongoing development of wireless sensor networks (WSNs) demands not only low-power sensors and less system cost but also good performance. Considering this background, investigating a new technology to satisfy both requirements is an important issue for current development of wireless sensor network systems. In this paper, we consider the situation that the sensor nodes in WSNs are deployed in a harsh environment such that both channel fading and unexpected sensor faults may occur. This work then proposes to combine channel estimation and sensor-fault protection using error-correcting coding technique so as to free the costly devices of channel estimation and equalization from fusion centers. Simulations, performed to examine the performance of our proposed blind-detection scheme, show that it can compete with the conventional training-sequence-based fusion in performance when the training sequences are retained for information bearing. By this new approach, the complexity in fusion centers can be reduced and the transmitted power of sensors decreased without sacrificing the performance.

Further results on fault-tolerant distributed classification using error-correcting codes

In this paper, we consider the distributed classification problem in wireless sensor networks. The DCFECC-SD approach employing the binary code matrix has recently been proposed to cope with the errors caused by both sensor faults and the effect of fading channels. The DCFECC-SD approach extends the DCFECC approach by using soft decision decoding to combat channel fading. However, the performance of the system employing the binary code matrix could be degraded if the distance between different hypotheses can not be kept large. This situation could happen when the number of sensor is small or the number of hypotheses is large. In this paper, we design the DCFECC-SD approach employing the D-ary code matrix, where D>2. Simulation results show that the performance of the DCFECC-SD approach employing the D-ary code matrix is better than that of the DCFECC-SD approach employing the binary code matrix. Performance evaluation of DCFECC-SD using different number of bits of local decision information is also provided when the total channel energy output from each sensor node is fixed.