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The problem is joint detection and tracking of possibly several objects moving through a region of interest. A wireless sensor network (WSN), deployed in the region, collects the acoustic energy measurements and sends them to the fusion center for processing. The problem is cast in the sequential Bayesian estimation framework and solved using a particle filter. The number of objects is unknown and...
Errors due to sensor bias are often present in sensor data and can reduce the tracking accuracy and stability of multi-sensor systems. The other practical problem is that the target data reported by the sensors are usually not time-coincident or synchronous due to the different data. This paper deals with these problems and presents a new algorithm for estimation of both constant and dynamic biases...
Many problems involve joint decision and estimation, where qualities of decision and estimation affect each other. This paper proposes an integrated approach based on a new Bayes risk, which is a generalization of those for decision and estimation separately. Theoretical results of the optimal joint decision and estimation that minimizes the new Bayes risk are presented. The power of the new approach...
Vision-guided autonomous platforms require inertial stabilization of the imaging sensor. This is typically achieved by using a gimbaled system with inertial rate sensors, such as gyros. Using low-cost gyros requires estimation of their error parameters, such as bias and scale-factor. This paper presents a motion model-based method for robust estimation of these parameters via fusing the inertial measurements...
Over-the-horizon (OTH) radar and automatic identification system (AIS) are commonly used in the surveillance of maritime areas. This paper presents a method, which includes tracking and association algorithms, for fusing the information from these two types of systems into an overall maritime picture. Data to be fused consists of asynchronous track estimates from the OTH system and measurements obtained...
This work is concerned with the design of sensor fusion methods using the fault-tolerant interval functions proposed by K. Marzullo and U. Schmid. A trade-off exists between the precision of the interval functions and their tolerance to invalid input intervals. The study shows how the performances of the interval functions in terms of expected length and variance can be estimated from their asymptotic...
An algorithm for detection and tracking of multiple targets using bearings measurements from several sensors is developed. The algorithm is an implementation of a multiple hypothesis tracker with pruning of unlikely hypotheses. Tracking conditional on each hypothesis can be performed using any suitable filtering approximation. In this paper a range- parameterized unscented Kalman filter is used. Each...
The idea of particle filter is to represent probability density function (PDF) of nonlinear/non-Gaussian system by a set of random samples. One of the key issue of particle filter is the proposal distribution. In this paper, the iterated unscented Kalman filter (IUKF) is used to generate the proposal distribution for particle filter. The proposal distributions integrate the current observation, thus...
The ability to meaningfully assess performance is crucial for understanding, developing and comparing estimators. The optimality of an estimator relies on estimation criterion and there exists a significant gap between estimation criterion and application requirements, so the estimation criterion is not good for evaluating or comparing algorithms. Different viewpoints for performance comparison can...
The estimation of Markov jump systems (MJS) is widely used in target tracking, fault detection, signal processing and digital communications. However, the above researches all assume that state measurement and additional mode observation are synchronous which means both state measurement and mode observation at each sampling time arrive at the fusion centre at the same time. The problem of estimation...
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