The calculation of marginal association probabilities is the major computational bottleneck in the Joint
Probabilistic Data Association Filter (JPDAF). In this paper, we investigate approximations for the marginal associations that simplify the (computational complex) original association model in order to obtain efficient algorithms. In this context, we first discuss the Bakhtiar-Alavi algorithm and the Linear Multitarget Integrated Probabilistic Data Association (LMIPDA) algorithm. Second, we propose a fast novel approximation that exploits systematic combinations of the JPDAF measurement model with the Probabilistic Multi-Hypothesis Tracker (PMHT) measurement model. The discussed methods are evaluated by means of a tracking scenario with a high number of closely-spaced targets.
Proc. SPIE. 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI
KEYWORDS: Maritime surveillance, Detection and tracking algorithms, Visualization, Sensors, Monte Carlo methods, Distance measurement, Optical tracking, Electronic filtering, Data integration, Process modeling
In this article, we present an evaluation of several multi-target tracking methods based on simulated scenarios in the
maritime domain. In particular, we consider variations of the Joint Integrated Probabilistic Data Association (JIPDA)
algorithm, namely the Linear Multi-Target IPDA (LMIPDA), Linear Joint IPDA (LJIPDA), and Markov Chain Monte
Carlo Data Association (MCMCDA). The algorithms are compared with respect to an extension of the Optimal
Subpattern Assignment (OSPA) metric, the Hellinger distance and further performance measures. As no single algorithm
is equally well fitted to all tested scenarios, our results show which algorithms fits best for specific scenarios.
Telepresent walking creates the sensation of walking through a
target environment, which is not directly accessible to a human,
e.g. because it is remote, hazardous, or of inappropriate scale. A mobile teleoperator replicates user motion and collects visual
and auditory information from the target environment, which is
then sent and displayed to the user. While walking freely about the user environment, the user perceives the target environment with the sensors of the teleoperator and feels as if walking through the target environment. Without additional processing of the user's motion data, the size of the target environment to be explored is limited to the size of the user environment. Motion compression extends telepresent walking to arbitrarily large target environments without making use of scaling or walking-in-place metaphors. Both travel distances and turning angles are mapped with ratio 1:1.
Proc. SPIE. 5084, Location Services and Navigation Technologies
KEYWORDS: Digital filtering, Complex systems, Estimation theory, Linear filtering, Time metrology, Antennas, Dynamical systems, Nonlinear filtering, Stochastic processes, Global system for mobile communications
Within the existing GSM standard, several measurements are available that can be used for estimating the position of a cellular phone. First, the timing advance (TA) gives an estimate for the distance to the serving base station. Second, the signal strengths (RXLEV) of neighbouring base stations can also be interpreted as distance information. Both TA and RXLEV are subject to measurement errors caused for example by shadowing, reflections, and fast fading. Thus, a nonlinear set-theoretic estimation technique based on pseudo ellipsoids is applied. The uncertainty regions in the original space defined by the measurements are transformed into a hyperspace of higher dimension and described by pseudo ellipsoids. An approximation of the set intersection of the pseudo ellipsoids can be calculated recursively by a linear set-theoretic filter. The resulting pseudo ellipsoid is transformed back into the original space, and the position estimate is calculated as center of gravity of the resulting uncertainty region. The algorithm is evaluated based on the data of an extensive field trial in a rural area. Compared to pure cell ID, the accuracy is significantly increased by using TA and RXLEV, reducing the mean error by half.
In this paper, nonlinear Bayesian filtering techniques are applied to the localization of mobile radio communication devices. The application of this approach is demonstrated for the localization of DECT mobile telephones in a scenario with several base stations and a mobile handset. The received signal power, measured by the mobile
handsets, is related to their position by nonlinear measurement equations. These consist of a deterministic part, modeling the received signal power as a function of the position, and a stochastic part, describing model errors and measurement noise. Additionally, user models are considered, which express knowledge about the motion
of the user of the handset. The new Prior Density Splitting Mixture Estimator (PDSME), a Gaussian mixture filtering algorithm, significantly improves the localization quality compared to standard filtering techniques as the Extended Kalman Filter (EKF).
This paper is concerned with recursively estimating the internal
state of a nonlinear dynamic system by processing noisy measurements and the known system input. In the case of continuous states, an exact analytic representation of the probability
density characterizing the estimate is generally too complex for
recursive estimation or even impossible to obtain. Hence, it is replaced by a convenient type of approximate density characterized by a finite set of parameters. Of course, parameters are desired that systematically minimize a given measure of deviation between
the (often unknown) exact density and its approximation, which in general leads to a complicated optimization problem. Here, a new framework for state estimation based on progressive processing is proposed. Rather than trying to solve the original problem, it is exactly converted into a corresponding system of explicit ordinary first-order differential equations. Solving this system over a finite "time" interval yields the desired optimal density
We consider the problem of simultaneously locating an observer and a set of environmental landmarks with respect to an inertial coordinate system, when both the observer position and the landmark positions are initially uncertain. For solving this problem, a new state estimator is introduced, which allows the problem to be consistently solved locally.
KEYWORDS: Digital signal processing, Detection and tracking algorithms, Cameras, Image processing, Fourier transforms, Signal processing, Algorithm development, Optical pattern recognition, Automatic control, Correlation function
In this paper, we present an algorithm for fast calculation of the normalized cross correlation and its application to the problem of template matching. Given a template t, whose position is to be determined in an image f, the basic idea of the algorithm is to represent the template, for which the normalized cross correlation is calculated, as a sum of rectangular basis functions. Then the correlation is calculated for each basis function instead of the whole template. The result of the correlation of the template t and the image f is obtained as the weighted sum of the correlation functions of the basis functions. Depending on the approximation, the algorithm can by far outperform Fourier-transform based implementations of the normalized cross correlation algorithm and it is especially suited to problems, where many different templates are to be found in the same image f.
This work presents new results for state estimation based on noisy observations suffering from two different types of uncertainties. The first uncertainty is a stochastic process with given statistics. The second uncertainty is only known to be bounded, the exact underlying statistics are unknown. State estimation tasks of this kind typically arise in target localization, navigation, and sensor data fusion. A new estimator has been developed, that combines set theoretic and stochastic estimation in a rigorous manner. The estimator is efficient and, hence, well-suited for practical applications. It provides a continuous transition between the two classical estimation concepts, because it converges to a set theoretic estimator, when the stochastic error goes to zero, and to a Kalman filter, when the bounded error vanishes. In the mixed noise case, the new estimator provides solution sets that are uncertain in a statistical sense.