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Automotive technology has been recently challenged with the issue of ensuring and improving road safety. Several academic institutions and automobile manufacturers are making efforts to develop technology for automotive safety. This study proposes an object recognition system based on the adaptive boosting algorithm that integrates a laser range finder and a camera. The laser range finder is used...
Feature fusion plays an important role in target recognition, especially when single sensor's recognition capability is limited under severe situations. In view of shortcomings of Multi-set Canonical Correlation Analysis (MCCA) and its supervised modified methods in using category information in fusion projection rule learning, a generalized discriminative learning version of MCCA, termed as GDMCCA,...
How to quantify the uncertainty information consisted in the body of evidence (BOE) in the framework of Dempster-Shafer evidence theory is still an open issue. A few uncertainty measures have been proposed in Dempster-Shafer evidence theory framework, but these studies mainly focused on the mass function itself and the scale of the frame of discernment (FOD) is totally ignored. Since the existing...
This paper focuses on addressing the data fusion problems in asynchronous sensor networks using distribute particle filter (DPF). Generally, the type of the local information communicated between sensors and the time synchronization of the local information are two major issues for DPF algorithms, which have significant influence on fusion accuracy and communication requirements. To address these...
In this paper, we attack the estimation problem in Kalman filtering when the measurements are contaminated by outliers. We employ the Laplace distribution to model the underlying non-Gaussian measurement process. The maximum posterior estimation is solved by the majorization minimization (MM) approach. This yields an MM based robust filter, where the intractable ℓ1 norm problem is converted into an...
The ability to recognize physical activity, such as sedentary, driving, riding, daily activities and effective training, is useful for health conscious users to catalogue their daily activities and to develop good exercise routines. Conventional activity recognition algorithms require complex calculations, which are not suitable for wearable devices developed on low-cost, low-power hardware platforms...
With the development of deep space exploration, the requirements of accuracy and real time for navigation services become higher, so that the traditional ground based network can hardly meet the user's needs. And the accuracy of existing autonomous navigation method, such as celestial navigation, cannot meet the requirement either. In order to improve the performance of autonomous navigation for deep...
In multiple-target tracking problem, data association technique plays an significant role. When targets move closely or crosswise, performances of conventional data association algorithms which use kinematic information only may be degraded. Actually, beside the kinematic information, sensors always can obtain feature information about the target, and incorporating the features into data association...
In previous works, it has been shown that the estimation problem of a thrusting/ballistic object in the three-dimensional space can be solved with two-dimensional measurements (azimuth and elevation angles starting from the launch time) assuming the launch point is perfectly known. In this paper, the problem is extended to estimate the target's trajectory with measurements starting after the launch...
An efficient subspace-based two-step direction finding method is proposed for uniform linear arrays. It improves the estimation accuracy for small sample size and coherent sources by diminishing the undesirable terms and utilizing the Toeplitz structure of the sample covariance matrix. Furthermore, it works well even using single snapshot, therefore, it is a good candidate to track the direction-of-arrival...
This paper presents a novel and an improved approach for estimating the position of a vehicle using vehicle-infrastructure cooperative localization. In our previous work we presented a Factor Graph based solution which added the topology (inter-vehicle distance) as a constraint while localizing the vehicle using data from sensors from both inside and outside the vehicle. This paper extends the work...
Predictive analytics and data fusion techniques are being regularly used for analysis in Quantitative Risk Management (QRM). The primary risk metric of interest, Value-at-Risk (VaR), has always been difficult to robustly estimate for different data types. The classical Monte Carlo simulation (MCS) approach (denoted henceforth as classical approach) assumes the independence of loss severity and loss...
A problem of state estimation with destination constraint is considered in this paper. An anti-radiation missile (ARM) often moves towards the target along a trajectory which is almost linear in the X-Y plane. The linear constraint for trajectory and target position are known as priori and can be used to enhance the performance of a tracking filter. In this paper, a destination constrained Kalman...
We consider the problem of choosing the best subset of sensors that results in a prescribed error probability Pe in Bayesian setting. Since minimizing the error probability is often difficult to evaluate and manipulate, conventional methods adopt Bhattacharyya distance instead of it. In fact, Chernoff distance is the best achievable exponent in the Bayesian error probability and it is more accurate...
The dynamic grid map illustrates the environment of robots with moving and static obstacles. Nuss et al. describe in [1] an implementation of this grid map, in which the state of the grid cells is to be modeled as a random finite set (RFS) based on a stochastic measurement system. For a real-time implementation this approach was approximated with Dempster-Shafer (DS). For this Nuss et al. design the...
In this paper, a new centralized algorithm is developed to estimate the registration error and target states jointly based on the generalized labeled multi-Bernoulli (GLMB) filter. The bias pseudo-measurements are calculated with the tracks generated by the GLMB filter. Then, the bias estimates are computed to compensate the measurements for multi-target tracking. Since the estimates of the sensor...
The Fisher information matrix is applied to evaluate the performance of three different navigation methods for the constellation around the libration points. Where the X-ray pulsar relative navigation is the main method, and the starlight Doppler relative navigation and the intersatellite links are integrated with it respectively. Their measurement quantities are respectively the time difference of...
Device-free localization (DFL) is an emerging wireless network target localization technique that does not need to attach any electronic device with the target. It is remaining as a challenging research problem due to the weak wireless signals and the uncertain wireless communication environment. In this paper, a novel Gaussian Process (GP) based wireless propagation model is proposed to describe...
The multiple hypothesis tracker (MHT) is a popular algorithm for solving multi-target tracking (MTT) problem in cluttered environment. It is known as a maximum a posterior (MAP) estimator which enumerates all possible global hypotheses and dedicates to find the most likely solution based on the received reports. However, its practical application is often limited by the complexity of data association...
Multispectral face recognition systems are widely used in various access control applications. The vulnerability of multispectral face recognition sensors towards low-cost Presentation Attack Instrument (PAI) such as printed photos used in attacks has emerged as a serious security threat. In this paper, we present a novel framework to detect presentation attacks against an extended multispectral face...
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