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The problem of single-sensor bearings-only tracking continues to present challenges to tracking algorithms, particularly in certain difficult scenarios such as ones with high bearing rates. In such scenarios, the performance of the recently introduced shifted Rayleigh filter (SRF) is compared with that of other techniques such as extended Kalman filter (EKF), unscented Kalman filter (UKF) and particle...
Distributed image steganography (DIS) (Y.S. Wu et al., 2004) is a new method of concealing secret information in several host images, leaving smaller traces than conventional steganographic techniques, and requiring a collection of affected images for secret information retrieval. Fusion system designs of the future will require enhanced security measures for distributed data communication. DIS, compared...
In this paper a hybrid Kalman filter is derived for the tracking of ground based targets. The propagation is performed using unscented Kalman filter equations, while the updates are performed using extended Kalman filter equations. The novel feature of this hybrid filter is that terrain information has been incorporated to improve the accuracy of state estimates. This information, termed trafficability,...
Fixed interval smoothing for systems with nonlinear process and measurement models is studied and applied to the assimilation of sensor data in a Chemical, Biological, Radiological or Nuclear (CBRN) incident scenario. A two-filter smoother that uses a Backward Sigma-Point Information Filter, and also a forward-backward Rauch-Tung-Striebel (RTS) smoothing form are re-derived using the weighted statistical...
The exploitation of bistatic Doppler measurements for multistatic tracking is considered. It is found through simulation, that, while the velocity estimation of the standard extended Kalman filter is improved in monostotic situations and multistatic situations where measurement errors are small, a degradation in performance is observed in multistatic situations where the measurement errors are realistically...
Geolocation with three or more unmanned aerial vehicles (UAVs) based on time-difference-of-arrivals (TDOA) is possible but has implementation problems including UAV trajectory optimization, measurement association, and communication bandwidth limitations. The complexity of each of these problems is manageable with a simpler system of two netted UAVs that processes multiple TDOA measurements collected...
The problem of tracking objects moving in Cartesian space with sensors delivering polar measurements has been under investigation of several researchers for quite some time now. Different proposals for using measurement conversion techniques in combination with a linear Kalman filter have been made in order to reduce the range bias that shows up in the filter estimates when a Cartesian pseudo-measurement...
The interacting multiple model (IMM) algorithm is a widely accepted state estimation scheme for solving maneuvering target tracking problems, which are generally nonlinear. During the IMM filtering process, serious errors can arise when a Gaussian mixture of posterior probability density functions is approximated by a single Gaussian. Particle filters (PFs) are effective in dealing with nonlinearity...
This paper considers the recursive estimation of emitter location using time difference of arrival measurements formed by the correlation of signals received by two unmanned aerial vehicles. The time difference of arrival measurement defines an hyperbola of possible emitter locations. This hyperbola is used as a measurement in a nonlinear Alter. The performance of two such filters, an extended Kalman...
Target tracking from incomplete measurements of distinct sensors in a sensor network is a task of data fusion, present in a lot of applications. Difficulties in tracking using extended Kalman filters lead to unstable behavior, mainly caused by difficult initialization. Instead of using numerical batch-estimators, we offer an analytical approach to initialize the filter from a minimum number of observations...
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...
Distributed Kalman filters are often used in multisensor target tracking where the fusion center receives local estimates and fuses them to obtain the global target state estimate. With such a fusion architecture, each local tracker can communicate less frequently with the fusion center than the local filter update rate. The global target state estimate via track fusion is usually less accurate than...
Negative information provides important additional knowledge that is not exploited for sensor data fusion tasks by default. This paper presents a new approach to incorporate such information about unoccupied, observed areas or missing measurements in the Kalman filtering process. For this purpose, a combination with a grid-based method is proposed to generate a visibility map. This enables a plausibility...
An algorithm is developed for joint tracking and detection of multiple maneuvering targets using a wireless sensor network. The target existence probability framework is adopted in which a collection of tentative tracks, each characterised by a posterior density and existence probability, is maintained. Track state posterior densities are approximated using the unscented Kalman filter and the interacting...
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...
In this paper, first an enhanced neuro-fuzzy method for modeling nonlinear system is presented In this method we use EM algorithm for identification of local models, which gain us model mismatch covariance. The achieved model can be stated in state space model as a linear time-varying system. As the noise and model mismatch covariance is known, Kalman filter can be easily used for centralized estimation...
A physical activity monitoring system by data fusion in body sensor networks is presented in this paper, which targets at providing body status information in real time and identifying body activities. By fusion of data collected from several accelerometer sensors placed on different parts of the body, the activities can be identified and tracked Mathematical approaches employed in the system include...
This paper describes a novel technique to obtain radar biases estimates that can effectively reduce mismatches in track association algorithms. This is accomplished by matching ship-borne radar images to geo-referenced satellite images. The matching is performed through the minimization of the averaged partial Hausdorff distance between data points in each image. The minimization rapidly yields robust...
Data assimilation in the context of puff based dispersion models is studied. A representative two dimensional Gaussian puff atmospheric dispersion model is used for the purpose of testing and comparing several data assimilation techniques. A continuous nonlinear observation model, and a quantized probabilistic nonlinear observation model, are used to simulate the measurements. The quantized model...
An alternating directions method is presented for joint maximum a posteriori estimation of target track and sensor field using bistatic range data. The algorithm cycles over two sub-algorithms: one improves the target state estimate conditioned on sensor field state, and the other improves the sensor field state estimate conditioned on target state. Nonlinearities in the sub-algorithms are mitigated...
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