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This paper aims to find an appropriate approach to improve estimation accuracy of bearings-only tracking (BOT) and Doppler bearing tracking (DBT) by making use of the constraint on target speed. Targets usually travel within a valid speed zone so this contextual information (speed inequality constraint) should hypothetically help tracking algorithms (filters) achieve better accuracy. However the inequality...
With the ubiquity of information distributed in networks, performing recursive Bayesian estimation using distributed calculations is becoming more and more important. There are a wide variety of algorithms catering to different applications and requiring different degrees of knowledge about the other nodes involved. One recently developed algorithm is the distributed Kalman filter (DKF), which assumes...
In this paper we introduce a new method of deciding, if a trajectory is following a pre-defined path. This is achieved by representing hypotheses as trajectories themselves using Accumulated State Densities. Live tracking data is incorporated into the trajectories via out of sequence processing. Through this, we gain two representations of the sensor data, each conditioned on a hypotheses. By using...
We consider the general continuous-discrete nonlinear filtering problem. In particular, the prediction step involving the numerical solution of the Fokker-Planck equation for the time evolution of the state probability density is known to be a challenging problem as it suffers from the curse of dimensionality. In this contribution a novel approach based on Kronecker tensor decomposition of the matrix...
The optimal non-linear filter estimates can be obtained by solving the Fokker-Planck equation (FPE) for the time propagation, together with the Bayesian measurement inclusion. An issue faced when solving the FPE is the curse of dimensionality. Recently, a tensor based approach has been proposed, which is said to be suitable for high dimensional problems. Then Bayesian measurement inclusion also presents...
This paper presents a concept on how a military formation can be jointly protected by linking available single-platform protection systems. To achieve this, a mobile ad hoc network is established between different vehicles carrying heterogeneous sensors, specifically acoustic and ESM sensors, emulating the protection systems. Distributed data fusion is applied to provide a situation overview for localizing...
For linear-Gaussian non-deterministic dynamics, that is, systems with non-zero process noise, it is well known that tracklet fusion based on equivalent measurement is optimal only for full communication rate, i.e., if the local posterior probabilities or estimates are communicated and fused after each observation and update time. Despite this constraint, tracklet fusion has become very popular because...
In multiple target tracking target occlusion or shadowing is a common occurrence. A target may be occluded by an existing structure, or in many cases, by another moving target in the environment. In this paper we consider a UWB-based range-only person tracking system. Occlusion regions induced by moving targets in the scenario are defined followed by a derivation of an occlusion likelihood function...
The Ensemble Kalman Filter (EnKF) is a Kalman based particle filter which was introduced to solve large scale data assimilation problems where the state space is of very large dimensionality. It also achieves good results when applied to a target tracking problem, however, due to its Gaussian assumption for the prior density, the performance can be improved by introducing Gaussian mixtures. In this...
In tracking and sensor data fusion applications, the full information on kinematic object properties accumulated over a certain discrete time window up to the present time is contained in the conditional joint probability density function of the kinematic state vectors referring to each time step in this window. This density is conditioned by the time series of all sensor data collected the present...
This paper focuses on determining the optimum placement of a given number of sensors for estimating the position of a moving target using range-difference measurements. We define a region of interest and generate several random trajectories with the dynamic white noise acceleration model. After obtaining those trajectories that populate the area we compute the posterior Cramér-Rao lower bound iteratively...
In this paper a new interpretation of linear estimation in the context of classical mechanics is presented. In this context, Accumulated State Densities can be interpreted as the Lagrange function of a “least action” principle that provides the expectation vectors for filtering and retrodiction as a solution. The superposition principle, which states that the solution of this algorithm is a linear...
When localizing multiple tag-free targets using ultra-wideband sensors, targets closer to the sensor occlude targets further from the sensor and in turn these targets are not detected. In this paper we first model the occlusion region of each target. Based on this model, targets unresolved in sensor data and occluded by other targets are updated. Experimental results demonstrate that the incorporation...
The distributed Kalman filter requires the measurement covariances of remote radar nodes to be known at all radar nodes. This is not possible for a radar network, as the true measurement covariances depend on the radar-target geometry and the fluctuating signal-to-noise ratio. This paper tackles this problem using the double debiased distributed Kalman filter (D3KF) which utilizes a radar model to...
In this paper, a direct connection between the covariance debiasing methodology for the distributed Kalman (DKF) filter in [1] and the federated Kalman filter is shown. In particular, it can be seen that for a unique choice of the information gain hypothesis of the DKF, the covariance debiasing becomes equivalent to the federated Kalman filter. As the complexity of the covariance calculation for the...
Originally, the Accumulated State Density (ASD) has been proposed to provide an exact solution to the out-of-sequence measurement problem. The posterior probability density function of the joint states accumulated over time was derived for a centralized fusion, in which time delayed data may appear. On the other hand, an exact solution for T2TF has been published as the Distributed Kalman Filter (DKF)...
In track fusion, the measurements of individual sensors for each target are processed to generate local state estimates, which are then fused to obtain the global state estimate for the target. When there is no process noise or the fusion rate equals the sensor observation rate, the standard tracklet fusion or equivalent measurement fusion algorithm computes the optimal centralized estimate by extracting...
The passive non-cooperative localization and tracking of mobile terminals in urban scenarios, called blind mobile localization (BML), is a highly demanding task which occurs for instance in safety, emergency and security applications with non-subscribed phone user locations. Due to the urban environment and physical propagation effects multiple signals which have traveled along different multipaths...
For the purpose of tracking maneuverable targets and estimating the maneuver mode, a multiple model filter similar to the interacting multiple model (IMM) filter is used. The dependency of the mode transition probabilities on the state is taken into account. By the use of the information about the mode that is contained in the target state, a faster convergence of the mode estimate to the true value...
In this paper, a novel approach for distributed bearings-only tracking is presented. In the past years, the literature on tracking has focused more and more on the distributed Kalman filter, which yields the optimal state estimate, given that the sensor model of all sensors in the system is known to each local processor. Since this condition is hardly feasible in practical applications, various approximations...
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