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Track-to-track association and sensor bias estimation are two important problems in multi-target multi-sensor tracking system. Track-to-track association becomes more complex in the presence of sensor bias and incorrect track association will lead to poor bias estimation results. Solving these two problems jointly would be attractive. This paper proposes a recursive joint track-to-track association...
Most target tracking algorithms work with data at the plot or detection level; that is, after an initial signal processing step of thresholding and centroiding that delivers point “hits” for data association and filtering. The GFMT (general frequency modulation tracker) and HPMHT (histogram probabilistic multi-hypothesis tracker), on the other hand, work directly with pixellated observation data,...
The multiple-model multi-Bernoulli (MM-MB) filter is a new attractive approach for estimating multiple maneuvering targets in the presence of clutter, missed detection and data association uncertainty. In this paper, we extend the Gaussian Mixture (GM) MM-MB filter to nonlinear models by using unscented transform techniques. Moreover, in order to improve the robustness and numerical stability of the...
Visual animal tracking is a challenging problem generally requiring extended target models, group tracking and handling of clutter and missed detections. Furthermore, the dolphin tracking problem we consider includes basin constraints, shadows, limited field of view and rapidly changing light conditions. We describe the whole pipeline of a solution based on a ceiling-mounted fisheye camera that includes...
A fusion methodology for tracks represented by Gaussian mixtures is proposed for distributed maneuvering target tracking with unknown correlation information between the local agents. For this purpose, Chernoff fusion is applied to the Gaussian mixtures provided by the local interacting multiple-model (IMM) filters. Chernoff fusion of Gaussian mixtures is achieved using a recently proposed method...
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...
Track association has not received as much attention as track fusion in distributed multi-sensor multitarget tracking, especially for targets whose motion models involve process noise. One exception is an association metric that uses the cross-covariance of the track state estimates at a single time. For track fusion, it has been shown that the centralized state estimate can be obtained by fusion...
A likelihood function for a multi-sensor, passive sonar Bayesian tracker may use data directly from the array elements or, alternatively, from the output of a conventional beamformer (CBF). Here, we compare the performance of a Bayesian tracker when using element- versus CBF-level data. We observed that, provided the CBF's beams are sufficiently closely spaced, the tracker's performance with CBF-level...
We present a Bayesian tracker for broadband passive sonar. The Bayesian formulation in Cartesian coordinates facilitates multi-sensor fusion of a distributed field of receivers. The likelihood functions used in the tracker are formulated so as to leverage a priori knowledge about the signal-of-interest's bandwidth and spectral mask when available. The use of a priori knowledge about the signal enables...
Single- and multi-target tracking are both typically based on the hidden Markov chain (HMC) model. That is, the target process is a Markov chain, observed by an independent observation process. Since HMC independence assumptions are invalid in many practical applications, the pairwise Markov chain (PMC) model has been proposed as an approach for weakening them. Petetin and Desbouvries subsequently...
Modern aircraft are capable of maneuvers exceeding those possible by purely aerodynamic design. This capability, called supermaneuverability, includes rapid changes in acceleration and high-G turns that are not feasible from traditional aircraft. Furthermore, newer aircraft often have a low radar cross-section (RCS) profile and/or RCS which varies rapidly with look angle. This paper summarizes the...
Filtering algorithms that use different forms of numerical integration to handle measurement and process non-linearities, such as the cubature Kalman filter, can perform extremely poorly in many applications involving angular measurements. We demonstrate how such filters can be modified to take into account the circular nature of the angular measurements, dramatically improving performance. Unlike...
Conventional tracking algorithms rely upon the hypothesis of one detection per target for each frame. However, very fine spatial resolution radars represent widespread systems that provides data for which this hypothesis could be no longer valid. This problem is often called in the literature extended target tracking. In this paper we propose to use the well-established random matrix theory to deal...
The use of random matrices for tracking extended objects has received high attention in recent years. It is an efficient approach for tracking objects that give rise to more than one measurement per time step. In this paper, the concept of random matrices is used to track surface vessels using highresolution automotive radar sensors. Since the radar also receives a large number of clutter measurements...
Long-haul sensor networks can be found in many real-world applications, such as tracking and/or monitoring of one or more dynamic targets in space. In such networks, sensors are remotely deployed over a large geographical area, whereas a remote fusion center fuses the information provided by these sensors in order to improve the accuracy of the final estimates of certain target characteristics. We...
The task of large-area visual monitoring for the protection of critical and public infrastructures calls for reliable automated visual surveillance systems. Reliability in this context implies that a high detection accuracy of critical events shall be maintained independent from observation conditions, appearance and pose variations of observed objects (persons, cars), while accomplishing a low-rate...
A novel radar energy control strategy based on an Interacting Multiple Model Particle Filter (IMMPF) tracking method is presented in this paper, which controls the radiated power of radar according to predicted tracking Cramér-Rao Lower Bounds (CRLB). Firstly, the computation model of the CRLB as the relation model between the radiated power and the tracking performance is built. Secondly, the power...
Small vessels or unmanned surface vehicles only have a limited amount of space and energy available. If these vessels require an active sensing collision avoidance system it is often not possible to mount large sensor systems like X-Band radars. Thus, in this paper an energy efficient automotive radar and a laser range sensor are evaluated for tracking surrounding vessels. For these targets, those...
We consider the control of two UAVs tracking an evasive moving ground vehicle. The UAVs are small fixed-wing aircraft equipped with gimbaled cameras and must coordinate their control actions so that at least one UAV is always close to the target. The control actions of the UAVs are computed based on noisy measurements of the UAVs' current state and vision-based measurements of the target's position...
In this paper, a particle filtering algorithm is proposed for the challenging problem of multiple target tracking in ultra-wideband (UWB) radar sensor networks. The particle weights are derived analytically for the case of one transmitter and several receivers. The performance of the proposed particle algorithm is then evaluated through numerical results with comparison to the well-known Kalman filter...
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