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This paper will specifically undertake the task of improving the passive sonar system using self-organizing map. Localizing multiple targets is a challenging problem as passive sonar sensors are only able to detect the targets' bearing angle. An effective way to find the targets location is by triangulation. However, in multi-sensor multi-target environment, ghost targets are introduced during the...
Range and angle measurement errors may be correlated when centroid image processing is applied on radar images of extended targets. This paper describes how a model of the correlation between target heading and measurement error can be used to improve the accuracy of tracking filters. The performance of the presented tracking algorithms is tested using a trajectory generator based on jump Markov nonlinear...
This paper presents a fusion process developed for the future armoured vehicle system (FAVS) technical demonstration (TD) project. One of the project objectives was to develop, optimize and demonstrate a multi-sensor suite mounted on an army vehicle to detect and identify targets while the platform was moving. The sensors consisted of a cooled infrared camera, millimetre-wave radar and a defensive...
A novel method involved the time-varying tracking model under the nonlinear state-space evolved system is presented, in which the expectation-maximization (EM) algorithm is used to identify the state transition matrix f and the process noise covariance Q online. The typical maneuvering models, as described, essentially, are prior models and use fixed and constant evolved matrix and designed noise...
Energy based detection measures sensor received signal strength (RSS) transmitted from a target. In this paper, we propose a new approach for estimating a moving target trajectory over a sensor field via energy based detections as an alternative to trilateration positioning or nonlinear estimation. In 2D case, possible target locations described by a RSS ratio from two sensors are approximated using...
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,...
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...
This paper describes a generalization of Murty's algorithm generating ranked solutions for classical assignment problems. The generalization extends the domain to a general class of zero-one integer linear programming problems that can be used to solve multi-frame data association problems for track-oriented multiple hypothesis tracking (MHT). The generalized Murty's algorithm mostly follows the steps...
Probabilistic multi-hypothesis tracking (PMHT) is an algorithm for tracking multiple targets when measurement-to- target assignments are unknown and must be jointly estimated with the target tracks. Multi-frame assignment PMHT (MF- PMHT) is an algorithm designed to mitigate some performance problems associated with PMHT. In MF-PMHT, the PMHT algorithm is applied to multi-frame sequences in the last...
This paper is concerned with performance prediction of multiple target tracking system. Effects of misassociation are considered in a simple (linear) framework so as to provide closed-form expressions of the probability of correct association. In this paper, we focus on the development of explicit approximations of this probability for a unique false measurements. Rigorous calculations allow us to...
PMHT algorithm, as proposed, promises high performance multi target tracking in clutter with (relatively) modest computational resources. However, when applied to practical target tracking situations, a number of problems need to be overcome. PMHT assumes fixed number of tracks, and furthermore it assumes that all tracks are true tracks. No track quality measure is provided within PMHT to enable false...
Multiple ground targets tracking with a GMTI (ground moving target indicator) sensor is considered a challenging problem in order to establish battlefield assessment. An IMM algorithm with a variable structure is adapted to the road network and used to track multiple manoeuvring ground targets. However, the case of undetected targets due to terrain elevation or Doppler obscuration was not taken into...
Decentralized multisensor-multitarget tracking has numerous advantages over single-sensor or single-platform tracking. In this paper, we present a solution to one of the main problems of decentralized tracking, namely, distributed information transfer and fusion among the participating platforms. This paper presents a hierarchial multi-level decision mechanism for collaborative distributed data fusion...
We consider the problem of state estimation for a dynamic system driven by unobserved, correlated inputs. We model these inputs via an uncertain set of temporally correlated dynamic models, where this uncertainty includes the number of modes, their associated statistics, and the rate of mode transitions. The dynamic system is formulated via two interacting graphs: a hidden Markov model (HMM) and a...
Tracking objects using multiple sensors is more efficient than those using one sensor. In this paper, we proposed a method to fuse data from multiple sensors in Gaussian mixture probability hypothesis density filter. This method can avoid the data association problem in multi-sensor multi-object tracking. Moreover, it is more reliable and less computational than particle probability hypothesis density...
The question tackled here is the time allocation of radars in a multitarget environment. At a given time radars can only observe a limited part of the space; it is therefore necessary to move their axis with respect to time, in order to be able to explore the overall space facing them. Such sensors are used to detect, to locate and to identify targets which are in their surrounding aerial space. In...
Fusing out-of-sequence information is a very important problem for multi-sensor target tracking systems. The challenge is in dealing with measurements that arrive from the various sensors at a central processor out-of-sequence; that is, signals arriving with a measurement relating to a time previous to the time of the current state. The problem of how to deal with these updates has received much attention...
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...
In this paper a novel multiple model particle filter algorithm for tracking ground targets on constrained paths is developed The algorithm is designed to let the different modes be represented by constrained likelihood models, whereas the state dynamics are the same for all models. The mixing procedure is performed over the likelihood models and the mixing parameters are calculated in a standard interacting...
In this paper we will consider several algorithms for tracking closely spaced objects. In particular we will concentrate on various particle filter implementations. One particular problem when using a joint multi target particle filter is the so-called mixed labelling problem. This problem amounts to the fact that different particles will have a different labelling w.r.t. target identity. The combination...
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