다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

A new recursive algorithm is proposed for jointly estimating the time-varying number of targets and their states from a sequence of observation sets in the presence of data association uncertainty, detection uncertainty, noise, and false alarms. The approach involves modelling the respective collections of targets and measurements as random finite sets and applying the probability hypothesis density (PHD) recursion to propagate the posterior intensity, which is a first-order statistic of the random finite set of targets, in time. At present, there is no closed-form solution to the PHD recursion. This paper shows that under linear, Gaussian assumptions on the target dynamics and birth process, the posterior intensity at any time step is a Gaussian mixture. More importantly, closed-form recursions for propagating the means, covariances, and weights of the constituent Gaussian components of the posterior intensity are derived. The proposed algorithm combines these recursions with a strategy for managing the number of Gaussian components to increase efficiency. This algorithm is extended to accommodate mildly nonlinear target dynamics using approximation strategies from the extended and unscented Kalman filters

The Gaussian Mixture Probability Hypothesis Density Filter

Orthogonal frequency division multiplexing (OFDM) is a modulation technique which is now used in most new and emerging broadband wired and wireless communication systems because it is an effective solution to intersymbol interference caused by a dispersive channel. Very recently a number of researchers have shown that OFDM is also a promising technology for optical communications. This paper gives a tutorial overview of OFDM highlighting the aspects that are likely to be important in optical applications. To achieve good performance in optical systems OFDM must be adapted in various ways. The constraints imposed by single mode optical fiber, multimode optical fiber and optical wireless are discussed and the new forms of optical OFDM which have been developed are outlined. The main drawbacks of OFDM are its high peak to average power ratio and its sensitivity to phase noise and frequency offset. The impairments that these cause are described and their implications for optical systems discussed.

OFDM for Optical Communications

https://www.alaakhamis.org/teaching/MCT200/reading/Information%20Fusion.pdf

Multisensor data fusion: A review of the state-of-the-art

The multitarget recursive Bayes nonlinear filter is the theoretically optimal approach to multisensor-multitarget detection, tracking, and identification. For applications in which this filter is appropriate, it is likely to be tractable for only a small number of targets. In earlier papers we derived closed-form equations for an approximation of this filter based on propagation of a first-order multitarget moment called the probability hypothesis density (PHD). In a recent paper, Erdinc, Willett, and Bar-Shalom argued for the need for a PHD-type filter which remains first-order in the states of individual targets, but which is higher-order in target number. In this paper we show that this is indeed possible. We derive a closed-form cardinalized PHD (CPHD) filter, which propagates not only the PHD but also the entire probability distribution on target number.

PHD filters of higher order in target number

The Gaussian mixture probability hypothesis density (GM-PHD) recursion is a closed-form solution to the probability hypothesis density (PHD) recursion, which was proposed for jointly estimating the time-varying number of targets and their states from a sequence of noisy measurement sets in the presence of data association uncertainty, clutter, and miss-detection. However the GM-PHD filter does not provide identities of individual target state estimates, that are needed to construct tracks of individual targets. In this paper, we propose a new multi-target tracker based on the GM-PHD filter, which gives the association amongst state estimates of targets over time and provides track labels. Various issues regarding initiating, propagating and terminating tracks are discussed. Furthermore, we also propose a technique for resolving identities of targets in close proximity, which the PHD filter is unable to do on its own.

/pdf/data-association-and-track-management-for-the-gaussian-1cz4gtzpnz.pdf

Data Association and Track Management for the Gaussian Mixture Probability Hypothesis Density Filter

The probability hypothesis density (PHD) filter is a practical alternative to the optimal Bayesian multi-target Alter based on finite set statistics. It propagates the PHD function, a first-order moment of the full multi-target posterior density. The peaks of the PHD function give estimates of target states. However, the PHD filter keeps no record of target identities and hence does not produce track-valued estimates of individual targets. We propose two different schemes according to which PHD filter can provide track-valued estimates of individual targets. Both schemes use the probabilistic data-association functionality albeit in different ways. In the first scheme, the outputs of the PHD filter are partitioned into tracks by performing track-to-estimate association. The second scheme uses the PHD filter as a clutter filter to eliminate some of the clutter from the measurement set before it is subjected to existing data association techniques. In both schemes, the PHD filter effectively reduces the size of the data that would be subject to data association. We consider the use of multiple hypothesis tracking (MHT) for the purpose of data association. The performance of the proposed schemes are discussed and compared with that of MHT.

Novel data association schemes for the probability hypothesis density filter

The Gaussian mixture probability hypothesis density filter (GM-PHD Filter) was proposed recently for jointly estimating the time-varying number of targets and their states from a noisy sequence of sets of measurements which may have missed detections and false alarms. The initial implementation of the GM-PHD filter provided estimates for the set of target states at each point in time but did not ensure continuity of the individual target tracks. It is shown here that the trajectories of the targets can be determined directly from the evolution of the Gaussian mixture and that single Gaussians within this mixture accurately track the correct targets. Furthermore, the technique is demonstrated to be successful in estimating the correct number of targets and their trajectories in high clutter density and shows better performance than the MHT filter

The GM-PHD Filter Multiple Target Tracker

Energy efficient white LEDs are increasingly being used for indoor lighting but unlike conventional lighting, the intensity of the light emitted by white LEDs can be modulated at high frequencies. Described is a novel positioning technique based on transmitting signals with known sinusoidal components from a number of LEDs. The receiver uses the resultant optical signal at the receiver photodiode to calculate the receiver position. Proof of concept experiments show that white LEDs designed for indoor lighting can be used as the transmitters. The modulation bandwidth of white LEDs is large enough to give a range of localisation that is compatible with typical indoor applications.

Indoor localisation using white LEDs

In this paper, we present a new form of orthogonal frequency division multiplexing (OFDM) for intensity modulated direct detection (IM/DD) systems. This new form of OFDM uses asymmetrically clipped optical OFDM (ACO-OFDM) on the odd frequency subcarriers and DC biased optical OFDM (DCO-OFDM) on the even subcarriers. By using a form of interference cancellation both the ACO-OFDM and the DCO-OFDM signals can be recovered at the receiver. It is shown that the DCO-OFDM component causes no interference on the odd frequency subcarriers so the receiver processing for the ACO-OFDM signal is identical to a conventional ACO-OFDM system. The ACO-OFDM signal causes clipping noise which falls on the even subcarriers. However, this interference can be estimated and cancelled at the receiver. The cancellation process results in a 3 dB noise penalty in the DCO-OFDM component. Simulation results are presented for an additive white Gaussian noise channel and it is shown that the new technique can achieve better optical power performance than the conventional schemes.

K. Panta

Papers

Data Association and Track Management for the Gaussian Mixture Probability Hypothesis Density Filter

Novel data association schemes for the probability hypothesis density filter

The GM-PHD Filter Multiple Target Tracker

Indoor localisation using white LEDs

A novel technique to simultaneously transmit ACO-OFDM and DCO-OFDM in IM/DD systems