Ozgur Erdinc

The Bin-Occupancy Filter and Its Connection to the PHD Filters

An algorithm that is capable not only of tracking multiple targets but also of ldquotrack managementrdquo-meaning that it does not need to know the number of targets as a user input-is of considerable interest. In this paper we devise a recursive track-managed filter via a quantized state-space (ldquobinrdquo) model. In the limit, as the discretization implied by the bins becomes as refined as possible (infinitesimal bins) we find that the filter equations are identical to Mahlers probability hypothesis density (PHD) filter, a novel track-managed filtering scheme that is attracting increasing attention. Thus, one contribution of this paper is an interpretation of, if not the PHD itself, at least what the PHD is doing. This does offer some intuitive appeal, but has some practical use as well: with this model it is possible to identify the PHDs ldquotarget-deathrdquo problem, and also the statistical inference structures of the PHD filters. To obviate the target death problem, PHD originator Mahler developed a new ldquocardinalizedrdquo version of PHD (CPHD). The second contribution of this paper is to extend the ldquobin-occupancyrdquo model such that the resulting recursive filter is identical to the cardinalized PHD filter.

Gaussian mixture cardinalized PHD filter for ground moving target tracking

The cardinalized probability hypothesis density (CPHD) filter is a recursive Bayesian algorithm for estimating multiple target states with varying target number in clutter. In particular, the Gaussian mixture variant (GMCPHD) for linear, Gaussian systems is a candidate for real time multi target tracking. The present work addresses the following three issues: (i) we show the equivalence between the GMCPHD filter and the standard Multi Hypothesis Tracker (MHT) in the case of single targets; (ii) using a Gaussian sum approach, we extend the GMCPHD filter by employing digital road maps for road constraint targets. The utilization of such external information leads to more precise tracks and faster and more reliable target number estimates; (iii) we model the effect of Doppler blindness by a target state dependent detection probability, leading to more stable target number estimation in the case of low Doppler targets.

GMTI Tracking via the Gaussian Mixture Cardinalized Probability Hypothesis Density Filter

The cardinalized probability hypothesis density (CPHD) filter is a recursive Bayesian algorithm for estimating multiple target states with a varying target number in clutter. In particular the Gaussian mixture variant (GMCPHD), which provides closed-form prediction and update equations for the filter in the case of linear Gaussian systems, is a candidate for real time multi-target tracking. The following three issues are addressed. First we show the equivalence between the GMCPHD filter and the standard multi hypothesis tracker (MHT) in the case of a single target. Second by using a Gaussian sum approach, we extend the GMCPHD filter to incorporate digital road maps for road constrained targets. The use of such external information leads to more precise tracks and faster and more reliable target number estimates. Third we model the effect of Doppler blindness by a target state-dependent detection probability, which leads to a more stable target-number estimation in the case of low-Doppler targets.

The Problem of Test Latency in Machine Diagnosis

The impact of delayed sensor alarm data upon a diagnostic inference engine appears not to be well appreciated. In this paper, we illustrate the effect of sensor latency, and we propose an inference approach to obviate it.

The CPHD and R-RANSAC trackers applied to the VIVID dataset

In this work, two multitarget trackers - the Cardinalized Probability Hypothesis Density (CPHD) filter and the Recursive Random Sample Consensus (R-RANSAC) algorithm - were applied to three scenarios of the Video Verification of IDentity (VIVID) dataset provided by DARPA. The dataset consists of real video data of multiple cars observed from an unmanned aerial vehicle (UAV) and includes challenging situations such as dense traffic and occlusions. The same detector output was given to each tracker and the same metrics of performance were computed in order to ensure fair comparison of the two tracking approaches. The results show the CPHD did better overall, which was to be expected given that it is the more mature approach.

Fast Diagnosis With Sensors of Uncertain Quality

This correspondence presents an approach to the detection and isolation of component failures in large-scale systems. In the case of sensors that report at rates of 1 Hz or less, the algorithm can be considered real time. The input is a set of observed test results from multiple sensors, and the algorithms main task is to deal with sensor errors. The sensors are assumed to be of threshold test (pass/fail) type, but to be vulnerable to noise, in that occasionally true failures are missed, and likewise, there can be false alarms. These errors are further assumed to be independent conditioned on the systems diagnostic state. Their probabilities, of missed detection and of false alarm, are not known a priori and must be estimated (ideally along with the accuracies of these estimates) online, within the inference engine. Further, recognizing a practical concern in most real systems, a sparsely instantiated observation vector must not be a problem. The key ingredients to our solution include the multiple-hypothesis tracking philosophy to complexity management, a Beta prior distribution on the sensor errors, and a quickest detection overlay to detect changes in these error rates when the prior is violated. We provide results illustrating performance in terms of both computational needs and error rate, and show its application both as a filter (i.e., used to ldquocleanrdquo sensor reports) and as a standalone state estimator.

Scalable human-in-the-loop decision support

A scalable human-in-the-loop decision support system has been built around an active learning algorithm operating on aircraft engine time series data. The system integrates hierarchical clustering and active learning algorithms backed by a big data analytics ecosystem with a browser-based user interface. This combination enables multiple expert users to efficiently train a model to classify aircraft engine behavior by prioritizing the segments of flight for human analysis. This system lowers the time required from human experts by eliminating unnecessary labelling effort and supporting the aircraft maintenance industrys service technicians.

Multistatic sensor placement : A tracking perspective

Sonar tracking using measurements from multistatic sensors has shown promise: there are benefits in terms of robustness, complementarity (covariance-ellipse intersection) and of course simply due to the increased probability of detection that naturally accrues from a well-designed data fusion system. It is not always clear what the placement of the sources and receivers that gives the best fused measurement covariance for any target--or at least for any target that is of interest--might be. In this paper, we investigate the problem as one of global optimization, in which the objective is to maximize the information provided to the tracker. We assume that the number of sensors is known, so that the optimization is done in a continuous space. We consider di.erent scenarios and numbers of sensors. The strong variability of target strength as a function of aspect is integral to the cost function we optimize. Numerical results are given, these suggesting that certain sensor geometries should be used. We have a number of intuitive suggestions that do not involve optimization for sensor layout.

Spectral multiscale coverage with the feature aided CPHD tracker

A closed loop approach for surveillance was developed leveraging the Spectral Multiscale Coverage (SMC) algorithm for sensor management coupled with the Cardinalized Probability Hypothesis Density (CPHD) multitarget tracker. Additionally, the CPHD was formulated such that it is able to ingest features, if available. Simulations with fixed and mobile sensors (the latter, tasked by the SMC) providing data to the tracker underlined the benefits of sensor fusion with respect to standard metrics of performance.