Airborne Infrared Search and Track Systems

Infrared search and track (IRST) systems are required for fighter aircraft to enable them to passively search, detect, track, classify, and prioritise multiple airborne targets under all aspects, look-up, look-down, and co-altitude conditions and engage them at as long ranges as possible. While the IRST systems have been proven in performance for ground-based and naval-based platforms, it is still facing some technical problems for airborne applications. These problems arise from uncertainty in target signature, atmospheric effects, background clutter (especially dense and varying clouds), signal and data processing algorithms to detect potential targets at long ranges and some hardware limitations such as large memory requirement to store and process wide field of view data. In this paper, an overview of airborne IRST as a system has been presented with detailed comparative simulation results of different detectionitracking algorithms and the present status of airborne IRST

Interacting Multiple Model Algorithm with the Unscented Particle Filter (UPF)

AbstractCombining interacting multiple model (IMM) and unscented particle filter (UPF), a new multiple model filtering algorithm is presented. Multiple models can be adapted to targets' high maneuvering. Particle filter can be used to deal with the nonlinear or non-Gaussian problems and the unscented Kalman filter (UKF) can improve the approximate accuracy. Compared with other interacting multiple model algorithms in the simulations, the results demonstrate the validity of the new filtering method

Novel methods for multi-target tracking with applications in sensor registration and fusion

Maintaining surveillance over vast volumes of space is an increasingly important capability for the defence industry. A clearer and more accurate picture of a surveillance region could be obtained through sensor fusion between a network of sensors. However, this accurate picture is dependent on the sensor registration being resolved. Any inaccuracies in sensor location or orientation can manifest themselves into the sensor measurements that are used in the fusion process, and lead to poor target tracking performance. Solutions previously proposed in the literature for the sensor registration problem have been based on a number of assumptions that do not always hold in practice, such as having a synchronous network and having small, static registration errors. This thesis will propose a number of solutions to resolving the sensor registration and sensor fusion problems jointly in an efficient manner. The assumptions made in previous works will be loosened or removed, making the solutions more applicable to problems that we are likely to see in practice. The proposed methods will be applied to both simulated data, and a segment of data taken from a live trial in the field

Analgorithmic Framework for Automatic Detection and Tracking Moving Point Targets in IR Image Sequences

Imaging sensors operating in infrared (IR) region of electromagnetic spectrum are gaining importance in airborne automatic target recognition (ATR) applications due to their passive nature of operation. IR imaging sensors exploit the unintended IR radiation emitted by the targets of interest for detection. The ATR systems based on the passive IR imaging sensors employ a set of signal processing algorithms for processing the image information in real-time. The real-time execution of signal processing algorithms provides the sufficient reaction time to the platform carrying ATR system to react upon the target of interest. These set of algorithms include detection, tracking, and classification of low-contrast, small sized-targets. Paper explained a signal processing framework developed to detect and track moving point targets from the acquired IR image sequences in real-time.Defence Science Journal, Vol. 65, No. 3, May 2015, pp.208-213, DOI: http://dx.doi.org/10.14429/dsj.65.816

Radar networks: A review of features and challenges

Networks of multiple radars are typically used for improving the coverage and tracking accuracy. Recently, such networks have facilitated deployment of commercial radars for civilian applications such as healthcare, gesture recognition, home security, and autonomous automobiles. They exploit advanced signal processing techniques together with efficient data fusion methods in order to yield high performance of event detection and tracking. This paper reviews outstanding features of radar networks, their challenges, and their state-of-the-art solutions from the perspective of signal processing. Each discussed subject can be evolved as a hot research topic.Comment: To appear soon in Information Fusio

The branching fraction of long-lived kaon going to muon-anti-muon

This thesis presents the results of a measurement of the branching fraction of the K\sb{L}\to\mu\bar{\mu} decay. This decay is scGIM supressed in the Standard Model, and provides a useful test of that model. Additionally, the degree of deviation from the unitary limit provides a useful test for several other models. The experiment was performed at the Brookhaven National Laboratory Alternating Gradient Synchrotron facility. The apparatus consisted of a two-magnet mass spectrometer, together with dual electromagnetic and muon particle identification systems. A total of 281 K\sb{L}\to\mu\bar{\mu} events were observed. Normalizing to the 15,768 K\sb{L}\to\pi\bar{\pi} events observed results in a branching fraction of \Gamma(K\sb{L}\to\mu\bar{\mu})/\Gamma(K\sb{L}\to all) = (7.6 $\pm$ 0.5) $\times$ 10\sp{-9}

The University Defence Research Collaboration In Signal Processing

This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations. The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour

Inertial Motion Capturing : Rigid Body Pose and Posture Estimation with Inertial Sensors

This dissertation is about estimating poses from inertial sensor data, that is estimating orientations and positions. Both poses of single rigid bodies as well as poses of so called skeletons, i.e. systems of jointed rigid bodies, are covered. The key insight into orientation estimation of a single rigid body is to view it as the fusion of sensor data and its dynamics model with prior information. To this end, three different Kalman Filter variations are presented, which fuse the same sensor data and the same dynamics with three different priors. It turns out that the classical model to correct the inclination in an orientation estimator, namely comparing the accelerometer measurement with (negative) gravity, is equivalent to the assumption that the rigid body does not accelerate on long-term average. Assuming that the velocity is zero on long-term average or that the rigid body stays at the same position on long-term average are alternative assumptions and both priors also yield orientation estimators. Moreover, the orientation estimator resulting from the position assumption also estimates a position, which is locally accurate - it follows the accelerometer measurements - but does not drift unboundedly, which it would if the position were obtained by integrating according to the dynamic model only. The focus here is more on the interplay of inertial sensor data and its dynamic model with prior information than it is on practical applications. For instance, for the integrated position to be a usable quantity, the estimate has to be conditioned on the long-term average of the position being zero instead of the velocity or acceleration being zero. In the second, bigger part of this dissertation the posture of a skeleton, i.e. the poses of all the skeleton's bodies, are estimated, again using inertial sensor data only. Notably, no magnetometers are used to recover the rotations around the vertical. Without magnetometers, the rotation of the skeleton as a whole around the vertical, of course, can not be estimated. However, to asses the skeleton's posture, it is also not important. If inertial sensor data of all bodies is fused with the prior information that a skeleton's bodies are jointed using hinges and spherical joints, the relative orientations of the bodies become observable completely: If two accelerometers of two jointed bodies measure the acceleration of a motion, then the relative orientation of those two bodies can be recovered from the directions of the accelerometer measurements, if effects due to movements of the joints are compensated for. The posture estimator that exploits this insight is developed and used in the sensor suit SIRKA, which is workwear with inertial sensors embedded into the clothing. On computationally very limited hardware, which is completely integrated into the suit, the estimator yields posture estimates in real-time. To make this possible, a technique to decouple the sensor's sampling rate from the estimation rate is introduced. Moreover, the sensor orientations and positions inside the suit are almost arbitrary and do not need adjustment. Instead, they are calibrated automatically. The motion capturing workwear is used in a real-world setting, estimating the posture of a worker welding steel on a shipyard. That would not be possible using a motion capturing suit relying on magnetometers