The challenge of ownship navigation for an airborne platform in the absence of precise navigation information is an important problem. In this paper, the problem is solved using the assumed known GPS locations of landmarks by casting it in a Bayesian state-space framework. It is assumed that no information is available from the navigation sensors. The platform kinematic state is inferred by using a nonlinear ﬁlter, such as the extended Kalman ﬁlter. The performance is assessed as a function of the density of landmarks and platform manoeuvres in a simulation environment.
Visual image tracking involves the estimation of the motion of any desired targets in a surveillance region using a sequence of images. A standard method of isolating moving targets in image tracking uses background subtraction. The standard background subtraction method is often impacted by irrelevant information in the images, which can lead to poor performance in image-based target tracking. In this paper, a B-Spline based image tracking is implemented. The novel method models the background and foreground using the B-Spline method followed by a tracking-by-detection algorithm. The eﬀectiveness of the proposed algorithm is demonstrated.
The problem of tracking a number of time-varying slow-moving targets in the presence of clutter and false alarms is particularly challenging for the ground moving target indication (GMTI) application. It requires adaptive clutter cancellation techniques such as space-time adaptive processing to deal with the mainbeam clutter. In addition, GMTI radars are also used for generating synthetic aperture radar (SAR) imagery. In this paper, we analysis the performance of the joint probabilistic data association (JPDA) filter for varying coherent processing intervals (CPI) by using experimental airborne radar data with a view towards a more efficient use of GMTI and SAR modes of an airborne AESA radar.
An airborne EO/IR (electro-optical/infrared) camera system comprises of a suite of sensors, such as a narrow and wide field of view (FOV) EO and mid-wave IR sensors. EO/IR camera systems are regularly employed on military and search and rescue aircrafts. The EO/IR system can be used to detect and identify objects rapidly in daylight and at night, often with superior performance in challenging conditions such as fog. There exist several algorithms for detecting potential targets in the bearing elevation grid. The nonlinear filtering problem is one of estimation of the kinematic parameters from bearing and elevation measurements from a moving platform. In this paper, we developed a complete model for the state of a target as detected by an airborne EO/IR system and simulated a typical scenario with single target with 1 or 2 airborne sensors. We have demonstrated the ability to track the target with `high precision' and noted the improvement from using two sensors on a single platform or on separate platforms. The performance of the Extended Kalman filter (EKF) is investigated on simulated data. Image/video data collected from an IR sensor on an airborne platform are processed using an image tracking by detection algorithm.
Proc. SPIE. 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI
KEYWORDS: Digital filtering, Particles, Linear filtering, Monte Carlo methods, Gaussian filters, Particle filters, Electronic filtering, Nonlinear filtering, Systems modeling, Filtering (signal processing)
The Probability Hypothesis Density Filter (PHD) is a multitarget tracker for recursively estimating the number
of targets and their state vectors from a set of observations. The PHD filter is capable of working well in
scenarios with false alarms and missed detections. Two distinct PHD filter implementations are available in the
literature: the Sequential Monte Carlo Probability Hypothesis Density (SMC-PHD) and the Gaussian Mixture
Probability Hypothesis Density (GM-PHD) filters. The SMC-PHD filter uses particles to provide target state
estimates, which can lead to a high computational load, whereas the GM-PHD filter does not use particles, but
restricts to linear Gaussian mixture models. The SMC-PHD filter technique provides only weighted samples
at discrete points in the state space instead of a continuous estimate of the probability density function of the
system state and thus suffers from the well-known degeneracy problem. This paper proposes a B-Spline based
Probability Hypothesis Density (S-PHD) filter, which has the capability to model any arbitrary probability
density function. The resulting algorithm can handle linear, non-linear, Gaussian, and non-Gaussian models and
the S-PHD filter can also provide continuous estimates of the probability density function of the system state. In
addition, by moving the knots dynamically, the S-PHD filter ensures that the splines cover only the region where
the probability of the system state is significant, hence the high efficiency of the S-PHD filter is maintained at
all times. Also, unlike the SMC-PHD filter, the S-PHD filter is immune to the degeneracy problem due to its
continuous nature. The S-PHD filter derivations and simulations are provided in this paper.