ADS-B is a widely used protocol that transmits aircraft’s position, velocity among other data. The protocol is not encrypted leading to the need of validation. A validation algorithm is proposed that makes use of Time Difference of Arrival localization to validate the position and velocity of ADS-B transmitting targets. Nowadays, Air navigation service providers (ANSP) commonly have at least one TDOA localization system in operation, allowing for cost effective implementation. Validation is achieved by using a Particle Filter and hypothesis tests. A novel method is used where the initial density is generated effectively based on the first set of TDOA measurements. Validation is possible when two or more ground stations receive the same ADS-B transmission, therefore the Particle Filter is designed to process such measurements. The algorithm is tested on data provided by Air Traffic Control The Netherlands’ North sea surveillance system. Results show that the validation works and that the algorithm is able to detect spoofing. Based on spoofed ADS-B messages and true TDOA measurements, the real and fake target can be detected when the distance is roughly 750 to 1000 meters (depending on the situation and the various tuning parameters). In addition, validation based on two or more ground stations per measurements has the effect that the validation area is increased, when compared to traditional filters that require 4 ground stations for tracking.

The sensor resource management problem in a multi-object tracking scenario is considered. In order to solve it, a dynamic budget balancing algorithm is proposed which models the different sensor tasks as partially observable Markov decision processes. Those are being solved by applying a combination of Lagrangian relaxation and policy rollout. The algorithm converges to a solution which is close to the optimal steady-state solution. This is shown through simulations of a two-dimensional tracking scenario. Moreover, it is demonstrated how the algorithm allocates the sensor time budgets dynamically to a changing environment and takes predictions of the future situation into account.

In this paper we present a method for the tracking of interacting targets disregarding whether or not the targets are close to each other. The method relies on parametric modeling of assumptions about targets interactive motion. Our filtering solution incorporates the parameters of the model in the state vector to perform on-line parameter estimation and exploitation. The proposed method is applied in a simulated Multiple Target Tracking application with radar track-before-detect measurements. Numerical experiments show that this approach results in estimation error reduction, allows detection of interactive target behaviors and reduce labeling uncertainty in closely-spaced targets tracking.

Recently several new results for Cramer-Rao lower bounds (CRLB's) in dynamical systems have been obtained. Several different approaches and approximations have been presented. For the general case of target tracking with a detection probability smaller than one and possibly in the presence of false measurements, two main approaches have been presented. One is the so called information reduction factor (IRF) approach and the other the enumeration (ENUM) approach, also referred to as conditioning approach. It has been shown that the ENUM approach leads to a strictly larger covariance matrix than the IRF approach, still being a lower bound of on the performance however. Thus, the ENUM approach provides a strictly tighter bound on the attainable performance. It has been conjectured that these bounds converge to one another in the limit or equivalently after an initial transition stage. In this paper we show, using some recent results on the so called modified Riccati (MR) equation and by means of counter examples, that this conjecture does not hold true in general. We also prove that it does hold true in the special case of deterministic target motion. Furthermore, we show that the detection probability has an influence on the limiting behaviors of the bounds. The various results are illustrated by means of representative examples.

In this paper the so called mixed labeling problem inherent, or at least thought to be inherent to a joint state multi target particle filter implementation is treated. The mixed labeling problem would be prohibitive for track extraction from a joint state multi target particle filter. It is shown and proven using the theory of Markov chains, that the mixed labeling problem is inherently self-resolving in a particle filter. It is also shown that the factors influencing this capability are the number of particles and the number of resampling steps.

This paper deals with target tracking for extended objects in a track before detect context. In the scope of this paper a target is called extended if its physical size is large enough to occupy multiple (radar) resolution cells, e.g. in range and/or azimuth. We show how the existing track before detect approach can be amended in order to deal with extended targets. The algorithm, that we propose, will jointly estimate on-line both the standard kinematic parameters of the target, i.e. position and velocity, as well as the size or extent of the target. The estimation is performed by means of a particle filter. It is shown that the extended target approach is significantly superior in terms of performance to a point target approach in case the target is extended