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 problem of the limited accuracy of precipitation Doppler spectrum moments estimation measured by fast azimuthally scanning weather radars is addressed. A novel approach for the Doppler moment estimation based on maximum likelihood estimation is proposed. A simplified semianalytical parametric model for the precipitation power spectral density (PSD) as a function of the velocity parameters of the scatterers and the finite radar observation time is derived for typical precipitation-like weather conditions. An inverse problem for estimating the Doppler moments from measurements of the PSD is formulated and solved. It is demonstrated that the variance of the estimation of the Doppler moments approaches the Cramer Rao Lower Bound (CRB) when the observation time approaches infinity. The performance of the proposed approach is compared with some classical techniques and another realization of the maximum likelihood approach based on simulated and experimental data. The results indicate the superiority of the proposed approach, especially for short observation time. Furthermore, a scanning strategy to accurately estimate the Doppler moments based on the true velocity dispersion of the scatterers is provided with the help of the proposed approach.

The challenge of avoiding aliasing in the Doppler spectrum for precipitation is addressed. A novel integrative signal processing approach has been proposed to address the research gaps from various disciplines. The proposed approach consists of several steps. First, an aperiodic way of sampling the echoes (aperiodic sampling refers to aperiodic pulse train in the context of radar echoes in slow time) has been proposed by which the maximum unambiguous Doppler frequency (velocity) is enhanced. Second, the Doppler spectrum moment estimation is performed with the help of a parametric form of its covariance. The performance of the moment estimation is assessed by the bias and the variance in the estimated counterparts. The theoretical variance for the parameter estimation is also derived. An aperiodic pulse train design recommendation has been proposed for adequately and unambiguously estimating the Doppler moments for one extended target (like precipitation). Finally, a spectrum reconstruction technique is implemented after the moment estimation on simulated radar echo samples for a realistic precipitation-like event. The comparison with the other approaches proves its superiority for parameter estimation and Doppler spectrum reconstruction.

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.

In this paper, a track selection problem for multi-target tracking in a multifunction radar network is studied using the concepts from game theory. The problem is formulated as a non-cooperative game, and specifically as an anti-coordination game, where each player aims to differ from what other players do. The players' utilities are modeled using a proper tracking accuracy criterion and, under different assumptions on the structure of these utilities, the corresponding Nash equilibria are characterized. To find an equilibrium, a distributed algorithm based on the best-response dynamics is proposed. Finally, computer simulations are carried out to verify the effectiveness of the proposed algorithm in a multi-target tracking scenario.

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

In this paper, we present a new result that can be used for detection purposes. We show that when estimating the a posteriori probability density of a possible signal in noise by means of a particle filter, the output of the filter, i.e., the unnormalized weights, can be used to approximately construct the likelihood ratio, which arises in many different detection schemes.

The paper presents a parallel architecture for a pel-recursive motion estimation algorithm. It is a linear array of processors, each consisting of an initialization part, a data-routing part and an updating part. The initializing part performs a prediction of the motion vector. The routing parts constitute a routing path along which previous-frame data is routed from processors that store to processors that request such data. A clocked version of the router is presented with some detail. The updating part calculates an update to the predicted motion vector. The architecture proposed is derived in a systematic way and is parameterized w.r.t. certain window sizes. It is thus completely different from the few existing pel-recursive motion estimation architectures.>

A parallel architecture for a pel-recursive motion estimation algorithm is presented. It is a linear array of processors, each consisting of an initialization part, a data-routing part, and an updating part. The initializing part performs a prediction of the motion vector. The routing parts constitute a routing path along which previous-frame data are routed from processors that store to processors that request such data. The router can be data driven or clocked. The latter approach is presented in more detail. The updating part calculates an update to the predicted motion vector. The architecture proposed is derived in a systematic way and is parameterized with respect to certain window sizes. It is thus completely different from the few existing pel-recursive motion estimation architectures.>

In this paper adaptive algorithms for pel-recursive displacement estimation are introduced. The proposed algorithms are similar in form to the original Wiener-based algorithm, where the extensions are the appropriate tuning of the so-called damping parameter and the use of a linear search technique. The proposed techniques maintain the order of complexity of the original Wiener-based displacement estimator but they are more robust and exhibit a higher rate of convergence. The adaption of the damping parameter in a Total Least Squares sense improves the performance of the estimator with only a modest increase in the computational complexity. The application of a bi-section linear search technique is the most effective extension, but the most computational intensive too.

An algorithm is proposed that estimates displacement vectors and segments each frame into three regions: unchanged, predictable, and unpredictable. The segmentation result is used for the adaptation of the causal window and the initial estimate, both essential estimation information. The algorithm consists of a two-stage detector where the stages are separated by a Wiener-based displacement estimator. The detectors are efficient, suboptimal, maximum a posteriori estimators of special first-order 2-D Markov random-field models. The detectors are shown to have a substantially improved performance compared with detectors previously used, and the prediction error is reduced compared with existing schemes. Initial experiments indicate good coding efficiency.>