Daniel Svensson

Shooting two birds with two bullets: How to find Minimum Mean OSPA estimates

Most area-defense formulations follow from the assumption that threats must first be identified and then neutralized. This is reasonable, but inherent to it is a process of labeling: threat A must be identified and then threat B, and then action must be taken. This manuscript begins from the assumption that such labeling (A & B) is irrelevant. The problem naturally devolves to one of Random Finite Set (RFS) estimation: we show that by eschewing any concern of target label we relax the estimation procedure, and it is perhaps not surprising that by such a removal of constraint (of labeling) performance (in terms of localization) is enhanced. A suitable measure for the estimation of unla-beled objects is the Mean OSPA (MOSPA). We derive a general algorithm which provided the optimal estimator which minimize the MOSPA. We call such an estimator a Minimum MOSPA (MMOSPA) estimator.

Set JPDA Filter for Multitarget Tracking

In this article, we show that when targets are closely spaced, traditional tracking algorithms can be adjusted to perform better under a performance measure that disregards identity. More specifically, we propose an adjusted version of the joint probabilistic data association (JPDA) filter, which we call set JPDA (SJPDA). Through examples and theory we motivate the new approach, and show its possibilities. To decrease the computational requirements, we further show that the SJPDA filter can be formulated as a continuous optimization problem which is fairly easy to handle. Optimal approximations are also discussed, and an algorithm, Kullback-Leibler SJPDA (KLSJPDA), which provides optimal Gaussian approximations in the Kullback-Leibler sense is derived. Finally, we evaluate the SJPDA filter on two scenarios with closely spaced targets, and compare the performance in terms of the mean optimal subpattern assignment (MOSPA) measure with the JPDA filter, and also with the Gaussian-mixture cardinalized probability hypothesis density (GM-CPHD) filter. The results show that the SJPDA filter performs substantially better than the JPDA filter, and almost as well as the more complex GM-CPHD filter.

A New Multiple Model Filter With Switch Time Conditions

The interacting multiple model filter has long been the method of choice for performing target tracking using multiple motion models. The filter finds a suboptimal solution to a problem which has the implicit assumption that immediate model shifts have the highest probability. When the sampling rate of the underlying continuous process is high compared to the target dynamics, this is not a reasonable assumption. Instead, changes in dynamics persist for some time. In this paper we propose an alternative switching model, which forces the dynamic models to persist for at least a model-specific time. The model is semi-Markov in nature, with a sojourn time probability mass function which is zero for a model-specific number of time steps, and then follows a geometrical distribution. Through this assumption a less complex problem in terms of model hypotheses arises, and to that problem we derive a state estimation algorithm that is close to optimal when the model assumptions are valid. Three other semi-Markov-based multiple-model filters are discussed and compared to in a qualitative sense. We also derive a new aircraft motion model for start and termination of turns. Finally, the proposed filter is evaluated on a benchmark scenario for tracking, and the results show a performance increase compared to the interacting multiple model (IMM) filter for the trajectories considered.

Multitarget Sensor Resolution Model and Joint Probabilistic Data Association

In the design of target tracking algorithms, the aspect of sensor resolution is rarely considered. Instead, it is usually assumed that all targets are always resolved, and that the only uncertainties in the data association are which targets that are detected, and which measurement each detected target gave rise to. However, in situations where the targets are closely spaced in relation to the sensor resolution, this assumption is not valid, and may lead to degraded tracking performance due to an incorrect description of the data. We present a framework for handling sensor resolution effects for an arbitrary, but known, number of targets. We propose a complete multitarget sensor resolution model that can be incorporated into traditional Bayesian tracking filters. Further, the exact form of the posterior probability density function (pdf) is derived, and two alternative ways of approximating that exact posterior density with a joint probabilistic data association (JPDA) filter are proposed. Evaluations of the resulting filters on simulated radar data show significantly increased tracking performance compared with the JPDA filter without a resolution model.

Joint probabilistic data association filter for partially unresolved target groups

In many surveillance problems the observed objects are so closely spaced that they cannot always be resolved by the sensor(s). Typical examples for partially unresolved measurements are the surveillance of aircraft in formation, and convoy tracking for ground surveillance. Ignoring the limited sensor resolution in a tracking system may lead to degraded tracking performance, in particular unwanted track-losses. In this paper, we further discuss a recently presented extension of the resolution model by Koch and van Keuk to the case of arbitrary object numbers, and it is shown how that model can be incorporated into the Joint Probabilistic Data Association Filter (JPDAF). Further, through simulations of a ground target tracking scenario, it is shown how the incorporation of the resolution model improves tracking performance when targets are partially unresolved.

Multitarget sensor resolution model for arbitrary target numbers

In many surveillance problems the observed objects are so closely spaced that they cannot always be resolved by the sensor(s). Typical examples for partially unresolved measurements are the surveillance of aircraft in formation, and convoy tracking for ground surveillance. Ignoring the limited sensor resolution in a tracking system may lead to degraded tracking performance, in particular unwanted track-losses. In this paper we extend the resolution model by Koch and van Keuk, given for two partially unresolved objects, to the case of arbitrary object numbers. We also derive the effects of the resolution model to the multi-target likelihood function and the possible data associations. Further, it is shown how the model can be integrated into the Joint Probabilistic Data Association Filter (JPDAF).

Channel Tracking for AF MIMO Relaying Systems

In this paper, we consider the problem of channel estimation in multiple-input multiple-output (MIMO) amplify-and-forward (AF) relaying systems operating over time varying channels. Only data at the receiving end are assumed available for the estimation. By employing a first-order autoregressive (AR) model for characterizing the time-varying nature of the channels to be estimated, we derive an expectation-maximization (EM) Kalman filter (KF) that utilizes the received signal at the destination to track the individual channel links. The extended KF algorithm is also derived and compared to the proposed EM-based KF. Our simulation results show that the proposed EM-based KF offers better estimation performance with less complexity when compared to the EKF algorithm.

Multiple Model Filtering with Switch Time Conditions

The interacting multiple model filter has long been the preferred method to handle multiple models in target tracking. The filter finds a suboptimal solution to a problem, which implicitly assumes that immediate model shifts have the highest probability. We argue that this model-shift property does not capture the typical nature of maneuvering targets, namely that changes in target dynamics persist for some time. In this paper, we propose an adjusted switch time assumption that forces the dynamic models to remain fixed for a specified time. The modified filtering problem has lower complexity, and we derive a state estimation algorithm that is close to optimal in many scenarios. From Monte Carlo simulations, the new filter is found to yield a 20% decrease in root mean square position error, compared to the interacting multiple model filter in situations where the switch-time conditions are fulfilled.

Mitigation methods for FOPEN radar impact on wireless communications

The paper provides methods for suppressing interference from ultra wide band FOPEN radar on communication signals within the FOPEN band. The paper accounts for theory, simulations and experiments concerning these methods. A foregoing analysis of the required radar emission thresholds, is provided in another paper, submitted to ITS 2014.

Target existence probability in the distributed Kalman filter

In this paper, the target existence probability for a single target in clutter is derived. More specifically, the paper considers target existence in the distributed Kalman filter. First, a conceptual solution is derived explicitly for a two-sensor case, and second a moment-matching approximation is performed, which enables computational tractability. The results can be generalized to arbitrary numbers of sensors.