Performance Evaluation of Simultaneous Sensor Registration and Object Tracking Algorithm

Reliable object tracking with multiple sensors requires that sensors are registered correctly with respect to each other. When an environment is Global Navigation Satellite System (GNSS) denied or limited – such as underwater, or in hostile regions – this task is more challenging. This paper performs uncertainty quantification on a simultaneous tracking and registration algorithm for sensor networks that does not require access to a GNSS. The method uses a particle filter combined with a bank of augmented state extended Kalman filters (EKFs). The particles represent hypotheses of registration errors between sensors, with associated weights. The EKFs are responsible for the tracking procedure and for contributing to particle state and weight updates. This is achieved through the evaluation of a likelihood. Registration errors in this paper are spatial, orientation, and temporal biases: seven distinct sensor errors are estimated alongside the tracking procedure. Monte Carlo trials are conducted for the uncertainty quantification. Since performance of particle filters is dependent on initialisation, a comparison is made between more and less favourable particle (hypothesis) initialisation. The results demonstrate the importance of initialisation, and the method is shown to perform well in tracking a fast (marginally sub-sonic) object following a bow-like trajectory (mimicking a representative scenario). Final results show the algorithm is capable of achieving angular bias estimation error of 0.0034 o , temporal bias estimation error of 0.0067 s, and spatial error of 0.021m

A Gaussian Process Regression based Dynamical Models Learning Algorithm for Target Tracking

Maneuvering target tracking is a challenging problem for sensor systems because of the unpredictability of the targets' motions. This paper proposes a novel data-driven method for learning the dynamical motion model of a target. Non-parametric Gaussian process regression (GPR) is used to learn a target's naturally shift invariant motion (NSIM) behavior, which is translationally invariant and does not need to be constantly updated as the target moves. The learned Gaussian processes (GPs) can be applied to track targets within different surveillance regions from the surveillance region of the training data by being incorporated into the particle filter (PF) implementation. The performance of our proposed approach is evaluated over different maneuvering scenarios by being compared with commonly used interacting multiple model (IMM)-PF methods and provides around $90\%$ performance improvement for a multi-target tracking (MTT) highly maneuvering scenario.Comment: 11 pages, 10 figure

A Gaussian process based method for multiple model tracking

Manoeuvring target tracking faces the challenge caused by the target motion model uncertainty, i.e., unknown model types or uncertain model parameters. Multiple-model (MM) methods have been generally considered to deal with this challenge, in which a bank of elemental filters is run simultaneously to provide a joint decision and estimation of motion model and localisation. However, if the uncertainty of the target trajectory increases, such as the target moves under mixed manoeuvring behaviours with time-varying parameters, more filters with different model assumptions have to be taken into account to match the motion of the target, which may lead to prohibitive computational complexity. To address this problem, we establish a training based algorithm which can learn the actual motion model as a Gaussian process (GP) regression. Then, by integrating the trained GP into the particle filter (PF), a GP-PF based tracking method is developed to track the manoeuvring targets in real-Time. Monte Carlo experiments show that the proposed method had much lower tracking root mean square error (RMSE) and robustness compared with the most commonly used MM methods

Adaptive kernel Kalman filter

Sequential Bayesian filters in non-linear dynamic systems require the recursive estimation of the predictive and posterior probability density function (pdf). This paper introduces a Bayesian filter called the adaptive kernel Kalman filter (AKKF). The AKKF approximates the arbitrary predictive and posterior pdf of hidden states using the kernel mean embedding (KME) in reproducing kernel Hilbert space (RKHS). In parallel with the KME, some particles in the data space are used to capture the properties of the dynamic system model. Specifically, particles are generated and updated in the data space. Moreover, the corresponding kernel weight means vector and covariance matrix associated with the particles' kernel feature mappings are predicted and updated in the RKHS based on the kernel Kalman rule (KKR). Simulation results are presented to confirm the improved performance of our approach with significantly reduced numbers of particles by comparing with the unscented Kalman filter (UKF), particle filter (PF), and Gaussian particle filter (GPF). For example, compared with the GPF, the AKKF provides around 50% logarithmic mean square error (LMSE) tracking performance improvement in the bearing-only tracking (BOT) system when using 50 particles

Implementation of adaptive kernel Kalman filter in stone soup

The recently proposed adaptive kernel Kalman filter (AKKF) is an efficient method for highly nonlinear and high-dimensional tracking or estimation problems. Compared to other nonlinear Kalman filters (KFs), the AKKF has significantly improved performance, reducing computational complexity and avoiding resampling. It has been applied in various tracking scenarios, such as multi-sensor fusion and multi-target tracking. By using existing Stone Soup components, along with newly established kernel-based prediction and update modules, we demonstrate that the AKKF can work in the Stone Soup platform by being applied to a bearing–only tracking (BOT) problem. We hope that the AKKF will enable more applications for tracking and estimation problems, and the development of a whole class of derived algorithms in sensor fusion systems

Introduction: Rethinking the Impact of the Inter-American Human Rights System

This chapter introduces the central themes of the book and argues that the Inter-American Human Rights System (IAHRS) is activated by political actors and institutions in ways that transcend traditional compliance perspectives and that have the potential to meaningfully alter politics and provoke positive domestic human rights change. The chapter identifies key gaps in existing human rights scholarship, particularly in relation to the IAHRS, and outlines three core perspectives on the System’s impact on human rights. It offers a synthesis of the key findings of the volume, and provides reflections on the future prospects of the System by locating it in its broader global context

Rethinking justice beyond human rights. Anti-colonialism and intersectionality in the politics of the Palestinian Youth Movement

This article discusses the politics of the Palestinian Youth Movement (PYM) – a contemporary social movement operating across a number of Arab and western countries. Unlike analysis on the Arab Uprisings which focused on the national dimension of youth activism, we explore how the PYM politics fosters and upholds an explicitly transnational anti-colonial and intersectional solidarity framework, which foregrounds a radical critique of conventional notions of self-determination based on state-framed human rights discourses and international law paradigms. The struggle becomes instead framed as an issue of justice, freedom and liberation from interlocking forms and hierarchies of oppression. KEYWORDS: Palestine, transnational social movements, intersectionality, human rights, anti-colonialis

Multichannel Online Blind Speech Dereverberation with Marginalization of Static Observation Parameters in a Rao-Blackwellized Particle Filter

Room reverberation leads to reduced intelligibility of audio signals and spectral coloration of audio signals. Enhancement of acoustic signals is thus crucial for high-quality audio and scene analysis applications. Multiple sensors can be used to exploit statistical evidence from multiple observations of the same event to improve enhancement. Whilst traditional beamforming techniques suffer from interfering reverberant reflections with the beam path, other approaches to dereverberation often require at least partial knowledge of the room impulse response which is not available in practice, or rely on inverse filtering of a channel estimate to obtain a clean speech estimate, resulting in difficulties with non-minimum phase acoustic impulse responses. This paper proposes a multi-sensor approach to blind dereverberation in which both the source signal and acoustic channel are directly estimated from the distorted observations using their optimal estimators. The remaining model parameters are sampled from hypothesis distributions using a particle filter, thus facilitating real-time dereverberation. This approach was previously successfully applied to single-sensor blind dereverberation. In this paper, the single-channel approach is extended to multiple sensors. Performance improvements due to the use of multiple sensors are demonstrated on synthetic and baseband speech examples