This paper focuses on the multi-target tracking using the Stone Soup framework. In particular, we aim at evaluation of two multi-target tracking scenarios based on the simulated class-B dataset and ADS-B class-A dataset provided by OpenSky Network. The scenarios are evaluated w.r.t. selection of a local state estimator using a range of the Stone Soup metrics. Source code with scenario definitions and Stone Soup set-up are provided along with the paper.

This paper deals with state estimation of nonlinear stochastic dynamic models. In particular, the stochastic integration rule, which provides asymptotically unbiased estimates of the moments of nonlinearly transformed Gaussian random variables, is reviewed together with the recently introduced stochastic integration filter (SIF). Using SIF, the respective multi-step prediction and smoothing algorithms are developed in full and efficient square-root form. The stochastic-integration-rule-based algorithms are implemented in Python (within the Stone Soup framework) and in MATLAB® and are numerically evaluated and compared with the well-known unscented and extended Kalman filters using the Stone Soup defined tracking scenario.

The International Society of Information Fusion (ISIF) Stone Soup project seeks to bring together advances in target tracking through an open-source repository of software libraries. Additionally, the ISIF uncertainty reasoning working group provides an open-source ontology. This paper seeks to demonstrate the correspondence between the open source tracking repository and the Uncertainty Representation and Reasoning Evaluation Framework (URREF) ontology. For example, many target tracking challenge problems propose a scenario for data fusion techniques to solve, from which various performance metrics are considered for evaluation. The Stone Soup framework has the MetricGenerator class and the URREF has the accuracy class. The example presented in the paper utilizes the cubature Kalman filter to determine the impact of corrupted measurements on the track accuracy as an instance of the Stone Soup and URREF metrics.

In this paper, we investigate the problem of source location estimation in wireless sensor networks (WSNs) based on quantized data in the presence of false information attacks. Using a Gaussian mixture to model the possible attacks, we develop a maximum likelihood estimator (MLE) to locate the source with sensor data corrupted by injected false information, and call the approach quantized received signal strength with a Gaussian mixture model (Q-RSS-GM). The Cramer-Rae lower bound (CRLB) for this estimation problem is also derived to evaluate the estimator's performance. Simulation results show that the proposed estimator is robust in various cases with different attack probabilities and parameter mismatch, and it significantly outperforms the approach that ignores the possible false information attacks.

In this paper, sparsity-promoting sensor selection algorithms for target tracking with quantized data are developed. We formulate sensor selection as an optimization problem that aims to strike a balance between estimation accuracy and the number of selected sensors. To cope with sensor selection problems in large-scale wireless sensor networks (WSNs), we propose a fast centralized optimization algorithm based on the alternating direction method of multipliers (ADMM). We further develop a low-complexity distributed version of the ADMM where each sensor makes a local sensor selection decision. The simulation results show that the proposed centralized and distributed algorithms activate the most informative sensors and yield very good tradeoff between the estimation performance and the cost of sensing and communication. For large scale sensor networks, the distributed ADMM algorithm is more efficient and has lower computational load per sensor node.

In this paper, we propose a new maximumlikelihood (ML) target location estimator which uses quantized sensor data and wireless channel statistics in a wireless sensor network. The novelty of our approach comes from the fact that imperfect channel statistics between wireless sensors and the fusion center are incorporated in the localization algorithm. We call this approach “channel-aware target localization”. Furthermore, we derive the Cramer-Rao lower bound as a performance bound for our channel-aware ML estimator. Simulation results are presented to show that the performance of the channel-aware ML location estimator is quite close to its theoretical performance bound even with relatively small number of sensors and it has superior performance compared to that of the channel-unaware ML estimator.

This paper presents a new framework for distributed target detection in wireless sensor networks (WSNs). In our previous work, for multiple networked sensors collaboratively detecting the presence or absence of a target in the sensor field, every sensor uses an identical threshold for local decision-making. In this paper, we propose a framework where the sensors in the network collaboratively decide and select non-identical thresholds to improve network-wide detection performance in a dynamic manner. This threshold selection scheme is based on a new statistical metric called False Discovery Rate (FDR). Assuming a signal attenuation model, where the received signal power decays as the distance from the target increases, various performance indices like system level probability of detection and probability of false alarm are studied. Analytical and simulation results are provided for system level probability of false alarm and probability of detection. Performance comparison between the proposed approach and the classical identical local sensor threshold approach is provided to demonstrate the effectiveness of this scheme.