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 unlabeled 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

Efficient characterization of labeling uncertainty in closely-spaced targets tracking

In this paper we propose a novel solution to the labeled multi-target tracking problem. The method presented is specially effective in scenarios where the targets have once moved in close proximity. When this is the case, disregarding the labeling uncertainty present in a solution (after the targets split) may lead to a wrong decision by the end user. We take a closer look at the main cause of the labeling problem. By modeling the possible crosses between the targets, we define some relevant labeled point estimates. We extend the concept of crossing objects, which is obvious in one dimension, to scenarios where the objects move in multiple dimensions. Moreover, we provide a measure of uncertainty associated to the proposed solution to tackle the labeling problem. We develop a novel, scalable and modular framework in line with it. The proposed method is applied and analyzed on the basis of one-dimensional objects and two-dimensional objects simulation experiments

Marginal multi-Bernoulli filters: RFS derivation of MHT, JIPDA and association-based MeMBer

Recent developments in random finite sets (RFSs) have yielded a variety of tracking methods that avoid data association. This paper derives a form of the full Bayes RFS filter and observes that data association is implicitly present, in a data structure similar to MHT. Subsequently, algorithms are obtained by approximating the distribution of associations. Two algorithms result: one nearly identical to JIPDA, and another related to the MeMBer filter. Both improve performance in challenging environments.Comment: Journal version at http://ieeexplore.ieee.org/document/7272821. Matlab code of simple implementation included with ancillary file

Метод обработки данных в комплексе прибрежных РЛС средней дальности

The article presents the basic principles of design and development of integrated middle range Coastal Surveillance System (CSS) used for water surface lookout. It provides solutions for such missions as command and control of maritime forces, border monitoring and control, prevention of illegal activities such as piracy, smuggling, illegal immigration, illegal fishing, supporting search and rescue (SAR) operations, and creates a common situation awareness picture of the Naval Theatre. The system structure diagram is designed to solve computational overload problem when processing large volume of data received from radar stations. The measurement-level fusion algorithm is developed based on the JPDA framework, in which radar data received from a single or group of radars and AIS data is aggregated in a processing center. The servers and workstations make use of local area network (LAN), using standard Gigabit Ethernet technologies for local network communications. Acquisition, analysis, storage and distribution of target data is executed in servers, then the data is sent to automated operator stations (console), where functional operations for managing, identifying and displaying of target on digital situational map are performed.Представлены основные принципы проектирования и построения интегрированной системы прибрежных РЛС средней дальности для наблюдения за надводной обстановкой в акваториях с интенсивным движением малых судов. Система предоставляет решения для таких целей, как командование и контроль над морскими силами, пограничный мониторинг и контроль, предотвращение незаконной деятельности (пиратства, контрабанды, незаконной иммиграции, незаконного промысла), поддержки операций по поиску и спасению и т. д. Комплекс прибрежного наблюдения должен интегрировать данные нескольких радаров SCORE 3000 и сообщения от автоматической идентификационной системы (АИС) и обеспечивать корреляцию данных этих систем. Разработана структурная схема системы, обеспечивающая распределение вычислительной нагрузки при обработке большого объема данных, поступающих от РЛС. Предложен алгоритм на основе структуры Joint Probabilistic Data Association (JPDA) для объединения радиолокационных данных от одной или нескольких РЛС и данных АИС, поступающих в центр обработки информации. Сбор, хранение, анализ и распределение данных осуществляются на компьютерном сервере, функции управления, идентификации и отображения целей на цифровой карте реализуются на автоматизированном рабочем месте

Aspects of MMOSPA Estimation

We expand upon existing literature regarding using Minimum Mean Optimal Sub-Pattern Assignment (MMOSPA) estimates in multitarget tracking, noting its advantages in comparison to Maximum Likelihood (ML) and Minimum Mean Squared Error (MMSE) estimation, and look at the practical computation of MMOSPA estimates. We demonstrate the use of MMOSPA estimation in a two-target tracking scenario as well as outside of tracking in a radar angular superresolution scenario