Single target tracking for noncooperative bistatic radar with unknown signal illumination

Wang, Sen; Bao, Qing

doi:10.1016/j.sigpro.2021.107991

Cited by 10 publications

(2 citation statements)

References 37 publications

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“…Standard single-target tracking algorithms assume that the target is a point, limiting it to one measurement per target per time only [5][6][7][8]. Researchers have studied the direct position determination (DPD) of targets using radio signals, including techniques such as time difference of arrival (TDOA), frequency difference of arrival (FDOA), and directions of arrival (DOA) [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A Network-Group Target State and Network Topology Estimation Method Based on Signals Containing Delay, Doppler and Address

Zhang,

Hu,

Cui

et al. 2024

Remote Sensing

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Network-group targets are a set of objectives that adhere to a shared communication protocol, perform common tasks, and exhibit relatively coordinated movements. Typically, network-group targets emit radar and communication signals. However, they often employ a silent mode to evade our perception. Despite this, they continue to exchange data through their communication networks. By intercepting the communication signals of these targets, this paper proposes a method for estimating the state and network topology of network-group targets based on random finite set (RFS) theory. This method is based on the modeling of network-group targets using a labeled multi-Bernoulli (LMB) RFS. In state estimation, the usual method involves extracting the localization parameters from the signals in the first step and use these parameters for target tracking in the second step. This study focused on estimating the kinematic states of network-group targets using communication signals containing delay and Doppler information received by multiple mobile sensor platforms. The proposed method considers the coherency between frequency-hopping pulses in the signals, resulting in an improved estimation performance. In addition, considering that network-group targets require network access for information exchange, graph theory concepts were utilized to estimate the network topology of network-group targets by address measurement. The simulation results validated the effectiveness of the proposed method and demonstrated its superior performance.

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Section: Introductionmentioning

confidence: 99%

A Network-Group Target State and Network Topology Estimation Method Based on Signals Containing Delay, Doppler and Address

Zhang,

Hu,

Cui

et al. 2024

Remote Sensing

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“…Conhecer os valores das variáveis de estado para cada instante de tempo de um sistema dinâmico pode ser útil na realização de várias atividades na engenharia, como diagnóstico de falhas, detecção de falhas e supervisão (Isermann, 1997;Pulido et al, 2019;Deng et al, 2020); projeto de algoritmos de controle (Inoue et al, 2015;Sato, 2019); rastreamento de alvo (Wang and Bao, 2021;Xu et al, 2021;; navegação de robôs (Zhibin et al, 2020;Blok et al, 2019), entre outras aplicações. Em muitas situações práticas, as variáveis de estado não podem ser medidas, entre outros motivos, por impossibilidades físicas ou questões de custo na utilização de instrumentos de medição (Monticelli, 2000).…”

Section: Introdu ç ãOunclassified

Rastreamento de trajetória de veículo terrestre não linear estocástico multivariável utilizando filtro de partículas

Velozo

Souza

2021

Procedings Do XV Simpósio Brasileiro De Automação Inteligente

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State estimation of stochastic nonlinear dynamical systems has several applications in many areas of engineering. Among these areas, the system states can be estimated in order to track mobile objects such as, for instance, autonomous vehicles and mobile robots in a given space. A particle filter can be used for this purpose due to its efficiency and precision in systems with generic characteristics. In this article, an application of the particle filter for tracking of a land vehicle with free mobility in a two-dimensional space will be presented. Numerical simulations are carried out considering different scenarios of process and measurement noises. Through these simulations, a comparative analysis between the particle filter and the unscented Kalman filter is performed. Resumo: A estimação de estados de sistemas dinâmicos não lineares estocásticos tem diversas aplicações em várias áreas da engenharia. Dentre estas áreas, os estados do sistema podem ser estimados com objetivo de rastrear objetos móveis como, por exemplo, veículos autônomos e robôs móveis em um dado espaço. Um filtro de partículas pode ser utilizado para essa finalidade devido à sua eficiência e precisão em sistemas com características genéricas. Neste artigo, uma aplicação do filtro de partículas para o rastreamento de um veículo terrestre com mobilidade livre no espaço bidimensional será apresentado. Simulações numéricas são realizadas considerando diferentes cenários de ruídos de processo e medição. Por meio destas simulações, uma análise comparativa entre o filtro de partículas e o filtro de Kalman unscented é realizada.

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State estimation with unknown measurement losses: A detector-based approach

Lin,

Cai,

et al. 2024

Information Sciences

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Single target tracking for noncooperative bistatic radar with unknown signal illumination

Cited by 10 publications

References 37 publications

A Network-Group Target State and Network Topology Estimation Method Based on Signals Containing Delay, Doppler and Address

A Network-Group Target State and Network Topology Estimation Method Based on Signals Containing Delay, Doppler and Address

Rastreamento de trajetória de veículo terrestre não linear estocástico multivariável utilizando filtro de partículas

State estimation with unknown measurement losses: A detector-based approach

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