Direct position determination using single moving rotating linear array: Noncoherent and coherent processing

Wu, Guizhou; Zhang, Min; He, Chaoxin; Guo, Fei

doi:10.1016/j.cja.2019.07.027

Cited by 5 publications

(2 citation statements)

References 32 publications

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“…The state transition density function, given by (7), is used for this calculation. There are L sensor platforms that listen to signals in time slots of time step k. The signals captured in the occupied time slots are sent to a platform to form a column vector, denoted as r k , as shown in (22); each signal in the occupied time slots is transmitted to an address recognizer (represented by the confusion matrix in (9) in this paper) to obtain address measurements z t k and z r k for the transmitter and receiver, respectively. The augmented measurement for the occupied time slots is denoted as z k , as shown in (7), and the measurement at time step k is denoted as Z k .…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…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]. Specifically, regarding the moving radiation sources, the estimation of the location by utilizing TDOA and FDOA information was achieved through fixed receiving stations [10].…”

Section: Introductionmentioning

confidence: 99%

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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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