Signal fusion‐based targets detection in the presence of clutter and subspace interference for multiple‐input‐multiple‐output radar

Li, Zhihua; Su, Hongtao; Zhou, Shenghua; Hu, Qinzhen

doi:10.1049/iet-rsn.2018.5012

Cited by 6 publications

(2 citation statements)

References 30 publications

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“…As compared to conventional radars, MIMO radars have the perks of better detection performance, increased coverage, and higher accuracy of target localization [ 21 , 22 ]. In [ 23 ], a detection algorithm based on the fusion of signal for frequency diversity MIMO radar is proposed. The proposed algorithm secured a unique and better solution for the given optimizations and improved detection performance at lesser communication expense.…”

Section: Brief Review Of Existing Work On Passive Multistatic Radarmentioning

confidence: 99%

Cooperative Fusion Based Passive Multistatic Radar Detection

Asif

Kandeepan

2021

Sensors

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Passive multistatic radars have gained a lot of interest in recent years as they offer many benefits contrary to conventional radars. Here in this research, our aim is detection of target in a passive multistatic radar system. The system contains a single transmitter and multiple spatially distributed receivers comprised of both the surveillance and reference antennas. The system consists of two main parts: 1. Local receiver, and 2. Fusion center. Each local receiver detects the signal, processes it, and passes the information to the fusion center for final detection. To take the advantage of spatial diversity, we apply major fusion techniques consisting of hard fusion and soft fusion for the case of multistatic passive radars. Hard fusion techniques are analyzed for the case of different local radar detectors. In terms of soft fusion, a blind technique called equal gain soft fusion technique with random matrix theory-based local detector is analytically and theoretically analyzed under null hypothesis along with the calculation of detection threshold. Furthermore, six novel random matrix theory-based soft fusion techniques are proposed. All the techniques are blind in nature and hence do not require any knowledge of transmitted signal or channel information. Simulation results illustrate that proposed fusion techniques increase detection performance to a reasonable extent compared to other blind fusion techniques.

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Section: Brief Review Of Existing Work On Passive Multistatic Radarmentioning

confidence: 99%

Cooperative Fusion Based Passive Multistatic Radar Detection

Asif

Kandeepan

2021

Sensors

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“…M ULTITARGET detection and tracking, which includes estimation of targets existence states and estimation of target kinematic states, is a basic problem in the area of maritime reconnaissance, drug enforcement and traffic control [1][2][3]. Most multitarget detection and tracking algorithms are assumed that one target generates only one measurement in a single scan with detection probability less than unity [4][5][6]. However, the situation that multiple measurements generated by one target in one scan often occur due to, such as multipath propagation phenomenon in underwater sound transmission or sky-wave over-the-horizon radar (OTHR), multipath effects in millimeter-wave wireless communication or extended nature of targets with a high resolution radar [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Joint Multitarget Detection and Tracking in Multipath Environment Using Expectation Maximization Algorithm

Liu

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Joint detection and tracking is fundamentally important in signal processing, navigation, and radar applications. Especially, multitarget detection and tracking in multipath environments is a promising issue and broadening range for joint detection and tracking. However, its wide adoption in realworld systems is challenged by the unknown data association, complex and changeable motion environment, and data mutual coupling. To address those problems, we propose a joint detection and tracking scheme based on expectation maximization (EM) algorithm. By alternately computing the complete likelihood function and optimizing it with respect to target existence state (detection) and target kinematic state (tracking), the proposed scheme iteratively optimizes estimation and data association. Furthermore, we provide a hybrid forward and backward algorithm during the M-step to deal with the coupling of the target existence state and target kinematic state. We also provide a convergence speed-up algorithm based on K-means method and stochastic initialization method to accelerate convergence speed. Finally, we verify the effectiveness of the proposed joint detection and tracking scheme by conducting extensive simulations with different tracking scenarios. The simulation results show that the proposed algorithm not only improves the detection and tracking performance but also stays stable performance in complex and changeable motion environment and high noise background.

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