Moving Target Localization in Bistatic Forward Scatter Radars: Performance Study and Efficient Estimators

Hamdollahzadeh, Mohammad; Amiri, Rouhollah; Behnia, Fereidoon

doi:10.1109/taes.2019.2934007

Cited by 16 publications

(10 citation statements)

References 16 publications

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“…Signal source localization has applications in many fields, such as surveillance, guidance, and tracking [1][2][3]. In terms of whether to actively transmit electromagnetic signals, sensors for target localization can be divided into active sensors and passive sensors.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Signal Source Localization with Angle-Only Measurements in Passive Sensor Networks

Wang,

Zhou,

Gong

et al. 2024

Remote Sensing

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Some passive sensors can provide only relative angles of a signal source. To obtain the signal source location, multiple passive sensors can be constructed into a passive sensor network through communication links. This paper investigates the source localization problem with angle-only measurements in three-dimensional space. First, we present an intersection localization method, which estimates the target position by minimizing the sum of distances between lines formed by angle-only measurements. It has the same target position estimate as the widely used least-squares (LS) method, but with a lower computational cost. Furthermore, considering the differences in measurement accuracy of sensors, the weighted least-squares (WLS) algorithm can achieve better localization performance than the LS method. Unfortunately, since the coefficient matrix and the noise vector are correlated, the WLS method is biased. The bias-compensation WLS (BCWLS) method is also presented in this paper to reduce the bias by estimating the correlation between the coefficient matrix and the pseudolinear noise vector. To evaluate the performance of the presented algorithms, numerical simulations are conducted, indicating that the superiority of the intersection localization method in computational cost and the superiority of the BCWLS method in localization accuracy.

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Section: Introductionmentioning

confidence: 99%

Three-Dimensional Signal Source Localization with Angle-Only Measurements in Passive Sensor Networks

Wang,

Zhou,

Gong

et al. 2024

Remote Sensing

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“…Since an intruder can enter the protected area with an arbitrary path, the planner must build a continuous barrier coverage with radars [16]. Unlike monostatic radar, multistatic radar is an essential extension of bistatic radar, which consists of separated transmitters and receivers [17]. The multistatic radar has superior performance, including strong anti-jamming capacity, ease of anti-low-altitude penetration, and ease of anti-stealth aircraft [18−21].…”

Section: Introductionmentioning

confidence: 99%

Optimization method of linear barrier coverage deployment for multistatic radar

Feng

Wang

2023

J. of Syst. Eng. Electron.

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To address the problem of building linear barrier coverage with the location restriction, an optimization method for deploying multistatic radars is proposed, where the location restriction splits the deployment line into two segments. By proving the characteristics of deployment patterns, an optimal deployment sequence consisting of multiple deployment patterns is proposed and exploited to cover each segment. The types and numbers of deployment patterns are determined by an algorithm that combines the integer linear programming (ILP) and exhaustive method (EM). In addition, to reduce the computation amount, a formula is introduced to calculate the upper threshold of receivers' number in a deployment pattern. Furthermore, since the objective function is non-convex and non-analytic, the overall model is divided into two layers concerning two suboptimization problems. Subsequently, another algorithm that integrates the segments and layers is proposed to determine the deployment parameters, such as the minimum cost, parameters of the optimal deployment sequence, and the location of the split point. Simulation results demonstrate that the proposed method can effectively determine the optimal deployment parameters under the location restriction.

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“…Passive radar based on GNSS has been researched for more than twenty years. GNSSbased passive radar has the advantages of wide coverage [1,2] and good concealment. However, the great challenge is that the scattering signal is extremely weak.…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation for Uniformly Accelerating Moving Target in the Forward Scatter Radar Network

Zheng

et al. 2022

Remote Sensing

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Passive radar based on the global navigation satellite positioning system (GNSS) has become the focus of attention in the field of radar. A parameter estimation method is proposed in the forward scatter radar (FSR) network based on GNSS to extend the application scenarios. For uniformly accelerating moving targets, only the instant times when the target crosses the individual baselines are used to retrieve the target motion parameters. The target position, velocity, and acceleration information can be obtained. Firstly, the minimum network configuration is derived theoretically. Then, the effects of crossing time error, station location error, transmitting/receiving station deployment, and target height on the accuracy are analyzed through Monte Carlo simulations. Finally, the simulation results indicate that the target position estimation error is in the order of 100 m. This paper provides the fundamental theory of aerial target positioning with a GNSS-based FSR network.

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Moving Target Localization in Bistatic Forward Scatter Radars: Performance Study and Efficient Estimators

Cited by 16 publications

References 16 publications

Three-Dimensional Signal Source Localization with Angle-Only Measurements in Passive Sensor Networks

Three-Dimensional Signal Source Localization with Angle-Only Measurements in Passive Sensor Networks

Optimization method of linear barrier coverage deployment for multistatic radar

Parameter Estimation for Uniformly Accelerating Moving Target in the Forward Scatter Radar Network

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