Efficient Closed-Form Solution for 3-D Hybrid Localization in Multistatic Radars

Kazemi, Seyed Amir Reza; Amiri, Rouhollah; Behnia, Fereidoon

doi:10.1109/taes.2021.3082664

Cited by 14 publications

(7 citation statements)

References 45 publications

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“…Passive sensors do not emit electromagnetic signals; therefore, they have a strong concealment [5]. Generally speaking, the types of passive sensors' measurements include time delay (TD), Doppler shift (DS), angle of arrival (AOA), angle rate, or their combinations [6,7]. Among them, signal source localization with angle-only measurements has attracted considerable attention for many years, and it has been applied in many fields, including radar, sonar, navigation, and communications [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…Due to the nonlinearity of the signal source position and the angle-only measurement, signal source localization based on angle-only measurements is a nonlinear estimation problem [12,13]. To solve this problem, a pseudolinear equation regarding the signal source position is constructed by linearizing the angle measurement equation [6,8,10,14]. The least-squares (LS) method is an efficient and intuitive method to solve the pseudolinear overdetermined equation [15].…”

Section: Introductionmentioning

confidence: 99%

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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%

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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“…For target position estimation, some studies utilize TD information as the measurement, and presents some effective localization algorithms such as the singular value decomposition (SVD) algorithm in [5], the two stage weighted least squares (2WLS) algorithm in [6], the convex optimization in [7], the majorization-minimization in [8], to estimate the target position. When the receiver has goniometric capability, TD and AOA measurements can be jointly used to enhance localization accuracy, such as the 2WLS solution in [9], the one stage weighted least squares (1WLS) solution in [10], [11], quadratic constraint [12], and the convex solution in [13]. For the position and velocity estimation of moving targets, DS measurement can also be used in addition to TD measurement to estimate accurately the target position and velocity, such as the iterative Lagrange programming neural network based algorithm in [14], the TSWLS and its variants in [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…For the position and velocity estimation of moving targets, DS measurement can also be used in addition to TD measurement to estimate accurately the target position and velocity, such as the iterative Lagrange programming neural network based algorithm in [14], the TSWLS and its variants in [15][16][17][18][19]. However, the above mentioned studies employ either one (TD-based localization [5][6][7][8]) or two (hybrid TD-AOA localization [9][10][11][12][13], hybrid TD-DS localization [14][15][16][17][18][19]) of the TD, DS and AOA measurements for target location. The hybrid TD-DS-AOA localization method, which uses all three types of measurements, offers theoretically higher localization accuracy and robustness by providing estimates for both target position and velocity.…”

Section: Introductionmentioning

confidence: 99%

Overcoming Unknown Measurement Noise Powers in Multistatic Target Localization: A Cyclic Minimization and Joint Estimation Algorithm

Yang¹,

Huang²,

Hu³

et al. 2023

RADIOENGINEERING

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This paper investigates the issue of multistatic target localization using measurements including angle of arrival (AOA), time delay (TD), and Doppler shift (DS).We delve into a practically driven nonideal localization scenario where the measurement noise powers remain unknown. An algorithm that jointly estimates target position-velocity and measurement noise powers is proposed. Initially, an optimization model for the joint estimation is developed following the maximum likelihood estimation criterion. Subsequently, we cyclically minimize the optimization model to yield estimates for target position-velocity and measurement noise powers. The Cramér-Rao lower bound (CRLB) for this joint estimation is also derived. Contrary to existing algorithms, our proposed method eliminates the need for prior knowledge of measurement noise powers, simultaneously estimating the target position-velocity and measurement noise powers. Simulation results indicate superior localization accuracy with our algorithm, particularly in scenarios with unknown measurement noise powers. Furthermore, at moderate noise levels, the algorithm's estimation accuracy for target position-velocity and measurement noise powers meets the CRLB.

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“…Currently, these systems commonly use static illuminators like broadcast television signals and communication base stations. This technology has reached a relatively mature stage, with various methods such as the linearization method based on Time-Difference-Of-Arrival (TDOA) and Angle-Of-Arrival (AOA) parameters [2][3][4]. Other notable methods include the quasi-Newtonian spectral method of nonlinear equation algorithms [5], and the utilization of Kalman filter class [6] and its extended class methods [7].…”

Section: Introductionmentioning

confidence: 99%

A Novel Target Detection Method Based on Multi-Parameter Space for Mobile Passive Multistatic Radar

Zhang,

Liu,

Dong

et al. 2023

Remote Sensing

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Deploying Passive Multistatic Radar (PMR) on mobile platforms provides covert and cost-effective monitoring over a large area, offering certain advantages in countermeasure. However, mobile PMR faces significant challenges, such as Doppler distortion and phase deviations. A multi-parameter space target detection method is proposed for mobile PMR to achieve target detection in three-dimensional environments. By estimating the Doppler Frequency Rate (DFR), applying bistatic range phase compensation, and implementing azimuth time integration, frame division, and data fusion, the detection accuracy and the Signal-to-Noise Ratio (SNR) are improved. Simulation results indicate that the proposed method significantly enhances the SNR and produces accurate detection results, demonstrating its efficacy.

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Efficient Closed-Form Solution for 3-D Hybrid Localization in Multistatic Radars

Cited by 14 publications

References 45 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

Overcoming Unknown Measurement Noise Powers in Multistatic Target Localization: A Cyclic Minimization and Joint Estimation Algorithm

A Novel Target Detection Method Based on Multi-Parameter Space for Mobile Passive Multistatic Radar

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