Two Methods for Target Localization in Multistatic Passive Radar

Malanowski, Mateusz; Kulpa, Krzysztof

doi:10.1109/taes.2012.6129656

Cited by 243 publications

(173 citation statements)

References 12 publications

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“…When multiple transmitters and one receiver are present, at least K transmitters are necessary to determine the target location uniquely and unambiguously in a K-dimensional space [5]. However, most of the LS methods require at least one extra transmitter for finding the target location clearly [20].…”

Section: Localisation From Brsmentioning

confidence: 99%

“…The BR is defined as the sum of the receiver-target range R tRx j and the target-transmitter range R tTx i as well [5]. According to this definition, the BR is (see (5)) By rearranging the above equation, we have…”

Section: Localisation From Brsmentioning

confidence: 99%

“…This delay is related to the bistatic range (BR), which is the sum of transmitter-target and target-receiver ranges [5]. The locus of points of the constant BR is an ellipsoid with a known transmitter and a known receiver as its foci.…”

Section: Introductionmentioning

confidence: 99%

“…The intersection of these curves is taken as the estimated target position. At least three ellipsoids are required to determine the target location unambiguously in three-dimensional (3D) space [5].…”

Section: Introductionmentioning

confidence: 99%

“…There is a rapidly growing literature on the BR-based method [2,[5][6][7][8][9][10][11][12], which indicates the importance of this issue. Nevertheless, most studies in target localisation have been conducted with the range-difference (RD)-based methods.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Weighted least squares target location estimation in multi‐transmitter multi‐receiver passive radar using bistatic range measurements

Noroozi

Sebt

2016

IET Radar, Sonar & Navigation

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This study proposes a weighted least squares method to determine the position of an individual target in the presence of multiple transmitters and multiple receivers from time difference of arrival measurements. The method is based on the intersection of the ellipsoids defined by bistatic range measurements from a number of transmitters and receivers. The solution is derived from the minimisation of the weighted equation error energy. In addition, the method requires no initial guess of the solution to find the target location. A weighting matrix is obtained in two different conditions in order to generate a substantial improvement in the performance of the method, which one of them leads to an approximate maximum-likelihood estimator and the other one results in a best linear unbiased estimator. A detailed theoretical error analysis associated with this method is presented and the Cramer-Rao lower bound is also derived. Simulations are included to examine the algorithm's performance and corroborate the theoretical developments.

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Section: Localisation From Brsmentioning

confidence: 99%

Section: Localisation From Brsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Weighted least squares target location estimation in multi‐transmitter multi‐receiver passive radar using bistatic range measurements

Noroozi

Sebt

2016

IET Radar, Sonar & Navigation

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A bias‐reduced solution for target localization with illuminator of opportunity passive radar

Wang³

2019

Int J Communication

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Summary An improved solution for target localization on the basis of the realistic distance‐dependent noises in illuminator of opportunity passive radar and an algebraic reduction method of the bias, which exists in the two‐stage weighted least squares (TSWLS) method is proposed. The classic TSWLS and its improved solutions have great localization performances just on the basis of two approximations; that is, setting the noise to a constant and ignoring the high‐order terms of measurement noises. It is these two approximations that lead to a suboptimal solution with bias. The bias of TSWLS has a significant influence on the target localization in illuminator of opportunity passive radar that has lower measurement accuracy and higher noises than common radars. Therefore, this paper starts by taking into consideration the realistic distance‐dependent characteristics of time‐of‐arrival (TOA) and frequency‐of‐arrival (FOA) noises and improving TSWLS method. Then, the bias of the improved TSWLS method is analyzed and an algebraic bias‐reduced solution on the basis of weighted least squares (WLS) is developed. Numerical simulations demonstrate that, compared with the existing methods, the proposed bias‐reduced solution attains the Cramer‐Rao lower bound (CRLB) better and reduces the bias effectively.

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A Novel Weighted-Distance Centralized Detection Method in Passive MIMO Radar

Song

Zhu

2023

Lecture Notes in Computer Science

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Two Methods for Target Localization in Multistatic Passive Radar

Cited by 243 publications

References 12 publications

Weighted least squares target location estimation in multi‐transmitter multi‐receiver passive radar using bistatic range measurements

Weighted least squares target location estimation in multi‐transmitter multi‐receiver passive radar using bistatic range measurements

A bias‐reduced solution for target localization with illuminator of opportunity passive radar

A Novel Weighted-Distance Centralized Detection Method in Passive MIMO Radar

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