Exact Solution for Elliptic Localization in Distributed MIMO Radar Systems

Amiri, Rouhollah; Behnia, Fereidoon; Sadr, Mohammad Amin Maleki

doi:10.1109/tvt.2017.2762631

Cited by 94 publications

(49 citation statements)

References 26 publications

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“…As we can see from Figure 4, in near-field case, the RMSPE and RMSVE curves of Nor-TSWLS and Ddn-TSWLS are obviously closer to the CRLB than Aux-TSWLS when 0 is beyond 20 dB, while the bias-reduction solution is closest to CRLB curve and has the highest accuracy. Figure 5 shows the localization accuracy degradation, which coincides with Yang et al, 14 Amiri et al, 11 and Noroozi 19 for the worse geometry of the far-field case. Meanwhile, Nor-TSWLS relies heavily on the results in the first stage WLS for the use of the covariance matrix of the first stage WLS estimates instead of the covariance matrix of the measurement noises.…”

Section: Numerical Simulationssupporting

confidence: 80%

“…The root mean square position error (RMSPE) and the root mean square velocity error (RMSVE) are always used to evaluate the accuracy of localization method . The position bias (Pbias) and the velocity bias (Vbias) can be used to evaluate the performance of bias reduction in this paper . Thus, we are concerned with RMSPE, RMSVE, Pbias, and Vbias to evaluate the performance of different methods.…”

Section: Bias‐reduced Solutionmentioning

confidence: 99%

“…Figure shows the localization accuracy degradation, which coincides with Yang et al, Amiri et al, and Noroozi for the worse geometry of the far‐field case. Meanwhile, Nor‐TSWLS relies heavily on the results in the first stage WLS for the use of the covariance matrix of the first stage WLS estimates instead of the covariance matrix of the measurement noises.…”

Section: Numerical Simulationsmentioning

confidence: 99%

“…[13][14][15][16] The position bias (Pbias) and the velocity bias (Vbias) can be used to evaluate the performance of bias reduction in this paper. 11,14,20 Thus, we are concerned with RMSPE, RMSVE, Pbias, and Vbias to evaluate the performance of different methods. RMSPE, RMSVE, Pbias, and Vbias are defined as…”

Section: Bias Reductionmentioning

confidence: 99%

“…12,13,20,24 The near-field case means target position is roughly inside of the spatial geometry formed by the illuminators and receivers, while the far-field case refers to the target lies outside of the spatial geometry formed by the illuminators and receivers. In recent years, many scholars 11,14,19 use this way of division and consider both cases in their papers and therefore we also simulate the near-field localization and the far-field localization.…”

Section: Numerical Simulationsmentioning

confidence: 99%

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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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Section: Numerical Simulationssupporting

confidence: 80%

Section: Bias‐reduced Solutionmentioning

confidence: 99%