A single sensor passive localization algorithm using second difference of time delay

Zhang, Shangyu; Huang, Zhen; He, Jiazhi

doi:10.1109/globalsip.2017.8308658

Cited by 5 publications

(7 citation statements)

References 15 publications

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“…The SDCTD only depends on the distance between the emitter and the sensors. The algorithm proposed in [19] divides the TOA sequence into four parts, takes the corresponding TOAs of each part, and then uses a group of four TOAs to obtain SDCTD. A N T /4 × 1 measurement is obtained.…”

Section: B Eliminate Unknown Parametersmentioning

confidence: 99%

“…A N T /4 × 1 measurement is obtained. Different from the algorithm in [19], a new calculation model is proposed. Four adjacent TOAs are used as a group to calculate the SDCTD and (N T − 2) × 1 measurement can be obtained.…”

Section: B Eliminate Unknown Parametersmentioning

confidence: 99%

“…The LM algorithm we propose here is similar to that proposed in [19]. Because the performance of the algorithm is sensitive to the initial value, it converges to the local optimal solution even if the initial value is not set well.…”

Section: Localization Algorithmmentioning

confidence: 99%

“…It identified the signal period first and then jointly estimates the source position and the signal period using an iterative maximum likelihood estimator. Zhang et al [19], [20] used the second difference of the time delays to eliminate the unknown start transmission time and period. In [21], [22], Wan reconstructed the main-beam pattern (MBP) curve of the radar antenna, and then exploits the reconstructed MBP curve to estimate the scanning cycle of radar using a single unmanned aerial vehicle.…”

Section: Introductionmentioning

confidence: 99%

“…Different from TDOA that only uses the relative position relationship between sensors when receiving the same pulse, SDCTD uses the periodicity within the transmitted TOA sequence. We build on our prior conference paper [19] that only uses signal periodicity, and propose a new calculation model for SDCTD, which increases the number of equations and improves the localization performance. A new algorithm that jointly exploits TDOA and SDCTD measurements for localization is proposed, which improves the performance in presence of two kinds of TOA errors.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Sensor Passive Localization Using Second Difference of Coherent Time Delays With Incomplete Measurements

et al. 2019

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The passive localization using multiple sensors is considered for emitter transmitting pulse signals with unknown start transmission time and period. Time of arrival (TOA) of a pulse is estimated at every sensor, and it is difficult for sensors to intercept all pulses during the observation time in practice, as there maybe obstacle on the line of sight or the signal is too weak to be detected, namely, incomplete measurements. In this paper, an algorithm based on a combination of a new second difference of coherent time delays (SDCTD) measurement and traditional time difference of arrival (TDOA) is proposed to improve localization performance in the presence of two kinds of TOA errors, including the independent estimation errors and common errors among multiple sensors. The Cramer-Rao lower bound for the proposed algorithm is derived, and the simulations validate that the performance of the proposed algorithm significantly outperforms existing algorithms, especially in the case of pulse loss.INDEX TERMS Multi-sensor passive localization, incomplete measurements, second difference of coherent time delays, time difference of arrival, Cramer-Rao lower bound.

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Section: B Eliminate Unknown Parametersmentioning

confidence: 99%

Section: B Eliminate Unknown Parametersmentioning

confidence: 99%

Section: Localization Algorithmmentioning

confidence: 99%