An Experimental Multi-Target Tracking of AM Radio-Based Passive Bistatic Radar System via Multi-Static Doppler Shifts

Zhou, Xueqin; Ma, Hong; Xu, Hua

doi:10.3390/s21186196

Cited by 2 publications

(3 citation statements)

References 31 publications

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“…In (9), the target states p k and v k served as the estimation parameters and can be solved with the least square method, requiring individual solutions for each parameter. The maximum likelihood function is expressed as follows.…”

Section: Tse Using Doppler Sensorsmentioning

confidence: 99%

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Joint Estimation Algorithm of Target State and Measurement Bias for Doppler Sensor

Sun,

Yan,

Yang

et al. 2023

IEEE Access

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In recent years, Doppler-only sensors have demonstrated an excellent estimation performance for target tracking. Most of the existing tracking algorithms that utilize Doppler sensors rely on the assumption that sensors capture only measurement errors, disregarding measurement biases. However, in many practical situations, there can be significant measurement biases, which can severely compromise the performance of target state estimation. Recognizing this issue, the study proposes a new joint estimation algorithm that is exclusively reliant on Doppler-only sensors. The proposed algorithm not only provides the estimated results of the target state, but also the measurement bias of the Doppler sensor in use. The approach unfolds in two stages: the first stage is to estimate the target state without considering the measurement bias; the second is to perform bias compensation using the least squares method, and the target state and measurement bias are jointly estimated by linearization of the measurement equation. To validate the efficacy of this method, we analyzed the Cramer-Rao lower bound (CRLB) for measurement bias estimation and designed simulations under both static and moving sensor scenarios to assess its performance. The results indicate that the proposed algorithm can effectively estimate the target state, outperforming Kalman filter (KF) in both the moving and static sensor scenario. The root mean square error (RMSE) of the bias estimation can approach the CRLB.

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Section: Tse Using Doppler Sensorsmentioning

confidence: 99%

“…Merging the DOA and TOA with filters can significantly improve the target localization performance. The Doppler frequency shift is a commonly used measurement for Doppler radar, which is simple to evaluate and has been widely used [8], [9]. Target estimation using Doppleronly shift measurement is an ancient problem researched in various studies [10], [11].…”

mentioning

confidence: 99%

Joint Estimation Algorithm of Target State and Measurement Bias for Doppler Sensor

Sun,

Yan,

Yang

et al. 2023

IEEE Access

View full text Add to dashboard Cite

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“…A less theoretically and straightforward multiple sensors extension of the MeMBer filter is iterated-corrector MeMBer (IC-MeMBer) filter [21] which processes the measurement subsets from different sensors in a sequential manner. Our previous work on related subject had detected and tracked multiple civil aviation targets successfully by iterating the filter update stage for each measurement subset [22]. Nevertheless, the results are sensitive to the sensor order.…”

Section: Introductionmentioning

confidence: 99%

A Multisource Multi-Bernoulli Filter for Multistatic Radar

Zhou

Jin

et al. 2022

IEEE Access

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Compared with conventional monostatic or bistatic radar, multistatic radar has wider coverage and better performance localization and tracking accuracy. However, the multistatic radar architecture poses challenges to the implementation for multitarget tracking in coping with highly uncertainty of data association for the fusion of multisource information. In this paper, the theoretically rigorous formulas for the multisource multi-Bernoulli (MeMBer) filter are derived by using the Finite set statistics (FISST) calculus built on the standard MeMBer filter. The multisource MeMBer filter propagates a set of MeMBer parameters approximately characterizing the multisource corrected posterior multitarget random finite set (RFS). Since the equations for the proposed filter multisource corrector are computationally intractable, we go further to develop an analytic Sequential Monte Carlo (SMC) implementation of multisource MeMBer recursion. The theoretical analysis and simulations show that the proposed filter perform well and accommodate nonlinear multistatic radar tracking scenario under the approximate conditions.INDEX TERMS Multistatic radar, finite set statistics, multi-Bernoulli filter, multitarget tracking.

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An Experimental Multi-Target Tracking of AM Radio-Based Passive Bistatic Radar System via Multi-Static Doppler Shifts

Cited by 2 publications

References 31 publications

Joint Estimation Algorithm of Target State and Measurement Bias for Doppler Sensor

Joint Estimation Algorithm of Target State and Measurement Bias for Doppler Sensor

A Multisource Multi-Bernoulli Filter for Multistatic Radar

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