Doppler Frequency-Only and T2 / R Radar Based Moving Target Location

Xiao, Yang-Can; Wei, Ping; Yuan, Ting

doi:10.1109/icr.2006.343394

Cited by 7 publications

(5 citation statements)

References 23 publications

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“…where r j,i 's are the frequencies of the received signals radiated from the j th transmitter and received by the i th receiver , f j,i 's are the actual noisless frequencies of the transmitted signal from the j th transmitter at the i th receiver and n j,i 's are measurement errors which are modelled as the spatially white, i.i.d., gaussian random processes with N(0, σ 2 ) [24,56].…”

Section: First Methods : Differantiated Doppler -Tldandv-ddmentioning

confidence: 99%

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Target localization methods for frequency-only MIMO radar

Kalkan

Baykal

2010

2010 IEEE 18th Signal Processing and Communications Applications Conference

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Section: First Methods : Differantiated Doppler -Tldandv-ddmentioning

confidence: 99%

“…In [24], CRB is derived for moving target localization in a doppler frequency-only radar system,which includes 2 transmitters and one reveivers. In here, observations are assumed as the received frequencies and the CRB is derived when frequency measurement error is white and gaussian.…”

Section: Literature Overviewmentioning

confidence: 99%

Target localization methods for frequency-only MIMO radar

Kalkan

Baykal

2010

2010 IEEE 18th Signal Processing and Communications Applications Conference

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“…(13) The estimation problem above is solved as described in [22]. The following is a summary of the solution steps.…”

Section: Tse Using Doppler Sensorsmentioning

confidence: 99%

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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“…Focusing on the optimal sensor placement, numerical experiments were carried out in [32] to evaluate the performance of the Doppler-shift localization of moving submarine targets with 4 or 6 fixed sensors using different sensor geometries, viz., rectangle, regular hexagon, line and L-shape. Simulation results on the geometric dilution of precision (GDOP) for bistatic Doppler-shift target localization using two transmitters and one receiver were reported in [33]. Recently optimal sensor placement that only optimizes the velocity estimation accuracy was introduced in [30] for the scenario of a moving target localized by stationary Doppler-shift sensors, where the optimal sensor placement turns out to be the same as the rangeonly based localization outlined in [21].…”

Section: Introductionmentioning

confidence: 99%

Optimal sensor placement for Doppler shift target localization

Nguyen

Doğançay

2015

2015 IEEE Radar Conference (RadarCon)

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A key component of cognitive radar is intelligent signal processing which encompasses an algorithmic decisionmaking process for adaptive transmitted waveform selection and online optimal path planning for moving radar platforms in order to deal with non-stationary and uncertain surrounding environments. This paper focuses on optimal radar trajectories in the cognitive radar context. In particular, the optimal sensor placement problem for stationary target localization by multiple moving Doppler-shift radars is considered. A solution based on maximizing the determinant of the Fisher information matrix is proposed and analyzed. The analytical findings of the paper are demonstrated by way of extensive simulations incorporating unmanned aerial vehicles equipped with Doppler radar in a number of different scenarios.

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Doppler Frequency-Only and T2 / R Radar Based Moving Target Location

Cited by 7 publications

References 23 publications

Target localization methods for frequency-only MIMO radar

Target localization methods for frequency-only MIMO radar

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

Optimal sensor placement for Doppler shift target localization

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