Range, radial velocity, and acceleration MLE using radar LFM pulse train

Abatzoglou, Theagenis J.; Gheen, Gregory

doi:10.1109/7.722676

Cited by 97 publications

(79 citation statements)

References 9 publications

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“…As the parameter A is linearly in the echo data and it can be solved in terms of the other parameters in the minimisation procedure. So, when the parameters [n R 0 , v, a, b] suffice to [37] […”

Section: Mle For Manoeuvring Targetmentioning

confidence: 99%

Long‐time coherent integration detection of weak manoeuvring target via integration algorithm, improved axis rotation discrete chirp‐Fourier transform

Rao

Tao

Xie

et al. 2015

IET Radar, Sonar & Navigation

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Range/Doppler migrations, which result from the integration time increasing and the target's manoeuvring motion, will affect the coherent integration performance severely. To deal with range/Doppler migration, a novel coherent integration algorithm, improved axis rotation discrete chirp-Fourier transform (IAR-DCFT), is proposed. IAR-DCFT could eliminate range migration via improved axis rotation transform, and realise the compensation of Doppler migration and coherent integration via discrete chirp-Fourier transform. IAR-DCFT may be regarded as tri-dimensional motion parameter filter banks, which is analogous to moving target detection that can be treated as Doppler filter banks, and estimate a target's velocity, acceleration and jerk simultaneously. Then the derivations of maximumlikelihood estimator and likelihood ratio test detector show that IAR-DCFT is the optimal estimator and a detector. The performance of the optimal estimator is verified by comparing with Cramer-Rao lower bound. Subsequently, the detailed performance analyses of IAR-DCFT are provided, including coherent integration gain, coherent integration time, multi-target detection and computational complexity. Furthermore, the authors introduce the generalisation of IAR-DCFT, that is, multi-range-cell associated IAR-DCFT (MR-IAR-DCFT), which can be applied to detect a target with high-manoeuvring motion or used in a longer time integration case. Finally, some numerical experiments are given to verify the performance of IAR-DCFT and MR-IAR-DCFT.

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Section: Mle For Manoeuvring Targetmentioning

confidence: 99%

Long‐time coherent integration detection of weak manoeuvring target via integration algorithm, improved axis rotation discrete chirp‐Fourier transform

Rao

Tao

Xie

et al. 2015

IET Radar, Sonar & Navigation

View full text Add to dashboard Cite

show abstract

“…which is the usual expression of the roundtrip for the extended 'stop-go' model for the fast moving targets in the ISAR [7][8][9][10][11][12][13][14][15][16][17]. In that model, only the slant range caused by the motion during the pulse propagating time is considered, and the motion during the pulse transmitting and receiving times has been ignored.…”

Section: Accurate Echo Geometrymentioning

confidence: 99%

“…Besides, any target's motion causes the Doppler effect in a burst. To obtain an HRRP, the target's motion in a single burst should be compensated for well, and a variety of motion compensation methods have been proposed for the ISAR [7][8][9][10][11][12] and the SF-ISAR [13][14][15][16][17] in the past decades. The translational motion compensation in the ISAR usually consists of two steps: envelope alignment and phase autofocus.…”

Section: Introductionmentioning

confidence: 99%

Stepped frequency synthetic preprocessing algorithm for inverse synthetic aperture radar imaging in fast moving target echo model

Zhou

Sun

Xia³

et al. 2014

IET Radar, Sonar & Navigation

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This paper first introduces an accurate inverse synthetic aperture radar echo model for the fast moving targets. In this model, the target's motion during the time periods of the pulse transmitting, propagating and receiving have been taken into account, and some problems, which have been ignored in the 'stop-go' model, may occur, for example, the varied ambiguity numbers and the drifted frequency centre. These problems will cause many difficulties in obtaining a high resolution range profile (HRRP) synthesis. To deal with these problems, a stepped frequency synthetic preprocessing (SFSP) algorithm is presented. The key of the proposed algorithm is synthetic preprocessing, including Doppler ambiguity suppression, echo coherence restoration and frequency centre correction, respectively. By completing the preprocessing, the HRRP can be achieved by a bandwidth synthesis and a well-focused image can be obtained by a keystone transform and an azimuth process, respectively. The simulation results validate the proposed SFSP algorithm by comparing it with the algorithm in the existing extended 'stop-go' model.

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“…This observation leads to the idea of decoupling the N -dimensional optimization problem into N one-dimensional optimization problems. The cost function |ϕ k | will produce sinusoidal harmonics up to the 12th order, but due to the observation in (37), only harmonics up to the 2nd order are required for sufficient accuracy. Thus, the criterion can be written in the following general form:…”

Section: Cost Function Approximationmentioning

confidence: 99%

Adaptive Mobile Sensor Positioning for Multi-Static Target Tracking

Zhan

Casbeer

Swindlehurst

2010

IEEE Trans. Aerosp. Electron. Syst.

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Unmanned Air Vehicles (UAVs) are playing an increasingly prominent role in both military and civilian applications. In this paper, we focus on the use of multiple UAV agents in a target tracking application where performance is improved by exploiting each agent's maneuverability. Local time-delay and Doppler measurements made at each UAV are used as inputs to an Extended Kalman Filter (EKF) which tracks the target's position and velocity. Two simple metrics are defined to quantify the accuracy of the tracking algorithm, and heading feedback to the UAVs is used to minimize the metric and improve tracking performance. A simplified version of one of the algorithms that reduces computational complexity is also presented. Simulations demonstrate the significant improvement that results when the UAV sensors are allowed to be optimally positioned during tracking.

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Range, radial velocity, and acceleration MLE using radar LFM pulse train

Cited by 97 publications

References 9 publications

Long‐time coherent integration detection of weak manoeuvring target via integration algorithm, improved axis rotation discrete chirp‐Fourier transform

Long‐time coherent integration detection of weak manoeuvring target via integration algorithm, improved axis rotation discrete chirp‐Fourier transform

Stepped frequency synthetic preprocessing algorithm for inverse synthetic aperture radar imaging in fast moving target echo model

Adaptive Mobile Sensor Positioning for Multi-Static Target Tracking

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