A Novel Dynamic Programming Algorithm for Track-Before-Detect in Radar Systems

Grossi, Emanuele; Lops, Marco; Venturino, Luca

doi:10.1109/tsp.2013.2251338

Cited by 173 publications

(65 citation statements)

References 41 publications

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“…We use the following simulated measurement data to test the performance of the proposed algorithm, which is widely used in the current study and some representative PF-TBD algorithms [2][3][4][5][6]. Meanwhile, the CV (constant velocity) and CT (coordinate turn) motion models are used for testing.…”

Section: Simulation Resultsmentioning

confidence: 99%

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A Particle Filter Track-Before-Detect Algorithm Based on Hybrid Differential Evolution

Zhang

Wang

2015

Algorithms

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Abstract:In this paper, we address the problem of detecting and tracking targets with a low signal-to-noise ratio (SNR) by exploiting hybrid differential evolution (HDE) in the particle filter track-before-detect (PF-TBD) context. Firstly, we introduce the Bayesian PF-TBD method and its weaknesses. Secondly, the HDE algorithm is regarded as a novel particle updating strategy, which is proposed to optimize the performance of the PF-TBD algorithm. Thirdly, we combine the systematic resampling approach to enhance the performance of the proposed algorithm. Then, an improved PF-TBD algorithm based on the HDE method is proposed. Experiment results indicate that the proposed method has better performance in detecting and tracking than previous algorithms when the targets have a low SNR.

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Section: Simulation Resultsmentioning

confidence: 99%

“…A large number of TBD algorithms have been developed in the past several decades including direct maximum likelihood [3], the Hough transform [4], dynamic programming [5,6], and so on. These methods generally require discretization of the state space and are very computationally intensive.…”

Section: Introductionmentioning

confidence: 99%

A Particle Filter Track-Before-Detect Algorithm Based on Hybrid Differential Evolution

Zhang

Wang

2015

Algorithms

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“…An alternative is to simultaneously estimate the object trajectory and select data samples for pulse integration accordingly. Trajectory estimation using the outputs of the matched filter tuned to the probing waveform is often referred to as track-before-detect (see, e.g., [3], [4]). …”

Section: Introductionmentioning

confidence: 99%

Simultaneous tracking and long time integration for detection in collaborative array radars

Kim

Üney

Mulgrew

2017

2017 IEEE Radar Conference (RadarConf)

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Abstract-In this work, we focus on the detection of manoeuvring low signal to noise ratio (SNR) objects in multiple collaborating radars. Collaboration involves having the knowledge of the locations of the transmitters and their transmission characteristics up to a synchronisation term which has to be estimated during the operation. We propose a local processing algorithm, which performs simultaneous trajectory estimation and long time integration of pulse returns in both the local channel and the remote channels. The synchronisation of the remote channels is achieved by simultaneously diverting beams towards both the tested point of detection and the transmitters. Detection is made by using a Neyman-Pearson test. Overall, this scheme enables us to exploit a statistical MIMO effect for the objects in the field of view and integrate multiple pulse returns while taking into account the object trajectory leading to the capability of detecting low SNR and manoeuvring objects. We demonstrate the efficacy of our approach through simulations.

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“…Increasing integration time is an effective way to improve the detection performance of weak targets. It is well-known that long-time integration methods can be categorised into three kinds: incoherent integration [2][3][4][5][6][7], coherent integration [8][9][10][11][12][13][14][15][16][17][18][19][20][21] and hybrid integration [22]. Without utilising the phase information of target's echo, the integration performance of incoherent integration is the worst but the realisation of algorithm is simple.…”

Section: Introductionmentioning

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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A Novel Dynamic Programming Algorithm for Track-Before-Detect in Radar Systems

Cited by 173 publications

References 41 publications

A Particle Filter Track-Before-Detect Algorithm Based on Hybrid Differential Evolution

A Particle Filter Track-Before-Detect Algorithm Based on Hybrid Differential Evolution

Simultaneous tracking and long time integration for detection in collaborative array radars

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

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