Using Phase to Improve Track-Before-Detect

Davey, Samuel J.; Rutten, Mark; Cheung, Brian

doi:10.1109/taes.2012.6129673

Cited by 60 publications

(31 citation statements)

References 15 publications

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“…The per-frame detection sensitivity, the estimation accuracy and the computation requirement [27–29] are used as the measures of performance (MOPs) in this paper. The per-frame detection sensitivity is the detected-frame rate as a function of SNR for a prescribed false-report rate.…”

Section: Performance Analysismentioning

confidence: 99%

Detection and Tracking of a Moving Target Using SAR Images with the Particle Filter-Based Track-Before-Detect Algorithm

Gao

2014

Sensors

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A novel approach to detecting and tracking a moving target using synthetic aperture radar (SAR) images is proposed in this paper. Achieved with the particle filter (PF) based track-before-detect (TBD) algorithm, the approach is capable of detecting and tracking the low signal-to-noise ratio (SNR) moving target with SAR systems, which the traditional track-after-detect (TAD) approach is inadequate for. By incorporating the signal model of the SAR moving target into the algorithm, the ambiguity in target azimuth position and radial velocity is resolved while tracking, which leads directly to the true estimation. With the sub-area substituted for the whole area to calculate the likelihood ratio and a pertinent choice of the number of particles, the computational efficiency is improved with little loss in the detection and tracking performance. The feasibility of the approach is validated and the performance is evaluated with Monte Carlo trials. It is demonstrated that the proposed approach is capable to detect and track a moving target with SNR as low as 7 dB, and outperforms the traditional TAD approach when the SNR is below 14 dB.

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Section: Performance Analysismentioning

confidence: 99%

Detection and Tracking of a Moving Target Using SAR Images with the Particle Filter-Based Track-Before-Detect Algorithm

Gao

2014

Sensors

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“…It is desirable to estimate this quantity, however, this requires more samples than one can collect at this sampling rate within a coherent processing interval (CPI) [5]. Moreover, in [6], it is argued that taking the phase of the complex reflection coefficient into account improves the detection performance. [7] proposes an algorithm, which uses both the modulus and the phase of the complex data, collected with a sampling rate much higher than the aforementioned rate.…”

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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“…Particle filter track-before-detect (PF-TBD) was first proposed by Salmond [8], in parallel with the work by Boers [9]. These algorithms have since been extended in [10][11][12], which form a single target filter for two targets. The literature [10] presents a performance comparison of three particle filters using several different particle proposal densities designed for a single target.…”

Section: Introductionmentioning

confidence: 99%

“…The literature [10] presents a performance comparison of three particle filters using several different particle proposal densities designed for a single target. In [11], a new model for solving the problem of detecting a single target arrival and tracking its state in a TBD context is presented. The work in [12] shows a modeling setup and algorithm for a multiple target recursive Bayesian TBD application in a radar context.…”

Section: Introductionmentioning

confidence: 99%

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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Using Phase to Improve Track-Before-Detect

Cited by 60 publications

References 15 publications

Detection and Tracking of a Moving Target Using SAR Images with the Particle Filter-Based Track-Before-Detect Algorithm

Detection and Tracking of a Moving Target Using SAR Images with the Particle Filter-Based Track-Before-Detect Algorithm

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

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

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