Tracking Airborne Targets Hidden in Blind Doppler Using Current Statistical Model Particle Filter

Shi, Zhiguo; Hong, Shaohua; Chen, Kang Sheng

doi:10.2528/pier08012407

Cited by 22 publications

(19 citation statements)

References 31 publications

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“…Multiple target tracking (MTT) is applied widely in the areas of autonomous surveillance, computer vision, audio signal processing and wireless communication [1][2][3][4][5][6]. Recently, joint detection and tracking for multiple targets has been drawn much attention.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Scan Mixture Particle Filter for Joint Detection and Tracking of Multiple Targets

Liu¹,

Han²,

Yao³

et al. 2013

PIER B

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Abstract-In this paper, a novel algorithm named multi-scan mixture particle filter is proposed for joint detection and tracking for a varying number of targets. The posterior distribution of multiple target state in a single-target state space is a multi-mode distribution with each mode corresponding to either a target or clutter. A general global posterior distribution is adopted in this work, which consists of existing components and new components. The new components are generated at each time step to capture the new modes due to newly appeared targets or clutter. In order to distinguish targets from clutter, multiple scan information is incorporated. The history of each component's associate weights is stored in a multi-scan sliding window, which is used to judge whether the component is from a target or clutter. Moreover, a novel sampling method which combines the likelihood sampling and prior sampling is proposed to draw particles from the desired parts of the state space at each time step. From the simulation results, it could be seen that the proposed algorithm can effectively detect the appearance/disappearance of the targets as well as track the existing target.

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Section: Introductionmentioning

confidence: 99%

A Multi-Scan Mixture Particle Filter for Joint Detection and Tracking of Multiple Targets

Liu¹,

Han²,

Yao³

et al. 2013

PIER B

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“…If the initial point of symbol is not correct, it causes decrease of system performance by intercarrier interference (ICI) and intersymbol interference (ISI) [15]. The frequency offset is caused by Doppler shift and carrier frequency offset [16]. Interference damages orthogonality among the sub-carriers.…”

Section: Introductionmentioning

confidence: 99%

“…So, OFDM system was selected as the standards of IEEE 802.11a, IEEE 802.11g, high wireless LAN of HIPERLAN/2, IEEE 802. 16 BWA, DAB and DVB-T which need high data rate [9][10][11][12]. But, main disadvantage is that OFDM system is sensitive against frequency offset [13].…”

Section: Introductionmentioning

confidence: 99%

An Elaborate Frequency Offset Estimation for Ofdm Systems

Nam¹,

Yoon²,

Song³

2008

PIER Letters

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Abstract-In this paper, a carrier frequency offset estimation scheme in orthogonal frequency division multiplexing is proposed. We focus on increasing the correlation of each PN sequence code repeated in one preamble. We aim at getting an efficiency like using many preambles. The proposed method can improve the performance of the system by estimating fine frequency offset. Also, the proposed method enhances reliability by maximizing the number of correlations compared with the established method in time domain.

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“…The key idea is to represent the target posterior probability density function (PDF) of the state given the observations by a set of random particles with their associated weights. PFs have shown great promise in various target tracking problems [20][21][22][23][24][25][26][27] since they were proposed. However, for MTT problems, multi-target PF requires intensive computations and it is difficult to find an efficient importance density for the multitarget PF [15].…”

Section: Introductionmentioning

confidence: 99%

Simplified Particle PHD Filter for Multiple-Target Tracking: Algorithm and Architecture

Hong¹,

Wang²,

Shi

et al. 2011

PIER

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Abstract-In this paper, we propose a simplified particle probability hypothesis density (PHD) filter and its hardware implementation for multiple-target tracking (MTT). In the proposed algorithm, the update step of particle PHD filter is simplified and the time-varying number of measurements is arranged in combination series/parallel mode. This may result in fixed hardware architecture and therefore present a convenient hardware implementation of particle PHD filter. Simulation results indicate that for the MTT problems, this proposed simplified algorithm shows similar performance with the standard particle PHD filter but has faster processing rate. Experiment study shows that the proposed simplified algorithm can be efficiently implemented in hardware and can effectively solve the MTT problems.

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Tracking Airborne Targets Hidden in Blind Doppler Using Current Statistical Model Particle Filter

Cited by 22 publications

References 31 publications

A Multi-Scan Mixture Particle Filter for Joint Detection and Tracking of Multiple Targets

A Multi-Scan Mixture Particle Filter for Joint Detection and Tracking of Multiple Targets

An Elaborate Frequency Offset Estimation for Ofdm Systems

Simplified Particle PHD Filter for Multiple-Target Tracking: Algorithm and Architecture

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