Offline Performance Prediction of PDAF With Bayesian Detection for Tracking in Clutter

Zeng, Tao; Zheng, Li; Li, Yang; Chen, Xinliang; Li, Teng

doi:10.1109/tsp.2012.2230168

Cited by 18 publications

(6 citation statements)

References 18 publications

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“…Using the parametric PDA [4], the association probability is as follows:

β_{i} (k) = {\begin{matrix} e_{i} / (b + true \sum_{j = 1}^{m_{k}} e_{j}) & i = 1, \dots m_{k} \\ b / (b + true \sum_{j = 1}^{m_{k}} e_{j}) & i = 0 \end{matrix}

where

e_{i} ≜ \exp (- \frac{1}{2} {boldnormal v}_{i}^{T} (k) S^{- 1} (k) v_{i} (k))

;

b ≜ λ {| 2 π boldnormal S (k) |}^{\frac{1}{2}} (1 - P_{d} P_{g} / P_{d})

P_{d}

is the probability of detection, and

P_{g}

is the probability that the target measurement falls into the validation region.…”

Section: A Novel Probabilistic Data Associationmentioning

confidence: 99%

“…Due to the sea clutter, filter algorithms, such as a Kalman filter, and a series of improved filter algorithms like EKF (extended Kalman filtering) and UKF (unscented Kalman filter), are used in target tracking systems [2], and the data association is also adopted which can confirm the probability of measurement coming from the target. Previous studies have shown that the nearest-neighbor (NN) [3] algorithm works reasonably well with targets in sparse scenarios, and the probabilistic data association (PDA) [4] is suitable to track a single target in a cluttered environment, which, considering all of the measurements, falls into the validation gate. For the case of more than one target in the cluttered environment, an extension of probabilistic data association (PDA) was derived, called joint probabilistic data association (JPDA) [5], where the association probabilities are computed from the joint likelihood functions corresponding to the joint hypotheses associating all of the returns to different permutations of the targets and clutter points.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Probabilistic Data Association for Target Tracking in a Cluttered Environment

Xiao¹,

Li²,

Li³

et al. 2016

Sensors

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The problem of data association for target tracking in a cluttered environment is discussed. In order to improve the real-time processing and accuracy of target tracking, based on a probabilistic data association algorithm, a novel data association algorithm using distance weighting was proposed, which can enhance the association probability of measurement originated from target, and then using a Kalman filter to estimate the target state more accurately. Thus, the tracking performance of the proposed algorithm when tracking non-maneuvering targets in a densely cluttered environment has improved, and also does better when two targets are parallel to each other, or at a small-angle crossing in a densely cluttered environment. As for maneuvering target issues, usually with an interactive multi-model framework, combined with the improved probabilistic data association method, we propose an improved algorithm using a combined interactive multiple model probabilistic data association algorithm to track a maneuvering target in a densely cluttered environment. Through Monte Carlo simulation, the results show that the proposed algorithm can be more effective and reliable for different scenarios of target tracking in a densely cluttered environment.

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“…Using the parametric PDA [4], the association probability is as follows:

β_{i} (k) = {\begin{matrix} e_{i} / (b + true \sum_{j = 1}^{m_{k}} e_{j}) & i = 1, \dots m_{k} \\ b / (b + true \sum_{j = 1}^{m_{k}} e_{j}) & i = 0 \end{matrix}

where

e_{i} ≜ \exp (- \frac{1}{2} {boldnormal v}_{i}^{T} (k) S^{- 1} (k) v_{i} (k))

;

b ≜ λ {| 2 π boldnormal S (k) |}^{\frac{1}{2}} (1 - P_{d} P_{g} / P_{d})

P_{d}

is the probability of detection, and

P_{g}

is the probability that the target measurement falls into the validation region.…”

Section: A Novel Probabilistic Data Associationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Probabilistic Data Association for Target Tracking in a Cluttered Environment

Xiao¹,

Li²,

Li³

et al. 2016

Sensors

View full text Add to dashboard Cite

show abstract

“…To mitigate these problems, we proposed an HYCA method for the PDAF-BD algorithm [79]. For a Direct data-association…”

Section: Integrated Detection and Tracking Algorithmmentioning

confidence: 99%

Advanced technology of high-resolution radar: target detection, tracking, imaging, and recognition

Liang

Liu

2019

Sci. China Inf. Sci.

Self Cite

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In recent years, the performances of radar resolution, coverage, and detection accuracy have been significantly improved through the use of ultra-wideband, synthetic aperture and digital signal processing technologies. High-resolution radars (HRRs) utilize wideband signals and synthetic apertures to enhance the range and angular resolutions of tracking, respectively. They also generate one-, two-, and even threedimensional high-resolution images containing the feature information of targets, from which the targets can be precisely classified and identified. Advanced signal processing algorithms in HRRs obtain important information such as range-Doppler imaging, phase-derived ranging, and micro-motion features. However, the advantages and applications of HRRs are restricted by factors such as the reduced signal-to-noise ratio (SNR) of multi-scatter point targets, decreased tracking accuracy of multi-scatter point targets, high demands of motion compensation, and low sensitivity of the target attitude. Focusing on these problems, this paper systematically introduces the novel technologies of HRRs and discusses the issues and solutions relevant to detection, tracking, imaging, and recognition. Finally, it reviews the latest progress and representative results of HRR-based research, and suggests the future development of HRRs.

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“…In other words, the integration of detection with the target tracking method supplies the detector with feedback information from the tracker, and the feedback has the form of a posterior distribution on the target location. This couple relationship between the detector and the tracker was revealed in [7,8,9,10] by analyzing the Riccati equation iterative operation and track results, which demonstrated that the prior information from the tracker can be used to determine the optimal detection threshold. One advantage of integrating detection with tracking is that the Bayesian detector can use the feedback from the tracker as a priori information for its hypothesis test.…”

Section: Introductionmentioning

confidence: 99%

A New Multiple Hypothesis Tracker Integrated with Detection Processing

Wang

Sun

et al. 2019

Sensors

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In extant radar signal processing systems, detection and tracking are carried out independently, and detected measurements are utilized as inputs to the tracking procedure. Therefore, the tracking performance is highly associated with detection accuracy, and this performance may severely degrade when detections include a mass of false alarms and missed-targets errors, especially in dense clutter or closely-spaced trajectories scenarios. To deal with this issue, this paper proposes a novel method for integrating the multiple hypothesis tracker with detection processing. Specifically, the detector acquires an adaptive detection threshold from the output of the multiple hypothesis tracker algorithm, and then the obtained detection threshold is employed to compute the score function and sequential probability ratio test threshold for the data association and track estimation tasks. A comparative analysis of three tracking algorithms in a clutter dense scenario, including the proposed method, the multiple hypothesis tracker, and the global nearest neighbor algorithm, is conducted. Simulation results demonstrate that the proposed multiple hypothesis tracker integrated with detection processing method outperforms both the standard multiple hypothesis tracker algorithm and the global nearest neighbor algorithm in terms of tracking accuracy.

show abstract

Offline Performance Prediction of PDAF With Bayesian Detection for Tracking in Clutter

Cited by 18 publications

References 18 publications

A Novel Probabilistic Data Association for Target Tracking in a Cluttered Environment

A Novel Probabilistic Data Association for Target Tracking in a Cluttered Environment

Advanced technology of high-resolution radar: target detection, tracking, imaging, and recognition

A New Multiple Hypothesis Tracker Integrated with Detection Processing

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