Passive Coherent Location With Unknown Transmitter States

Guo, Yunfei; Kirubarajan, Thiagalingam; Wong, S.K.; Jassemi-Zargani, Rahim

doi:10.1109/taes.2017.2649739

Cited by 13 publications

(2 citation statements)

References 39 publications

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“…4) Posterior Cramer-Rao lower bound (PCRLB): The posterior Cramer-Rao lower bound is applied to evaluate the performance of proposed estimation method. According [52] and [53], the PCRLB on the augmented state estimation error covariance matrix P k+1 is the inverse of the Fisher information matrix (FIM) J k+1 , thus…”

Section: B Configuration Of the Longitudinal Tracking Algorithmsmentioning

confidence: 99%

Ground Vehicle Tracking Using Context-Based Sojourn Time Dependent Markov Model and Pseudo-Measurement

Tian

Cen

et al. 2020

IEEE Access

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Tracking maneuvering vehicles in complex dynamic environment is a challenging problem for advanced driver assistance system and autonomous driving systems. Most conventional vehicle tracking algorithms can not model the vehicle dynamic exactly because of the uncertain moving behavior. However, due to the on-road capability, vehicles have to subject to various constraints imposed by traffic rules and roads. Taking advantage of those context information can refine and improve the performance of tracking as it provides additional prior information for vehicles' dynamic behavior. To achieve this goal, this paper presents a novel context-enhanced tracking approach that exploits the context information to reduce the uncertainty of dynamic estimation. A new context-based sojourn-time dependent semi-Markov (STDM) model, called sojourn-time dependent semi-Markov variable structure interacting multiple model (STDM-VSIMM), is proposed to describe the vehicle's longitudinal acceleration process. In order to cope with the context information into STDM model, a context-based Bayesian network is presented to replace the fixed model transition probabilities with sojourn-time dependent transition probabilities. Compared with traditional interacting multiple model tracking with fixed transition probability, this adaptation switching strategy makes the vehicle motion sequence closer to the natural behavior and improve the tracking performance. Furthermore, a novel pseudo-measurement is constructed to formulate the road-map constraint in tracking process for reducing uncertain on mobility constraints. Simulation results shows that the proposed STDM-VSIMM can achieve better performance after considering the context. INDEX TERMS Vehicle tracking, context-enhanced, sojourn-time dependent Markov model, pseudomeasurements.

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Section: B Configuration Of the Longitudinal Tracking Algorithmsmentioning

confidence: 99%

Ground Vehicle Tracking Using Context-Based Sojourn Time Dependent Markov Model and Pseudo-Measurement

Tian

Cen

et al. 2020

IEEE Access

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show abstract

“…Third, the ambient co-frequency interference also brings a lot of clutter. To the best of our knowledge, there is no explicit contribution concerning multi-target tracking of the non-cooperative bistatic radar under low detection probability and high clutter, and researchers mainly address target-tracking of the passive radar with commercial transmitters [21,[24][25][26][27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Improved probability hypothesis density filter for multi‐target tracking of non‐cooperative bistatic radar

Wang

Bao

Pan

2021

IET Radar Sonar & Navi

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Non-cooperative bistatic radar refers to the passive bistatic radar using a non-cooperative radar as the illuminator of opportunity. Limited by the non-cooperation and bistatic configuration, multi-target tracking of the non-cooperative bistatic radar is confronted with challenges of low detection probability and high clutter. Based on the sequential Monte Carlo implementation of probability hypothesis density (PHD) filter, this study proposes an improved PHD (I-PHD) filter. Similar to the tracking gate in data association, whether each candidate target is detected at each time is determined and recorded, and the sequential probability ratio test distinguishes a real target and false target based on this record. To distinguish miss detection and target death, a new survival probability dependent on target position is defined, and the survival probability of a specific target drops rapidly when it is located near the boundary and moves outwards. To label targets and extract their states, a new method of track identification and state estimation is proposed. The simulation results illustrate that the proposed I-PHD filter can effectively track multiple targets of non-cooperative bistatic radar under low detection probability and high clutter and has superior performance than competing methods.

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Single target tracking for noncooperative bistatic radar with unknown signal illumination

Wang

Bao

2021

Signal Processing

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Passive Coherent Location With Unknown Transmitter States

Cited by 13 publications

References 39 publications

Ground Vehicle Tracking Using Context-Based Sojourn Time Dependent Markov Model and Pseudo-Measurement

Ground Vehicle Tracking Using Context-Based Sojourn Time Dependent Markov Model and Pseudo-Measurement

Improved probability hypothesis density filter for multi‐target tracking of non‐cooperative bistatic radar

Single target tracking for noncooperative bistatic radar with unknown signal illumination

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