Ground target tracking with RCS estimation based on signal strength measurements

Mertens, Michael; Ulmke, Martin; Koch, Wolfgang

doi:10.1109/taes.2015.140866

Cited by 24 publications

(17 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…. , 100 together with the threshold values at which, we would reject the null hypothesis at αsig = 0.05 significance level 4 . The obtained values are far above the thresholds which means that the null hypothesis can be rejected with a very low error probability (p).…”

Section: A Rss and Time Dependencymentioning

confidence: 88%

See 1 more Smart Citation

Characterization of Received Signal Strength Perturbations Using Allan Variance

Luo

Casaseca‐de‐la‐Higuera

McClean

et al. 2018

IEEE Trans. Aerosp. Electron. Syst.

View full text Add to dashboard Cite

Abstract-The received signal strength (RSS) of wireless signals conveys important information that has been widely used in wireless communications, localisation and tracking. Traditional RSS-based research and applications model the RSS signal using a deterministic component plus a white noise term. This paper investigates the assumption of white noise to have a further understanding of the RSS signal and proposes a methodology based on the Allan Variance (AVAR) to characterise it. Using AVAR, we model the RSS unknown perturbations as correlated random terms. These terms can account for both coloured noise or other effects such as shadowing or smallscale fading. Our results confirm that AVAR can be used to obtain a flexible model of the RSS perturbations, as expressed by coloured noise components . The study is complemented by introducing two straightforward applications of the proposed methodology: 1) The modelling and simulation of RSS noise using Wiener processes, and 2) RSS localisation using the extended Kalman filter.

show abstract

Section: A Rss and Time Dependencymentioning

confidence: 88%

“…The absence of shadowing obstacles and nearby objects prevented for correlation effects due to temporal fading. 4 The null hypothesis would be rejected with 5% error probability. …”

Section: A Rss and Time Dependencymentioning

confidence: 98%

Characterization of Received Signal Strength Perturbations Using Allan Variance

Luo

Casaseca‐de‐la‐Higuera

McClean

et al. 2018

IEEE Trans. Aerosp. Electron. Syst.

View full text Add to dashboard Cite

show abstract

“…Hence, the work does not evaluate the label switching problem. In [ 48 ], the amplitude information is exploited based on the CPHD filter for GMTI tracking, but the DBZ masking problem is not involved there. [49] used the PHD filter to process the GMTI experimental data, but no detailed algorithm description is given there.…”

Section: B Related Workmentioning

confidence: 99%

Tracking multiple maneuvering targets hidden in the DBZ based on the MM-PHD filter

Wu¹

2020

Preprint

View full text Add to dashboard Cite

<p><a></a><a></a><a>For a ground moving target indication (GMTI) radar, the presence of </a><a></a><a></a><a></a><a>Doppler blind zone (DBZ)</a> results in many short tracks with frequent label switching, which seriously deteriorates the tracking performance. When the DBZ masking is coupled with targets maneuvering, tracking multiple maneuvering targets hidden in the DBZ becomes very challenging, which is reflected in the fact that there is no public research on this issue. To overcome this complicated problem, we propose a practical and fully functional GMTI multi-maneuvering-target tracker based on the multiple model probability hypothesis density (MM-PHD) filter. Unlike the standard MM-PHD filter, the proposed tracker utilizes the Doppler information and incorporates the minimum detectable velocity (MDV) to suppress the DBZ masking. Furthermore, to cope with the problems of the fixed initiation and no label output of the standard MM-PHD filter, the resulting MM-PHD filter with the Doppler and MDV information is augmented with measurement-driven adaptive track initiation and track label propagation, which are necessary for a practical tracker and also required for evaluating the overall GMTI tracking performance. Finally, numerical examples show that the proposed tracker outperforms significantly the existing ones, thus verifying its effectiveness.</p> <p> </p>

show abstract

“…The state estimation is an important research topic in many fields such as signal processing, target tracking, system identification, navigation and so on [1]- [3], [5], [6]. A large number of filtering approaches have been developed to address the problems of nonlinear state estimation [4].…”

Section: Introductionmentioning

confidence: 99%

“…Robust filtering methods were reported to solve the performance degradation involved in nonlinear Kalman-based filters [1], [17], [18]. The H∞ filter with H∞ norm as the performance standard is a robust filter for a variety of system uncertainties [4], [19].…”

Section: Introductionmentioning

confidence: 99%

A Novel Fitting H-Infinity Kalman Filter for Nonlinear Uncertain Discrete-Time Systems Based on Fitting Transformation

et al. 2020

View full text Add to dashboard Cite

The classical Kalman-based filtering algorithm, such as extended Kalman filter or unscented Kalman filter, commonly assumes that the accurate system model or precise noise statistics is known for using. Hence, these filters are not robust estimation to practical systems and always with poor stability, low precision or even divergence since uncertain items exist. In order to tackle these issues, a novel scheme referred to as the fitting H-infinity Kalman filter (FHKF) is proposed and used for robust estimation of the nonlinear uncertain systems. The hardcore of FHKF is the fitting transformation, which is a numerical approximation approach to get the estimation values of coefficient matrix based on least weighted squares. Moreover, FHKF is proposed by applying the coefficient matrix to the structure of the extended H-infinity Kalman filter. Based on the stochastic stability lemma, the stability analysis is presented to verify the error boundness of the proposed filtering. Its efficiency is demonstrated by taking Monte Carlo simulation for the uncertain system and practical experiments in the INS/GPS integrated navigation. INDEX TERMS Fitting transformation, fitting H-infinity Kalman filter, stability analysis, uncertain system.

show abstract

Ground target tracking with RCS estimation based on signal strength measurements

Cited by 24 publications

References 22 publications

Characterization of Received Signal Strength Perturbations Using Allan Variance

Characterization of Received Signal Strength Perturbations Using Allan Variance

Tracking multiple maneuvering targets hidden in the DBZ based on the MM-PHD filter

A Novel Fitting H-Infinity Kalman Filter for Nonlinear Uncertain Discrete-Time Systems Based on Fitting Transformation

Contact Info

Product

Resources

About