Single Space Based Sensor Bias Estimation Using a Single Target of Opportunity

Belfadel, Djedjiga; Bar‐Shalom, Yaakov; Willett, Peter

doi:10.1109/taes.2019.2895709

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…However, it needs to measure the relative distance between neighboring sensors to provide mutual observation information, which increases the sensor burden. The literature [7] proposes a method for spatial registration of a single sensor using batch nonlinear least squares estimation, which is shown to be statistically valid by the evaluation of the Clamero lower bound, but the method requires high-precision satellite coordinates of the sensor. Lu Z. and Zhu M. et al [8] proposed an iterative design spatial registration algorithm based on expectation maximization (EM), but the model is too ideal and far from engineering applications.…”

Section: Introductionmentioning

confidence: 99%

A Spatial Registration Method for Multi-UAVs Based on a Cooperative Platform in a Geodesic Coordinate Information-Free Environment

Dai,

Lu,

2023

Applied Sciences

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The satellite navigation system of Unmanned Aerial Vehicles (UAVs) is susceptible to external interference in a complex environment, resulting in the loss of their own geodetic coordinate information. A spatial registration method for multi-UAVs based on a cooperative platform in a geodesic coordinate information-free environment is proposed to solve this problem. The mutual observation information between UAVs is approximated by the observation information of the cooperative platform. Indirect observation information of the target can be obtained on account of mutual observation. On the basis of this, a close-range spatial registration algorithm without the geodetic coordinate information of UAVs is designed by means of the right-angle translation method. Finally, the Kalman filtering technique is used to track maritime targets. In this paper, the proposed method is verified by a simulation experiment and a practical experiment. The proposed method is 90% effective in reducing systematic errors. The tracking accuracy after alignment is significantly better than that of the original trajectory.

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Section: Introductionmentioning

confidence: 99%

A Spatial Registration Method for Multi-UAVs Based on a Cooperative Platform in a Geodesic Coordinate Information-Free Environment

Dai,

Lu,

2023

Applied Sciences

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“…Possible sources of bias include sensor alignment bias, sensor altitude bias, location bias, etc. [17]. In this work, we consider only the measurement biases, i.e., the biases in the elevation and bearing angles.…”

Section: Introductionmentioning

confidence: 99%

Ground Target Tracking Using an Airborne Angle-Only Sensor with Terrain Uncertainty and Sensor Biases

Mitra¹,

Balachandran²,

Kirubarajan³

2022

Sensors

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Airborne angle-only sensors can be used to track stationary or mobile ground targets. In order to make the problem observable in 3-dimensions (3-D), the height of the target (i.e., the height of the terrain) from the sea-level is needed to be known. In most of the existing works, the terrain height is assumed to be known accurately. However, the terrain height is usually obtained from Digital Terrain Elevation Data (DTED), which has different resolution levels. Ignoring the terrain height uncertainty in a tracking algorithm will lead to a bias in the estimated states. In addition to the terrain uncertainty, another common source of uncertainty in angle-only sensors is the sensor biases. Both these uncertainties must be handled properly to obtain better tracking accuracy. In this paper, we propose algorithms to estimate the sensor biases with the target(s) of opportunity and algorithms to track targets with terrain and sensor bias uncertainties. Sensor bias uncertainties can be reduced by estimating the biases using the measurements from the target(s) of opportunity with known horizontal positions. This step can be an optional step in an angle-only tracking problem. In this work, we have proposed algorithms to pick optimal targets of opportunity to obtain better bias estimation and algorithms to estimate the biases with the selected target(s) of opportunity. Finally, we provide a filtering framework to track the targets with terrain and bias uncertainties. The Posterior Cramer–Rao Lower Bound (PCRLB), which provides the lower bound on achievable estimation error, is derived for the single target filtering with an angle-only sensor with terrain uncertainty and measurement biases. The effectiveness of the proposed algorithms is verified by Monte Carlo simulations. The simulation results show that sensor biases can be estimated accurately using the target(s) of opportunity and the tracking accuracies of the targets can be improved significantly using the proposed algorithms when the terrain and bias uncertainties are present.

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“…Multiple factors in various applications lead to the appearance of gross errors. Examples include miscalibrations of sensors, other configuration aberrations like errors in sensor location or alignment, clock errors, or malfunctioning [11]. In addition, environmental inconsistencies can introduce biases in observations e.g.…”

Section: Introductionmentioning

confidence: 99%

Variational-Based Non-linear Bayesian Filtering with Observations Subjected to Gross Errors

Chughtai¹,

Majal²,

Tahir³

et al. 2021

Preprint

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State estimation of dynamical systems is crucial for providing new decision-making and system automation information in different applications. However, the assumptions on the standard computational models for sensor measurements can be violated in practice due to different types of data abnormalities such as random and gross errors. In this work, we focus on the occurrence of gross errors and propose a robust filter for their detection and mitigation during state estimation of nonlinear dynamical systems. We model the presence of gross errors within the generative structure of the state-space models. Subsequently, employing the theory of Variational Bayes and general Gaussian filtering, we devise a recursive filter which we call the Bias Detecting and Mitigating (BDM) filter. As the error detection mechanism is embedded within the filter structure its dependence on any external detector is obviated. Simulations verify the performance gains of the proposed BDM filter compared to similar Kalman filtering-based approaches in terms of robustness to temporary and persistent bias presence.

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Single Space Based Sensor Bias Estimation Using a Single Target of Opportunity

Cited by 8 publications

References 11 publications

A Spatial Registration Method for Multi-UAVs Based on a Cooperative Platform in a Geodesic Coordinate Information-Free Environment

A Spatial Registration Method for Multi-UAVs Based on a Cooperative Platform in a Geodesic Coordinate Information-Free Environment

Ground Target Tracking Using an Airborne Angle-Only Sensor with Terrain Uncertainty and Sensor Biases

Variational-Based Non-linear Bayesian Filtering with Observations Subjected to Gross Errors

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