A simplified αβ based Gaussian sum filter

Only target kinematic information is used in most conventional tracking systems, such as a radar or a sonar. Target attitude angles, which provide information about future trajectory curvature before radar measurement, can be used to improve tracking accuracy. The aim of this study is to track three-dimensional (3D) target with attitude angles (yaw and pitch) and radar measurement. Target velocity variations in each coordinate under attitude angles are derived after motion analysis under yaw and pitch angles separately. Then tracking models, the state vector of which includes attitude angles, are presented for target tracking. Targeting at non-zero mean characteristics of attitude measurement, and based on analysing Gaussian sum filter (GSF) and cubature Kalman filter (CKF), a Gaussian sum CKF (GSCKF) is presented to improve the filtering ability of non-linear non-Gaussian systems. Meanwhile, tracking models with different attitude components are established according to the changing law during target motion, and manoeuvering attitude angles are estimated through model switch. A comparison of performance with and without the use of attitude angles shows the benefits of attitude angleaided 3D target tracking. Simulation results of GSFs with different sub-filters demonstrate that the performance of the presented GSCKF has improved over conventional GSFs.

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“…The future positions of target in Y and Z coordinates which can be derived in the same way are (see equation (18) at the bottom of the next page)…”

Section: Target Motion Under Attitude Anglementioning

confidence: 99%

“…Gaussian mixture Kalman filter (GMKF), a recursive estimator combining GSF and model order reduction, was presented [17]. The αβ‐GSF combining GSF and αβ filter was presented [18]. GSFs with linear filters as sub‐filter are not suitable for non‐linear systems.…”

Section: Introductionmentioning

confidence: 99%

Model‐switched Gaussian sum cubature Kalman filter for attitude angle‐aided three‐dimensional target tracking

Zhang

Shan

2015

IET Radar, Sonar & Navigation

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“…The improved fuzzy alpha-beta filter has been proposed in [29] to track the highly maneuvering targets. A simplified alpha-beta filter based on Gaussian sum has been used by [30]. An EP-based α-β-γ-δ filter has been used in [31] for the target tracking.…”

Section: Related Workmentioning

confidence: 99%

“…If we derive the -filter, then we have to presume that a system is adequately estimated by a model with two interval positions. In the first step, we can do the initialization, as shown in Equations (29,30) [52], [53] [54].…”

Section: + Denotes the Opposite Ofmentioning

confidence: 99%

An Improved Alpha Beta Filter Using a Deep Extreme Learning Machine

et al. 2021

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This paper introduces new learning to the prediction model to enhance the prediction algorithms' performance in dynamic circumstances. We have proposed a novel technique based on the alpha-beta filter and deep extreme learning machine (DELM) algorithm named as learning to alpha-beta filter. The proposed method has two main components, namely the prediction unit and the learning unit. We have used the alpha-beta filter in the prediction unit, and the learning unit uses a DELM. The main problem with the conventional alpha-beta filter is that the values are generally selected via the trial-anderror technique. Once the alpha-beta values are chosen for a specific problem, they remain fixed for the entire data. It has been observed that different alpha-beta values for the same problem give different results. Hence it is essential to tune the alpha-beta values according to their historical behavior for certain values. Therefore, in the proposed method, we have addressed this problem and added the learning module to the conventional -filter to improve the -filter's performance. The DELM algorithm has been used to enhance the conventional alpha-beta filter algorithm's performance in dynamically changing conditions. The model performance has been measured using indoor environmental values of temperature and humidity. The relative improvement in the proposed learning prediction model's accuracy was 7.72% and 16.47% in RMSE and MSE metrics. The results show that the proposed model outperforms in terms of the result as compared to the conventional alpha-beta filter.INDEX TERMS Alpha-beta filter, learning algorithm, prediction algorithm, deep extreme learning machine, energy prediction.

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“…Although µ also depends on R xv , it is determined from known measurements of the noise parameters (B x , B v ) and sampling interval T as indicated in Equation (18). Thus, the optimal gains are determined by a D , and its appropriate presetting is essential for the proposed strategy.…”

Section: Optimal Gain Design Using the Rms Indexmentioning

confidence: 99%

Performance Analysis and Design Strategy for a Second-Order, Fixed-Gain, Position-Velocity-Measured (α-β-η-θ) Tracking Filter

Saho

Masugi

2017

Applied Sciences

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Featured Application: Design and evaluation of monitoring systems in intelligent vehicles, robots, and so on.Abstract: We present a strategy for designing an α-β-η-θ filter, a fixed-gain moving-object tracking filter using position and velocity measurements. First, performance indices and stability conditions for the filter are analytically derived. Then, an optimal gain design strategy using these results is proposed and its relationship to the position-velocity-measured (PVM) Kalman filter is shown. Numerical analyses demonstrate the effectiveness of the proposed strategy, as well as a performance improvement over the traditional position-only-measured α-β filter. Moreover, we apply an α-β-η-θ filter designed using this strategy to ultra-wideband Doppler radar tracking in numerical simulations. We verify that the proposed strategy can easily design the gains for an α-β-η-θ filter based on the performance of the ultra-wideband Doppler radar and a rough approximation of the target's acceleration. Moreover, its effectiveness in predicting the steady state performance in designing the position-velocity-measured Kalman filter is also demonstrated.

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A simplified αβ based Gaussian sum filter

Cited by 9 publications

References 22 publications

Model‐switched Gaussian sum cubature Kalman filter for attitude angle‐aided three‐dimensional target tracking

Model‐switched Gaussian sum cubature Kalman filter for attitude angle‐aided three‐dimensional target tracking

An Improved Alpha Beta Filter Using a Deep Extreme Learning Machine

Performance Analysis and Design Strategy for a Second-Order, Fixed-Gain, Position-Velocity-Measured (α-β-η-θ) Tracking Filter

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