Frequency estimation of narrow band signals in Gaussian noise via Unscented Kalman Filter

The location of the vehicle is a basic parameter for self-driving cars. The key problem of localization is the noise of the sensors. In previous research, we proposed a particle-aided unscented Kalman filter (PAUKF) to handle the localization problem in non-Gaussian noise environments. However, the previous basic PAUKF only considers the infrastructures in two dimensions (2D). This previous PAUKF 2D limitation rendered it inoperable in the real world, which is full of three-dimensional (3D) features. In this paper, we have extended the previous basic PAUKF’s particle weighting process based on the multivariable normal distribution for handling 3D features. The extended PAUKF also raises the feasibility of fusing multisource perception data into the PAUKF framework. The simulation results show that the extended PAUKF has better real-world applicability than the previous basic PAUKF.

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Adaptive complex unscented Kalman filter for frequency estimation of time-varying signals

IET Science Measurement & Technology ◽

10.1049/iet-smt.2009.0003 ◽

2010 ◽

Vol 4 (2) ◽

pp. 93-103 ◽

Cited By ~ 21

Author(s):

P.K. Dash ◽

B.K. Panigrahi ◽

S. Hasan

Keyword(s):

Kalman Filter ◽

Unscented Kalman Filter ◽

Frequency Estimation ◽

Time Varying ◽

Estimation Of Time

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Underwater surveillance in non-Gaussian noisy environment

Measurement and Control ◽

10.1177/0020294019877515 ◽

2020 ◽

Vol 53 (1-2) ◽

pp. 250-261

Author(s):

B Omkar Lakshmi Jagan ◽

S Koteswara Rao

Keyword(s):

Kalman Filter ◽

Lower Bound ◽

Extended Kalman Filter ◽

Gaussian Noise ◽

Unscented Kalman Filter ◽

Doppler Frequency ◽

Gaussian Mixture ◽

Noisy Environment ◽

Sampled Measurements ◽

Non Gaussian

The aim of this paper is to evaluate the performance of different filtering algorithms in the presence of non-Gaussian noise environment for tracking underwater targets, using Doppler frequency and bearing measurements. The tracking using Doppler frequency and bearing measurements is popularly known as Doppler-bearing tracking. Here the measurements, that is, bearings and Doppler frequency, are considered to be corrupted with two types of non-Gaussian noises namely shot noise and Gaussian mixture noise. The non-Gaussian noise sampled measurements are assumed to be obtained (a) randomly throughout the process and (b) repeatedly at some particular time samples. The efficiency of these filters with the increase in non-Gaussian noise samples is discussed in this paper. The performance of filters is compared with that of Cramer-Rao Lower Bound. Doppler-bearing extended Kalman filter and Doppler-bearing unscented Kalman filter are chosen for this work.

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Three-Dimensional Submarine-to-Submarine Passive Target Tracking in the Presence of Non-Gaussian Noises

International Journal of e-Collaboration ◽

10.4018/ijec.2021070101 ◽

2021 ◽

Vol 17 (3) ◽

pp. 1-24

Author(s):

Kavitha Lakshmi M. ◽

Koteswara Rao S. ◽

Subrahmanyam Kodukula

Keyword(s):

Kalman Filter ◽

Target Tracking ◽

Gaussian Noise ◽

Target Location ◽

Unscented Kalman Filter ◽

Three Dimensional ◽

Noise Environment ◽

Cubature Kalman Filter ◽

Highly Nonlinear ◽

Non Gaussian

In underwater surveillance, three-dimensional target tracking is a challenging task. The angles-only measurements (i.e., bearing and elevation) obtained by hull mounted sensors are considered to appraise the target motion parameter. Due to noise in measurements and nonlinearity of the system, it is very hard to find out the target location. For many applications, UKF is best estimator that remaining algorithms. Recently, cubature Kalman filter (CKF) is also popular. It is proposed to use UKF (unscented Kalman filter) and CKF (cubature Kalman filter) algorithms that minimize the noise in measurements. So far, researchers carried out this work (target tracking) in Gaussian noise environment, whereas in this paper same work is carried out for non-Gaussian noise environment. The performance evaluation of the filters using Monte-Carlo simulation and Cramer-Rao lower bound (CRLB) is accomplished and the results are analyzed. Result shows that UKF is well suitable for highly nonlinear systems than CKF.

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