In strap down inertial navigation system (SINS), an interferometric fiber optic gyroscope (IFOG) is sensitive device to measure the rotation rate of an object. The IFOG output sustains with noise and random drift errors, which are influenced by the uncertainties of the external environment (like temperature, pressure, vibration) and sensor itself. Random drift is the main error source and it degrades the IFOG accuracy. To improve the precision of IFOG and to suppress these noises, the drift modeling and noise compensation methods are required. In this paper, a residual based adaptive unscented Kalman filter (AUKF) is proposed for denoising the IFOG signal. In this algorithm, window average method is used for estimating the measurement noise covariance matrix (R) based on covariance matching technique. The proposed algorithm is utilized for denoising IFOG test signal under static and dynamic environment. Allan variance analysis is used to analyze and quantify the noise sources of IFOG sensor. Based on the suggested technique, the angle random walk (N) and bias instability (Bs) values are reduced by an order of 10 times compared with actual value. The performance improvement of proposed algorithm in maneuvering condition is indicated by the reduced root mean square error values (RMSE). The performance of the proposed algorithm is compared with Unscented Kalman filter (UKF) algorithm. Simulation result reveals that the proposed algorithm is a valid solution for drift denoising t

Abstract This paper presents an extended Kalman filter for pose estimation using noise covariance matrices based on sensor output. Compact and lightweight nine-axis motion sensors are used for motion analysis in widely various fields such as medical welfare and sports. A nine-axis motion sensor includes a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer. Information obtained from the three sensors is useful for estimating joint angles using the Kalman filter. The extended Kalman filter is used widely for state estimation because it can estimate the status with a small computational load. However, determining the process and observation noise covariance matrices in the extended Kalman filter is complicated. The noise covariance matrices in the extended Kalman filter were found for this study based on the sensor output. Postural change appears in the gyroscope output because the rotational motion of the joints produces human movement. Therefore, the process noise covariance matrix was determined based on the gyroscope output. An observation noise covariance matrix was determined based on the accelerometer and magnetometer output because the two sensors’ outputs were used as observation values. During a laboratory experiment, the lower limb joint angles of three participants were measured using an optical 3D motion analysis system and nine-axis motion sensors while participants were walking. The lower limb joint angles estimated using the extended Kalman filter with noise covariance matrices based on sensor output were generally consistent with results obtained from the optical 3D motion analysis system. Furthermore, the lower limb joint angles were measured using nine-axis motion sensors while participants ran on a treadmill for about 90 seconds. The experiment results demonstrated the effectiveness of the proposed method for human pose estimation.

compared: (1) The noise covariance is IID, such that Ky.) = o7I; and (2) The noise covariance is as derived … y > 1.6. The reliability, defined in the previous sense, could not be a valid measure of performance of … kN forward solves, where k is a small integer. We note that B’J7=~!'JB is a square matrix with dimension

Accurate mapping of localization error distribution is essential for assessing passive sensor systems and guiding sensor placement. However, conventional analytical methods like the Geometrical Dilution of Precision (GDOP) rely on idealized error models, failing to capture the complex, heterogeneous error distributions typical of real-world environments. To overcome this challenge, we propose a novel data-driven framework that reconstructs high-fidelity localization error maps from sparse observations in TDOA-based systems. Specifically, we model the error distribution as a tensor and formulate the reconstruction as a tensor completion problem. A key innovation is our physics-informed regularization strategy, which incorporates prior knowledge from the analytical error covariance matrix into the tensor factorization process. This allows for robust recovery of the complete error map even from highly incomplete data. Experiments on a real-world dataset validate the superiority of our approach, showing an accuracy improvement of at least 27.96% over state-of-the-art methods.

Mainlobe interference can severely degrade the performance of distributed phased-array radar systems in the presence of strong jamming or low-reflectivity targets. This paper introduces a signal-data dual-domain cooperative antijamming and localization (SDCAL) framework that integrates adaptive complete ensemble empirical mode decomposition with improved blind source separation and wavelet optimization (CEEMDAN-WOBSS) for signal-level denoising and separation. Following source separation, CFAR-based pulse compression is applied for precise range estimation, and multi-node data fusion is then used to achieve three-dimensional target localization. Under low signal-to-noise ratio (SNR) conditions, the adaptive CEEMDAN-WOBSS approach reconstructs the signal covariance matrix to preserve subspace rank, thereby accelerating convergence of the separation matrix. The subsequent pulse compression and CFAR detection steps provide reliable inter-node distance measurements for accurate fusion. The simulation results demonstrate that, compared to conventional blind-source-separation methods, the proposed framework markedly enhances interference suppression, detection probability, and localization accuracy-validating its effectiveness for robust collaborative sensing in challenging jamming scenarios.