This paper proposes a method based on modulation transfer function (MTF) to restore the power envelope of noisy reverberant speech by using a Kalman filter with linear prediction (LP). Its advantage is that it can simultaneously suppress the effects of noise and reverberation by restoring the smeared MTF without measuring room impulse responses. This scheme has two processes: power envelope subtraction and power envelope inverse filtering. In the subtraction process, the statistical properties of observation noise and driving noise for power envelope are investigated for the criteria of the Kalman filter which requires noise to be white and Gaussian. Furthermore, LP coefficients drastically affect the Kalman filter performance, and a method is developed for deriving LP coefficients from noisy reverberant speech. In the dereverberation process, an inverse filtering method is applied to remove the effects of reverberation. Objective experiments were conducted under various noisy reverberant conditions to evaluate how well the proposed Kalman filtering method based on MTF improves the signal-to-error ratio (SER) and correlation between restored power envelopes compared with conventional methods. Results showed that the proposed Kalman filtering method based on MTF can improve SER and correlation more than conventional methods.

In cognitive radar systems (CRSs), target scattering coefficients (TSC) can be utilized to improve the performance of target identification and classification. This work considers the problem of TSC estimation for temporally correlated target. Multiple receive antennas are adopted to receive the echo waveforms, which are interfered by the signal-dependent clutter. Unlike existing estimation methods in time domain, a novel estimation method based on Kalman filtering (KF) is proposed in frequency domain to exploit the temporal TSC correlation, and reduce the complexity of subsequent waveform optimization. Additionally, to minimize the mean square error of estimated TSC at each KF iteration, in contrary to existing works, we directly model the design process as an optimization problem, which is non-convex and cannot be solved efficiently. Therefore, we propose a novel method, similar in some way to semi-definite programming (SDP), to convert the non-convex problem into a convex one. Simulation results demonstrate that the estimation performance can be significantly improved by the KF estimation with optimized waveform.

Rotate magnetic field can be used for ranging, especially the environment where electronic filed suffers a deep fading and attenuation, such as drilling underground. However, magnetic field is still affected by the ferromagnetic materials, e.g., oil casing pipe. The measurement error is not endurable for single measurement. In this paper, the Geometric Predicted Unscented Kalman Filtering (GP-UKF) algorithm is developed for rotate magnetic ranging system underground. With GP-UKF, the Root Mean Square Error (RMSE) can be suppressed. It is really important in a long range detection by magnetic field, i.e., more than 50 meters.

In this letter, we present a real-time orientation estimation and motion tracking scheme using interacting multiple model (IMM) based Kalman filtering method. Two nonlinear filters, quaternion-based extended Kalman filter (QBEKF) and gyroscope-based extended Kalman filter (GBEKF) are utilized in the proposed IMM-based orientation estimator for sensor motion state estimation. In the QBEKF, measurements from gyroscope, accelerometer and magnetometer are processed; while in the GBEKF, sole measurements from gyroscope are processed. The interacting multiple model algorithm is used for fusing the estimated states via adaptive model weighting. Simulation results validate the proposed design concept, and the scheme is capable of reducing overall estimation errors in sensor motion tracking.

This paper focuses on fusion estimation algorithms weighted by matrices and scalars, and relationship between them is considered. We present new algorithms that address the computation of matrix weights arising from multidimensional estimation problems. The first algorithm is based on the Cholesky factorization of a cross-covariance block-matrix. This algorithm is equivalent to the standard composite fusion estimation algorithm however it is low-complexity. The second fusion algorithm is based on an approximation scheme which uses special steady-state approximation for local cross-covariances. Such approximation is useful for computing matrix weights in real-time. Subsequent analysis of the proposed fusion algorithms is presented, in which examples demonstrate the low-computational complexity of the new fusion estimation algorithms.

In this letter, a new estimation filtering is proposed when a delay between signal generation and signal estimation exists. The estimation filter is developed under a maximum likelihood criterion using only the finite observations on the delay interval. The proposed estimation filter is represented in both matrix form and iterative form. It is shown that the filtered estimate has good inherent properties such as time-invariance, unbiasedness and deadbeat. Via numerical simulations, the performance of the proposed estimation filtering is evaluated by the comparison with that of the existing fixed-lag smoothing, which shows that the proposed approach could be appropriate for fast estimation of signals that vary relatively quickly. Moreover, the on-line computational complexity of the proposed estimation filter is shown to be maintained at a lower level than the existing one.

One of the system greatly affecting the performance of a database server is the size-division of buffer pools. This letter proposes an adaptive control method of the buffer pool sizes. This method obtains the nearly optimal division using only observed response times in a comparatively short duration.

Although the multiuser detection scheme based on Kalman filtering (K-MUD) proposed by Zhang and Wei, is referred to as a "blind" algorithm, in fact it is not really blind because it is conditioned on perfect knowledge of system parameter, power of the desired user. This paper derives an algorithm to estimate the power of the user of interest, and proposes a completely blind multiuser detection. Computer simulations show that the proposed parameter estimation scheme obtains excellent effect, and that the new detection scheme has nearly the same performance as the K-MUD, there is only slight degradation at very low input signal-to-interference ratios (SIR).

This paper considers the problem of recursive filtering for linear discrete-time systems with uncertain observation. A new approximate adaptive filter with a parallel structure is herein proposed. It is based on the optimal mean square combination of arbitrary number of correlated estimates which is also derived. The equation for error covariance characterizing the mean-square accuracy of the new filter is derived. In consequence of parallel structure of the filtering equations the parallel computers can be used for their design. It is shown that this filter is very effective for multisensor systems containing different types of sensors. A practical implementation issue to consider this filter is also addressed. Example demonstrates the accuracy and efficiency of the proposed filter.

This paper proposes an algorithm that adaptively estimates time-varying noise variance used in Kalman filtering for real-time speech signal enhancement. In the speech signal contaminated by white noise, the spectral components except dominant ones in high frequency band are expected to reflect the noise energy. Our approach is first to find the dominant energy bands over speech spectrum using LPC. We then calculate the average value of the actual spectral components over the high frequency region excluding the dominant energy bands and use it as the noise variance. The resulting noise variance estimate is then applied to Kalman filtering to suppress the background noise. Experimental results indicate that the proposed approach achieves a significant improvement in terms of speech enhancement over those of the conventional Kalman filtering that uses the average noise power over silence interval only. As a refinement of our results, we employ multiple-Kalman filtering with multiple noise models and improve the intelligibility.

In this paper, we propose an idea for intramedia synchronization control using a method of end-to-end delay monitoring to estimate future delay in delay compensation protocol. The estimated value by Kalman filtering at the presentation site is used for feedback control to adjust the retrieval schedule at the source according to the network conditions. The proposed approach is applicable for the real time retrieving application where `tightness' of temporal synchronization is required. The retrieval schedule adjustment is achieved by two resynchronization mechanisms-retrieval offset adjustment and data unit skipping. The retrieval offset adjustment is performed along with a buffer level check in order to compensate for the change in delay jitter, while the data unit skipping control is performed to accelerate the recovery of unsynchronization period under severe conditions. Simulations are performed to verify the effectiveness of the proposed scheme. It is found that with a limited buffer size and tolerable latency in initial presentation, using a higher efficient delay estimator in our proposed resynchronization scheme, the synchronization performance can be improved particularly in the critically congested network condition. In the study, Kalman filtering is shown to perform better than the existing estimation methods using the previous measured jitter or the average value as an estimate.

This letter proposes a map-matching method for automotive navigation systems. The proposed method utilizes the innovation of the Kalman filter algorithm and can achieve more accurate positioning than the correlation method which is generally used for the navigation systems. In this letter, the performance of the proposed algorithm is verified by some simulations.