Kernel adaptive filter

About: Kernel adaptive filter is a research topic. Over the lifetime, 8771 publications have been published within this topic receiving 142711 citations.

Least mean M-estimate algorithms for robust adaptive filtering in impulse noise

Abstract: This paper proposes two gradient-based adaptive algorithms, called the least mean M estimate and the transform domain least mean M-estimate (TLMM) algorithms, for robust adaptive filtering in impulse noise. A robust M-estimator is used as the objective function to suppress the adverse effects of impulse noise on the filter weights. They have a computational complexity of order O(N) and can be viewed, respectively, as the generalization of the least mean square and the transform-domain least mean square algorithms. A robust method fur estimating the required thresholds in the M-estimator is also given. Simulation results show that the TLMM algorithm, in particular, is more robust and effective than other commonly used algorithms in suppressing the adverse effects of the impulses.

Adaptive estimation of time delays in sampled data systems

Abstract: An adaptive technique is developed which iteratively determines the time delay between two sampled signals that are highly correlated. Although the procedure does not require a priori information on the input signals, it does require that the signals have a unimodal or periodically unimodal cross-correlation function. The adaptive delay algorithm uses a gradient technique to find the value of the adaptive delay that minimizes the mean-squared (MS) error function. This iterative algorithm is similar to the adaptive filter coefficient algorithm developed by Widrow. However, the MS error function for the adaptive delay is not quadratic, as it is in the adaptive filter. A statistical analysis determines the value of the convergence parameter which effects rapid convergence of the adaptive delay. This convergence parameter is a function of the power of the input signal. Computer simulations are presented which verify that the adaptive delay correctly estimates the time delay difference between two sinusoids, including those in noisy environments. The adaptive delay is also shown to perform correctly in a time delay tracking application.

Novel data association schemes for the probability hypothesis density filter

Abstract: The probability hypothesis density (PHD) filter is a practical alternative to the optimal Bayesian multi-target Alter based on finite set statistics. It propagates the PHD function, a first-order moment of the full multi-target posterior density. The peaks of the PHD function give estimates of target states. However, the PHD filter keeps no record of target identities and hence does not produce track-valued estimates of individual targets. We propose two different schemes according to which PHD filter can provide track-valued estimates of individual targets. Both schemes use the probabilistic data-association functionality albeit in different ways. In the first scheme, the outputs of the PHD filter are partitioned into tracks by performing track-to-estimate association. The second scheme uses the PHD filter as a clutter filter to eliminate some of the clutter from the measurement set before it is subjected to existing data association techniques. In both schemes, the PHD filter effectively reduces the size of the data that would be subject to data association. We consider the use of multiple hypothesis tracking (MHT) for the purpose of data association. The performance of the proposed schemes are discussed and compared with that of MHT.

Time-Domain Signal Analysis Using Adaptive Notch Filter

Abstract: Noise reduction and signal decomposition are among important and practical issues in time-domain signal analysis. This paper presents an adaptive notch filter (ANF) to achieve both these objectives. For noise reduction purpose, the proposed adaptive filter successfully extracts a single sinusoid of a possibly time-varying nature from a noise-corrupted signal. The paper proceeds with introducing a chain of filters which is capable of estimating the fundamental frequency of a signal composed of harmonically related sinusoids, and of decomposing it into its constituent components. The order of differential equations governing this algorithm is 2n+1, where n is the number of constituent sinusoids that should be extracted. Stability analysis of the proposed algorithm is carried out based on the application of the local averaging theory under the assumption of slow adaptation. When compared with the conventional Fourier analysis, the proposed method provides instantaneous values of the constituting components. Moreover, it is adaptive with respect to the fundamental frequency of the signal. Simulation results verify the validity of the presented algorithm and confirm its desirable transient and steady-state performances as well as its desirable noise characteristics

Improved SMC implementation of the PHD filter

Abstract: The paper makes two contributions. First, a new formulation of the PHD filter which distinguishes between persistent and newborn objects is presented. This formulation results in an efficient sequential Monte Carlo (SMC) implementation of the PHD filter, where the placement of newborn object particles is determined by the measurements. The second contribution is a novel method for the state and error estimation from an SMC implementation of the PHD filter. Instead of clustering the particles in an ad-hoc manner after the update step (which is the current approach), we perform state estimation and, if required, particle clustering, within the update step in an exact and principled manner. Numerical simulations indicate a significant improvement in the estimation accuracy of the proposed SMC-PHD filter.