Adaptive cancelation of self-generated sensory signals in a whisking robot

Sensory signals are often caused by one's own active movements. This raises a problem of discriminating between self-generated sensory signals and signals generated by the external world. Such discrimination is of general importance for robotic systems, where operational robustness is dependent on the correct interpretation of sensory signals. Here, we investigate this problem in the context of a whiskered robot. The whisker sensory signal comprises two components: one due to contact with an object (externally generated) and another due to active movement of the whisker (self-generated). We propose a solution to this discrimination problem based on adaptive noise cancelation, where the robot learns to predict the sensory consequences of its own movements using an adaptive filter. The filter inputs (copy of motor commands) are transformed by Laguerre functions instead of the often-used tapped-delay line, which reduces model order and, therefore, computational complexity. Results from a contact-detection task demonstrate that false positives are significantly reduced using the proposed scheme

A Recursive Least M-Estimate Algorithm for Robust Adaptive Filtering in Impulsive Noise: Fast Algorithm and Convergence Performance Analysis

This paper studies the problem of robust adaptive filtering in impulsive noise environment using a recursive least M-estimate algorithm (RLM). The RLM algorithm minimizes a robust M-estimator-based cost function instead of the conventional mean square error function (MSE). Previous work has showed that the RLM algorithm offers improved robustness to impulses over conventional recursive least squares (RLS) algorithm. In this paper, the mean and mean square convergence behaviors of the RLM algorithm under the contaminated Gaussian impulsive noise model is analyzed. A lattice structure-based fast RLM algorithm, called the Huber Prior Error Feedback-Least Squares Lattice (H-PEF-LSL) algorithm1 is derived. It has an order O(N) arithmetic complexity, where N is the length of the adaptive filter, and can be viewed as a fast implementation of the RLM algorithm based on the modified Huber M-estimate function and the conventional PEF-LSL adaptive filtering algorithm. Simulation results show that the transversal RLM and the H-PEF-LSL algorithms have better performance than the conventional RLS and other RLS-like robust adaptive algorithms tested when the desired and input signals are corrupted by impulsive noise. Furthermore, the theoretical and simulation results on the convergence behaviors agree very well with each other.published_or_final_versio

Robust detail-preserving signal extraction

We discuss robust filtering procedures for signal extraction from noisy time series. Particular attention is paid to the preservation of relevant signal details like abrupt shifts. moving averages and running medians are widely used but have shortcomings when large spikes (outliers) or trends occur. Modifications like modified trimmed means and linear median hybrid filters combine advantages of both approaches, but they do not completely overcome the difficulties. Better solutions can be based on robust regression techniques, which even work in real time because of increased computational power and faster algorithms. Reviewing previous work we present filters for robust signal extraction and discuss their merits for preserving trends, abrupt shifts and local extremes as well as for the removal of outliers. --

A Huber recursive least squares adaptive lattice filter for impulse noise suppression

This paper proposes a new adaptive filtering algorithm called the Huber Prior Error-Feedback Least Squares Lattice (H-PEF-LSL) algorithm for robust adaptive filtering in impulse noise environment. It minimizes a modified Huber M-estimator based cost function, instead of the least squares cost function. In addition, the simple modified Huber M-estimate cost function also allows us to perform the time and order recursive updates in the conventional PEF-LSL algorithm so that the complexity can be significantly reduced to O(M), where M is the length of the adaptive filter. The new algorithm can also be viewed as an efficient implementation of the recursive least M-estimate (RLM) algorithm recently proposed by the authors [1], which has a complexity of O(M 2). Simulation results show that the proposed H-PEF-LSL algorithm is more robust than the conventional PEF-LSL algorithm in suppressing the adverse influence of the impulses at the input and desired signals with small additional computational cost.published_or_final_versio

Partition based vector filtering technique for suppression of noise in digital color images

A partition-based adaptive vector filter is proposed for the restoration of corrupted digital color images. The novelty of the filter lies in its unique three-stage adaptive estimation. The local image structure is first estimated by a series of center-weighted reference filters. Then the distances between the observed central pixel and estimated references are utilized to classify the local inputs into one of preset structure partition cells. Finally, a weighted filtering operation, indexed by the partition cell, is applied to the estimated references in order to restore the central pixel value. The weighted filtering operation is optimized off-line for each partition cell to achieve the best tradeoff between noise suppression and structure preservation. Recursive filtering operation and recursive weight training are also investigated to further boost the restoration performance. The proposed filter has demonstrated satisfactory results in suppressing many distinct types of noise in natural color images. Noticeable performance gains are demonstrated over other prior-art methods in terms of standard objective measurements, the visual image quality and the computational complexity

A robust M-estimate adaptive filter for impulse noise suppression

In this paper, a robust M-estimate adaptive filter for impulse noise suppression is proposed. The objective function used is based on a robust M-estimate. It has the ability to ignore or down weight large signal error when certain thresholds are exceeded. A systematic method for estimating such thresholds is also proposed. An advantage of the proposed method is that its solution is governed by a system of linear equations. Therefore, fast adaptation algorithms for traditional linear adaptive filters can be applied. In particular, a M-estimate recursive least square (M-RLS) adaptive algorithm is studied in detail. Simulation results show that it is more robust against individual and consecutive impulse noise than the MN-LMS and the N-RLS algorithms. It also has fast convergence speed and a low steady state error similar to its RLS counterpart.published_or_final_versio

Recursive Least M-estimate (RLM) adaptive filter for robust filtering in impulse noise

This paper proposes a recursive least M-estimate (RLM) algorithm for robust adaptive filtering in impulse noise. It employs an M-estimate cost function, which is able to suppress the effect of impulses on the filter weights. Simulation results showed that the RLM algorithm performs better than the conventional RLS, NRLS, and OSFKF algorithms when the desired and input signals are corrupted by impulses. Its initial convergence, steady-state error, computational complexity, and robustness to sudden system change are comparable to the conventional RLS algorithm in the presence of Gaussian noise alone.published_or_final_versio

A robust M-estimate adaptive equaliser for impulse noise suppression

In this paper, a FIR adaptive equaliser for impulse noise suppression is proposed. It is based on the minimization of an M-estimate objective function which has the ability to ignore or down-weight a large error signal when it exceeds certain thresholds. An advantage of the proposed method is that its solution is governed by a system of linear equations, called the M-estimate normal equation. Therefore, traditional fast algorithms like the recursive least squares algorithm can be applied. Using a robust estimation of the thresholds and the recursive least square algorithm, an M-estimate RLS (M-RLS) algorithm is developed. Simulation results show that the proposed algorithm has better convergence performance than the N-RLS and MN-LMS algorithms when the input signal of the equaliser is corrupted by individually or consecutive impulse noises. It also shares the low steady state error of the traditional RLS algorithm.published_or_final_versio

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

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 for 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. © 2000 IEEE.published_or_final_versio

A robust statistics based adaptive lattice-ladder filter in impulsive noise

In this paper, a new robust adaptive lattice-ladder filter for impulsive noise suppression is proposed. The filter is obtained by applying the non-linear filtering technique in [l] and the robust statistic approach to the gradient adaptive lattice filter. A systematic method is also developed to determine the corresponding threshold parameters for impulse suppression. Simulation results showed that the performance of the proposed algorithm is better than the conventional RLS, N-RLS, the gradient adaptive lattice normalised-LMS (GAL-NLMS), RMN and ATNA algorithms when the input and desired signals are corrupted by individual and consecutive impulses. The initial convergence, steady-state error, computational complexity and tracking capability of the proposed algorithm are also comparable to the conventional GAL-NLMS algorithm.published_or_final_versio