Improved Proportionate Affine Projection Algorithm Research Articles

Non-uniform [formula omitted]-norm constraint based underwater acoustic channel adaptive estimation

It is known that the underwater acoustic channels (UAC) exhibit non-uniform cluster-sparse characteristics, meaning that the channel impulse response (CIR) typically consists of a large number of near-zero taps and with only a few none-zero ones assemble in clusters in a structured manner, moreover, the cluster structure is non-uniformly distributed on the time domain. In this paper, an improved proportionate affine projection algorithm (IPAPA) with the non-uniform l21-norm constraint is proposed for non-uniform cluster-sparse UAC estimation. Firstly, the auxiliary channel information is obtained via the correlation of training sequence, whereby the priori cluster-sparse structure positioning of UAC is realized. Then the non-uniform l21-norm is added on the IPAPA for the final accurate channel estimation: it encourages correlation among coefficients inside each group via the l2 norm and facilitates sparsity across all groups using the l1 norm. The results of numerical simulations and sea trial show that the proposed UAC estimation algorithm can achieve a better performance in terms of mean square error (MSE) compared to existing sparse channel estimation methods.

Adaptive feedback cancellation with prediction error method and howling suppression in train public address system

Train public address (PA) system is an important component of Passenger Information System. Acoustic feedback is a well-known problem in public address systems. It will cause howling under certain conditions, which can severely degrade sound quality and limit the maximum allowable amplification of the PA systems. The general adaptive feedback cancellation (AFC) algorithms lead to a biased estimation because of the correlation between source and feedback signals. In order to solve the problem and achieve both fast convergence and low steady-state misalignment, a novel variable step-size improved proportionate affine projection algorithm (IPAPA) based on mixed error cost function with prediction error method (PEM) and howling suppression is proposed in this paper. The experimental results demonstrate the effectiveness of the proposed algorithm.

Steady-State and Tracking Analyses of the Improved Proportionate Affine Projection Algorithm

The performance analysis of the improved proportionate affine projection (IPAP) algorithm is performed in this brief. Based on the energy-conservation arguments, the steady-state analysis of the IPAP algorithm is performed, which provides a general expression of steady-state excess mean-square error for the proportionate-type affine projection algorithms. The tracking behavior is also studied and the step size that optimizes the tracking performance is provided. Simulation results confirm the accuracy of the proposed expressions under different operating scenarios.

Mixed Norm Constrained Sparse APA Algorithm for Satellite and Network Echo Channel Estimation

We propose an improved proportionate affine projection algorithm (PAPA), which is realized by integrating a hybrid-norm constraint into the affine projection algorithm (APA) to estimate the cluster sparse signals that are happened in satellite and network echo channels. The proposed algorithm optimizes the hybrid ${l_{2,0}}$ -norm of the filter coefficients, and the PAPA is a special case of the proposed method. Moreover, an enhanced PAPA is modified for the cluster sparse channel estimations. Various simulation experiments are performed to verify that the proposed algorithms are superior to the APA, PAPA, and related sparse algorithms with different input signals and various parameters.

An efficient implementation of the Memory Improved Proportionate Affine Projection Algorithm

In this paper, an efficient implementation of the Memory Improved Proportionate Affine Projection Algorithm (MIPAPA) is developed, for the adaptive identification of linear in parameters’ systems. The proposed algorithm offers an exact implementation of the MIPAPA scheme, while maintaining the computational burden at an affordable level. The performance of the proposed approach is illustrated by means of computer simulation, in the context of adaptive identification of sparse systems. Moreover, it is shown that the proposed algorithm can be applied for the efficient estimation of the communication channel in the transmission over optical fiber links using intensity modulated direct detection ON/OFF signaling.

A Set-membership Approach to Improved Proportionate Affine Projection Algorithm

A Set-membership Approach to Improved Proportionate Affine Projection Algorithm