Conventional RLS Algorithm Research Articles

An Adaptive Algorithm for Calculating Crosstalk Error for Blind Source Separation

The crosstalk error is widely used to evaluate the performance of blind source separation. However, it needs to know the global separation matrix in advance, and it is not robust. In order to solve these problems, a new adaptive algorithm for calculating crosstalk error is presented, which calculates the crosstalk error by a cost function of least squares criterion, and the robustness of the crosstalk error is improved by introducing the position information of the maximum value in the global separation matrix. Finally, the method is compared with the conventional RLS algorithms in terms of performance, robustness and convergence rate. Furthermore, its validity is verified by simulation experiments and real world signals experiments.

Performance Analysis of MSFRLS-VFF Based Real-Time Adaptive Noise Canceller with RLS and APA Algorithms Using TMS320C6713 Processor

In this paper, the real-time adaptive noise canceller (ANC) system is implemented using modified sigmoid function variation based recursive least square with variable forgetting factor (MSFRLS-VFF) algorithm. The experiment is performed on DSP TMS320C6713. The performance of MSFRLS-VFF algorithm is evaluated and comparison is made with conventional RLS and affine projection algorithm (APA). In the experimental setup, the different types of noises are artificially added into the clean signals to make the signal noisy at different input SNR levels (− 5 dB to 10 dB). The result shows that MSFRLS-VFF algorithm provides superior performance than RLS and APA algorithm (the best performance achieved for SNR improvement is 2.2 dB over RLS and 2.1 dB over APA at − 5 dB input SNR with filter order 10). Also, the MSFRLS-VFF algorithm provides minimum mean square error than RLS and APA algorithms, however computational complexity of APA algorithm is less as compared to MSFRLS-VFF and RLS algorithms. The output error-free signal obtained after MATLAB simulation and from TMS320C6713 DSP shows the similar result that proves the correctness of the setup.

Efficient System Tracking With Decomposable Graph-Structured Inputs and Application to Adaptive Equalization With Cyclostationary Inputs

This paper introduces the graph-structured recursive least squares (GS-RLS) algorithm, which is a very efficient means to track a linear time-varying system when the inputs to the system have structure that can be modeled using a decomposable Gaussian graphical model. For graphs with small clique sizes, it is shown that GS-RLS can achieve tracking performance very close to that of the conventional RLS algorithm for a fraction of the computational cost. In particular, after proving that the outputs of wide-sense stationary time-varying communication channels have graphical model structure if the inputs are cyclostationary, significant computational gains are realized for adaptive equalization of the time-varying underwater acoustic communication channel using the GS-RLS algorithm. This is verified using field data from the SPACE08 underwater acoustic communication experiment.

QR-based incremental minimum-Wilcoxon-norm strategies for distributed wireless sensor networks

When outliers are present in the desired data, the conventional distributed adaptive estimation algorithms exhibit poor performance. To alleviate this shortcoming a novel distributed robust incremental strategy based on QR decomposition and the Wilcoxon score is proposed which convergence speed is faster than the other previous techniques. To demonstrate the potential of this algorithm simulation study is carried out for the distributed estimation of parameters in the presence of weak to strong outliers in the desired data. The results show that the performance of the new algorithm is robust against outliers compared to conventional incremental RLS algorithm. Further to achieve low communication overhead, a new scheme is introduced and its performance has been assessed through simulation study. It is observed that the proposed scheme even though exhibits slightly inferior performance but offers substantially reduction in terms of communication overhead.

$L_1$-Regularized STAP Algorithms With a Generalized Sidelobe Canceler Architecture for Airborne Radar

In this paper, we propose novel l1-regularized space-time adaptive processing (STAP) algorithms with a generalized sidelobe canceler architecture for airborne radar applications. The proposed methods suppose that a number of samples at the output of the blocking process are not needed for sidelobe canceling, which leads to the sparsity of the STAP filter weight vector. The core idea is to impose a sparse regularization (l1-norm type) to the minimum variance criterion. By solving this optimization problem, an l1-regularized recursive least squares (l1-based RLS) adaptive algorithm is developed. We also discuss the SINR steady-state performance and the penalty parameter setting of the proposed algorithm. To adaptively set the penalty parameter, two switched schemes are proposed for l1-based RLS algorithms. The computational complexity analysis shows that the proposed algorithms have the same complexity level as the conventional RLS algorithm (O((NM)2)), where NM is the filter weight vector length), but a significantly lower complexity level than the loaded sample covariance matrix inversion algorithm (O((NM)3)) and the compressive sensing STAP algorithm (O((NsNd)3), where N8Nd >; NM is the angle-Doppler plane size). The simulation results show that the proposed STAP algorithms converge rapidly and provide a SINR improvement using a small number of snapshots.

Modified RLS algorithm with enhanced tracking capability for MIMO channel estimation

A new RLS adaptive filtering algorithm has been developed by enforcing a constraint on the a posteriori error of the conventional exponentially-weighted RLS algorithm and modifying its coefficient update equation. The motivation behind the new algorithm is to attain improved tracking capability when the target system features fast variations in time. The proposed algorithm requires only slightly more computations than the conventional RLS algorithm and is specifically suitable for estimation and tracking of fast-fading MIMO channels. Simulations demonstrate that a MIMO channel estimator utilising the proposed algorithm outperforms those using the conventional RLS algorithm, the set-membership-based BEACON algorithm and the extended RLS algorithm without requiring prior knowledge of the Doppler shift.

Clipped Input RLS Applied to Vehicle Tracking

A new variation to the RLS algorithm is presented. In the clipped RLS algorithm (CRLS), proposed in updating the filter weights and computation of the inverse correlation matrix, the input signal is quantized into three levels. The convergence of the CRLS algorithm to the optimum Wiener weights is proved. The computational complexity and signal estimation error is lower than that of the RLS algorithm. The CRLS algorithm is used in the estimation of a noisy chirp signal and in vehicles tracking. Simulation results in chirp signal detection shows that this algorithm yields considerable error reduction and less computation time in comparison to the conventional RLS algorithm. In the presence of strong noise, also using the proposed algorithm in tracking of 59 vehicles shows an average of 3.06% reduction in prediction error variance relative to conventional RLS algorithm.

On-line adaptive parameter estimator tuning

This paper discusses a systematic technique for tuning a parameter estimator. The estimator used in this work is a discrete version of a projection algorithm estimator. Although the development is explained for linear models, the method is equally applicable to any type of linear or non-linear model that is linear with respect to the estimated parameters. The estimation scheme includes a first-order filter in the error dynamics equation and individual parameter adaptation equations. A simple analytical formula was derived for on-line tuning of the filter gain based on the maximisation of the decay of the norm of the output and parameter errors. The tuning of the parameter adaptation gains is also done based on the maximisation of the decay of this norm. The measurement noise problem was addressed using a specific dead zone approach. The algorithm performance is compared to the conventional RLS algorithm.

Noncoherent space-time equalization

In this paper, noncoherent equalization is combined with multiple receive antennas. The resulting noncoherent space-time equalization (NSTE) schemes are analyzed and compared with the corresponding coherent receivers. In particular, noncoherent linear equalization (NLE), noncoherent decision-feedback equalization (NDFE), and noncoherent sequence estimation (NSE) are considered. For NLE and NDFE novel approximations for the signal-to-distortion ratio (SDR) are derived and verified by simulations. It is shown that NSTE can suppress interfering users and exploit diversity as efficiently as coherent STE. However, NSTE is more robust against channel phase variations than the combination of coherent STE and synchronization. Robust noncoherent recursive least squares (NC-RLS) algorithms, which compare favorably with the conventional RLS algorithm with additional carrier synchronization loop, are proposed for fast filter adaptation.

Multistage group-blind adaptive multiuser detector for reverse link in direct-sequence CDMA systems

We develop a novel multistage group-blind multiuser detection technique for reverse link in multi-cell direct sequence (DS) code-division multiple access (CDMA) systems. This approach fully utilizes all known users' information, performs interference cancellation (IC) to suppress the interferers within the cell, and exploits blind adaptive decision-directed (DD) MMSE detector to suppress the inter-cell interference. This process can be made iterative and in the multistage process, the tentative decision bits from DD-MMSE detectors can be fed back to the IC part to make interference regeneration more accurate. A simplified RLS (SRLS) algorithm is derived particularly for DD-MMSE detectors and steady-state SIR performance of the SRLS algorithm is analyzed. It is shown that the proposed detector with SRLS algorithm is sufficient to perform accurate multiuser detection and has significant performance gain over that of the pure blind detectors and the non-blind detectors for the given scenario. Moreover, compared with the conventional RLS algorithm, the SRLS algorithm has lower computational complexity and the convergence speed is faster. We also develop two group-blind channel estimation methods to estimate the effective signature waveforms in multipath fading channel. It is seen that the group-blind channel estimation methods using updated received signal correlation matrix can effectively combat the mismatch problem faced by pure blind detectors.

A 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 the 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.

Split Recursive Least-Squares: algorithms, architectures, and applications

In this paper, a new computationally efficient algorithm for adaptive filtering is presented. The proposed Split Recursive Least-Squares (Split RLS) algorithm can perform the approximated RLS with O(N) complexity for signals having no special data structure to be exploited (e.g., the signals in multichannel adaptive filtering applications, which are not shifts of a single-channel signal data), while avoiding the high computational complexity (O(N/sup 2/)) required in the conventional RLS algorithms. Our performance analysis shows that the estimation bias will be small when the input data are less correlated. We also show that for highly correlated data, the orthogonal preprocessing scheme can be used to improve the performance of the Split RLS. Furthermore, the systolic implementation of our algorithm based on the QR-decomposition RLS (QRD-RLS) array as well as its application to multidimensional adaptive filtering is also discussed. The hardware complexity for the resulting array is only O(N) and the system latency can be reduced to O(log/sub 2/ N). The simulation results show that the Split RLS outperforms the conventional RLS in the application of image restoration. A major advantage of the Split RLS is its superior tracking capability over the conventional RLS under nonstationary environments.

Tracking capability and floating‐point error analysis in multirate complex recursive weighted least squares algorithm

AbstractThis paper presents an analysis of a multirate complex recursive weighted least squares (MC‐RLS) algorithm based on an analytic signal. Conventional adaptive filters for multiple sinusoid extraction have been based on lattice filter structures or gradient methods because of their low computational cost and/or low sensitivity to quantization errors. On the other hand, the RLS algorithm is easy to introduce assuming time‐variant quantities in the algorithm when sinusoid frequencies have the time‐varying property. However, the relationship between the tracking capability and the quantization error of the least‐squares (LS) algorithm in the transversal structure has not been reported. In addition, an improvement algorithm for these errors have not been reported. In this paper, we shall describe a new RLS algorithm in a transversal filter structure with a superior transient property, reduced sensitivity to quantization errors, and low computational cost. First, an analytic signal‐based autoregressive model is introduced and the MC‐RLS algorithm is shown. Then, using an excess mean‐square error of the MC‐RLS algorithm, the tracking capability shown to be unaffected by the analytic transform and the decimation. In addition, the floatingpoint error and the computational cost of the MC‐RLS algorithm are analyzed. It is shown that both floatingpoint error and computational cost are smaller than those of conventional RLS algorithms that use real signals.

Adaptive array beamforming based on an efficient technique

This paper presents a new processing technique for adaptive array beamforming. We first properly decompose the optimal weight vector associated with the original array beamformer into several adaptive weight subvectors. An adaptive algorithm based on the conventional RLS algorithm is derived to update the weight subvectors. The required computational complexity is also evaluated for the proposed technique. The convergence rate of the proposed technique is mainly affected by the size of the largest weight subvector. Faster convergence speed and less array sensitivity to the pointing errors can be achieved by using the proposed technique. Several simulation results are included for illustration and comparison.

An adaptive algorithm for stable decision-feedback filtering

A method of decision-feedback filtering that is particularly well suited for data that have undergone spectral nulling is introduced. The lack of spectral content causes a large eigenvalue spread of the data correlation matrix, with deleterious effects on the performance of conventional adaptive algorithms. An adaptive decision-feedback equalizer update algorithm that does not have these undesirable properties is presented. The algorithm calculates an iterative regularized inverse solution for the decision-feedback equalizer coefficients and applies appropriate attenuation (regularization) at frequencies lacking signal energy. This automatic estimation of the significant signal components provides an improvement over other methods that use matrix arithmetic to perform the same task. The other features of the algorithm are linear computational complexity and fast tracking capability. Simulation results that compare the algorithm to the conventional LMS and RLS algorithms are presented. >