# \(\ell _1\)-regularized recursive total least squares based sparse system identification for the error-in-variables

- Jun-seok Lim
^{1}Email author and - Hee-Suk Pang
^{1}

**Received: **21 September 2015

**Accepted: **22 August 2016

**Published: **31 August 2016

## Abstract

In this paper an \(\ell _1\)-regularized recursive total least squares (RTLS) algorithm is considered for the sparse system identification. Although recursive least squares (RLS) has been successfully applied in sparse system identification, the estimation performance in RLS based algorithms becomes worse, when both input and output are contaminated by noise (the error-in-variables problem). We proposed an algorithm to handle the error-in-variables problem. The proposed \(\ell _1\)-RTLS algorithm is an RLS like iteration using the \(\ell _1\) regularization. The proposed algorithm not only gives excellent performance but also reduces the required complexity through the effective inversion matrix handling. Simulations demonstrate the superiority of the proposed \(\ell _1\)-regularized RTLS for the sparse system identification setting.

### Keywords

Adaptive filter TLS RLS Convex regularization Sparsity \(\ell\)1-norm## Background

There has been a recent interest in adaptive algorithms to handle sparsity in various signals and systems (Gu et al. 2009; Chen et al. 2009; Babadi et al. 2010; Angelosante et al. 2010; Eksioglu 2011; Eksioglu and Tanc 2011; Kalouptsidis et al. 2011). The idea is to exploit a priori knowledge about sparsity in a signal that needs to be processed for system identification. Several algorithms based on the least-mean square (LMS) (Gu et al. 2009; Chen et al. 2009) and the recursive least squares (RLS) (Babadi et al. 2010; Angelosante et al. 2010; Eksioglu 2011; Eksioglu and Tanc 2011) techniques have been reported with different penalty or shrinkage functions. In a broad range of signal processing applications, not only the system output is corrupted by measurement noise, but also the measured input signal may often be corrupted by the additive noise due to such as sampling error, quantization error and wide-band channel noise. However, the algorithms for sparsity can handle only the corrupted output case. It is necessary to derive an algorithm to handle a noisy case of both noisy input and noisy output (i.e. the error-in-variables problem).

One of the potential counterparts to handle the error-in-variables problem is the total-least-squares estimator (TLS) that seeks to minimize the sum of squares of residuals on all of the variables in the equation instead of minimizing the sum of squares of residuals on only the response variable. Golub and Van Loan introduced the TLS problem to the field of numerical analysis (Golub and Loan 1980); consequently, other researchers have developed and analyzed adaptive algorithms that employ the TLS formulation and its extensions (Dunne and Williamson 2000, 2004; Feng et al. 1998; Arablouei et al. 2014; Davila 1994; Arablouei et al. 2015). Recursive based algorithms were also studied along with adaptive TLS estimators (Soijer 2004; Choi et al. 2005). The algorithms were denoted as recursive total least squares (RTLS) or sequential total least squares (STLS). These algorithms recursively calculate and track the eigenvector corresponding to the minimum eigenvalue (the TLS solution) from the inverse covariance matrix of the augmented sample matrix. Some TLS based algorithms were proposed for the sparse signal processing (Tanc 2015; Arablouei 2016; Zhu et al. 2011; Dumitrescu 2013; Lim and Pang 2016). The algorithms in Tanc (2015), Arablouei (2016) utilized the gradient based method. The algorithms in Zhu et al. (2011), Dumitrescu (2013) were based on the block coordinate descent method. In Lim and Pang (2016), the TLS method was applied to handle the group sparsity problem.

In this paper, we consider the \(\ell _1\) regularization for the RTLS cost function, in which the recursive procedure is derived from the generalized eigendecomposition method in Davila (1994) and Choi et al. (2005), and the regularization approach outlined in Eksioglu and Tanc (2011) is used in order to handle the sparsity. We develop the update algorithm for the \(\ell _1\)-regularized RTLS using results from subgradient calculus. As a result, we propose the algorithm superior to the algorithm of Eksioglu and Tanc (2011) in the error-in-variables. We also reduce the total complexity by utilizing the inverse matrix update effectively. The proposed algorithm improves the sparse system estimation performance in the error-in-variables with only a little additional complexity. We provide simulation results to examine the performance of the proposed algorithm in comparison with the algorithm of Eksioglu and Tanc (2011).

## Sparse system identification problem

*k*, performs filtering and obtains the output \(y(k) = {\mathbf{x}}^T(k){\mathbf{w}}_o (k)\), where \({\mathbf{w}}_o (k) = [w_1 (k), \ldots ,w_M (k)]^T\) is an M–length finite-impulse-response (FIR) system that represents the actual system. For system identification, an adaptive filter with M coefficients \({\hat{\mathbf{w}}}(k)\) is employed in such a way that observes \({\mathbf{x}}(k)\) and produces an estimate \(\hat{y}(k) = {\mathbf{x}}^T(k){{\hat{\mathbf{w}}}}(k)\). The system identification scheme then compares the output of the actual system

*y*(

*k*) and the adaptive filter \(\hat{y}(k)\), resulting in an error signal \(e(k) = y(k) + n(k) - \hat{y}(k) = \tilde{y}(k) - \hat{y}(k)\), where

*n*(

*k*) is the measurement noise. In this context, the goal of an adaptive algorithm is to identify the system by minimizing the cost function defined by

Especially, we call *M*-th order \({\mathbf{w}}_o (k)\) sparse system, when the number of nonzero coefficients \(K \ll M\). In order to estimate the *M*-th order sparse system, most estimation algorithms exploit non-zero coefficients of the system to obtain performance benefits and/or a computational complexity reduction (Gu et al. 2009; Chen et al. 2009; Babadi et al. 2010; Angelosante et al. 2010; Eksioglu 2011; Eksioglu and Tanc 2011).

## \(\ell _1\)-regularized RTLS (recursive total least squares)

*k*-th time step. The subgradient of \(f({{\tilde{\mathbf{w}}}})\) is \(\nabla ^s\left\| {{\tilde{\mathbf{w}}}} \right\| _1 = \mathrm{sgn}({{\tilde{\mathbf{w}}}})\) (Babadi et al. 2010; Kalouptsidis et al. 2011), and sgn (\(\cdot\)) is the component-wise sign function. In (11), the regularized parameter,\(\gamma _k\), is time-varying, which governs a tradeoff between the approximation error and the penalty function. From (11), we obtain

## A simplified way to solve \(\ell _1\)-regularized RTLS

## Simulation results

In this experiment, we follow the experiment scenario in Eksioglu and Tanc (2011). The true system function \({\mathbf{w}}\) has a total of N = 64 taps, where only S of them are nonzero. The nonzero coefficients are positioned randomly and take their values from an \(N(0,1/\mathrm{S})\) distribution. The input signal is \(x_k \sim N(0,1)\). Noise is added to both the input and the output, and the additive input and output noises in this paper are \(n_{in,k} \sim N(0,\sigma _{in}^2 )\) and \(n_{out,k} \sim N(0,\sigma _{out}^2 )\), respectively. These additional noises are necessary to experiment the errors-in-variables problem. The proposed \(\ell _1\)-RTLS algorithm is realized with the automatic \(\gamma _k\) using (17). The \(\rho\) value in (17) is taken to be the true value of \(f({\mathbf{w}})\) as in Eksioglu and Tanc (2011), that is \(\rho = \left\| {{\mathbf{w}}_o } \right\| _1\) for the \(\ell _1\)-RTLS. We also compare the ordinary RLS and the \(\ell _1\)-RLS of Eksioglu and Tanc (2011) with the proposed \(\ell _1\)-RTLS.

Comparison of MSD in case of noisy input and noisy output

Forgetting factor | Algorithm | S = 4 (dB) | S = 8 (dB) | S = 16 (dB) | S = 64 (dB) |
---|---|---|---|---|---|

0.999 | RLS | −34.0 | −33.5 | −33.8 | −33.7 |

\(\ell _1\)-RLS | −34.8 | −33.6 | −33.5 | −33.7 | |

\(\ell _1\)-RTLS | −35.4 | −35.0 | −34.9 | −34.8 | |

(\(\ell _1\)-RLS)-(\(\ell _1\)-RTLS) | 0.6 | 1.4 | 1.4 | 1.1 | |

0.9995 | RLS | −35.7 | −35.9 | −36.1 | −35.9 |

\(\ell _1\)-RLS | −36.0 | −35.8 | −35.8 | −35.9 | |

\(\ell _1\)-RTLS | −38.1 | −38.0 | −38.0 | −38.0 | |

(\(\ell _1\)-RLS)-(\(\ell _1\)-RTLS) | 2.1 | 2.2 | 2.2 | 2.1 | |

0.9999 | RLS | −39.0 | −39.3 | −38.6 | −38.6 |

\(\ell _1\)-RLS | −38.9 | −39.1 | −38.5 | −38.6 | |

\(\ell _1\)-RTLS | −44.0 | −44.1 | −43.6 | −43.6 | |

(\(\ell _1\)-RLS)-(\(\ell _1\)-RTLS) | 5.1 | 5.0 | 5.1 | 5.0 | |

1 | RLS | −39.0 | −38.5 | −38.6 | −38.9 |

\(\ell _1\)-RLS | −39.0 | −38.5 | −38.6 | −38.9 | |

\(\ell _1\)-RTLS | −45.0 | −44.6 | −44.8 | −44.7 | |

(\(\ell _1\)-RLS)-(\(\ell _1\)-RTLS) | 6.0 | 6.1 | 6.2 | 5.8 |

## Conclusion

In this paper, we propose an \(\ell _1\)-regularized RTLS for sparse system identification. The proposed algorithm keeps good performance in case of both noisy input and noisy output. We develop the recursive procedure for total least squares solution with an \(\ell _1\)-regularized cost function. We also present a simplified solution requiring only a little additional complexity in order to integrate the regularization factor. Simulations show that the introduced \(\ell _1\)-regularized RTLS algorithm shows better performance than RLS and \(\ell _1\)-regularized RLS in the sparse system with noisy input and noisy output.

## Declarations

### Authors' contributions

This paper considers the sparse system identification. Especially, authors contribute to propose a new recursive sparse system identification algorithm, when both input and output are contaminated by noise. The algorithm not only gives excellent performance but also reduces the required complexity. Both authors read and approved the final manuscript.

### Acknowledgements

This paper was supported by Agency for Defense Development (ADD) in Korean (15-106-104-027).

### Competing interests

The authors declare that they have no competing interests.

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## Authors’ Affiliations

## References

- Angelosante D, Bazerque J, Giannakis G (2010) Online adaptive estimation of sparse signals: where RLS meets the l1-norm. IEEE Trans Signal Process 58:3436–3447View ArticleGoogle Scholar
- Arablouei R, Werner S, Dogancay K (2014) Analysis of the gradient-decent total least-squares algorithm. IEEE Trans Signal Process 62:1256–1264View ArticleGoogle Scholar
- Arablouei R, Dogancay K, Werner S (2015) Recursive total least-squares algorithm based on inverse power method and dichotomous coordinate-descent iterations. IEEE Trans Signal Process 63:1941–1949 View ArticleGoogle Scholar
- Arablouei R (2016) Fast reconstruction algorithm for perturbed compressive sensing based on total least-squares and proximal splitting. Signal Process. doi:https://doi.org/10.1016/j.sigpro.2016.06.009 Google Scholar
- Babadi B, Kalouptsidis N, Tarokh V (2010) SPARLS: the sparse RLS algorithm. IEEE Trans Signal Process 58:4013–4025View ArticleGoogle Scholar
- Chen Y, Gu Y, Hero A (2009) Sparse LMS for system identification. In: Paper presented at IEEE international conference on acoustics, speech and signal processing, Taiwan, 19–24 April, 2009Google Scholar
- Choi N, Lim J, Sung K (2005) An efficient recursive total least squares algorithm for training multilayer feedforward neural networks. LNCS 3496:558–565Google Scholar
- Davila C (1994) An efficient recursive total least squares algorithm for FIR adaptive filtering. IEEE Trans Signal Process 42:268–280View ArticleGoogle Scholar
- Dumitrescu B (2013) Sparse total least squares: analysis and greedy algorithms. Linear Algebra Appl 438:2661–2674View ArticleGoogle Scholar
- Dunne B, Williamson G (2000) Stable simplified gradient algorithms for total least squares filtering. In: Paper presented at the 32nd annual asilomar conference on signals, systems, and computers, Pacific Grove, Oct 29–Nov 1 2000Google Scholar
- Dunne B, Williamson G (2004) Analysis of gradient algorithms for TLS-based adaptive IIR filters. IEEE Trans Signal Process 52:3345–3356View ArticleGoogle Scholar
- Eksioglu E (2011) Sparsity regularized RLS adaptive filtering. IET Signal Process 5:480–487View ArticleGoogle Scholar
- Eksioglu E, Tanc A (2011) RLS algorithm with convex regularization. IEEE Signal Process Lett 18:470–473View ArticleGoogle Scholar
- Feng D, Bao Z, Jiao L (1998) Total least mean squares algorithm. IEEE Trans Signal Process 46:2122–2130View ArticleGoogle Scholar
- Golub G, Van Loan C (1980) An analysis of the total least squares problem. SIAM J Numer Anal 17:883–893View ArticleGoogle Scholar
- Gu Y, Jin J, Mei S (2009) Norm constraint LMS algorithm for sparse system identification. IEEE Signal Process Lett 16:774–777View ArticleGoogle Scholar
- Kalouptsidis N, Mileounis G, Babadi B, Tarokh V (2011) Adaptive algorithms for sparse system identification. Signal Process 91:1910–1919View ArticleGoogle Scholar
- Lim J, Pang H (2016) Mixed norm regularized recursive total least squares for group sparse system identification. Int J Adapt Control Signal Process 30:664–673View ArticleGoogle Scholar
- Moon T, Stirling W (2000) Mathematical methods and algorithm for signal processing. Prentice Hall, New JerseyGoogle Scholar
- Soijer MW (2004) Sequential computation of total least-squares parameter estimates. J Guidance 27:501–503View ArticleGoogle Scholar
- Tanc A (2015) Sparsity regularized recursive total least-squares. Digital Signal Process 40:176–180View ArticleGoogle Scholar
- Zhu H, Leus G, Giannakis G (2011) Sparsity-cognizant total least-squares for perturbed compressive sampling. IEEE Trans Signal Process 59:2002–2016View ArticleGoogle Scholar