Sung Jun Ban

An enhanced positioning scheme for chirp spread spectrum ranging

In this paper, we propose an algorithm for acquiring enhanced position coordinates when using the chirp spread spectrum (CSS) specified in IEEE 802.15.4a. Because the measured distances are generally perturbed by measurement noise, the extended Kalman filter (EKF) can be applied to trilateration to treat the variation in the measured distances due to the noise. In practice, the average value of many measured distances obtained using the CSS is not equal to the true distance, because the noise in the measurement has a nonzero mean; that is, a nonzero average error exists. Because the EKF is only suitable for a case with zero mean noise, the average error needs to be eliminated in advance. For this purpose, we propose a minimization criterion that determines weighting parameters. The average error is reduced by multiplying measured distances by the weighting parameters. To verify the performance of the proposed method, we conduct experiments for the CSS-based positioning of given targets, and compare the results for the EKF with and without the proposed algorithm. From the results, we can find that the coordinates estimated by the EKF with the proposed algorithm are more accurate.

Fast communication: A variable step-size adaptive algorithm for direct frequency estimation

A frequency estimator based on the linear prediction property of sinusoidal signals has recently been proposed for the direct estimation of real tone frequency in white noise. We propose a variable step-size direct frequency estimator (VS-DFE), which shows improved performance on account of the use of a dynamically updated step-size instead of a fixed step-size. A comparison of the simulation results of the proposed estimator with those of conventional estimators clearly indicates its superior performance.

Adaptive Regularization Parameter for Pseudo Affine Projection Algorithm

An adaptive regularized affine projection (AR-AP) algorithm has recently been proposed in order to achieve good misalignment performance. However, the algorithm requires high computational complexity. To overcome this problem, we propose an adaptive regularized pseudo affine projection (AR-PAP) algorithm. Additionally, we derive the lower bound of the regularization parameter in order to guarantee the stability of the AR-PAP algorithm. Simulation results show that the performance of the proposed algorithm is better than that of the AR-AP algorithm despite its significantly lower complexity.

A derivation of the excess mean square error for affine projection algorithms using the condition number

This paper presents a numerical expression for the excess mean-square error (EMSE) of affine projection(AP) algorithms, and the expression is proportional to the data reuse factor and the condition number of the input data. For the condition number, a recently reported technique developed by using E-norm is adopted instead of L2-norm. The proposed expression offers an insight into the mechanics of the AP algorithm by including the condition number in the EMSE. Our simulation results show a relatively good match between the proposed expression and the practice.

Convex combination of direct frequency estimators

Direct frequency estimator (DFE) has recently been proposed for unbiased frequency estimation of a real tone in white noise. This paper proposes to improve the performance of the DFE using a convex combination of two DFEs. Each DFE is adapted independently to retain its performance advantage, and the estimation results are then adaptively combined for optimal performance. The convex combination scheme offers improved tracking and accuracy. Simulations show that the proposed estimator performs better than the conventional DFE.

A normalized LMF (XE-NLMF) algorithm with variable data-reusing

The normalized least-mean-fourth (XE-NLMF) algorithm has a faster convergence rate and lower misalignment performance than the normalized least-mean-squares (NLMS) algorithm in sub-Gaussian noise environments. However, the XE-NLMF algorithm shows convergence performance degradation in highly correlated input signals. To overcome the problem, we propose an XE-NLMF algorithm with variable data-reusing. Through computer simulations, we confirmed that the proposed algorithm has a better convergence performance than the conventional XE-NLMF algorithm for colored input signals.

Affine Projection Algorithm with Improved Data-Selective Method Using the Condition Number

Recently, a data-selective method has been proposed to achieve low misalignment in affine projection algorithm (APA) by keeping the condition number of an input data matrix small. We present an improved method, and a complexity reduction algorithm for the APA with the data-selective method. Experimental results show that the proposed algorithm has lower misalignment and a lower condition number for an input data matrix than both the conventional APA and the APA with the previous data-selective method.