A secondary EWMA-based dictionary learning algorithm for polynomial phase signal denoising.

Under the influence of additive white Gaussian noise, sparse representation cannot effectively remove noise associated with the polynomial phase signal (PPS) via most dictionary learning algorithms whose training data come from the noisy signal, such as K-SVD and RLS-DLA. In this paper, we present a novel dictionary learning algorithm based on secondary exponentially weighted moving average (SEWMA) to denoise PPS. In the proposed algorithm, we first estimate the signal-to-noise (SNR) of the PPS to set the optimal rate of a weighted decline using covariance matrix model. Second we use RLS-DLA to train the dictionary. Thirdly, SEWMA is used to refine atoms in the learned dictionary. In this way, the SNR of the reconstructed signal obtained using the proposed algorithm is clearly higher than that of other algorithms, whereas the mean squared error is lower than that of other algorithms. To obtain the optimal denoising performance, the optimal rate of a weighted decline is set based on the estimated SNR. Simulation results show that the proposed method outperforms the K-SVD, RLS-DLA in mean square error and the SNR.

The Time-of-Arrival Offset Estimation in Neural Network Atomic Denoising in Wireless Location.

With the increasing demand for wireless location services, it is of great interest to reduce the deployment cost of positioning systems. For this reason, indoor positioning based on WiFi has attracted great attention. Compared with the received signal strength indicator (RSSI), channel state information (CSI) captures the radio propagation environment more accurately. However, it is necessary to take signal bandwidth, interferences, noises, and other factors into account for accurate CSI-based positioning. In this paper, we propose a novel dictionary filtering method that uses the direct weight determination method of a neural network to denoise the dictionary and uses compressive sensing (CS) to extract the channel impulse response (CIR). A high-precision time-of-arrival (TOA) is then estimated by peak search. A median value filtering algorithm is used to locate target devices based on the time-difference-of-arrival (TDOA) technique. We demonstrate the superior performance of the proposed scheme experimentally, using data collected with a WiFi positioning testbed. Compared with the fingerprint location method, the proposed location method does not require a site survey in advance and therefore enables a fast system deployment.