In this paper, we introduce a new robust 1-bit compressive sensing reconstruction algorithm in the noisy environment, where a lot of signs are flipped. By calculating the number of inconsistencies, this algorithm can adaptively detect the positions where sign flips occur. Subsequently, the sparse signals can be recovered by using "corrected" measurements without a priori knowledge of the noise level. Numerical experiments show the high performance of signal recovery for the proposed algorithm in the presence of both measurement noise and transmission noise.

Hidden or partially observable degradation state of the equipment is frequently encountered in many engineering practices. This may encourage the state space modeling technique as a feasible way to estimate equipment's remaining useful life (RUL). However, most of the existing state space models falling into this category are based on the assumptions that the degradation process is linear or can be linearized. Therefore, modeling the hidden degradation process under a general nonlinear function and deriving the corresponding analytical form of the RUL distribution are still challenging and have not been well solved in literature. In this paper, we present a state-space-based prognostic model to address the above issues, in which the nonlinearity is characterized by an age-dependent general nonlinear function. Specifically, we model the degradation process as the unobservable nonlinear drift-based Brownian motion (BM) and apply extended Kalman filter (EKF) and expectation-maximization (EM) algorithm to estimate and update the degradation state and the unknown parameters of the established model jointly. Furthermore, we derive the analytical form of the RUL distribution approximately which incorporates the uncertainty of the estimation for hidden state and can be real-time updated based on the available observations. For verifying our approach, a numerical example and a case study for a NASA battery are provided, and the results show that both the parameters and the RUL are estimated accurately. We also consider several different nonlinear functions and compare them with the linear model. The comparative results demonstrate our approach is better than the results in the linear case.

In this letter, a stable and orthonormal version of the OJA algorithm (SOOJA) is investigated for principal and minor subspace extraction and tracking. The new algorithm presented here guarantees the orthonormality of the weight matrix at each iteration through a novel orthonormalization method. Moreover, it obtains both a high numerical stability and a low computational complexity. The superiority of the proposed algorithm to some existing subspace tracking algorithms is demonstrated using a classical example. Simulation results confirm the veracity of the subspace tracking algorithm advocated.

A new algorithm for estimating the frequencies of the two- dimensional (2-D) exponentials is presented. Via joint diagonalization of a set of matrices, the estimation of the automatically paired 2-D frequency parameters are obtained. Hence the proposed algorithm can avoid the computationally expensive 2-D parameters pairing procedure. Simulation results validate the high performance of the new algorithm.

A new blind channel estimator for multi-carrier PSK transmission is developed. By exploiting the structure of PSK signals, the proposed channel estimator can identify non-minumum phase channel using only the power spectrum of received signals. Unlike existing subspace-based channel estimators, our scheme is based on symbol-block-by-symbol-block approach, and, thus, the channel tracking ability is improved. Simulation results demonstrate that the proposed blind receiver exhibits satisfactory performance.

A totally blind ZP-OFDM receiver is derived in this letter. Unlike existing blind receivers that are based on the scheme that channels are estimated first and then equalizers (MMSE or ZF) are adopted to detect information symbols, our scheme is based on the symbol-block-by-symbol-block direct symbol estimation approach. The proposed receiver is also robust in rapid channel variation cases as long as channel responses remain almost constant during one symbol block. Simulation results show that the BER performance of our scheme and linear MMSE (and ZF) equalizers with perfect CSI are indistinguishable in SNR scenarios of interests, despite the fact that our scheme is totally blind.

The subspaces of FM m let transform are investigated. It is shown that some of the existing transforms like the Fourier transform, short-time Fourier transform, Gabor transform, wavelet transform, chirplet transform, the mean of signal, and the FM −1 let transform, and the butterfly subspace are all special cases of FM m let transform. Therefore the FM m let transform is more flexible for delineating both the linear and nonlinear time-varying structures of a signal.

Wigner-Ville distribution (WVD) is recognized as being a powerful tool and a nucleus in time-frequency representation (TFR) which gives an excellent time-frequency concentration, and more importantly, has many desirable properties. A major shortcoming of WVD is the inherent cross-term (CT) interference. Although solutions to this problem from the bulk of contributions to the literature concerning TFR are currently available, none has been able to completely eliminate the CT’s in WVD. It is therefore a common belief that if there exists an auxiliary time-frequency distribution (TFD) which has the same auto-terms (AT’s) as that in WVD, but has CT’s with the opposite sign, then, by adding the auxiliary TFD to WVD, an ideal TFD, which preserves the concentration of WVD while annihilating the CT’s, is readily obtained. However, we prove that the auxiliary TFD does not exist. Moreover, it is found that in general, CT free joint distributions with their concentrations close to that of WVD do not exist either.

We propose a new atom, namely, the dilated and translated windowed exponential frequency modulated functions (FM/sup m/let) for compactly characterizing both the signal's time-invariant and time-varying spectral contents. The superiority of the proposed method to some existing time-frequency distributions (TFDs) is demonstrated using a bat sonar signal.