Search

This paper presents a methodology to detect non-stationary frequency modulated signals under noise process uncertainty. The proposed method uses the combination of adaptive directional time–frequency distribution and modified Viterbi algorithm to robustly estimate the instantaneous frequency (IF) of the given signal. This IF information is then used to remove the frequency modulation from the given signal thus converting it into temporally correlated signal. This temporal correlation is then exploited to detect signals under noise power uncertainty. The effectiveness of the proposed detector is shown through numerical simulations. [ABSTRACT FROM AUTHOR]

This paper proposes a threshold-free method for grouping and selecting the singular spectrum analysis (SSA) components for performing the signal denoising via the empirical mode decomposition (EMD) approach. First, the total number of the groups of the SSA components is selected to be the same as the total number of the intrinsic mode functions (IMFs) of the signal. The SSA components are assigned to the group where the absolute correlation coefficient between the IMF and the SSA component is the highest. This grouping method is implemented using the matching pursuit algorithm. Then, the groups of the SSA components are selected based on the selection criterion used in an existing EMD denoising method. As the EMD denoising approach is a time-domain approach and the SSA components are represented in the transformed domain, our proposed method exploits both the time-domain and the transformed-domain information for performing the denoising. Computer numerical simulation results show that the signal-to-noise ratios of common practical signals denoised by our proposed method are higher than those denoised by the existing methods. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the problem of blind source separation (BSS). To solve the problem efficiently, a new algorithm is proposed. First, a parallel variable dual-matrix model (PVDMM) that considers all the numerical relations between a mixing matrix and a separating matrix is proposed. Different constrained terms are used to construct the cost function for every sub-algorithm. These constrained terms reflect a numerical relation. Therefore, a number of undesired solutions are excluded, the search region is reduced, and the convergence efficiency of the algorithm is ultimately improved. Parallel sub-algorithms are proven to converge to a separable matrix only if the cost function approaches zero. Second, a descent algorithm (DA) as a PVDMM optimizing approach is proposed in this paper as well. Apparently, the efficiency of the algorithm depends completely on the DA. Unlike the traditional descent method, the DA defines step length by solving inequality instead of merely utilizing the Wolfe- or Armijo-type search rule. Stimulation results indicate that the DA can improve computational efficiency. Under mild conditions, the DA has been proven to have strong convergence properties. Numerical results also show that the method is very efficient and robust. Finally, we applied the combinative algorithm to the BSS problem. Computer simulations illustrate its good performance. [ABSTRACT FROM AUTHOR]

Blindly separating the intercepted signals is a challenging problem in non-cooperative multiple input multiple output systems in association with space-time block code (STBC) where channel state information and coding matrix are unavailable. To our knowledge, there is no report on dealing with this problem in literature. In this paper, the STBC systems are represented with an independent component analysis (ICA) model by merging the channel and coding matrices as virtual channel matrix. Analysis shows that the source signals are of group-wise independence and the condition of mutual independence can not be satisfied for ordinary ICA algorithms when specific modulations are employed. A new multidimensional ICA algorithm is proposed to separate the intercepted signals in this case by jointly block-diagonalizing (JBD) the cumulant matrices. In this paper, JBD is achieved by a 2-step optimization algorithm and a contrast function is derived from the JBD criterion to remove the additional permutation ambiguity with explicit mathematical explanations. The convergence of the new method is guaranteed. Compared with the ICA-based channel estimation methods, simulations show that the new algorithm, which does not introduce additional ambiguities, achieves better performance with faster convergence in a non-cooperative scenario. [ABSTRACT FROM AUTHOR]

Distributed subarray antennas (DSAs), which are sparse arrays consisting of two or more far separated subarrays, have many advantages over uniform linear array, especially for direction of arrival (DOA) estimation. However, they are subject to manifold ambiguity, which has significant influence on DOA estimation. In order to solve the manifold ambiguity of uncorrelated sources for DSA, a novel method based on estimation of signal parameters via rotational invariance technique by utilizing optimal subarray partition is proposed in this paper. In the proposed method, an optimized reference estimation is obtained by the rotational invariance between the new subarrays with optimal partition of DSA. The high accuracy and unambiguous DOA estimations are then disambiguated easily according to the optimized reference estimation. In this way, the performance of disambiguated DOA estimation can be enhanced in cases of the low signal-to-noise ratio and the large spacing between the subarrays. Moreover, the ambiguity threshold effect of DOA estimation for DSA is analyzed by means of the maximum a posteriori estimator. Computer simulation results show good performance of the proposed method and the effectiveness of the ambiguity threshold for DSA. [ABSTRACT FROM AUTHOR]

A compressive radar cross section (RCS) measurement method is presented in this paper. This method relies on the theory of compressive sensing (CS). We first show that the RCS data have sparse expansions in some proper basis. According to the theory of CS, the full RCS data can be recovered from the partial measured data by convex optimization algorithms. Comparisons of the compressive measurement method and the traditional measurement method are demonstrated by means of numerical simulations as well as by real data measured in the outdoor range. [ABSTRACT FROM AUTHOR]

In a cognitive radio network, when primary user's (PU's) spectrum is periodically sensed, secondary user (SU) needs to wait during sensing interval, which causes interruption in SUs' transmission and significant reduction in achievable throughput. If continuous spectrum sensing is implemented, then a portion of a band is always unavailable for SUs' transmission, which also limits the throughput. In this paper, we propose two methods. With the first one, we achieve an increase in throughput, while SUs' transmission is uninterrupted, and in the second method, we let our algorithm dynamically choose time-band portion of a frame for spectrum sensing, causing minimal interference to PU than the one in first method and offering uninterrupted service to cognitive radio (CR) users. Simulation results show that achievable throughput is improved significantly in both methods. It also showed that the methods offer normalized throughput between 4.7 and 5.3 bits/s/Hz over a wider range of sensing band from 0.75 to 6 MHz, when PU's active phase probability is 0.2, whereas delay-oriented continuous spectrum sensing (DOCSS) scheme achieves this range within a narrower sensing band-especially at the optimal value. Results also showed that for an arbitrary sensing band, normalized throughput achieved by our methods is larger than the one achieved by DOCSS scheme. [ABSTRACT FROM AUTHOR]

This paper aims at the PID control system design for multivariable input and multivariable output (MIMO) processes. An improved version of a particle swarm optimization (PSO) algorithm is utilized to design PID control gains in MIMO control systems. In addition to the individual best and the global best particles, the velocity updating formula of the developed algorithm includes a new factor, the best particle of each sub-population, to enhance the search capacity. Based on the improved particle swarm optimization (IPSO), a complete design strategy is proposed for MIMO PID control systems. All control gains will be evolved to the optimal values by minimizing the system performance criterion. To show the efficiency of the proposed design method, a multivariable chemical process system with two inputs and two outputs is illustrated. Some experiment results, including different algorithm parameter settings and comparisons with other methods, are given. Numerical simulations indicate that the proposed method is superior to other optimal methods. [ABSTRACT FROM AUTHOR]

Binary sparse measurement matrices are widely used in compressed sensing (CS) due to their low computational complexity. However, binary sparse measurement matrices perform well in CS-based binary signal recovery only when the source signals are very sparse (e.g., k/ n=0.1, where k is the sparsity of the source signal, n is the length of the source signal). In this paper, we propose to construct a non-binary sparse measurement matrix to recover binary source signals which are not so sparse (e.g., k/ n=0.2) accurately with few measurements. The novel measurement matrix enables us to design a suboptimal and effective recovery algorithm by fully exploiting the structural features. Moreover, we analyze and estimate the un-recovery probability based on the tree structure to evaluate the recovery performance. The simulation results validate that non-binary sparse measurement matrices can be used to recover binary source signals which are not so sparse, the recovery performance of non-binary sparse measurement matrices is better than that of binary sparse measurement matrices in terms of the un-recovery probability. [ABSTRACT FROM AUTHOR]

Usually Acoustic Echo Cancellers (AECs) are realized by adaptive Finite duration Impulse Response (FIR) filter having large number of coefficients and Least Mean Square (LMS) as an adaptive algorithm resulting in slow convergence speed and poor tracking performance of these adaptive filters. In this paper, we have proposed a Multiple Sub-filter (MSF) parallel structure based on multipath acoustic echo model using the basis that each sub-filter will compensate the echo contributed by each path of multipath acoustic channel. To realize the MSF, modified Generalized Autocorrelation-based Estimator (MAE) has been used to estimate time delay associated with each path while the order of each sub-filter has been estimated using Power Spectral Density (PSD) method. Accuracy Percentage (AP) performance measure has been used to characterize the performance of the estimator. Simulation results show that the performance of the MAE improves with the increase in SNR and/or decrease in number of multipath. Using these estimates MSF based AEC is constructed. The convergence performance of MSF-based AEC has been studied, via computer simulation, and compared with the conventional Single Long length adaptive Filter (SLF)-based canceller for different SNRs and number of multipath. The results of MSF have been found to be very encouraging in almost all of the various situations considered. Subsequently, the tracking behavior has also been studied with variation in the channel parameters of the multipath model. The proposed MSF can track variations in the channel parameters of the multipath model faster as compared to the conventional echo canceller. [ABSTRACT FROM AUTHOR]

In this paper, the joint transceiver design issue is investigated for multi-cell systems with multiple mobile terminals (MTs). An optimization problem is formulated based on the criterion of mean squared error minimization for the joint design of the cooperative precoder of all multi-input multi-output (MIMO) cells and the cooperative decoder of all MIMO MTs. With a fixed decoder, the optimum precoder is first derived in a closed-form expression, where no exchange of data streams among all of the MIMO cells is required due to the limited capability of the backhaul. Subsequently, an optimum decoder is then obtained in an analytical expression with the fixed precoder. Based on these two expressions, an iterative algorithm with guaranteed convergence is finally developed to carry out the joint optimization of the cooperative precoders and decoders. Simulation results demonstrating the effectiveness of the proposed algorithm are also provided. [ABSTRACT FROM AUTHOR]

Due to the environmental noise, variance is uncertain and unknown. The energy-based detection (ED) technology has many shortcomings in cognitive radio. In the paper, an energy-autocorrelation detection (EAD) algorithm is proposed to overcome these challenges, taking advantage of the different characteristics of Gauss white noise and signal. Two statistics are structured based on energy and autocorrelation of samples. This spectrum sensing algorithm can lead to stable and accurate detection performance without any prior information on noise and signal. It is testified in the simulation that the energy-autocorrelation-based detection is much better than energy-based detection; moreover, the impact of some parameters of the algorithm is also simulated and discussed. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the recursive computation of the cost functions for the least-squares-type algorithms for multivariable linear regressive models. It is shown that the proposed recursive computation formulas for the cost functions can also be extended to the estimation algorithm of the multivariable equation error models, e.g., the controlled autoregressive models. The simulation results indicate that the proposed algorithm is effective. [ABSTRACT FROM AUTHOR]

For a multivariable controlled autoregressive system with autoregressive noises, its corresponding identification model contains a parameter matrix and a parameter vector. This paper presents the hierarchical gradient-based iterative (HGI) algorithm to interactively estimate the parameter matrix and the parameter vector by using the hierarchical identification principle and the gradient search. The simulation results show that the HGI algorithm is effective. [ABSTRACT FROM AUTHOR]

control-theoretic approach in designing Digital-to-Analogue Converters and Digital Amplifiers which leads to improved performance in Audio and Multimedia applications is presented in this paper. The design involves an over-sampling and a pulse modulation component which is driven by a pulse generation algorithm based on the characteristics of the output filter. The theoretical model results in a family of digital circuits whose operation is verified by computer simulations achieving a performance of Signal-to-Noise Ratio of 147 dB at a switching rate of 90 MHz. Implementation and hardware complexity issues are discussed based on a FPGA realization of the algorithm. [ABSTRACT FROM AUTHOR]