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The fast finite-time output bipartite tracking of networked heterogeneous robotic systems with matrix-weighted digraphs, parametric uncertainties and external disturbances is studied in this article. Besides, solving the fast finite-time bipartite tracking problem in this paper implies that the system states are forced to reach the employed nonsingular finite-time sliding surface in a predefined time, which thus called fast finite-time control. To address the aforementioned issues, a fast finite time hierarchical control algorithm utilizing estimator methodologies are proposed. The Lyapunov stability theory is used to derive some sufficient requirements for performing output bipartite tracking in a fast finite time manner. To verify that the theoretical results are valid, numerical simulation examples are given. [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]

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]

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]

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]

Frequency-domain adaptive filter (FDAF) algorithms have been widely used in many fields in virtue of its fast convergence and low computational complexity. However, FDAF algorithms with fixed step-size cannot balance convergence rate and steady-state misadjustment. In this paper, we propose the switching step-size based FDAF (SSS-FDAF) algorithm that selects the optimal step-size at each iteration for the FDAF update by comparing the mean-square deviation (MSD) trends with different step-sizes, to obtain fast convergence and low steady-state misadjustment at the same time. Furthermore, a novel reset strategy is designed for guaranteeing the tracking capability of the proposed algorithm. Computer simulations of colored signals and real-world signals have demonstrated the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

The variational mode decomposition (VMD) and its variants aim to decompose a given signal into a set of narrow band modes. The analysis of these modes is usually based on the Fourier analysis. That is, the center frequencies of these modes are found without exploiting the local time varying information of the signal during the iteration in the existing algorithms for performing the VMD. To address this issue, this paper proposes a recursive windowed VMD (RWVMD) approach for performing the signal decomposition. First, the window is sliding across the signal. Then, the variational mode extraction is performed on each frame to obtain the first mode. Then, the difference between the first mode and the signal is computed to obtain the residual signal. The above process is repeated on the residual signal until the algorithm converges. The effectiveness of the RWVMD algorithm is demonstrated through the computer numerical simulations. It is found that the center frequency in the time frequency plane is more accurately matched with the characteristics of the original signal. [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]

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]

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]

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]

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, 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]

The short-time fractional Fourier transform (STFrFT) is beneficial for addressing non-stationary signals in many application settings. However, the STFrFT algorithm fails to obtain a high time–frequency (TF) concentration because of the uncertainty principle. To resolve these problems, we introduce a new algorithm that is referred to as the SSTFrFT, which is a combination of the synchroextracting transform and STFrFT. The main principle of this algorithm is to establish a synchroextracting operator based on the STFrFT and then to extract the TF coefficient of the ridgeline position in the TF distribution to improve the concentration. Using numerical simulations with two examples (linear frequency modulation signal and nonlinear frequency modulation signal), we illustrate how the algorithm can be useful in improving concentration. The instantaneous frequency estimation and energy distribution description are more accurate than traditional methods, such as the short-time Fourier transform, Wigner Ville distribution, synchrosqueezed transform, and STFrFT. Furthermore, we apply the algorithm to identify the frequency curve generated by the target's motion from Ice Multiparameter Imaging X-Band radar echo data from the sea clutter background. The test results of the SSTFrFT method that we developed can accurately distinguish moving targets and sea clutter, which suggest the possible utility of this approach for detection and motion characteristics of marine moving targets. [ABSTRACT FROM AUTHOR]

We propose a two-stage estimator to estimate chirp rate and initial frequency of the chirp signals in the presence of additive white Gaussian noise. In the first stage, the chirp rate estimation problem is reformulated as a single tone frequency estimation problem. Then, the frequency of single tone is estimated through a linear prediction approach. Using the chirp rate estimate in the first stage, we can convert the linear frequency modulated signal to a single tone. Similar to the first stage, the initial frequency is estimated via the linear prediction approach. The performance of the present method is assessed by comparison with Cramer-Rao lower bound and other existing methods through computer simulations. The proposed algorithm estimates well for different values of the chirp rate and initial frequency, as well as for different number of samples. In other words, this algorithm has uniform performance for various values of the signal parameters. [ABSTRACT FROM AUTHOR]

To solve the conflicting requirement of fast convergence and low steady-state misalignment, a variable step-size shrinkage set-membership affine projection algorithm is proposed, which is efficient for the correlated input signal and noisy input environments. The new variable step size is derived by minimizing the square of noise-free a posteriori error, and the shrinkage method is employed to estimate the second-order statistics of the noise-free a priori error vector. Moreover, the stability analysis of the algorithm is conducted. Simulations demonstrate the effectiveness of the proposed algorithm for various noisy input environments. [ABSTRACT FROM AUTHOR]

In this work, we consider the joint estimation of time delay, Doppler velocity and Doppler rate of unknown wideband signals, in the sense that the time delay migration over time and Doppler shift migration over signal frequency are non-ignorable. By partitioning the received signals into short-time segments, a novel wideband signal model is presented and the maximum likelihood (ML) estimator is derived. With better compensation of parameter migrations, higher estimation accuracy might be achieved through the derived ML estimator. The signal-specific Cramer-Rao lower bound analysis for the wideband cases is also presented. To avoid the multidimensional search of the ML estimator, a fast estimation algorithm based on the time-reverse transform and the second-order Keystone transform is proposed. In this fast estimation algorithm, these parameters could be estimated through successive one-dimensional searches. Simulation results show that the fast estimation algorithm could achieve comparable performance with the ML estimator with a much lower computational complexity. [ABSTRACT FROM AUTHOR]

Sparse adaptive filtering algorithms are utilized to exploit system sparsity as well as to mitigate interferences in many applications such as channel estimation and system identification. In order to improve the robustness of the sparse adaptive filtering, a novel adaptive filter is developed in this work by incorporating a correntropy-induced metric (CIM) constraint into the least logarithmic absolute difference (LLAD) algorithm. The CIM as an $$l_{0}$$ -norm approximation exerts a zero attraction, and hence, the LLAD algorithm performs well with robustness against impulsive noises. Numerical simulation results show that the proposed algorithm may achieve much better performance than other robust and sparse adaptive filtering algorithms such as the least mean p-power algorithm with $$l_{1}$$ -norm or reweighted $$l_{1}$$ -norm constraints. [ABSTRACT FROM AUTHOR]

The closed-form solution of linearly constrained minimum variance (CF-LCMV) suffers heavy computational burden from two-matrix inversion when computing the optimal vector. CF-LCMV is not an adaptive beamformer and performs poorly with low signal-to-interference-plus-noise-ratio (SINR) and small number of snapshots. In this study, we derive a low-complexity iterative adaptive LCMV (IA-LCMV) algorithm based on conjugate gradient (CG) technique with threefold advantages: first, IA-LCMV can remarkably alleviate the complexity of CF-LCMV; second, IA-LCMV can adjust output adaptively with comparable convergence speed. Finally, it shows robust performance against low SINR and small number of snapshots. Simulation results demonstrate the efficacy of our proposed algorithms. [ABSTRACT FROM AUTHOR]