Current Issue : January - March Volume : 2017 Issue Number : 1 Articles : 6 Articles
Nyquist folding receiver (NYFR) is a novel ultra-wideband receiver architecture which can realize wideband receiving\nwith a small amount of equipment. Linear frequency modulated/binary phase shift keying (LFM/BPSK) hybrid\nmodulated signal is a novel kind of low probability interception signal with wide bandwidth. The NYFR is an\neffective architecture to intercept the LFM/BPSK signal and the LFM/BPSK signal intercepted by the NYFR will\nadd the local oscillator modulation. A parameter estimation algorithm for the NYFR output signal is proposed.\nAccording to the NYFR prior information, the chirp singular value ratio spectrum is proposed to estimate the\nchirp rate. Then, based on the output self-characteristic, matching component function is designed to estimate Nyquist\nzone (NZ) index. Finally, matching code and subspace method are employed to estimate the phase change points and\ncode length. Compared with the existing methods, the proposed algorithm has a better performance. It also\nhas no need to construct a multi-channel structure, which means the computational complexity for the NZ\nindex estimation is small. The simulation results demonstrate the efficacy of the proposed algorithm....
An array signal recovery algorithm based on sparse signal reconstruction theory is proposed for a single-RF-channel\ndigital beamforming (DBF) array. A single-RF-channel antenna array is a low-cost antenna array in which signals are\nobtained from all antenna elements by only one microwave digital receiver. The spatially parallel array signals are\nconverted into time-sequence signals, which are then sampled by the system. The proposed algorithm uses these\ntime-sequence samples to recover the original parallel array signals by exploiting the second-order sparse structure of\nthe array signals. Additionally, an optimization method based on the artificial bee colony (ABC) algorithm is proposed\nto improve the reconstruction performance. Using the proposed algorithm, the motion compensation problem for\nthe single-RF-channel DBF array can be solved effectively, and the angle and Doppler information for the target can\nbe simultaneously estimated. The effectiveness of the proposed algorithms is demonstrated by the results of\nnumerical simulations....
We explore joint training strategies of DNNs for simultaneous dereverberation and acoustic modeling to improve the\nperformance of distant speech recognition. There are two key contributions. First, a new DNN structure incorporating\nboth dereverberated and original reverberant features is shown to effectively improve recognition accuracy over the\nconventional one using only dereverberated features as the input. Second, in most of the simulated reverberant\nenvironments for training data collection and DNN-based dereverberation, the resource data and learning targets are\nhigh-quality clean speech. With our joint training strategy, we can relax this constraint by using large-scale diversified\nreal close-talking data as the targets which are easy to be collected via many speech-enabled applications from\nmobile internet users, and find the scenario even more effective. Our experiments on a Mandarin speech recognition\ntask with 2000-h training data show that the proposed framework achieves relative word error rate reductions of 9.7\nand 8.6 % over the multi-condition training systems for the cases of single-channel and multi-channel with\nbeamforming, respectively. Furthermore, significant gains are consistently observed over the pre-processing\napproach using simply DNN-based dereverberation....
Traditional space-time adaptive processing (STAP) is a strategy for clutter suppression in airborne radar, which\nrequires a large number of computational complexity and secondary data. In order to address the problem,\nreduced-dimension (RD) STAP is generally used. We propose a novel RD STAP through searching the best channels as\nthe auxiliary channels to cancel the interference. Based on the estimation of the clutter Fourier basis vectors offline, a\nparameter named angle-Doppler correlation coefficient (ADC2) is constructed to evaluate the capability of each\nauxiliary channel in clutter suppression, and the best sets of RD channels can be selected. The proposed algorithm can\nachieve the best detection performance with the fixed number of auxiliary channel. When the degrees of freedom\n(DOF) are restricted to a small value, only one auxiliary channel is needed to guarantee the SINR loss less than 3 dB.\nTherefore, the requirement of the training sample can be reduced, which makes the proposed approach more\nsuitable for the heterogeneous clutter environments....
We propose a line-of-sight (LOS)/non-line-of-sight (NLOS) mixture source localization algorithm that utilizes the\nweighted least squares (WLS) method in LOS/NLOS mixture environments, where the weight matrix is determined in\nthe algebraic form. Unless the contamination ratio exceeds 50 %, the asymptotic variance of the sample median can\nbe approximately related to that of the sample mean. Based on this observation, we use the error covariance matrix\nfor the sample mean and median to minimize the weighted squared error (WSE) loss function. The WSE loss function\nbased on the sample median is utilized when statistical testing supports the LOS/NLOS state, while the WSE function\nusing the sample mean is employed when statistical testing indicates that the sensor is in the LOS state. To testify the\nsuperiority of the proposed methods, the mean square error (MSE) performances are compared via simulation....
Modern radar and communication systems require the detection and parameter estimation of signal under a\nbroadband radio frequency (RF) environment. The Nyquist folding receiver (NYFR) is an efficient analog-toinformation\n(A2I) architecture. It can use the compressive sensing (CS) techniques to break the limitations of the\nanalog-to-digital converter (ADC). This paper demonstrates the restricted isometry property (RIP) of the NYFR\ndeterministically by applying the Gershgorin circle theory. And, the NYFR suffers a poor RIP for the broadband\nsignal, which will lead the conventional CS algorithms to be invalid. So, we derive the Fourier spectrum of the\nbroadband signal, which covered multiple Nyquist zones and received by the NYFR. Then, the broadband signal\ncan be regarded as the block-sparse signal. And, the block CS algorithms are applied for recovering the signal\nbased on the analysis of the block-RIP. Finally, the simulation experiments demonstrate the validity of the findings....
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