基于状态空间模型的多辐射源运动状态分析

利用无源传感器对辐射源进行无源定位是重要的研究课题,受到广泛重视。而利用运动的单一传感器对运动的多个辐射源同时进行运动状态分析则是无源定位问题中的难点。状态空间模型可将辐射源的参数估计和辐射源的运动状态分析有机地联系在一起。本文基于状态空间模型和运动的单一均匀线阵,用信息论准则和ESRPIT算法对辐射源的个数、辐射源的发射频率和空间频率、空间频率变化率进行联合估计,进而对各个辐射源进行运动状态分析(TMA目标运动分析)。     

Turbo Source Extraction Algorithm and Noncancellation Source Separation Algorithms by Kurtosis Maximization

The kurtosis maximization criterion has been effectively used for blind spatial extraction of one source from an instantaneous mixture of multiple non-Gaussian sources, such as the kurtosis maximization algorithm by Ding and Nguyen, and the fast kurtosis maximization algorithm (FKMA) by Chi and Chen. By empirical studies, we found that the smaller the normalized kurtosis magnitude of the extracted source signal, the worse the performance of these algorithms. In this paper, with the assumption that each source is a non-Gaussian linear process, a novel blind source extraction algorithm, called turbo source extraction algorithm (TSEA), is proposed. The ideas of the TSEA are to exploit signal temporal properties for increasing the normalized kurtosis magnitude, and to apply spatial and temporal processing in a cyclic fashion to improve the signal extraction performance. The proposed TSEA not only outperforms the FKMA, but also shares the convergence and computation advantages enjoyed by the latter. This paper also considers the extraction of multiple sources, also known as source separation, by incorporating the proposed TSEA into the widely used multistage successive cancellation (MSC) procedure. A problem with the MSC procedure is its susceptibility to error propagation accumulated at each stage. We propose two noncancellation multistage (NCMS) algorithms, referred to as NCMS-FKMA and NCMS-TSEA, that are free from the error propagation effects. Simulation results are presented to show that the NCMS-TSEA yields substantial performance gain compared with some existing blind separation algorithms, together with a computational complexity comparison. Finally, we draw some conclusions.     

Despread data rate update multitarget adaptive array for CDMAsignals

The authors examine the performance of a blind adaptive array algorithm for spatial filtering of code division multiple access (CDMA) signals. The algorithm is based on decision direction and takes advantage of the processing gain of the CDMA system to give improved performance over a non-despread architecture     

Finite data performance analysis of MVDR beamformer with andwithout spatial smoothing

The finite-data performance of a minimum-variance distortionless response (MVDR) beamformer is analyzed with and without spatial smoothing, using first-order perturbation theory. In particular, expressions are developed for the mean values of the power gain in any direction of interest, the output power, and the norm of the weight-error vector, as a function of the number of snapshots and the number of smoothing steps. It is shown that, in general, the smoothing, in addition to decorrelating the sources, can alleviate the effects of finite-data perturbations. The above expressions are reduced to the case in which no spatial smoothing is used. These expressions are valid for an arbitrary array and for arbitrarily correlated signals. For this case, an expression for the variance of the power gain is also developed. For a single interference case it is shown explicitly how the SNR, spacing of the interference from the desired signal and the correlation between them influence the beamformer performance. Simulations verify the usefulness of the theoretical expressions     

A Method for the Direction-of-Arrival Estimation of Incoherently Distributed Sources

We consider the problem of estimating the nominal direction of arrival (DOA) of an incoherently distributed source. This problem is encountered due to the presence of local scatterers in the vicinity of a transmitter or due to signals propagating through a random inhomogeneous medium. Since the spatial covariance matrix has full rank for an incoherently distributed source, the performance of most high-resolution DOA estimation algorithms conceived under coherently distributed sources, as well as point source models, degrades when scattering is present. In addition, several DOA estimation techniques devised under a distributed source model require a high-dimensional nonlinear optimization problem. In this paper, we propose a novel method based on the conventional beamforming approach, which estimates the nominal DOA from a spatial maximum peak of the output power. The proposed method is computationally more attractive than the maximum likelihood (ML) estimator, although the performance degrades in comparison with the ML estimator, whose asymptotic performance is equivalent to the Cramer–Rao bound (CRB). We derive and compare the asymptotic performances of the proposed method and the redundancy-averaged covariance matching (RACM) method in the single-source case. The simulation results illustrate that the asymptotic performance of the proposed method is better than that of the RACM method.      

On maximum-likelihood localization of coherent signals

We consider the problem of localizing multiple signal sources in the special case where all the signals are known a priori to be coherent. A maximum-likelihood estimator (MLE) is constructed for this special case, and its asymptotical performance is analyzed via the Cramer-Rao bound (CRB). It is proved that the CRB for this case is identical to the CRB for the case that no prior knowledge on coherency is exploited, thus establishing a quite surprising result that, asymptotically, the localization errors are not reduced by exploiting this prior knowledge. Also, we prove that in the coherent case the deterministic signals model and the stochastic signals model yield MLE's that are asymptotically identical. Simulation results confirming these theoretical results are included     

Bilinear signal synthesis in array processing

Multiple source signals impinging on an antenna array can be separated by time-frequency synthesis techniques. Averaging of the time-frequency distributions (TFDs) of the data across the array permits the spatial signatures of sources to play a fundamental role in improving the synthesis performance. Array averaging introduces a weighting function in the time-frequency domain that decreases the noise levels, reduces the interactions of the source signals, and mitigates the crossterms. This is achieved independent of the temporal characteristics of the source signals and without causing any smearing of the signal terms. The weighting function may take noninteger values, which are determined by the communication channel, the source positions, and their angular separations. Unlike the recently devised blind source separation methods using spatial TFDs, the proposed method does not require whitening or retrieval of the source directional matrix. The paper evaluates the proposed method in terms of performance and computations relative to the existing source separation techniques based on quadratic TFDs.     

On the average near-far resistance for MMSE detection of directsequence CDMA signals with random spreading

The performance of a near-far-resistant, finite-complexity, minimum mean squared error (MMSE) linear detector for demodulating direct sequence (DS) code-division multiple access (CDMA) signals is studied, assuming that the users are assigned random signature sequences. We obtain tight upper and lower bounds on the expected near-far resistance of the MMSE detector, averaged over signature sequences and delays, as a function of the processing gain and the number of users. Since the MMSE detector is optimally near-far-resistant, these bounds apply to any multiuser detector that uses the same observation interval and sampling rate. The lower bound on near-far resistance implies that, even without power control, linear multiuser detection provides near-far-resistant performance for a number of users that grows linearly with the processing gain     

Maximum-likelihood source localization and unknown sensor locationestimation for wideband signals in the near-field

In this paper, we derive the maximum-likelihood (ML) location estimator for wideband sources in the near field of the sensor array. The ML estimator is optimized in a single step, as opposed to other estimators that are optimized separately in relative time-delay and source location estimations. For the multisource case, we propose and demonstrate an efficient alternating projection procedure based on sequential iterative search on single-source parameters. The proposed algorithm is shown to yield superior performance over other suboptimal techniques, including the wideband MUSIC and the two-step least-squares methods, and is efficient with respect to the derived Cramer-Rao bound (CRB). From the CRB analysis, we find that better source location estimates can be obtained for high-frequency signals than low-frequency signals. In addition, large range estimation error results when the source signal is unknown, but such unknown parameter does not have much impact on angle estimation. In some applications, the locations of some sensors may be unknown and must be estimated. The proposed method is extended to estimate the range from a source to an unknown sensor location. After a number of source-location frames, the location of the uncalibrated sensor can be determined based on a least-squares unknown sensor location estimator     

A two-dimensional RAKE receiver architecture with an FFT-based matched filtering

This work proposes a two-dimensional (2-D) RAKE receiver, which is a spatial-temporal matched filter implemented in the frequency domain. To form a beam pattern, we calculate the spatial frequency spectra of received signals on the antenna array using fast Fourier transform (FFT). After FFT beamforming, a bank of FFT-based matched filters is used to perform code matching. Afterward, the code-matched signals are summed up with maximal-ratio combining through a spatial-temporal channel-matched filter implemented in the frequency domain. This 2-D RAKE receiver includes a channel sounder that is used to estimate the spatial and temporal channel impulse response parameters, such as delays, directions of arrivals, and complex gains of multipath components. Monte Carlo simulations have been used to evaluate the receiver bit-error rate performance in both static channel and mobile radio channel environments. Simulation results show that the RAKE receiver performs well in both kinds of channels.     

Convergence analysis of a CM array for CDMA systems

The incorporation of directional sensitivity, provided by so-called adaptive antennas is useful in suppressing interfering signals that arise from spatially distinct mobile sources. The problem is that in a cellular radio environment where multipath exists, the standard adaptive antenna using reference signals may not properly lock on the desired signal. This is because the signal correlation matrix processed by the antenna may then be close to singular and standard algorithms fail. Also, most standard algorithms need to cooperate with the receiver for the spatial discrimination of signals. A smart antenna utilizing a blind algorithm is of interest since the antenna may not need to get any feedback from a receiver for the adjustment of weight coefficient for spatial processing and can stand alone to be plugged into any kind of receiver structure.In this paper, we address the convergence property of a Constant Modulus Algorithm which is a blind algorithm and, if employed, can provide no need for an antenna to cooperate with a receiver attached. By identifying a relationship between the weight coefficients and output signal amplitude, we also evaluate the performance of such a stand-alone antenna plus a CDMA matched filter reception. Our results show that for a three element CM array, the BER of a desired user with the other interfering users is much better than a conventional correlation receiver for a single user case since the array suppresses interferences and achieves array gain in SNR.     

Performance of High-Bitrate Multiple-Output Links Over Multimode Fiber With Intermodal Dispersion

We investigate novel transmission schemes over multimode fiber with multiple output detectors, providing more efficient utilization of the available spatial-temporal degrees of freedom of the system by combining coherent phase shift keying transmission with direct detection. We evaluate the statistics of the electrical charge generated by each detector, and its dependence on factors such as detector type, dimension and offset position. In the frequency-selective case, we reveal that temporal degrees of freedom resulting from nonoverlapping time pulses modify the decision variable statistics. We apply the ensuing model to propose a novel phase-modulated single input multiple output (SIMO) multimode fiber transmission system employing multiple detectors and multiple input multiple output (MIMO) space-time postdetection signal processing in order to mitigate the ISI stemming from intermodal dispersion.      

Soft information assisted space-time multiuser detection for highly loaded CDMA

This letter presents an effective space-time multiuser detector (MUD) with the assistance of soft information in multipath code division multiple access (CDMA) channels. The space-time MUD considered is a simple, separable spatial-temporal filter which consists of a single spatial filter and a single temporal filter. Based on the soft-decision outputs determined in the previous iteration, the soft information is then exchanged in the alternating updates of either the spatial filters or the temporal filters. Furnished simulations show that the proposed scheme can offer substantial performance improvement compared with previous works, especially in highly loaded scenarios.     

Blind estimation of space?time coded systems using the finite alphabet?constant modulus algorithm

Space-time coding introduces spatial and temporal correlations into signals transmitted from multiple antennas in order to provide diversity and coding gain at the receiver. In the field of blindly estimating space-time coded signals impinging on an antenna array, an effective scheme for jointly exploring the constant modulus (CM) and the finite alphabet (FA) constraints is presented. The CM constraints are explored algebraically, whereas the FA constraints are explored via an iterative algorithm considering the constraints imposed by the space-time coded signals. This leads to our novel FACM algorithm that offers a wide range of trade-offs between performance and complexity while it lends itself to an amenable (step-by-step) algorithmic implementation as well as an amenable algorithmic complexity     

Signal detection performance in Rayleigh fading environments with a moving antenna

The performances of a single-antenna handheld receiver in detecting a narrowband signal in a Rayleigh fading environment that is temporally static but decorrelates spatially are analysed. Of interest is comparing the detection performance of a static antenna with that of a moving antenna subject to constant processing time. It is shown that the net processing gain resulting from randomly moving the antenna relative to keeping it static can be large, namely over 11 dB in some cases, which is significant for numerous indoor applications. It is further demonstrated that, for a given utilisation scenario, there is an optimum number of spatial samples that maximise the processing gain advantage of the moving antenna. Generally, if the spatial trajectory of the antenna becomes too large, then the loss associated with the signal decorrelation dominates and undermines the gains achieved by the increased spatial diversity. Practical implementation issues including the sensitivity of the proposed method to trajectory estimation are investigated. An extensive set of measurements based on CDMA 2000 signals propagated from outdoor terrestrial base stations and captured in indoor multipath environments using static and moving antennas are utilised to experimentally substantiate these theoretical findings.     

Threshold vector field detectors

The problem of threshold or weak-signal detection in highly nonGaussian EMI is extended to vector fields, and narrow-band signals and interference. The emphasis is on a canonical formulation, illustrated by a number of specific examples. Spatial sampling with adaptive beam forming, as well as temporal sampling and all relevant vector field components, must be included for maximum processing gain. New results for a canonical theory of these vector detection cases are presented. Jointly and asymptotically locally optimum algorithms and performance measures are obtained. These results provide statistical-physical models of the EMI environment, and they include first-order probability distributions of vector EMI noise fields and received processes, with specific examples of EMI fields generated by randomly distributed electric and magnetic dipole sources, as well as more general sources. The effects of beamforming, selfdirecting beams, multiple field components, fading, and Doppler `smear' on signal detectability are included     

Reduced-rank adaptive detection of distributed sources using subarrays

We introduce a framework for exploring array detection problems in a reduced-dimensional space. This involves calculating a structured subarray transformation matrix for the detection of a distributed signal using large aperture linear arrays. We study the performance of the adaptive subarray detector and evaluate its potential improvement in detection performance compared with the full array detector with finite data samples. One would expect that processing on subarrays may result in performance loss in that smaller number of degrees of freedom is utilized. However, it also leads to a better estimation accuracy for the interference and noise covariance matrix with finite data samples, which will yield some gain in performance. By studying the subarray detector for general linear arrays, we identify this gain under various scenarios. We show that when the number of samples is small, the subarray detectors have a significant gain over the full array detector. In addition, the subarray processing can also be successfully applied to the problem of detecting moving sources in an underwater acoustic scenario. We validate our results by computer simulations.     

Multisource classification of remotely sensed data: fusion ofLandsat TM and SAR images

Proposes a new method for statistical classification of multisource data. The method is suited for land-use classification based on the fusion of remotely sensed images of the same scene captured at different dates from multiple sources. It incorporates a priori information about the likelihood of changes between the acquisition of the different images to be fused. A framework for the fusion of remotely sensed data based on a Bayesian formulation is presented. First, a simple fusion model is given, and then the basic model is extended to take into account the temporal attribute if the different data sources are acquired at different dates. The performance of the model is evaluated by fusing Landsat TM images and ERS-1-SAR images for land-use classification. The fusion model gives significant improvements in the classification error rates compared to the conventional single-source classifiers     

Spatiotemporal squeeze-and-excitation residual multiplier network for video action recognition

Aiming at the shortcomings of shallow networks and general deep models in two-stream network structure,which could not effectively learn spatial and temporal information,a squeeze-and-excitation residual network was proposed for action recognition with a spatial stream and a temporal stream.Meanwhile,the long-term temporal dependence was captured by injecting the identity mapping kernel into the network as a temporal filter.Spatiotemporal feature multiplication fusion was used to further enhance the interaction between spatial information and temporal information of squeeze-and-excitation residual networks.Simultaneously,the influence of spatial-temporal stream multiplication fusion methods,times and locations on the performance of action recognition was studied.Given the limitations of performance achieved by a single model,three different strategies were proposed to generate multiple models,and the final recognition result was obtained by integrating these models through averaging and weighted averaging.The experimental results on the HMDB51 and UCF101 datasets show that the proposed spatiotemporal squeeze-and-excitation residual multiplier networks can effectively improve the performance of action recognition.     

Parametric localization of distributed sources

Most array processing algorithms are based on the assumption that the signals are generated by point sources. This is a mathematical constraint that is not satisfied in many applications. In this paper, we consider situations where the sources are distributed in space with a parametric angular cross-correlation kernel. We propose an algorithm that estimates the parameters of this model using a generalization of the MUSIC algorithm. The method involves maximizing a cost function that depends on a matrix array manifold and the noise eigenvectors. We study two particular cases: coherent and incoherent spatial source distributions. The spatial correlation function for a uniformly distributed signal is derived. From this, we find the array gain and show that (in contrast to point sources) it does not increase linearly with the number of sources. We compare our method to the conventional (point source) MUSIC algorithm. The simulation studies show that the new method outperforms the MUSIC algorithm by reducing the estimation bias and the standard deviation for scenarios with distributed sources. It is also shown that the threshold signal-to-noise ratio required for resolving two closely spaced distributed sources is considerably smaller for the new method