Polyphase orthogonal sequences design for opportunistic array radar via HGA

Opportunistic array radar （OAR） is a new generation radar system based on the stealth of the platform, which can improve the modern radar performance effectively. Designing the orthogonal code sets with low autocorrelation and cross-correlation is a key issue for OAR. This paper proposes a novel hybrid genetic algorithm （HGA） and designs the polyphase orthogonal code sets with low autocorrelation and cross-correlation properties, which can be used in the OAR system. The novel algorithm combines with simulated annealing （SA） and genetic algorithm （GA）, adds in keeping best individuals and competition in small scope, and introduces grey correlation evaluation to evaluate fitness function. These avoid the premature convergence problem existed in GA and enhance the global searching capability. At last, the genetic results are optimized to obtain the best solution by using greedy algorithm. The simulation results show that the proposed algorithm is effective for the design of orthogonal phase signals used in OAR systems.

Schemes for synthesizing high-resolution range profile with extended OFDM-MIMO

Two novel schemes are proposed to synthesize high resolution range profile (HRRP) based on co-located multiple-input multiple-output (MIMO) system in the context of the joint radar and communication system. The difference between two schemes is the pattern of selecting pulses, which depends on the demand for the velocity information. The system, a type of frequency diverse array (FDA), takes full advantage of the phase-coded orthogonal frequency division multiplexing (OFDM) signal. Furthermore, the complete discrete form of the phase-coded OFDM echoes is utilized to derive the HRRP processing. The velocity estimation in the second scheme aims to eliminate velocity ambiguity, and high velocity can be retrieved exactly. Meanwhile, the imaging method is investigated with random frequency coding applied to an array. The desired performance of resolving velocity ambiguity and suppressing noise is shown by means of comparisons with previous work. The advantages in the radar imaging and the significance of the work are concluded in the end.

Angle estimation for bistatic MIMO radar with unknown mutual coupling based on three-way compressive sensing

The problem of angle estimation for bistatic multiple-input multiple-output radar in the present of unknown mutual coupling (MC) is investigated, and a three-way compressive sensing (TWCS) estimation algorithm is developed. To exploit the inherent multi-dimensional structure of received data, a trilinear tensor model is firstly formulated. Then the de-coupling operation is followed. Thereafter, the high-order singular value decomposition is applied to compress the high dimensional tensor to a much smaller one. The estimation of the compressed direction matrices are linked to the compressed trilinear model, and finally two over-complete dictionaries are constructed for angle estimation. Also, Cramer-Rao bounds for angle and MC estimation are derived. The proposed TWCS algorithm is effective from the perspective of estimation accuracy as well as the computational complexity, and it can achieve automatically paired angle estimation. Simulation results show that the proposed method has much better estimation accuracy than the existing algorithms in the low signal-to-noise ratio scenario, and its estimation performance is very close to the parallel factor analysis (PARAFAC) algorithm at the high SNR regions. © 2017 Beijing Institute of Aerospace Information.

Multifractal analysis of resting state fMRI series in default mode network: age and gender effects

Age-related changes of resting state in default mode network(DMN)may provide new clues to the developing mechanism of normal brain as well as early diagnosis and therapy of some neuropsychiatric disorders.The application of multifractal theory to functional magnetic resonance imaging(fMRI)signals has recently raised increasing attention.We aim to explore the multifractal characteristics underlying the resting state functional magnetic resonance imaging(rs-fMRI)series extracted from DMN,and two issues are mainly discussed:(1)whether there exist multifractals in rs-fMRI series;(2)whether it is possible to distinguish between the different ages or genders by means of multifractal characteristics.Results demonstrated the existence of multifractals in rs-fMRI series in DMN.In addition,slight differences between young subjects and middle-aged or elderly subjects can be successfully detected by Dasa,a modified measure we proposed.Furthermore,it is revealed that the rs-fMRI series from young subjects possess smaller averaged scale index and weaker long range correlation,while those from middle-aged or elderly people present increasing averaged scale index andstronger long range correlation.Whereas no significant statistical differences has been found between male and female group.Our results,therefore,highlight the potential usefulness of multifractal analysis in fMRI series of a certain brain region,and provide important insights into healthy aging in DMN.

Adapitive waveform design for distributed OFDM MIMO radar system in multi-target scenario

In order to improve detection and estimation performance of distributed OrthogonalFrequency-Division Multiplexing（OFDM） Multiple-Input Multiple-Output（MIMO） radar system in multi-target scene, we propose a novel approach of Adaptive Waveform Design（AWD） based on a constrained Multi-Objective Optimization（MOO）. The sparse measurement model of this radar system is derived, and the method based on decomposed Dantzig selectors is applied for the sparse recovery according to the block structures of the sparse vector and the system matrix. An AWD approach is proposed, which optimizes two objective functions, namely minimizing the upper bound of the recovery error and maximizing the weakest-target return, by adjusting the complex weights of the emitting waveform amplitudes. Several numerical simulations are provided and their results show that the detection and estimation performance of the radar system is improved significantly when this MOO-based AWD approach is applied to the distributed OFDM MIMO radar system. Especially, we verify the effectiveness of our AWD approach when the available samples are reduced severally and the technique of compressed sensing is introduced.

Estimation of multipath parameters in wireless communications using multi-way compressive sensing

This paper addresses the problem of joint angle and delay estimation(JADE) in a multipath communication scenario. A low-complexity multi-way compressive sensing(MCS) estimation algorithm is proposed. The received data are firstly stacked up to a trilinear tensor model. To reduce the computational complexity,three random compression matrices are individually used to reduce each tensor to a much smaller one. JADE then is linked to a low-dimensional trilinear model. Our algorithm has an estimation performance very close to that of the parallel factor analysis(PARAFAC) algorithm and automatic pairing of the two parameter sets. Compared with other methods, such as multiple signal classification(MUSIC), the estimation of signal parameters via rotational invariance techniques(ESPRIT), the MCS algorithm requires neither eigenvalue decomposition of the received signal covariance matrix nor spectral peak searching. It also does not require the channel fading information, which means the proposed algorithm is blind and robust, therefore it has a higher working efficiency.Simulation results indicate the proposed algorithm have a bright future in wireless communications.

Photonic-assisted single system for microwave frequency and phase noise measurement

We propose a photonic-assisted single system for measuring the frequency and phase noise of microwave signals in a large spectral range. Both the frequency and phase noise to be measured are extracted from the phase difference between the signal under testing and its replica delayed by a span of fiber and a variable optical delay line(VODL). The system calibration, frequency measurement, and phase noise measurement are performed by adjusting the VODL at different working modes. Accurate frequency and phase noise measurement for microwave signals in a large frequency range from 5 to 50 GHz is experimentally demonstrated.