Tao Shan

Sparse Discrete Fractional Fourier Transform and Its Applications

The discrete fractional Fourier transform is a powerful signal processing tool with broad applications for nonstationary signals. In this paper, we propose a sparse discrete fractional Fourier transform (SDFrFT) algorithm to reduce the computational complexity when dealing with large data sets that are sparsely represented in the fractional Fourier domain. The proposed technique achieves multicomponent resolution in addition to its low computational complexity and robustness against noise. In addition, we apply the SDFrFT to the synchronization of high dynamic direct-sequence spread-spectrum signals. Furthermore, a sparse fractional cross ambiguity function (SFrCAF) is developed, and the application of SFrCAF to a passive coherent location system is presented. The experiment results confirm that the proposed approach can substantially reduce the computation complexity without degrading the precision.

A Novel Two-Dimensional Sparse-Weight NLMS Filtering Scheme for Passive Bistatic Radar

In passive bistatic radars, weak target echoes may often be masked by direct path interference, multipath components, and strong target echoes, making weak target detection a challenging problem. The conventional 1-D adaptive cancelation algorithms, such as the normalized least mean square (NLMS), cannot effectively suppress strong target echoes when their Doppler frequencies spread. In addition, the continuous distribution of the NLMS weight vector does not match the sparse characteristics of strong multipath components and target echoes, thus resulting in degraded cancelation performance. Motivated by this fact, a novel 2-D sparse-weight NLMS filtering scheme is proposed by extending the NLMS to a 2-D structure, in which the weight vector is sparsely distributed and adaptively adjusted based on the sparse strong multipath components and target echoes.

A fast algorithm for multi-component LFM signal analysis exploiting segmented DPT and SDFrFT

This paper addresses the problem of estimating the chirp rates of multi-component linear frequency modulated signals, which is important in radar, sonar and navigation signal processing. The main difficulties in the estimation procedure lie in the cross-terms between multi-components and the high computation burden. To solve these problems, a novel algorithm that combines segmented discrete polynomial-phase transform and sparse discrete fractional Fourier transform is proposed to yield a significant reduction of the computational load with a satisfactory estimation performance. Simulation results are provided to demonstrate the effectiveness of the proposed approach.

Sparsity-based frequency-hopping spectrum estimation with missing samples

In this paper, we address the problem of spectrum estimation of frequency-hopping (FH) signals in the presence of random missing samples. The signals are analyzed within the bilinear time-frequency representation framework, where a time-frequency kernel is designed based on inherent FH signal structures. The designed kernel permits effective suppression of cross-terms and artifacts due to missing samples while preserving the FH signal auto-terms. The kernelled results are represented in the instantaneous autocorrelation function domain, which are then processed using sparse reconstruction methods for high-resolution estimation of the FH signal time-frequency spectrum. The proposed method achieves accurate FH signal spectrum estimation even when a large proportion of data samples is missing. Simulation results verify the effectiveness of the proposed method and its superiority over existing techniques.

Performance of order statistic clutter map CFAR

Cell average clutter map CFAR (CACM-CFAR) is analyzed at first. CACM-CFAR has better performance in the inhomogeneous clutter environment, but is affected by interference and self-masking which will cause the low probability of detection. To solve these problems, a novel algorithm named order statistic clutter map CFAR (OSCM-CFAR) is proposed, in which the clutter map is updated by the k-th ranked cell in the clutter map cell. The performance analysis shows that the probability of detection for slow moving targets is increased by using the OSCM-CFAR algorithm. And the same case occurs in the multi-target environment when the new method is used.

Detection of weak astronomical signals with frequency-hopping interference suppression

Abstract This paper addresses the detection of weak astronomical signals that are contaminated by strong frequency-hopping (FH) interferers and suffer from missing samples. The problem is considered in the time–frequency domain and we successively suppress artifacts due to missing samples, estimate and remove FH interferers, and detect the weak astronomical signals. More specifically, we first suppress the artifacts due to missing samples by developing a waveform-adaptive time–frequency kernel. The instantaneous spectra of the FH interferers are then estimated using a sparsity-based approach that takes the inherent properties of FH signals into account. Finally, a sparse coherent integrated cubic phase function is applied to effectively detect weak astronomical chirp components over a long integration time. Simulation results are provided to demonstrate the effectiveness of the proposed approach.

Automatic human fall detection in fractional fourier domain for assisted living

Fast and accurate detection of elderly falls can significantly reduce the rate of morbidity and mortality. In the past decade, extensive research has been performed to achieve real-time fall monitoring solutions. In this paper, we consider the radar-based modality and utilize the family of fractional Fourier transform to enhance the motion Doppler signature of falls. Compare with the conventional time-frequency analysis approaches, the proposed method achieves higher signal energy concentration and thus yields improved fall detection in low signal-to-noise ratio scenarios. Experimental results are used to validate the theoretical analysis and to demonstrate the feasibility of the proposed approach.

Interference suppression using joint spatio-temporal domain filtering in passive radar

This paper addresses the problem of interference cancellation in passive bistatic radar. The impact of fractional time delay, channel linearity and reference channel signal-to-noise-ratio (SNR) on the interference cancellation performance is analyzed, and the results show that the performance of adaptive cancellation algorithm is degraded by the fractional time delay, the difference between the frequency responses of the surveillance channel and the reference channel, and a low reference channel SNR. To solve this problem, a signal reconstruction method is adopted to improve the reference signal quality, and a joint spa-tiotemporal domain interference suppression method is proposed to effectively mitigate direct-path interference and multi-path interference resulting in significant target SNR improvement The effectiveness of the proposed methods is verified by real data experiments.

The migration compensation methods for DTV based passive radar

The compensation methods for range and Doppler migration in digital television (DTV) based passive radar are studied in this paper. Firstly, the problems of the compensation method based on Keystone transform (KT) and Chirp-Fourier transform (CFT) when applied to passive radar are analyzed, and then a comparison is made between this method and the compensation method based on envelope shift and multirate signal processing. The simulation analysis and real data verification imply that both of these methods can achieve effective compensation results. However, in DTV based passive radar, the compensation performance of the latter is distinctly superior to the former.

Liveness evaluation of multi-living agent system

Multi-living agent system (MLAS) is a new concept in the field of complex system research, which is peculiarly suitable for the design and analysis of a complex information system in a serious confrontation and tight constraint environment. How-ever, the universal method to quantitatively measure the living degree of an MLAS remains uncertain, which is critical to the self-organizing process. Therefore, a novel analytic hierarchy process (AHP) based method with dependent pairwise comparison matrix (PCM) for the evaluation of living degree of the MLAS is proposed, which eliminates the shortcoming of fixed PCM in traditional process. Furthermore, to avoid the annoying procedure of the consistency validation, the PCMs are appropriately reconstructed. Through an illustration of the netted radar system, the calculation detail is explicitly presented. Altogether, the advanced evaluation method successfully accomplishes the preset objective and promotes the development of the MLAS theory and AHP as well.