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High-precision ranging radar is of vital importance in many fields. For an extended target, by exploiting the target impulse response (TIR), the ranging precision may be improved. In this paper, inspired by the concept of cognitive radar (CR), we propose a new approach for extended target ranging, in which waveform can be adaptively adjusted according to the varying TIR. In this approach, waveform...
In this paper, we develop a model and method for communication OFDM SAR imaging. In real applications, communication transmitters transmits OFDM data frames which are usually much longer than traditional radar pulses, so the variation of distance from radar to target should be considered. We analyze the phase variation caused by the distance variation, and find that it causes a significant loss of...
This paper considers the detection of a distributed target in the partial observation scenario, where only a set of receiving data with missing entries is available. The target is assumed to be obscured by complex Gaussian disturbance (clutter plus noise) with structured covariance matrix. A detection scheme based on the Rao test is proposed in this case. The unknown parameters in the detector are...
In this paper, a new perturbation analysis algorithm for the MUltiple SIgnal Classification (MUSIC) estimator applied to a Hermitian Toeplitz covariance matrix is presented. Inspired by the perspective that the MUSIC algorithm can be recognized as a structured matrix approximation, the perturbation of parameter estimates can be predicted more accurately by seeking the minimum of a Frobenius norm....
With the popularization of driving-assistance and self-driving systems, the mutual interference will become a big challenge for automotive radars, which demands the introduction of spectrum sharing technology. Based on the radar ranging using OFDM signals and target tracking, the overall performance of ranging accuracy is considered to be the criterion of spectrum allocation. Aiming to optimally allocate...
This article presents a Bayesian track-before-detect (TBD) method based on Gaussian message passing to detect and track a target in the low SNR scene. Removing the threshold brings great computation demanding to TBD methods. Because of the distributive law and computation consistency, message passing can reduce the calculation load efficiently. Also, as a probabilistic inference algorithm, message...
This paper concerns sparse Bayesian learning (SBL) problem for group sparse signals. Group sparsity means that the signal components can be divided into groups, and the entries in one group are simultaneously zero or nonzero. In SBL, each group is controlled by a hyper-parameter. The marginal likelihood maximization (MLM) problem is to maximize the marginal likelihood of a given hyper-parameter by...
In this paper, we focus on the effective Space-Time Adaptive Processing (STAP) method in nonhomogeneous clutter environment. The nonhomogeneous clutter leads to the lack of sufficient training data for clutter covariance matrix estimation in traditional STAP methods. By utilizing the sparsity of the distribution of clutter in angle-Doppler domain, we build a factor graph model and develop a message...
Spectral correlation function (SCF) is an important tool for analyzing cyclostationary signals. Only a few kinds of communication signals have analytic form for their SCF. This paper proposes a simple analytic expression for SCF of the well-known GMSK signal using super-Gaussian function. Numerical results show the validity of our approximation.
Reconstructing signals that have a sparse representation has been studied in the recent years. However, most of the work dealing with this problem requires a low-coherent dictionary matrix. This article presents a novel procedure for sparse signal reconstruction with high coherent dictionary by partitioning the dictionary in the preprocessing step and addressing the reconstruction of hierarchical...
In this paper, we propose a novel model to study the efficiency of detecting latent connection relationships, represented by a given set of graphs, among N users. A subset of active nodes transmit following a common codebook over a multiple access Boolean channel. To maximize the error exponent of the structure detection, we formulate an optimization problem whose objective is to max-minimize the...
Detecting the presence of target subspace signals with unknown clutters is a well-known hard problem encountered in various signal processing applications. Traditional methods fails to solve this problem because prior knowledge of clutter subspace is required, which can not be obtained when target and clutter are intimately mixed. In this paper, we propose a novel subspace detector that can detect...
It has been widely recognized that structure information helps in sparse signal recovery. In this paper, a general form of block structure is considered, which is often referred to hierarchically sparse model. It is assumed that the unknown sparse signal can be divided into blocks, and a block contains either all zero components or a fraction of nonzero components. This model sits between the standard...
Feature selection in Fingerprint-based localization systems is of great importance, because of its capability to reduce the overhead in handling high- dimensional data while ensuring positioning accuracy. Several methods for such a task have been proposed, but they either do not consider the correlation between features, or propose an inefficient method to deal with the correlation. The study in this...
Compressive Sensing (CS) provides a new perspective for dimensionnality reduction without compromising performance. The theoretical foundation for most of existing studies of CS is a stable embedding (i.e., a distance-preserving property) of certain low-dimensional signal models such as sparse signals or signals in a union of linear subspaces. However, few existing literatures clearly discussed the...
Various structures and configurations make frame to be a powerful tool in the domain of signal processing. Among its numerous configurations, the ones which have drawn much attention recently are Equiangular Tight Frames (ETFs) and Grassmannian Frames. These frames both have optimality in coherence, thus bring robustness and optimal performance in applications such as digital fingerprint, erasure...
Classifying sparse signals with compressive measurements is different from the well-known sparse recovery, for its focus is minimizing the probability of false classification rather than error of recovery. This paper considered the way to decrease the probability of false classification for compressive classifier. It is proved rigorously that the probability of false classification could be reduced...
A novel high resolution ISAR imaging method based on adaptive sparse recovery is proposed in his paper. The ISAR signal in each range bin is sparsely represented by an over-complete chirplet basis matrix, which can be determined by an unknown parameter set. An adaptive parametric sparse recovery method is proposed to retrieve both the parameter set and the ISAR image. This goal is achieved by sequentially...
A method of Micro-Doppler component separation based on parametric sparse time-frequency representation is proposed in this paper. The received signal is decomposed into a family of parametric basis signals that are dependent of the target angular velocity, and the significant coefficients of the sparse solution indicate the initial phases and the Doppler amplitudes of Micro-Doppler components. The...
Based on the covariance-like fitting criterion we propose a direction of arrival (DOA) estimation algorithm that embeds a weighting scheme in the objective function without selection of any hyperparameters. With an assumption of uncorrelated sources, we formulate the problem of DOA estimation as a linearly constrained quadratic optimization under power constraints. Numerical results show that the...
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