Recent advances in signal processing such as sparse sensing has offered researchers new opportunities to study radar and sonar. The goal of this Special Issue is to publish the most recent results in the new advances of sparse sensing in radar and sonar signal processing.
Edited by: Moeness Amin, Jun-Hong Cui, Qilian Liang, Yiming Pi, Radmila Pribic and Sherwood Samn
In this paper, we proposed a detection-based orthogonal match pursuit (DOMP) algorithm for compressive sensing. Unlike the conventional greedy algorithm, our proposed algorithm does not rely on the priori know...
In this paper, multi-frequency co-prime arrays are employed to perform direction-of-arrival (DOA) estimation with enhanced degrees of freedom (DOFs). Operation at multiple frequencies creates additional virtua...
Multi-static passive radar (MPR) systems typically use narrowband signals and operate under weak signal conditions, making them difficult to reliably estimate motion parameters of ground moving targets. On the...
Synthetic aperture imaging is a high-resolution imaging technique employed in radar and sonar applications, which construct a large aperture by constantly transmitting pulses while moving along a scene of inte...
A general echo model is derived for the synthetic aperture radar (SAR) imaging with high resolution based on the scalar form of Maxwell’s equations. After analyzing the relationship between the general echo mo...
A coprime sensor array (CSA) is a non-uniform linear array obtained by interleaving two uniform linear arrays (ULAs) that are undersampled by coprime factors. A CSA provides the resolution of a fully populated...
Nonlinear sparse sensing (NSS) techniques have been adopted for realizing compressive sensing in many applications such as radar imaging. Unlike the NSS, in this paper, we propose an adaptive sparse sensing (A...
This paper deals with the problem of estimating the directions of arrival (DOA) of multiple source signals from a single observation vector of an array data. In particular, four estimation algorithms based on ...
In this paper, the application of vector analysis tool in the illuminated area and the Doppler frequency distribution research for the airborne pulse radar is studied. An important feature of vector analysis i...
This paper investigates techniques for angular superresolution using limited data of real beam scanning radar (RBSR). In order to improve the angular resolution of RBSR, many algorithms have been proposed. How...
Bi-static passive synthetic aperture radar (SAR) systems using ground broadcast and wireless network signals suffer from a low spatial resolution due to the narrow bandwidths and low carrier frequencies. By ex...
In this paper, we exploit the notion of partial sparsity for scene reconstruction associated with through-the-wall radar imaging of stationary targets under reduced data volume. Partial sparsity implies that t...
Recently with an emerging theory of ‘compressive sensing’ (CS), a radically new concept of compressive sensing radar (CSR) has been proposed in which the time-frequency plane is discretized into a grid. Random...
A novel adaptive beamforming algorithm against large direction-of-arrival (DOA) mismatch without using optimization toolboxes is proposed. In contrast to previous works, this new beamformer employs two reconst...
In this paper, a SAR target recognition method is proposed based on the improved joint sparse representation (IJSR) model. The IJSR model can effectively combine multiple-view SAR images from the same physical...
Radar obtains its parameters on a grid whose design supports resolution of underlying radar processing. Existing radar exploits a regular grid although the resolution changes with stronger echoes at shorter ra...
Non-contact respiration and heartbeat rates detection could be applied to find survivors trapped in the disaster or the remote monitoring of the respiration and heartbeat of a patient. This study presents an i...
In this paper, we propose a new supervised feature extraction algorithm in synthetic aperture radar automatic target recognition (SAR ATR), called generalized neighbor discriminant embedding (GNDE). Based on m...
The demand for navigating pedestrian by using a hand-held mobile device increased remarkably over the past few years, especially in GPS-denied scenario. We propose a new pedestrian dead reckoning (PDR)-based n...
This paper first proposes a novel image separation method based on the hyperanalytic shearlet. By combining the advantages of both the hyperanalytic wavelet transform and the shear operation, hyperanalytic she...
With the increasingly diverse and complex requirements of radar systems and communication systems, the application of multifunction-phased array radar has become a trend, and the digital multi-beamforming tech...
Based on the combination of time domain averaging and correlation, we propose an effective time domain averaging and correlation-based spectrum sensing (TDA-C-SS) method used in very low signal-to-noise ratio ...
Since the traditional CFAR algorithm is not suitable for high-resolution target detection of synthetic aperture radar (SAR) images, a new two-stage target detection method based on variance weighted informatio...
In this paper, a cooperative transmission protocol for cognitive radio systems is proposed. In this protocol, the primary system comprises a transmitter (PT), a receiver (PR), and a decode-and-forward relay (R...
Due to the sparse distribution of reflectors in space, wireless channels are commonly sparse. Thus, utilizing the sparsity of channels in the delay-Doppler domain, a channel estimation method based on compress...
In this paper, rate distortion performance of nested sampling and coprime sampling is studied. It is shown that with the increasing of distortion, the data rate decreases. With these two sparse sampling algori...
Compressive sensing can minimize the collection of redundant data in the acquisition step. However, it requires a huge amount of storage and creates a tremendous computation burden due to the size of random me...
The cyclostationarity feature of modulated signals was widely applied for signal detection and parameter estimation. Previous studies on blind recognition of the modulation type of a received signal are genera...
Classification in large-scale data is a key problem in big data domain. The theory of compressive sensing enables the recovery of a sparse signal from a small set of linear, random projections which provides a...