Guohua Wang

Bio: Guohua Wang is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Radar & Waveform. The author has an hindex of 12, co-authored 44 publications receiving 466 citations. Previous affiliations of Guohua Wang include Central South University & Chinese Ministry of Education.

Designing single/multiple sparse frequency waveforms with sidelobe constraint

Abstract: Sparse frequency waveform with narrow stopbands sparsely distributed over a wide spectrum band is preferred for many radar and communication systems operating in a highly congested spectrum environment In this paper, a new method for designing sparse frequency waveform with low range side lobes are proposed The basic idea is to achieve waveform total performance improvement by minimising a new effective penalty function based on both requirements for the power spectrum density and the range side lobe through an iterative algorithm The proposed approach is efficient in computation and flexible in designing sparse frequency waveform Several design examples are also presented to show the validity of the proposed method An extension to design multiple waveforms for multiple-input multiple-output radar is also presented

Multiband Radar Signal Coherent Fusion Processing With IAA and apFFT

Abstract: This letter proposes a new method for multiband radar signal fusion by making use of all-phase fast Fourier transform (apFFT) algorithm and iterative adaptive approach (IAA). Central to the proposed method are, first, the mutual incoherence compensation between various subbands by apFFT algorithm, and second, the application of IAA to the mutually coherent subband measurements for signal fusion. Taking advantage of both algorithms, the proposed method effectively improves the range resolution with low sidelobes and performs robustly in the presence of noise. In particular, it requires no model information and therefore enables flexible implementation for practical application. The feasibility and effectiveness of the proposed algorithm are validated through both numerical simulations and raw data processing results.

Clutter Rank of STAP in MIMO Radar With Waveform Diversity

Abstract: In recent years, multiple-input multiple-output (MIMO) radar systems with space-time adaptive processing (STAP) have been proposed to improve radar performance. For MIMO radars with STAP, one big concern is the clutter rank. Current studies employ the rule of time-bandwidth product to predict the clutter rank of MIMO radars using orthogonal waveforms. This paper investigates clutter rank estimation for MIMO radar systems with more flexible waveform diversity, where waveforms are not constrained to be orthogonal. Under general waveform assumption, we have derived the clutter covariance matrix as a function of waveform covariance matrix (WCM). The clutter rank is then found to be determined by the rank and structure of the WCM. For different waveform cases, the WCM may be either full rank or rank deficient. Rules for estimation of clutter rank in these cases are provided and demonstrated by numerical simulations.

Polar Format Algorithm for Spotlight Bistatic SAR with Arbitrary Geometry Configuration

Abstract: This paper presents an efiective Polar Format Algorithm (PFA) for spotlight bistatic synthetic aperture radar (SAR) with arbitrary geometry conflguration. Nonuniform interpolation and resampling are adopted when converting raw data from polar coordinates to Cartesian coordinates according to the characteristics of raw data samples in spatial frequency space. Thus, the proposed algorithm avoids both rotation transformation and the calculation of azimuth compensation factor and thereby avoids the corresponding approximate error appeared in the conventional PFA. Meanwhile, the proposed algorithm inherits the character of decomposing 2- D interpolation to two 1-D interpolations from conventional PFA algorithm applied in monostatic SAR imaging. Therefore, the processing ∞ow, computation e-ciency and performance of the proposed algorithm are the same as those of conventional PFA for monostatic spotlight SAR. Point target simulations are provided to validate the proposed algorithm.

A review on the long short-term memory model

Abstract: Long short-term memory (LSTM) has transformed both machine learning and neurocomputing fields. According to several online sources, this model has improved Google’s speech recognition, greatly improved machine translations on Google Translate, and the answers of Amazon’s Alexa. This neural system is also employed by Facebook, reaching over 4 billion LSTM-based translations per day as of 2017. Interestingly, recurrent neural networks had shown a rather discrete performance until LSTM showed up. One reason for the success of this recurrent network lies in its ability to handle the exploding/vanishing gradient problem, which stands as a difficult issue to be circumvented when training recurrent or very deep neural networks. In this paper, we present a comprehensive review that covers LSTM’s formulation and training, relevant applications reported in the literature and code resources implementing this model for a toy example.

Radar waveform design in a spectrally crowded environment via nonconvex quadratic optimization

Abstract: Radar signal design in a spectrally crowded environment is a very challenging and topical problem due to the increasing demand for both military surveillance/remote-sensing capabilities and civilian wireless services. This paper deals with the synthesis of optimized radar waveforms ensuring spectral compatibility with the overlayed licensed electromagnetic radiators. A priori information, for instance, provided by a radio environmental map (REM), is exploited to force a spectral constraint on the radar waveform, which is thus the result of a constrained optimization process aimed at improving some radar performances (such as detection, sidelobes, resolution, tracking). The feasibility of the waveform optimization problem is extensively studied, and a solution technique leading to an optimal waveform is proposed. The procedure requires the relaxation of the original problem into a convex optimization problem and involves a polynomial computational complexity. At the analysis stage, the waveform performance is studied in terms of trade-off among the achievable signal to interference plus noise ratio (SINR), spectral shape, and the resulting autocorrelation function (ACF).

Waveform Design for Active Sensing Systems: A Computational Approach

Abstract: Active sensing applications such as radar, sonar and medical imaging, demand proper designs of the probing waveform. A well-synthesized waveform can significantly increase the system performance in terms of signal-to-interference ratio, spectrum containment, beampattern matching, target parameter estimation and so on. The focus of this work is on designing probing waveforms using computational algorithms. We first investigate designing waveforms with good correlation properties, which are widely useful in applications including range compression, channel estimation and spread spectrum. We consider both the design of a single sequence and that of a set of sequences, the former with only auto-correlations and the latter with auto- and cross-correlations. The proposed algorithms leverage FFT (fast Fourier transform) operations and can efficiently generate long sequences that were previously difficult to synthesize. We present a new derivation of the lower bound for sequence correlations that arises from the proposed algorithm framework. We show that such a lower bound can be closely approached by the newly designed sequences. A two-dimensional extension of the time-delay correlation function is the ambiguity function (AF) that involves a Doppler frequency shift. We give an overview of AF properties and discuss how to minimize AF sidelobes in a discrete formation. Besides good correlation properties, we also consider the stopband constraint that is required in the scenario of avoiding reserved frequency bands or strong electronic jammer. We present an algorithm that accounts for both correlation and stopband constraints. We finally consider transmit beampattern synthesis, particularly in the wideband case. We establish the relationship between a desired beampattern and underlying waveforms by using the Fourier transform. We highlight the increased design freedom resulting from the waveform diversity of a MIMO (multi-input multi-output) system.

A new radar waveform design algorithm with improved feasibility for spectral coexistence

Abstract: Radar signal design in a spectrally crowded environment is currently a challenge due to the increasing requests for spectrum from both military sensing applications and civilian wireless services. The goal of this paper is to improve a previously devised algorithm for the synthesis of optimized radar waveforms fulfilling spectral compatibility with overlaid licensed radiators. The new technique achieves an enhanced spectral coexistence with the surrounding electromagnetic environment through a suitable modulation of the transmitted waveform energy, which was kept fixed at the maximum level in the previously devised algorithm. At the analysis stage, the waveform performance is studied in terms of trade-off among the achievable Signal to Interference Plus Noise Ratio (SINR), spectral shape, and the resulting Autocorrelation Function (ACF), also in situations where the previous technique cannot be applied.