Proof-of-principle experimental demonstration of quantum secure imaging based on quantum key distribution

We present a quantum secure imaging(QSI) scheme based on the phase encoding and weak+vacuum decoy-state BB84 protocol of quantum key distribution(QKD). It allows us to implement a computational ghost imaging(CGI) system with more simplified equipment and reconstructed algorithm by using a digital micro-mirror device(DMD) to preset the specific spatial distribution of the light intensity. What is more, the quantum bit error rate(QBER) and the secure key rate analytical functions of QKD are used to see through the intercept-resend jamming attacks and ensure the authenticity of the imaging information. In the experiment, we obtained the image of the object quickly and efficiently by measuring the signal photon counts with a single-photon detector(SPD), and achieved a secure key rate of 571.0 bps and a secure QBER of 3.99%, which is well below the lower bound of QBER of 14.51%. Besides, our imaging system uses a laser with invisible wavelength of 1550 nm, whose intensity is as low as single-photon, that can realize weak-light imaging and is immune to the stray light or air turbulence, thus it will become a better choice for quantum security radar against intercept-resend jamming attacks.

DOA estimation of high-dimensional signals based on Krylov subspace and weighted l_(1)-norm

With the extensive application of large-scale array antennas,the increasing number of array elements leads to the increasing dimension of received signals,making it difficult to meet the real-time requirement of direction of arrival(DOA)estimation due to the computational complexity of algorithms.Traditional subspace algorithms require estimation of the covariance matrix,which has high computational complexity and is prone to producing spurious peaks.In order to reduce the computational complexity of DOA estimation algorithms and improve their estimation accuracy under large array elements,this paper proposes a DOA estimation method based on Krylov subspace and weighted l_(1)-norm.The method uses the multistage Wiener filter(MSWF)iteration to solve the basis of the Krylov subspace as an estimate of the signal subspace,further uses the measurement matrix to reduce the dimensionality of the signal subspace observation,constructs a weighted matrix,and combines the sparse reconstruction to establish a convex optimization function based on the residual sum of squares and weighted l_(1)-norm to solve the target DOA.Simulation results show that the proposed method has high resolution under large array conditions,effectively suppresses spurious peaks,reduces computational complexity,and has good robustness for low signal to noise ratio(SNR)environment.

Sparse flight spotlight mode 3-D imaging of spaceborne SAR based on sparse spectrum and principal component analysis

The spaceborne synthetic aperture radar(SAR)sparse flight 3-D imaging technology through multiple observations of the cross-track direction is designed to form the cross-track equivalent aperture,and achieve the third dimensionality recognition.In this paper,combined with the actual triple star orbits,a sparse flight spaceborne SAR 3-D imaging method based on the sparse spectrum of interferometry and the principal component analysis(PCA)is presented.Firstly,interferometric processing is utilized to reach an effective sparse representation of radar images in the frequency domain.Secondly,as a method with simple principle and fast calculation,the PCA is introduced to extract the main features of the image spectrum according to its principal characteristics.Finally,the 3-D image can be obtained by inverse transformation of the reconstructed spectrum by the PCA.The simulation results of 4.84 km equivalent cross-track aperture and corresponding 1.78 m cross-track resolution verify the effective suppression of this method on high-frequency sidelobe noise introduced by sparse flight with a sparsity of 49%and random noise introduced by the receiver.Meanwhile,due to the influence of orbit distribution of the actual triple star orbits,the simulation results of the sparse flight with the 7-bit Barker code orbits are given as a comparison and reference to illuminate the significance of orbit distribution for this reconstruction results.This method has prospects for sparse flight 3-D imaging in high latitude areas for its short revisit period.

Equalization Reconstruction Algorithm Based on Reference Signal Frequency Domain Block Joint for DTMB-Based Passive Radar

Channel equalization plays a pivotal role within the reconstruction phase of passive radar reference signals.In the context of reconstructing digital terrestrial multimedia broadcasting(DTMB)signals for low-slow-small(LSS)target detection,a novel frequency domain block joint equalization algorithm is presented in this article.From the DTMB signal frame structure and channel multipath transmission characteristics,this article adopts a unconventional approach where the delay and frame structure of each DTMB signal frame are reconfigured to create a circular convolution block,facilitating concurrent fast Fourier transform(FFT)calculations.Following equalization,an inverse fast Fourier transform(IFFT)-based joint output and subsequent data reordering are executed to finalize the equalization process for the DTMB signal.Simulation and measured data confirm that this algorithm outperforms conventional techniques by reducing signal errors rate and enhancing real-time processing.In passive radar LSS detection,it effectively suppresses multipath and noise through frequency domain equalization,reducing false alarms and improving the capabilities of weak target detection.

A Novel Clutter Suppression Algorithm for Low-Slow-Small Targets Detecting Based on Sparse Adaptive Filtering

Passive detection of low-slow-small(LSS)targets is easily interfered by direct signal and multipath clutter,and the traditional clutter suppression method has the contradiction between step size and convergence rate.In this paper,a frequency domain clutter suppression algorithm based on sparse adaptive filtering is proposed.The pulse compression operation between the error signal and the input reference signal is added to the cost function as a sparsity constraint,and the criterion for filter weight updating is improved to obtain a purer echo signal.At the same time,the step size and penalty factor are brought into the adaptive iteration process,and the input data is used to drive the adaptive changes of parameters such as step size.The proposed algorithm has a small amount of calculation,which improves the robustness to parameters such as step size,reduces the weight error of the filter and has a good clutter suppression performance.

OPTIMIZATION ON ANTENNA PATTERN OF SPACEBORNE SAR WITH IMPROVED NSGA-Ⅱ

Optimization of antenna array pattern used in a spaceborne Synthetic Aperture Radar (SAR) system is considered in this study. A robust evolutionary algorithm, Non-dominated Sorting Genetic Algorithms (the improved NSGA-Ⅱ), is applied on a spaceborne SAR antenna pattern design. The system consists of two objective functions with two constraints. Pareto fronts are generated as a result of multi-objective optimization. After being validated by a test problem ZDT4, the algorithms are used to synthesize spaceborne SAR antenna radiation pattern. The good results with low Ambi- guity-to-Signal Ratio (ASR) and high directivity are obtained in the paper.

IMAGING AND MTI PROCESSING BASED ON DUAL-FREQUENCIES DUAL-APERTURES SPACEBORNE SAR

Based on dual-frequencies dual-apertures spaceborne SAR (Synthetic Aperture Radar), a new SAR system with four receiving channels and two operation modes is presented in this paper. SAR imaging and Moving Target Indication (MTI) are studied in this system. High resolution imaging with wide swath is implemented by the Mode I, and MTI is completed by the Mode II. High azimuth resolution is achieved by the Displaced Phase Center (DPC) multibeam technique. And the Coherent Accumulation (CA) method, which combines dual channels data of different carrier frequency, is used to enhance the range resolution. For the data of different carrier frequency, the two aperture inter- ferometric processing is executed to implement clutter cancellation, respectively. And the couple of clutter suppressed data are employed to implement Dual Carrier Frequency Conjugate Processing (DCFCP), then both slow and fast moving targets detection can be completed, followed by moving target imaging. The simulation results show the validity of the signal processing method of this new SAR system.

ADAPTIVE KURTOSIS OPTIMIZATION AUTOFOCUS ALGORITHM

This paper proposes an autofocus algorithm used for Synthetic Aperture Radar (SAR) images, called Adaptive Kurtosis Optimization Autofocus Algorithm (AKOAA). The AKOAA can reduce the differ-ence between initial value and real value in focusing by adaptively adjusting the initial value, therefore effec-tively improve the local extremum problem in the Contrast Optimization Autofocus Algorithm (COAA) and speed up the convergence velocity. The principle and realization method of AKOAA are thoroughly investi-gated, and experimental results using real L-band SAR data show that the focus speed of AKOAA is nearly doubled compared with that of the COAA, and the image contrast of AKOAA is improved as well.

Circular SAR processing using an improved omega-k type algorithm

An improved circular synthetic aperture radar（CSAR） imaging algorithm of omega-k（ω-k） type mainly for reconstructing an image on a cylindrical surface is proposed.In the typical CSAR ω-k algorithm,the rage trajectory is approximated by Taylor series expansion to the quadratic terms,which limits the valid synthetic aperture length and the angular reconstruction range severely.Based on the model of the CSAR echo signal,the proposed algorithm directly transforms the signal to the two-dimensional（2D） wavenumber domain,not using approximation processing to the range trajectory.Based on form of the signal spectrum in the wavenumber domain,the formula for the wavenumber domain interpolation of the w-k algorithm is deduced,and the wavenumber spectrum of the reference point used for bulk compression is obtained from numerical method.The improved CSAR ω-k imaging algorithm increases the valid synthetic aperture length and the angular area greatly and hence improves the angular resolution of the cylindrical imaging.Additionally,the proposed algorithm can be repeated on different cylindrical surfaces to achieve three dimensional（3D） image reconstruction.The 3D spatial resolution of the CSAR system is discussed,and the simulation results validate the correctness of the analysis and the feasibility of the algorithm.

Effect of orbital errors on the geosynchronous circular synthetic aperture radar imaging and interferometric processing

The geosynchronous circular synthetic aperture radar (GEOCSAR) is an innovative SAR system,which can produce high resolution three-dimensional (3D) images and has the potential to provide 3D deformation measurement.With an orbit altitude of approximately 36 000 km,the orbit motion and orbit disturbance effects of GEOCSAR behave differently from those of the conventional spaceborne SAR.In this paper,we analyze the effects of orbit errors on GEOCSAR imaging and interferometric processing.First,we present the GEOCSAR imaging geometry and the orbit errors model based on perturbation analysis.Then,we give the GEOCSAR signal formulation based on imaging geometry,and analyze the effect of the orbit error on the output focused signal.By interferometric processing on the 3D reconstructed images,the relationship between satellite orbit errors and the interferometric phase is deduced.Simulations demonstrate the effects of orbit errors on the GEOCSAR images,interferograms,and the deformations.The conclusions are that the required relative accuracy of orbit estimation should be at centimeter level for GEOCSAR imaging at L-band,and that millimeter-scale accuracy is needed for GEOCSAR interferometric processing.

REVERSE-RANGE-DOPPLER METHOD FOR AUTOMATED GEOCODING SAR IMAGES

Range-Doppler (RD) method and Reverse-Range-Doppler (RRD) method are combined together to achieve automatic geocoding of Synthetic Aperture Radar (SAR) image quickly and accurately in the paper. The RD method is firstly used to locate the four corners of the image, then the other pixels of the image can be located by Reverse-Range-Doppler (RRD) method. Resampling is performed at last. The approach has an advantage over previous techniques in that it does not require ground control points and is independent of spacecraft attitude knowledge or control. It can compensate the shift due to the assumed Doppler frequency in SAR image preprocessing. RRD simplifies the process of RD, therefore speeds up the computation. The experimental results show that a SAR image can be automated geocoded in 30 s using the single CPU (3 GHz) with 1 G memory and an accuracy of 10 m is attainable with this method.

Photonics-enabled distributed MIMO radar for high-resolution 3D imaging

Three-dimensional(3D)imaging radar is an advanced sensor applied in space surveillance and target recognition for supplying 3D geometric features and supporting visualization.However,high 3D resolution requires both broadband operation and a large 2D aperture,which are difficult and complex for conventional radars.This paper presents a photonics-enabled distributed multiple-input and multiple-output(MIMO)radar with a centralized architecture.By use of photonic multi-dimensional multiplexing,multi-channel signal generation and reception are implemented on a shared reference signal in a central office,enabling a highly coherent network with a simple structure.Additionally,a sparse array and a synthetic aperture are combined to efficiently reduce the required transceivers,further weakening the dilemma between system complexity and angular resolution.A 4×4 MIMO radar is established and evaluated in field tests.A high-resolution 3D image of a non-cooperative aircraft is obtained,in which rich details are displayed.From a comparison with electronics-based radar,significant resolution improvement is observed.The results verify the superior imaging capability and practicability of the proposed radar and its great potential to outperform conventional technologies in target classification and recognition applications.

Distributed UAV swarm based on spatial grid model

Unmanned Aerial Vehicle(UAV)swarm has become the inevitable trend of development,which will enjoy broad prospects to be applied in the future.However,the change of UAV application mode will certainly bring new technical challenges in the flight management,environmental perception,collaborative control as well as other fields.This paper considers that it can be an effective solution to realize the unified management of spatial information on the UAV platform by adopting the spatial grid model represented by the Geo SOT-3D,and to reduce the workload of flight management,airborne environmental perception,and ranging in the neighborhood through the association and query of all spatial grid data.