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In practical radar systems, amplitude and phase distortion exists in antenna channels, which has a bad effect on subsequent signal processing. So, waveform generator is needed to launch signals covering radar waveband to make a channel test. Ensuring accuracy and bandwidth of the waveform generator is a fundamental requirement. Accordingly, this paper proposed a full flow of wideband waveform generation, including hardware design, parameter selection and pre-distortion correction. Direct digital waveform synthesize (DDWS) is done in the first, second, or third Nyquist region without analog frequency conversion, ensuring the quality of the waveform. And pre-distortion measures are taken to correct the waveform reconstruction error. The waveform generator designed in this paper has been successfully applied in a digital array radar (DAR) system, which can generate 500MHz bandwidth LFM signal, covering multiple waveband including 0.65 GHz–1.15 GHz, 1.25 GHz–1.75 GHz, 1.85 GHz–2.35 GHz, 2.45 GHz–2.95 GHz.

Amainlobe interference suppression method is proposed in this paper, which can still work when the signal of interest (SOI) is present in the training data. In this method, the iterative adaptive approach (IAA) is applied to spatial spectrum estimation at first. Then, IAA spatial spectrum is used to reconstruct the interference-plus-noise covariance matrix (INCM). Next, the eigenvector associated with mainlobe interference in INCM is determined and the eigen-projection matrix can be calculated to suppress the mainlobe interference. Meanwhile, the sidelobe-interference-plus-noise covariance matrix (SINCM) can be reconstructed. Finally, the adaptive weight vector is obtained. One main advantage is that the proposed method can deal with coherent mainlobe interference and sidelobe interferences simultaneously. Simulation results demonstrate the effectiveness of the proposed method.

A modified spectrogram approach is presented for the analysis of frequency modulated radar signals, taking the entropy as the evaluation criterion. The characteristics of spectrogram are firstly introduced. After the analysis of performance evaluation for time-frequency distribution, entropy is studied as a quantitative parameter for concentration and cross-term suppression. An improved spectrogram method based on multi-window is proposed and verified by simulated data through entropy criterion.

In this paper, a novel detection method based on Fractional Fourier Transform is proposed. Compared with the conventional signal processing procedure, the proposed method achieved the similar performance to the matched filter without the estimation of transmitter signal parameters and the reconstruction of transmitted signal. The evaluations of the proposed method are summarized.

Aiming at pulse detection in practical complex environment applications, a new method based on frequency-domain constant false alarm rate (CFAR) detector is proposed in this paper. First, short time Fourier transform (STFT) is carried out. Then frequency-domain CFAR detector is used to judge whether signal exists or not in each STFT window. Finally the complete pulse is detected by sliding the STFT window. The threshold of the method is obtained and updated automatically according to the current noise level. The proposed method is robust to signal to noise ratio (SNR) and can work well without artificially setting detection threshold. Simulation results and real data applications validate the effectiveness of the proposed method.

This paper proposes a signal processing method of passive bistatic radar (PBR) emploiting a phased array radar as an illuminator. Compared with other opportunity illuminators, the transmitting signal of a radar usually has a better ambiguity function, which leads to higher range resolution. Two channels are needed in a PBR system. The reference channel is used to estimate radar signal parameters and reconstruct directly propagated signal. The surveillance channel is used to receive scattered wave. An array antenna and simultaneous multibeam algorithm are necessary in the surveillance channel due to the flexible beam scanning of the phased array radar. The procedure of the proposed method is explained in detail that is then followed by a field experiment. Preliminary results from the field experiment show that the proposed method can be applied to target localization successfully.

In this work, we present a binomial splitting Gaussian mixture unscented Kalman probability hypothesis density (BSGM-UKPHD) filter. The BSGM-UKPHD filter applies a binomial splitting strategy to the original Gaussian mixture unscented Kalman probability hypothesis density (GM-UKPHD) filter, to gain performance promotion when the measurement function is nonlinear. The binomial splitting approximates every Gaussian component of the predicted probability hypothesis density (PHD) with a sum of weighted Gaussian distributions that have smaller covariance. Thus the state update of the nonlinear measurements will cause smaller errors. The binomial splitting preserves the mean and covariance of the original Gaussian distribution, and uses weights from the standardized binomial distribution. Simulation results show that, the proposed BSGM-UKPHD filter outperforms the GM-UKPHD filter and the Gaussian mixture extended Kalman PHD (GM-EKPHD) filter.

An approach to wideband beamformer with frequency invariant property is proposed by optimising the space-time two-dimensional finite impulse response (FIR) filters. Frequency invariant beam pattern brings some restrictions on frequency bandwith and filter length. Using the Landau-Pollak theorem to estimate the rank of wideband signals, we give the lower bound of filter length respect to array element numbers and the relative bandwidth of wideband signal. With the expression of beam pattern using real and symmetric FIR filters, we develop corresponding optimisation formation considering the robustness of beamformer as well, which can be easily solved by the least mean square (LMS) algorithm. A design example is provided to show the effectiveness of the proposed method. Defence Science Journal, 2012, 62(4), pp.243-247 , DOI:http://dx.doi.org/10.14429/dsj.62.965

Data fusion using subbands, which can obtain a higher range resolution without altering the bandwidth, hardware, and sampling rate of the radar system, has attracted more and more attention in recent years. A method of ISAR imaging based on subbands fusion and high precision parameter estimation of geometrical theory of diffraction (GTD) model is presented in this paper. To resolve the incoherence problem in subbands data, a coherent processing method is adopted. Based on an all-pole model, the phase difference of pole and scattering coefficient between each sub-band is used to effectively estimate the incoherent components. After coherent processing, the high and low frequency sub-band data can be expressed as a uniform all-pole model. The gapped-data amplitude and phase estimation (GAPES) algorithm is used to fill up the gapped band. Finally, fusion data is gained by high precision parameter estimation of GTD-all-pole model with full-band data, such as scattering center number, scattering center type and amplitude. The experimental results of simulated data show the validity of the algorithm.

In this paper, we present a new direction of arrival (DOA) estimation algorithm for coherent wideband signals. This algorithm is based on the test of orthogonality of projected subspaces (TOPS) method which will fail to work in real environments where signals are highly correlated or coherent due to multipath propagation. In order to overcome the disadvantage, we combine spatial smoothing techniques with TOPS method so that the rank of covariance matrix is equal to the number of signal sources even signals received are coherent. Unlike other coherent wideband methods, such as the coherent signal subspace method (CSSM) and WAVES, the new method does not require any initial DOA estimation, thus avoiding errors brought by incorrect initial values. Simulations on computer and experiments in the anechoic chamber based on an 8-elements digital array radar test-bed operating at L & S band are carried out. Simulation and experimental results validate the effectiveness of proposed algorithm.

In this paper, we propose a new pulse edge detection method based on short time Fourier transform (STFT) and difference of boxes (DOB) filter. Firstly, detect the coarse starting and ending positions in frequency domain after STFT. Then obtain the precise pulse edge through DOB filter. It achieves a better performance than the classical energy detection (ED) method, especially when signal to noise ratio (SNR) is low. Simulation results and real data application validate the effectiveness of the proposed method.

Aircraft recognition is of great theoretical and practical significance in fields like remote sensing, navigation and traffic monitoring. It seems difficult to recognize aircraft in low-resolution SAR imagery because of difference between real image and simulated template induced by poor image quality and inherent simulation error. Aiming at this problem, an aircraft recognition method using peak feature matching is proposed. Firstly, the scattering centers of detected target are extracted in low-resolution SAR imagery using an adaptive threshold. Secondly, the extracted peak features are used to estimate the aircraft azimuth angle, which can be used to reduce the searching space in template database dramatically. Finally, a novel peak feature matching method using spatial distribution information of entire peak set is proposed to measure the similarity between detected target and simulated template. Experimental results demonstrate the good performance of the proposed method on a variety of low-resolution SAR imageries.

With regard to aircraft target detection in complex clouds background of infrared search system, this paper proposes a new target detection algorithm based on combination of intensity and edge of the target. Firstly, the algorithm segments the image by iterative OTSU segmentation method, at the same time, it detects the edge by morphological processing. Then, by the fusion decision of the combination of segmentation and edge result, it detects the real aircraft targets and eliminates the clouds false alarm. The algorithm overcomes the too much clouds false alarm problems of the traditional target detection method. The test data detection shows, the algorithm enables effective detection of aircraft target in complex clouds background with low-rate false warning. The algorithm has realized real-time processing and has been effectively applied to the development of the engineering sample of the Wide Field of View Infrared Search System.

The radar targets number increases from one to more when the ballistic missile is in the process of separating the lower stage rocket or casting covers or other components. It is vital to identify the warhead target quickly among these multiple targets for radar tracking. A fast recognition method of the warhead target is proposed to solve this problem by using kinematic features, utilizing fuzzy comprehensive method and information fusion method. In order to weaken the influence of radar measurement noise, an extended Kalman filter with constant jerk model (CJEKF) is applied to obtain more accurate target’s motion information. The simulation shows the validity of the algorithm and the effects of the radar measurement precision upon the algorithm’s performance.

Mn-doped TiO2 grown on reduced graphene oxide(rGO) was synthesized by one-pot hydrothermal method and the photocatalytic removal of Cr by the material was investigated under sunlight. The materials were characterized by a combination of scanning electron microscopy, transmission electron microscopy, X-ray diffraction, thermogravimetric analysis, Brunauer-Emmett-Teller method, UV-vis diffuse reflectance spectra, photoluminescence spectra, electrochemical impedance spectroscopy and X-ray photoelectron spectroscopy. Cr(total) removal efficiency of the material is 97.32% in 30min and 99.02% in 60min under sunlight irradiation, as the initial concentration of Cr(VI) is 20mg/L. The high photocatalytic activity under visible light is considered mainly due to the Mn-doping, and rGO plays an important role in the synergetic effect of adsorption and photocatalysis to sustain the high efficient removal of Cr(VI) and Cr(III). Cr(VI) adsorbed on the surface of rGO is reduced to Cr(III) by photo electrons which are transported through rGO, and the reaction product Cr(III) continues to be adsorbed. The process contributes to the release of abundant photocatalytic sites of Mn-TiO2 and improves photocatalytic efficiency. The excellent adsorption and photocatalytic effect with the explanation of the synergetic mechanism are very useful not only for fundamental research but also for the potential practical applications.

Amplitude-phase errors and mutual coupling errors among multi-channels in digital array radar (DAR) will seriously deteriorate the performance of signal processing such as digital beam-forming (DBF) and high resolution direction finding. In this paper, a combined algorithm for error calibration in DAR has been demonstrated. The algorithm firstly estimates the amplitude-phase errors of each channel using interior calibration sources with the help of the calibration network. Then the signals from far field are received and the amplitude-phase errors are compensated. According to the subspace theories, the relationship between the principle eigenvectors and distorted steering vectors is expressed, and the cost function containing the mutual coupling matrix (MCM) and incident directions is established. Making use of the properties of MCM of uniform linear array, Gauss-Newton method is implied to iteratively compute the MCM and the direction of arrival (DOA). Simulation results have shown the effectiveness and performance of proposed algorithm. Based on an 8-elements DAR test-bed, experiments are carried out in anechoic chamber. The results illustrate that the algorithm is feasible in actual systems.

Acoustic radar has the advantages of low cost, high resolution, and the simplicity for sampling and storage. Recent years, it has been widely studied and applied in fields such as target detection, recognition and imaging. However, the sound signal is usually wideband. The wideband correlation function is often used in active and passive detections. To address the problems of the errors and computational burden introduced by approximation and the interpolation of signal dilation, a Mellin transform based multi-pulse weak target detection method is proposed. The method can reduce the Mellin sampling numbers and the detection result can be updated pulse by pulse.

This paper describes a convenient technique for polarimetric calibration of a polarimetric inverse synthetic aperture radar (ISAR) system with giant aperture antennas. With this technique, only a polarimetric active radar calibrator (PARC) with two independently rotary antennas is needed to calibrate the radar system. A complete polarimetric measurement mathematical model is adopted without any assumption. The calibration method is on the basis of Pauli matrix decomposition. We use four Lexicographic calibrators provided by the PARC instead of three Pauli calibrators. For these reasons, the calculation of error coefficients in the model is easier and the calibration performance is better than that in other calibration methods, such as the generalized calibration technique (GCT) and target spinor calibration (TSC). An experimental validation is presented. Excellent agreement is obtained between the theoretical analysis and the experimental results. A 35 dB improvement in the system cross-polarization isolation is obtained using the proposed method.

Amplitude and phase errors among multi-channels in digital array radar will seriously deteriorate the performance of digital beam-forming (DBF) and high resolution direction finding. This paper describes a practical calibration algorithm that calibrates amplitude and phase errors coarsely using interior calibration source and then realizes precise calibration by eigen-decomposition approach. Based on an 8-elements digital array radar test-bed operating at L & S band, experiments are carried out in the anechoic chamber and outdoors. After calibration, the radiation pattern is nearly ideal and the direction of arrival (DOA) estimation of measured data achieves a good performance. The expected results illustrate that interior-auto calibration algorithm is fairly effective and feasible in actual systems.

In order to deal with the track coalescence problem of the joint probabilistic data association (JPDA) algorithm, a novel approach from a state bias removal point of view is developed in this paper. The factors that JPDA causes the state bias are analyzed, and the direct computation equation of the bias in the ideal case is given. Then based on the definitions of target detection hypothesis and target-to-target association hypothesis, the bias estimation is extended to the general and practical case. Finally, the estimated bias is removed from the state updated by JPDA to generate the unbiased state. The results of Monte Carlo simulations show that the proposed method can handle track coalescence and presents better performance when compared with the traditional methods.

Laser detection based on the “cat’s eye effect” has become the hot research project for its initiative compared to the passivity of sound detection and infrared detection. And the target detection is one of the core technologies in this system. The paper puts forward a method for detecting small targets based on cumulative weighted value of target properties using given data. Firstly, we make a frame difference to the images, then make image processing based on Morphology Principles. Secondly, we segment images, and screen the targets; then find some interesting locations. Finally, comparing to a quantity of frames, we locate the target. We did an exam to 394 true frames, the experimental result shows that the mathod can detect small targets efficiently.

In case of passive bistatic radar (PBR) using a phased array radar (PAR) as the non-cooperative illuminator, there are problems that signal parameters of the transmitter are unknown and signal coherence is damaged. Aiming at the issues, this article presents a target detection method for PBR using PAR as the non-cooperative illuminator. Firstly, the method uses the direct wave to estimate signal parameters, and then pulse compresses scattered wave. Secondly, the compressed wave is aligned by fast-time dimension so that a Range-Time (R-T) map can be obtained. Finally, the target is detected according to the trajectory in R-T map. The field experimental results demonstrate the effectiveness of this method.

According to the characteristics of passive radar, a new method of multi-target track processing based on Kalman filter is proposed, which is Track Searching based on Kalman Filter(TSKF). In this method, velocity method[1] is used firstly to find possible track and an adaptive association gate is adopted to associate with successive measurements. Secondly, Kalman filter is used to predict locations of successive measurements. Finally, Nearest Neighbor method is used to search all points belonging to the possible track and confirm the track. After that the algorithm need to go to a next cycle to find new target tracks. In order to demonstrate the validity of the proposed method, experiments with real data were conducted to compare with traditional Kalman method.

Passive bistatic radars (PBR) using illuminators of opportunity have attracted much attention over the last years. However, most PBR systems have been rather experimental set-ups tailored to a single frequency band or implemented as laboratory test devices. In case of PBR exploiting transmitters for sources of illumination, there is a problem that radar transmitting signal parameters are unknown, and radar signal processing cannot be conducted accurately. This paper presents a study to demonstrate the feasibility of using a Digital Array Passive Bistatic Radar (DA-PBR) system in weak target detection. The design considerations and the resulting DA-PBR structure are described and the evaluations of various measurement campaigns with this system are summarized.

At present, most of the passive radar system researches utilize FM radios, TV broadcasts, navigation satellites etc. as illuminators. The transmitted signals are not specifically designed radar waveforms. In this work, a frequency agile, phased array Air Surveillance Radar (ASR) is used as illuminator of opportunity for weak target detection. In this case, coherent integration methods are not applicable. Thus, a parametric non-coherent integration algorithm based on task de-interweaving is proposed. Numerical experiments verify that this method is effective in weak target detection.

A novel robust adaptive beamforming algorithm based on a reconstructed covariance matrix is proposed to combat the desired signal look direction error. First, the iterative adaptive approach (IAA) algorithm is performed to acquire the true signal direction and the spatial spectrum simultaneously. Secondly, the IAA spatial spectrum is used to reconstruct the interference-plus-noise covariance matrix. Compared with other reconstruction-based algorithms, this approach is capable of dealing with coherent interferences without any optimisation procedures.

Wideband digital array radar (WDAR) has the potential of achieving high resolution and providing multi-function simultaneously, but it is limited in practical use due to the effects of frequency-dependent array model errors. A method to jointly calibrate gain-phase perturbations and mutual coupling effects in WDRA is presented. First, a sparse-based method is deployed to calculate the narrowband calibration matrices at a set of discrete frequencies. Moreover, using a group of finite-impulse response filters, a wideband calibration matrix is constructed, which is capable of compensating the imperfections of array at any frequency in the band. The proposed method is simple and feasible for actual applications, which can be applied to arbitrary array geometries. Simulations and experimental results with measured data demonstrate the validity and effectiveness of the proposed method.

Digital orthogonal receiver is one of the key techniques in digital receiver of soft radar, and compressed sensing is attracting more and more attention in radar signal processing. In this paper, we propose a CS digital orthogonal receiver for wideband radar which utilizes compressed sampling in the acquisition of radar raw data. In order to reconstruct complex signal from sub-sampled raw data, a novel sparse dictionary is proposed to represent the real-valued radar raw signal sparsely. Using our dictionary and CS algorithm, we can reconstruct the complex-valued radar signal from sub-sampled echoes. Compared with conventional digital orthogonal radar receiver, the architecture of receiver in this paper is more simplified and the sampling frequency of ADC is reduced sharply. At the same time, the range profile can be obtained during the reconstruction, so the matched filtering can be eliminated in the receiver. Some experiments on ISAR imaging based on simulated data prove that the phase information of radar echoes is well reserved in our orthogonal receiver and the whole design is effective for wideband radar.

Point pattern matching (PPM) including the hard assignment and soft assignment approaches has attracted much attention. The typical probability based method is Coherent Point Drift (CPD) algorithm, which treats one point set(named model point set) as centroids of Gaussian mixture model, and then fits it to the other(named target point set). It uses the expectation maximization (EM) framework, where the point correspondences and transformation parameters are updated alternately. But the anti-outlier performance of CPD is not robust enough as outliers have always been involved in operation until CPD converges. So we proposed an automatic outlier suppression mechanism (AOS) to overcome the shortages of CPD. Firstly, inliers or outliers are judged by converting matching probability matrix into doubly stochastic matrix. Then, transformation parameters are fitted using accurate matching point sets. Finally, the model point set is forced to move coherently to target point set by this transformation model. The transformed model point set is imported into EM iteration again and the cycle repeats itself. The iteration finishes when matching probability matrix converges or the cardinality of accurate matching point set reaches maximum. Besides, the covariance should be updated by the newest position error before re-entering EM algorithm. The experimental results based on both synthetic and real data indicate that compared with other algorithms, AOS-CPD is more robust and efficient. It offers a good practicability and accuracy in rigid PPM applications.

This paper investigates the problem of detecting changes on multitemporal SAR imagery in an unsupervised way. A novel change indicator was developed to identify the temporal changes. It is computed by the local average of the amplitude ratio comparing the exponentiation of the local average of the logarithm–transformed amplitude ratio. Compared with the classical ratio of local means, the novel operator is more effective in identifying the changed pixels even the local means are preserved. The classification is implemented by an automatic thresholding algorithm derived from a new Riemannian metric defined in the differential geometry structure. The geodesic distance derived from the new Riemannian metric provides a way to compare the distance between the probability distributions of the changed class and the non-changed class. The probability density functions of the changed and non-changed classes are characterized over the photometric variable. By maximizing the distance between the probability density distributions of the two classes, the misclassification errors are minimized and the optimal threshold is achieved accordingly. Experiments were carried on portions of multi-temporal Radarsat-1 SAR data. The obtained accuracies confirm the effectiveness of the proposed approach.

In this paper, we propose an unsupervised change detection method using the labeled co-occurrence matrix on multitemporal SAR images. In SAR images, each land cover (LC) class has a distinct reflectivity to radar signals and presents a specific backscattering value. Generally, the amplitude of the SAR images can be roughly clustered into three classes according to the backscattering behaviors of the LC classes. The changes occurred between the images can be considered as a backscattering variation that is changed from one backscattering class into another. As a result, we analyzed the possible cases of the positive and negative backscattering variations, and merged the initial three backscattering classes into two classes with the pixel in the medium backscattering class being attached to the strong backscattering class and the low backscattering class respectively in a membership degree. Two pairs of fuzzy-label images are derived accordingly, where each pair of fuzzy-label images are computed from the multi-temporal SAR data. The labeled cooccurrence matrix is computed locally on each pair of fuzzy-label images by combining the membership values in a conjunctive operator, and the autocorrelation feature is extracted. The classifications are implemented by Otsu Nthresholding algorithm on the derived two autocorrelation features. The final binary change detection map is achieved by combining the obtained two classification results. Experiments were carried on portions of multi-temporal Radarsat-1 SAR data. The effectiveness of the proposed approach was confirmed.

In order to enhance the robustness of building recognition in forward-looking infrared (FLIR) images, an effective method based on big template is proposed. Big template is a set of small templates which contains a great amount of information of surface features. Its information content cannot be matched by any small template and it has advantages in conquering noise interference or incompleteness and avoiding erroneous judgments. Firstly, digital surface model (DSM) was utilized to make big template, distance transformation was operated on the big template, and region of interest (ROI) was extracted by the way of template matching between the big template and contour of real-time image. Secondly, corners were detected from the big template, response function was defined by utilizing gradients and phases of corners and their neighborhoods, a kind of similarity measure was designed based on the response function and overlap ratio, then the template and real-time image were matched accurately. Finally, a large number of image data was used to test the performance of the algorithm, and optimal parameters selection criterion was designed. Test results indicate that the target matching ratio of the algorithm can reach 95%, it has effectively solved the problem of building recognition under the conditions of noise disturbance, incompleteness or the target is not in view.

For infrared imaging systems with high sampling width applying to the traditional display device or real-time processing system with 8-bit data width, this paper presents a new high dynamic range compression and detail enhancement (DRCDDE) algorithm for infrared images. First, a bilateral filter is adopted to separate the original image into two parts: the base component that contains large-scale signal variations, and the detail component that contains high-frequency information. Then, the operator model for DRC with local-contrast preservation is established, along with a new proposed nonlinear intensity transfer function (ITF) to implement adaptive DRC of the base component. For the detail component, depending on the local statistical characteristics, we set up suitable intensity level extension criteria to enhance the low-contrast details and suppress noise. Finally, the results of the two components are recombined with a weighted coefficient. Experiment results by real infrared data, and quantitative comparison with other well-established methods, show the better performance of the proposed algorithm. Furthermore, the technique could effectively project a dim target while suppressing noise, which is beneficial to image display and target detection.

This paper investigates an imaging method for space debris by wideband radar. Because of the spinning of the space debris, the correlation of the adjacent high range resolution profile (HRRP) is undermined and the motion compensation method for dechirped echoes is invalid. Therefore, a wideband imaging method of space debris based on intermediate frequency sampling (DIFS) signals is proposed in this paper. The IF sampling technique has the advantage in maintaining the coherence of echo pulse, which eliminates the negative influence of the spin. Firstly, the accurate translational motion parameters of the target are estimated from the radar observations by using of polynomial fitting method. Then the translational motion compensation is carried out in frequency domain based on the target motion track. Finally, the improved back projection transform (BPT) method is used for image reconstruction, which transforms the echo from range-time domain to the scattering point distribution plane by coherent integral. A well-focused and high resolution image of the space debris without side lobe peaks can be obtained in the end. The simulation results indicate the validity of the proposed method in this paper.

In this paper the effects of orbits motion makes for scattering centers trajectory is analyzed, and introduced to scattering centers association, as a constraint. A screening method of feature points is presented to analysis the false points of reconstructed result, and the wrong association which lead these false points. The loop iteration between 3D reconstruction and association result makes the precision of final reconstructed result have a further improvement. The simulation data shows the validity of the algorithm.

High-resolution, multi-pixels and large field of view (FOV) infrared (IR) detector is an important research direction, which greatly improves the target detection capability. This paper addresses the infrared target detection under the guidance of attention mechanism. The Gabor filter is used to extract the elementary visual feature of infrared image for its orientation selectiveness. Then it researches the reasons that produce visual saliency in frequency domain, and provides the multichannel feature combination strategy to generate the feature map. Further, a novel saliency detection model using Fourier spectrum filtering, is presented to calculate feature regions of infrared image. Experimental results using a wide range of real IR images demonstrate that the proposed algorithm is robust and effective, yielding satisfying results for infrared target detection in large FOV with complex background and low SNR.

Radar imaging is an ill-posed linear inverse problem and compressed sensing (CS) has been proved to have tremendous potential in this field. This paper surveys the theory of radar imaging and a conclusion is drawn that the processing of ISAR imaging can be denoted mathematically as a problem of 2D sparse decomposition. Based on CS, we propose a novel measuring strategy for ISAR imaging radar and utilize random sub-sampling in both range and azimuth dimensions, which will reduce the amount of sampling data tremendously. In order to handle 2D reconstructing problem, the ordinary solution is converting the 2D problem into 1D by Kronecker product, which will increase the size of dictionary and computational cost sharply. In this paper, we introduce the 2D-SL0 algorithm into the reconstruction of imaging. It is proved that 2D-SL0 can achieve equivalent result as other 1D reconstructing methods, but the computational complexity and memory usage is reduced significantly. Moreover, we will state the results of simulating experiments and prove the effectiveness and feasibility of our method.

This paper proposes a new image registration method based on grade-by-grade matching in interferometric inverse synthetic aperture radar (InISAR) imaging system using two antennas. The causation and quantitative analysis of the offset between two ISAR images for different antennas along each baseline is analyzed. Strong scatterer centers (SSCs) are extracted from the ISAR images of each antenna by OTSU method firstly. A standard matching is calculated by the image centroid. Then a mapping of region of interest (ROI) and correlation is carried out to get the precise registration. Simulation results demonstrate that the offset between two ISAR images can be compensated effectively when the proposed method is used, achieving a high quality 3D InISAR image consequently.

In this paper, an X-band, 8-element wideband digital array radar (DAR) test-bed is presented, which makes use of a novel digital backend coupled with highly-integrated, multi-channel intermediate frequency (IF) digital receiver. Radar returns are received by the broadband antenna and then down-converted to the IF of 0.6GHz-3.0GHz. Four band-pass filters are applied in the front-end to divide the IF returns into four frequency bands with the instantaneous bandwidth of 500MHz. Every four array elements utilize a digital receiver, which is focused in this paper. The digital receivers are designed in a compact and flexible manner to meet the demands of DAR system. Each receiver consists of a fourchannel ADC, a high-performance FPGA, four DDR3 chips and two optical transceivers. With the sampling rate of up to 1.2GHz each channel, the ADC is capable of directly sampling the IF returns of four array elements at 10bits. In addition to serving as FIFO and controller, the onboard FPGA is also utilized for the implementation of various real-time algorithms such as DDC and channel calibration. Data is converted to bit stream and transferred through two low overhead, high data rate and multi-channel optical transceivers. Key technologies such as channel calibration and wideband DOA are studied with the measured data which is obtained in the experiments to illustrate the functionality of the system.

Aiming at solving the problem of detecting the wideband chirp signals under low Signal-to-Noise Ratio (SNR) condition, an effective signal detection algorithm based on Short-Time-Fourier-Transform (STFT) is proposed. Considering the characteristic of dispersion of noise spectrum and concentration of chirp spectrum, STFT is performed on chirp signals with Gauss window by fixed step, and these frequencies of peak spectrum obtained from every STFT are in correspondence to the time of every stepped window. Then, the frequencies are binarized and the approach similar to mnk method in time domain is used to detect the chirp pulse signal and determine the coarse starting time and ending time. Finally, the data segments, where the former starting time and ending time locate, are subdivided into many segments evenly, on which the STFT is implemented respectively. By that, the precise starting and ending time are attained. Simulations shows that when the SNR is higher than -28dB, the detection probability is not less than 99% and false alarm probability is zero, and also good estimation accuracy of starting and ending time is acquired. The algorithm is easy to realize and surpasses FFT in computation when the width of STFT window and step length are selected properly, so the presented algorithm has good engineering value.

�(SL0) algorithm is employed to reconstruct range profile from sub-sampled results, while 2D SL0 algorithm is improved to reconstruct the ISAR image of target. Compared with the traditional band-pass digital receiver, equivalent result of matched filter and ISAR imaging can be obtained using much fewer samples in our system. A series of simulation validates the design and demonstrate the feasibility of our sub-Nyquist sampling and reconstructing algorithm.