SIGNAL PROCESSING IN SATELLITE-BORNE RADAR ALTIMETER

A new Suboptimal Maximum Likelihood Estimation (SMLE) algorithm based on full-deramp model and its implementation in satellite-borne radar altimeter are presented, with emphasis on the influence of both the return fluctuation and the receiver noise on height and slope estimation precision. Some conclusions are obtained and verified by computer simulation.

ASSP-BASED RADAR SIGNAL PROCESSING

This paper proposes the Application Specific Signal Processor(ASSP)-based implementation of the real-time signal processing system in both spatial domain and time domain for a phased-array radar. This paper also proposes the system-on-silicon hardware design of some ASSPs including the adaptive beamformer, FFT appliation specific integrated circuit, clutter map former and update, moving target extractor and video integrator. The advantages of the processing system are compact, efficient, and robust.

Signal and Data Processing of Television Based on Multistatic Radar Systems

A television based multistatic radar system is described. The commercial television transmitter is used as the illuminator in the multistatic radar system. The reflected commercial television signals are measured by an array of sensors. A data processing scheme is developed that adapts to the poor signal processing ability. The innovation is focused on the construction of the observation space, which could reduce the non linearity error. The new method leads to better system stability than the traditional one. Monte Carlo simulation is utilized and compared with the traditional method.

Current progress in sparse signal processing applied to radar imaging

Sparse signal processing is a signal processing technique that takes advantage of signal’s sparsity,allowing signal to be recovered with a reduced number of samples.Compressive sensing,a new branch of the sparse signal processing,has become a rapidly growing research field.Sparse microwave imaging introduces the sparse signal processing theory to radar imaging to obtain new theories,new systems and new methodologies of microwave imaging.This paper first summarizes the latest application of sparse microwave imaging,including Synthetic Aperture Radar(SAR),tomographic SAR and inverse SAR.As sparse signal processing keeps evolving,an avalanche of results have been obtained.We also highlight its recent theoretical advances,including structured sparsity,off-grid,Bayesian approaches,and point out new research directions in sparse microwave imaging.

High Resolution Radar Real-Time Signal and Information Processing

Radar is an electronic device that uses radio waves to determine the range, angle, or velocity of objects. Real-time signal and information processor is an important module for real-time positioning, imaging, detection and recognition of targets. With the development of ultra-wideband technology, synthetic aperture technology, signal and information processing technology, the radar coverage, detection accuracy and resolution have been greatly improved, especially in terms of one-dimensional(1D) high-resolution radar detection, tracking, recognition, and two-dimensional(2D) synthetic aperture radar imaging technology. Meanwhile, for the application of radar detection and remote sensing with high resolution and wide swath, the amount of data has been greatly increased. Therefore, the radar is required to have low-latency and real-time processing capability under the constraints of size, weight and power consumption. This paper systematically introduces the new technology of high resolution radar and real-time signal and information processing. The key problems and solutions are discussed, including the detection and tracking of 1D high-resolution radar, the accurate signal modeling and wide-swath imaging for geosynchronous orbit synthetic aperture radar, and real-time signal and information processing architecture and efficient algorithms. Finally, the latest research progress and representative results are presented, and the development trends are prospected.

Radar Emitter Signal Recognition Based on Complexity Features

Intra-pulse characteristics of different radar emitter signals reflect on signal waveform by way of changing frequency, phase and amplitude. A novel approach was proposed to extract complexity features of radar emitter signals in a wide range of signal-to-noise ratio (SNR), and radial basis probability neural network (RBPNN) was used to recognize different radar emitter signals. Complexity features, including Lempel-Ziv complexity (LZC) and correlation dimension (CD), can measure the complexity and irregularity of signals, which mirrors the intra-pulse modulation laws of radar emitter signals. In an experiment, LZC and CD features of 10 typical radar emitter signals were extracted and RBPNN was applied to identify the 10 radar emitter signals. Simulation results show that the proposed approach is effective and has good application values because average accurate recognition rate is high when SNR varies in a wide range.

Radar Emitter Signal Recognition Using Wavelet Packet Transform and Support Vector Machines

This paper presents a novel method for radar emitter signal recognition. First, wavelet packet transform （WPT） is introduced to extract features from radar emitter signals. Then, rough set theory is used to select the optimal feature subset with good discriminability from original feature set, and support vector machines （SVMs） are employed to design classifiers. A large number of experimental results show that the proposed method achieves very high recognition rates for 9 radar emitter signals in a wide range of signal-to-noise rates, and proves a feasible and valid method.

Ocean surface current retrieval and imaging with a new shore-based X-band radar based on time-shifted up-and-down linear frequency modulated signal

This paper proposes a multifunction radar that can not only measure sea currents but also perform sea-surface imaging.The fundamental aspect of the proposed radar comprises transmitting time-shifted up-and-down continuous wave linear frequency modulated signals that allow for the offset of two one-dimensional range images of the sea surface that respectively correspond to the upward linear frequency modulated(LFM)signal and the downward LFM signal.Owing to the Doppler frequency shift from the sea surface,a range offset,which is proportional to the radial velocity of the sea surface,occurs between the upward and downward LFM signals.By using the least-squares linear fitting method in the transformed domain,the range offset can be measured and the current velocity can be retrieved.Finally,we verify the accuracy of current measurement with simulation results.

The Echo Modelling and Simulation of the Semi-Active Radar Seeker against a Sea Skimming Target

This paper has proposed a new modelling and simulating technique for the echo of the semi-active radar seeker against the sea skimming target. The echo modelling is based on the electromagnetic scattering mechanisms. A modified Four-path model based on the radar detection scene is used to describe the multipath scattering between the target and rough sea surface. A Facet-based Small Slope Approximation (FBSSA) method is employed to calculate the scattering from the sea surface. The Physical Optics (PO) and the Equivalent Edge Current (EEC) Method is used to calculate the target scattering. In the echo simulations. The results present the original echo and the echo processed by the signal processing procedures, where the clutter and multipath effect can be observed.

Investigation of system structure and information processing mechanism for cognitive skywave over-the-horizon radar

Based on the cognitive radar concept and the basic connotation of cognitive skywave over-the-horizon radar（SWOTHR）, the system structure and information processingmechanism about cognitive SWOTHR are researched. Amongthem, the hybrid network system architecture which is thedistributed configuration combining with the centralized cognition and its soft/hardware framework with the sense-detectionintegration are proposed, and the information processing framebased on the lens principle and its information processing flowwith receive-transmit joint adaption are designed, which buildand parse the work law for cognition and its self feedback adjustment with the lens focus model and five stages informationprocessing sequence. After that, the system simulation andthe performance analysis and comparison are provided, whichinitially proves the rationality and advantages of the proposedideas. Finally, four important development ideas of futureSWOTHR toward ＂high frequency intelligence information processing system＂ are discussed, which are scene information fusion, dynamic reconfigurable system, hierarchical and modulardesign, and sustainable development. Then the conclusion thatthe cognitive SWOTHR can cause the performance improvement is gotten.

ULTRAWIDE BAND RADAR SIGNAL DETECTION, ESTIMATION AND EXPERIMENT

With reference to the air target detection of ultra-wide band (UWB)/impulse radar(IR), the transient signal processing techniques was discussed. In weak UWB signal detection, the wavelet transforms and high order spectrum estimation techniques were preferred. In target characteristic analysis, a time domain bispectrum estimation algorithm was used to analyze the target impulse response, which could estimate accurately local scattering distribution of complex target. A free field IR experimental system installed in an anechoic chamber was used. With this system, experiments to several target models were made. The results of these experiments verified the signal processing method efficiency.

Impacts of space-time-frequency synchronization errors onwide-band target echo characteristics of bistatic/multistatic radar

Bistatic/multistatic radar has great potential advantages over its monostatic counterpart. However, the separation of a transmitter and a receiver leads to difficulties in locating the target position accurately and guaranteeing space-timefrequency synchronization of the transmitter and the receiver.The error model of space-time-frequency synchronization in a motion platform of bistatic/multistatic radar is studied. The relationship between the space synchronization error and the transmitter platform position, receiver platform position, moving state, and beam pointing error, is analyzed. The effect of space synchronization error on target echo power is studied. The target scattering characteristics are restructured by many separate scattering centers of the target in high frequency regions. Based on the scattering centers model of the radar target, this radar target echo model and the simulation method are discussed. The algorithm of bistatic/multistatic radar target echo accurately reflects the scattering characteristics of the radar target, pulse modulation speciality of radar transmitting signals, and spacetime-frequency synchronization error characteristics between the transmitter station and the receiver station. The simulation of bistatic radar is completed in computer, and the results of the simulation validate the feasibility of the method.

Recognition Method Based on Wigner-Hough Transform for Poly-Phase Code Radar Signal

In order to solve the recognition of polyphase code radar signal, this paper gives two methods based on Frank code, i.e. the high-order spectrum recognition method and the fractional Fourier transform (FRFT) method, by analyzing the micro characteristics of polyphase code signals in time and frequency domain respectively. And a recognition algorithm based on Wigner-Hough transform (WHT) is developed in this paper. We verify the validity of each method by computer simulation and give relative merits and demerits. A set of results demonstrate that the algorithm based on Wigner-Hough transform has better recognition performance in low signal-to-noise (SNR) than others.

Long term integration of radar signals with unknown Doppler shift for ubiquitous radar

Ubiquitous radar is a new radar system that provides continuous and uninterrupted multifunction capability within a coverage volume. Continuous coverage from close-in ＂pop-up＂ targets in clutter to long-range targets impacts selection of waveform parameters. The coherent processing interval （CPI） must be long enough to achieve a certain signal-to-noise ratio （SNR） that ensures the efficiency of detection. The condition of detection in the case of low SNR is analyzed, and three different cases that would occur during integration are discussed and a method to determine the CPI is presented. The simulation results show that targets detection with SNR as low as -26 dB in the experimental system can possibly determine the CPI.

A New Radar Target Recognition Method Based on Polarimetric Processing and Neural Learning

The new millimeter-wave(MMW) radar target recognition method proposed uses polarmetric information to obtain stable amplitudes of range profiles and neural learning to extract angle-invariant features of range profiles and polarimetric processing reduces speckle to enhance ability to discriminate targets, and in comparison with conventional approaches, subclass features obtained by the neural learning carries more information and thus makes the correctness of target classification higher and simulation results vended the validity of this approach.

Waveform Design for Cognitive Radar with Deterministic Extended Targets in the Presence of Clutter

Adjusting radar transmitted waveform to its environment is one of the most important roles in cognitive radar;having the capability of updating transmitted waveforms in different applications is a key point. It has been shown in many studies that if the waveform is designed according to the target and clutter characteristics, the detection performance will improve significantly. The uncertainty of the target radar signatures decreases via maximizing MI and the probability of extended target detection is increases via maximizing SNR. In this paper, a waveform design approach based on maximizing both SNR and MI and with regard to target and clutter shape is presented. The detection performance for proposed waveform is compared with previous proposed waveforms. The present paper compares different scenarios of target and clutter and using the probability of detection as a cost function to investigate the advantages and disadvantages of each waveform in different scenarios which are mainly discussed in this text. The desired waveform for cognitive radar is selected based on simultaneously making compromises between SNR and MI, which plays an important role in cognitive radar systems and based on the assumption addressed in the text, the best waveform transmitted into the environment.

Polarimetric super-resolution algorithm for radar range imaging via spatial smoothing processing

A full-polarimetric super-resolution algorithm with spatial smoothing processing is presented for one-dimensional（1-D）radar imaging.The coherence between scattering centers is minimized by using spatial smoothing processing（SSP）.Then the range and polarimetric scattering matrix of the scattering centers are estimated.The impact of different lengths of the smoothing window on the imaging quality is mainly analyzed with different signal-to-noise ratios（SNR）.Simulation and experimental results show that an improved radar super-resolution range profile and more precise estimation can be obtained by adjusting the length of the smoothing window under different SNR conditions.

Entropy function optimization for radar imaging

The convergence performance of the minimum entropy auto-focusing（MEA） algorithm for inverse synthetic aperture radar（ISAR） imaging is analyzed by simulation. The results show that a local optimal solution problem exists in the MEA algorithm. The cost function of the MEA algorithm is not a downward-convex function of multidimensional phases to be compensated. Only when the initial values of the compensated phases are chosen to be near the global minimal point of the entropy function, the MEA algorithm can converge to a global optimal solution. To study the optimal solution problem of the MEA algorithm, a new scheme of entropy function optimization for radar imaging is presented. First, the initial values of the compensated phases are estimated by using the modified Doppler centroid tracking （DCT）algorithm. Since these values are obtained according to the maximum likelihood （ML） principle, the initial phases can be located near the optimal solution values. Then, a fast MEA algorithm is used for the local searching process and the global optimal solution can be obtained. The simulation results show that this scheme can realize the global optimization of the MEA algorithm and can avoid the selection and adjustment of parameters such as iteration step lengths, threshold values, etc.

Angle estimation for bistatic MIMO radar with unknown mutual coupling based on three-way compressive sensing

The problem of angle estimation for bistatic multiple-input multiple-output radar in the present of unknown mutual coupling (MC) is investigated, and a three-way compressive sensing (TWCS) estimation algorithm is developed. To exploit the inherent multi-dimensional structure of received data, a trilinear tensor model is firstly formulated. Then the de-coupling operation is followed. Thereafter, the high-order singular value decomposition is applied to compress the high dimensional tensor to a much smaller one. The estimation of the compressed direction matrices are linked to the compressed trilinear model, and finally two over-complete dictionaries are constructed for angle estimation. Also, Cramer-Rao bounds for angle and MC estimation are derived. The proposed TWCS algorithm is effective from the perspective of estimation accuracy as well as the computational complexity, and it can achieve automatically paired angle estimation. Simulation results show that the proposed method has much better estimation accuracy than the existing algorithms in the low signal-to-noise ratio scenario, and its estimation performance is very close to the parallel factor analysis (PARAFAC) algorithm at the high SNR regions. © 2017 Beijing Institute of Aerospace Information.

Doppler radar by using single multicarrier pulse based on optical fibre delay lines

The proposed Doppler measurement technique shows that the Doppler measurements can be accomplished by a single pulse with multiple frequency components through optical fibre delay lines.Range and velocity ambiguity can be removed,and the velocity resolution can be improved dramatically by using long optical fibre delay lines.Furthermore,the velocity resolution can be modified by adjusting the length of optical fibre delay lines.In addition,the proposed radar can achieve high range resolution by using a single wideband pulse.As a result,the new approach can improve radar performance significantly.