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In comparing system performance for ground moving target indicator (GMTI) radar systems, various metrics are used. It is highly desirable that the metric be simple and powerful. Ideally it is a single number, or a plot. It is often the case that a single number is not sufficient to describe the radar performance under all operational conditions. In spite of this, it is still common to attempt to use a simple metric, such as the minimum detectable velocity (MDV). This paper discusses the concept of minimum detectable velocity with the goal of showing what this metric attempts to communicate, and what may not be properly communicated by this metric without careful attention. Basic parameters that affect the minimum detectable velocity are presented.

Various parameters can affect the ground moving target indicator (GMTI) radar performance. One such parameter that may often be overlooked is that of the unintended polarization of the antenna, e.g., the cross-polarization response. This paper discusses the issue of cross-polarization on clutter attenuation performance for GMTI radars.

The radar system uses space time adaptive processing as a type of signal processing technique. STAP uses antenna array with multiple channels to obtain two dimensional filtering. In ground moving target indicator system(GMTI),the echo is received by the radar from the target, these signals received not only contains the reflected echo from the target but also contains the ground returned signals, the ground returns are called as clutter. The total returned clutter is stronger in amplitude than the returned echo from the target, this poses a challenge for target detection in the presence of clutter and jammer interference, as this clutter signal's energy fills all range bins, clutter filtering is a critical part of ground moving target indicator (GMTI) system. Jamming signal is also a significant interference source to radar. STAP, filter the signal received in angular and Doppler domains to suppress both the clutter and jammer returns. The main goal of STAP is to reduce clutter and null the jammer interference.

In this paper, a linear frequency modulated continuous wave (LFMCW) radar system was designed and established for detecting the distance and velocity of people and vehicles. The radar operates at 35 GHz with the maximum sweep frequency band of 2 GHz and uses triangular wave modulation. Background learning method is introduced to improve the system performance. Learning and recording the stationary background results after Moving Target Indication (MTI) filter is used as the threshold value for detecting moving targets. The designed radar system measured the distance and velocity of walking people and the speed error is less than 0.5 m/s.

In this paper, the GMTI performance and limitation in terms of minimum detectable velocity (MDV) using ATI and STAP are investigated from a high-speed platform. Statistical characterizations of the ATI phase variable and the STAP output power are used to derive probability of detection vs. probability of false alarm for moving targets as a function of their velocity, SNR and CNR. The MDV for moving targets are computed for a certain probability of detection and false alarm rate under different conditions. Finally, the comparison between the MDV performances for ATI and STAP are carried out by computing the MDVs as functions of key parameters of the problem.

The phase-attached radar-communications (PARC) framework was recently proposed as a means to realize both functions via an FM waveform structure that is amenable to high-power transmitters. This implementation promotes the operation of both functions simultaneously without sacrificing transmission resources (i.e. power, time, frequency) needed for the primary radar mission. Here, the PARC framework is extended to an FM continuous-wave (FMCW) operation which maximizes both data throughput and energy on target. The FMCW PARC waveforms are tested in a open-air environment for ground-based moving target indication (MTI) and the results compared against traditional FMCW stretch processing. As with the original pulsed version of PARC, it is found that the FMCW version introduces Doppler-spread clutter resulting from range sidelobe modulation (RSM), thus necessitating subsequent steps be taken to compensate.

A new approach to ship detection, discrimination, and motion estimation is proposed and demonstrated using a combined polarimetry and GMTI technique from a spaceborne platform. Using a standard dual-pol SAR, the new method exploits a bright “shadow” in cross-pol imagery of a moving ship and its azimuth-shifted image to discriminate it from other stationary or quasi-stationary objects (e.g., icebergs) on the ocean surface and to estimate its motion parameters, such as radial speed and heading. The motion estimation results are compared to those obtained using the newly implemented Dual-Channel Polarimeric GMTI modes on RADARSAT-2. Unlike the ship wake approach, the proposed technique is applicable to and successfully demonstrated for both C-band and X-band radars (RADARSAT-2 and TerraSAR-X) and it is not dependent on the angle of incidence to achieve a good signal-to-clutter ratio and ship detectability. Physical mechanisms giving rise to ship “shadows” are also discussed.

Heartbeat frequency of a living being is a key characteristic as it lies within a certain range. The heart beat frequency of a human subject behind the wall has been identified by applying singular value decomposition (SVD) based clutter reduction technique along with moving target indicator (MTI) filtering. The application of SVD improves the signal to clutter noise ratio (SCNR) of a signal coming from a human subject and make it possible for life feature detectable by MTI for through the wall detection. The final detection of human subject by tracking its heartbeat frequency from its distance measured referred to the antenna position is provided in a range doppler (RD) plane.

There has been significant research into the use of reflected GPS signals for remote sensing. The reflected GPS signals may contain information on soil moisture, ocean waves, and altitude [1]–[4]. Space radar systems have also been a topic of intense interest for several decades, as a means of providing global coverage for both military and geoscience applications [5]–[7]. These systems implement Synthetic Aperture Radar (SAR), Ground Moving Target Indicator (GMTI), and Space Time Adaptive Processing (STAP) algorithms to detect targets and form images. Orbit selection is important for these systems to provide optimum coverage and performance. By using reflected GPS signals to implement space-based bistatic radar, a more robust and cost-effective system could be fielded. However, the performance analysis of such a system would be complex, since the orbits of the receiving spacecraft and the GPS constellation will be significantly different, and their interactions dynamic. This relationship between the receiving satellite's orbit and the GPS constellation was the subject of the research. A prototype antenna array was used to estimate the resolution and signal levels of the system for various transmitter/receiver relationships. Based on an understanding of the desired signal characteristics for bistatic GPS, a family of orbits was simulated to determine the dependency on inclinations and altitudes.

This paper proposes a low-complexity frequency-modulated continuous wave (FMCW) surveillance radar algorithm using random dual chirps in order to overcome the blind-speed problem and reduce the computational complexity. In surveillance radar algorithm, the most widely used moving target indicator (MTI) algorithm is proposed to effectively remove clutter. However, the MTI algorithm has a so-called &lsquo, blind-speed problem&rsquo, that cannot detect a target of a specific velocity. In this paper, we try to solve the blind-speed problem of MTI algorithm by randomly selecting two beat signals selected for MTI for each frame. To further reduce the redundant complexity, the proposed algorithm first performs one-dimensional fast Fourier transform (FFT) for range detection and performs multidimensional FFT only when it is determined that a target exists at each frame. The simulation results show that despite low complexity, the proposed algorithm detects moving targets well by avoiding the problem of blind speed. Furthermore, the effectiveness of the proposed algorithm was verified by performing an experiment using the FMCW radar system in a real environment.

International audience; Ground moving target indicator (GMTI) radar performance is strongly depending on the antenna geometry: length of the equivalent radar aperture (longer apertures enable lower minimum detectable velocity-MDV) and sparsity. Compact and sparse multiple-input multiple-output (MIMO) techniques have been investigated into this paper to point out potential benefits compared to SIMO configuration. In 2016 an experiment was conducted with ONERA BUSARD motoglider to collect MIMO GMTI dataset on instrumented ground vehicles. DDMA waveform has been operated as orthogonal pattern on transmit. Analysis of the data indicates improved clutter nulling and resulting MDV with the MIMO techniques compared to the conventional SIMO one.

The Identification and classification of targets is one of the key capabilities of Ground based C4ISR systems, military Command and Control Systems and Combat Management Systems. It is a precondition for situational awareness and supports operational users in decision making. A correct identification is an important prerequisite to prevent fratricide and civilian collateral damages and to complete the Situational Awareness. Modern Combat Management and Surveillance systems deal with thousands of tracked objects and such an operator is unable to handle the huge amount of targets and data in an operationally acceptable timeline. Therefore an automated identification and classification process is integrated in such military systems. Typical sensors used for this task are radars, IFF and ESM sensors complemented by sources like Tactical Data Links, civil and military Airspace Control Means and flight plans.

It is inevitable for the future battlefield target surveillance radar to utilize space-based platform. What's more, the higher the signal-to-clutter-noise ratio (SCNR) of the radar echo is, the more accurate information the radar will obtain from the target. Therefore, distributed spaceborne coherent aperture radar (DSCAR) technique is proposed for moving target indicator (MTI) to achieve higher SCNR. However, grating lobes and high sidelobes caused by the long baseline will degrade its detection performance. In this paper, aiming at the above problems, a new method based on Optimally Irreducible Arrays (OIAs) and Minimum Redundant Arrays (MRAs) has been proposed. Simulations are also carried out, showing the feasibility and the effectiveness of the method.

Ground moving target indication (GMTI) technology is an important development direction in the field of SAR signal processing, and the performance of GMTI is determined by clutter suppression directly. Since the trajectory of airborne platform is a circular arc and the beam directions of antennas are time-varying in the circular SAR (CSAR) mode, the classical channel registration methods for the strip-map SAR are no longer applicable. Based on the measured data of a CSAR system, channel calibration and channel equalization are investigated to eliminate the phase and amplitude differences of the clutter between the channels. Further, according to the complementary property of displaced phase center antenna (DPCA) and along-track interferometry (ATI) technologies, we propose a clutter suppression method in the complex image domain by utilizing the interference phase of ATI to weight the DPCA amplitude. The process results of measured data prove a good capability for clutter suppression both in homogeneous and inhomogeneous areas.

Recently, geosynchronous synthetic aperture radar (GEO-SAR), which can achieve continuous Earth observation missions with large coverage and short revisit period, has drawn extensive attention. The wide-swath imaging mode of GEO-SAR presents great potentials for bistatic observation applications. Geosynchronous bistatic SAR (GEO-BiSAR) can not only complete large scene imaging task, but also provide the conditions for ground moving-target indication (GMTI). In this paper, the 3D velocity estimation for moving target via GEO-BiSAR are investigated. And the velocity estimation problem is extended to three-dimensional (3D) domain, considering that targets always move in a 3D space. To solve the 3D velocity of moving targets via GEO-BiSAR, the 3D velocity raw signal model is established, and a method based on fractional Fourier transform (FrFT) is proposed. First, the Doppler centroids and Doppler rates in the GEO-BiSAR are estimated by the FrFT. Second, one of the Doppler rates is used to estimate the azimuth velocity. Then, the range and height velocity are obtained by solving a Doppler centroid equation set. Finally, simulation experiments are presented to demonstrate the effectiveness of the method.

In this paper, design and development of air surveillance radar software using QT creator application is presented. The software manufacture was performed in two stages, where the first stage is to design a flowchart and the second stage is to create the program using codes. Radar software must be integrated with hardware to display the results of the target detection. All detection results will be shown on the plan position indicator (PPI) display. From the detection results, all flying objects that can reflect radar signals will be shown in the display, i.e., airplanes, clouds, birds, mountains, tall buildings, as long as they are still in the radar detection range. The radar software has been tested with very significant good results. To only see moving objects such as high-speed aircrafts, moving target indicator (MTI) feature is enabled. The radar software was shown to be able to track objects and to create guard zones feature as an observation area.

Detection of targets with small Doppler frequencies of linear-frequency modulated continuous wave radars is the main task of this article. The moving target indicator (MTI) is used to reject the fixed targets and high-speed targets through the radar research area. In this work, targets with small Doppler frequencies can be detected perfectly based on the frequency response of a single delay line canceller followed by single delay line integrator. An enhancement of the proposed algorithm is achieved using a filter in the range direction of the range-Doppler processor scheme. The proposed filter is chosen with certain coefficients after the first fast Fourier transform processor in range to enhance the radar performance. The evaluation of the proposed algorithm is achieved at different slow Doppler scenarios of the target and compared with the traditional algorithm which uses only MTI processor. Another aspect that is important for evaluation of the proposed algorithm is the detection performance of the algorithms through the receiver operating characteristic curves. Implementation of the proposed algorithm using FPGA is performed in real time applications and it is found that it meets the simulation results.

In this paper, we present ground moving target indication (GMTI) signal processing algorithm encompassing clutter suppression, target detection and parameter estimation. One of the most significant yet least publicized is the need of the GMTI mode for a forward-looking airborne radar. The integration of GMTI mode in a forward-looking airborne radar allows reconnaissance and surveillance operations in all weather conditions. In this context, space time adaptive processing (STAP) offers a unique prospect of enabling the GMTI mode in forward looking airborne radar. STAP is a two-dimensional filter designed to suppress platform motion-induced clutter Doppler spread. Interference is characterized by a covariance matrix. In the case of a forward-looking airborne radar, the clutter Doppler is dependent on range. Clutter Doppler dependency on the range renders the training cells heterogeneous. The heterogeneity effects are particularly prominent in the near range bins. Non-homogeneous training cells have a deleterious effect on STAP performance. In this study, we propose an adaptive Doppler compensation to mitigate the degraded STAP performance in the near range bins. The adaptivity feature circumvents the need for the availability of radar parameters in real-time. The real time implementation of STAP is impeded by requirements of a large number of training samples and covariance matrix inversion. Therefore, there is a dire need to devise a framework to detect and estimate target parameters within the STAP. In this regard, we propose an efficient STAP algorithm to detect and estimate target parameters. STAP weights are applied to the input data to obtain a 3D array. The range projection of the 3D array is utilized to detect and estimate the range of the target, while the angle&ndash, Doppler projection is used to estimate spatial and temporal parameters of the target. Most of the literature on STAP is geared towards a known covariance matrix. The assumption of a known covariance matrix may degrade STAP performance because of the inherent mismatches between the actual and assumed target steering vectors. In this study, we estimate the covariance matrix based on the synthetic data generated from a model of an airborne phased array radar. The developed STAP algorithms closely mimic a real-time implementation scheme in an airborne radar platform. The results of the proposed algorithm are validated through target parameter estimation and STAP metrics on synthetic data.

This paper describes the analysis performed on coherent simultaneously recorded monostatic and bistatic sea clutter data. The data were generated using a networked pulsed radar system, namely, NetRAD. This analysis is completed in both the temporal and Doppler domains, and the parameters characterized are compared between multiple bistatic angles and different polarizations. The K-distribution model is used to assess the variation in the clutter amplitude statistics between multiple bistatic data and the corresponding monostatic data. Key characteristics of the Doppler data, such as the spectrum width, center of gravity (CoG), and variance of the spectral width, are evaluated as a function of bistatic angle allowing novel relationships to be defined. The results conclude that the bistatic Doppler data have a lower K-distribution shape parameter in the majority of bistatic angles compared with the simultaneous monostatic data. In addition, novel trends in the relationship between the clutter spectrum CoG and the clutter intensity are presented.

A high-pulse-repetition-frequency (PRF) radar can handle the high Doppler frequencies of clutter echoes received by a fast-moving airborne radar. However, high-PRF radar causes range ambiguity. In addition, the clutter is range dependent when the airborne radar works in a forward-looking geometry. The range ambiguity and range dependence will lead to severe performance degradation of the traditional space-time adaptive processing (STAP) methods. In this paper, a vertical frequency diverse array (FDA), which applies frequency diversity in the vertical of a planar array, is explored to circumvent the range ambiguity problem in STAP radar. A range-ambiguous clutter suppression approach is devised, which consists of vertical spatial frequency compensation and pre-STAP filtering. In the vertical-FDA radar, the vertical spatial frequency depends not only on the depression angle but also on the slant range. By using this characteristic, the range-ambiguous clutter can be separated in the vertical spatial frequency domain, and then, clutter suppression is achieved for each separated range region. As a result, both problems of range ambiguity and range dependence are solved. Simulation results are provided to demonstrate the effectiveness of the proposed method.

In this paper, for a multichannel synthetic aperture radar–ground moving target indication (SAR--GMTI) system, a new high-accuracy focusing and relocating method using instantaneous interferometry, i.e., carrying out interferometry operation in the azimuth time domain before focusing, is proposed. One of the key steps of this method is to perform instantaneous interferometry to get accurate equivalent cross-track velocity (ECV) estimation for cross-track motion compensation. After that, the signal from a moving target is concentrated in range, and along-track motion compensation becomes convenient. Motion compensation transforms a moving target into a stationary one; thus, the conventional SAR imaging algorithm can be applied to focus the moving target. Finally, a strategy for accurately relocating a moving target is presented. The processing results of simulated and measured data illustrate the effectiveness of the proposed method.

The detection of ground moving targets with arbitrary linear motion from an airborne multichannel radar via a long coherent processing interval is considered. The technique maximizes the target signal energy in synthetic aperture radar (SAR) images by matching to the target's linear motion profile. A reparameterization of the target's linear motion is developed that decouples the array processing from the image formation process. An explicit approach to forming SAR images focused with respect to a particular along-track velocity is presented. The ground clutter is canceled via an adaptive array technique, which also yields an estimate of the target radial velocity. Because the algorithm generates estimates of the target's along-track and cross-track velocity components, a unique determination of the target's motion parameters is possible. The approach is derived and investigated via simulated point spread functions. Algorithm performance is demonstrated on the GOTCHA SAR ground moving target indication data set.

Clutter suppression and ground moving target indication (GMTI) are challenging tasks for multichannel synthetic aperture radar (MC-SAR) systems. In recent years, robust principal component analysis (RPCA), such as the augmented Lagrange multiplier method (ALM) and go decomposition (GoDec) algorithm, has drawn considerable attention for its excellent performance in distinguishing the different parts from a set of correlative database. In this letter, an efficient along-track interferometry GoDec (ATI-GoDec) approach is proposed for GMTI in MC-SAR systems under a strong clutter background. The proposed method can be separated into two sections: the predetection and the postdetection. The predetection by an efficient ATI RPCA method can decrease missing targets, and postdetection with a novel magnitude and phase (M&P) detection has the ability to reduce the false targets. As a result, the proposed method can provide a more robust performance by a comparison to the traditional ATI detection. It can also widen the tolerant values of the preset cardinality and decrease the probability of false alarm compared with the conventional GoDec algorithm. Moreover, the proposed method only takes several iterations to reach the convergence by solving the optimization problem of the RPCA model, which makes it more efficient than the previous RPCA methods. The results by applying the proposed method into a set of real SAR data are consistent with the analysis presented in this letter.

Along-track multichannel synthetic aperture radar is usually used to achieve ground moving target detection and imaging. Nevertheless, there is a design dilemma between azimuth high resolution and wide swath (HRWS). To solve this problem in HRWS mode, we introduce a virtual multichannel (VMC) scheme. For each virtual channel, the low real pulse repetition frequency (PRF) improves the ability of resolving range ambiguity for wide-swath, and the high virtual PRF improves the capability of resolving Doppler ambiguity for azimuth high resolution. For multiple virtual channels, strong ground clutter is eliminated by the joint VMC processing. Furthermore, a detailed signal model of a moving target in the virtual channel is given, and the special false-peak effect in the azimuthal image is analyzed. Moreover, we propose a novel ground moving target processing method based on the VMC scheme and the clutter suppression interferometry (CSI) technique, which is called VMC-CSI. The integration of detection, location, velocity estimation, and imaging for ground moving targets can be achieved. Accounting for the unresolved main peak and false peak for a moving target, in the VMC-CSI method, we adopt a two-step scheme to estimate the radial velocity and along-track velocity, namely, rough estimation and precise estimation. Meanwhile, considering the same interferometric phases of the main peak and the false peak, we use false peaks first for the robustness of initial azimuth location estimation and remove false peaks afterward. Numerical simulations are provided for testing the effect of the false peak and the effectiveness of VMC-CSI.

Performance of space–time adaptive processing (STAP) with adaptive matched filter detection for maritime surveillance is assessed using simulated maritime surface targets embedded in real radar sea clutter data and compared with conventional pulse-Doppler processing. Pre-Doppler and post-Doppler suboptimal STAP are examined, with pulse repetition interval (PRI)-staggered post-Doppler shown to provide best overall detection and constant false alarm rate performance. A two-component clutter model fit is used to explain variations of clutter characteristics and detector performance with Doppler frequency.

Due to the advantage of short revisit time, airborne circular stripmap synthetic aperture radar (CSSAR) is an attractive tool for air-to-ground surveillance and reconnaissance. This paper deals with the issue of ground moving-target detection with an airborne CSSAR and proposes a new autofocus-based detection algorithm. The prominent features of the proposed algorithm are the incorporation of the Doppler ambiguity information into the autofocus-based moving-target detection and the ability to solve the problem of large range cell migration, which affects most of autofocus algorithms. The proposed algorithm can detect not only the targets whose azimuth chirp rates are different from that of the static clutter background but also the targets whose Doppler ambiguities are different from that of the static clutter background. Numerical results show that the proposed algorithm works well in both homogeneous and heterogeneous clutter backgrounds.

In synthetic aperture radar (SAR) images, moving targets are usually smeared and/or imaged at incorrect positions due to the target motions during the SAR integration time. Moreover, since a high-resolution wide-swath SAR system is operated with a rather low pulse repetition frequency, a moving target will cause multiple ghost targets in the reconstructed SAR image. A new space–time adaptive processing framework is proposed in this paper for removing moving target artifacts in SAR images. In this new framework, the dynamic steering vector concept is proposed. In addition, this paper develops a moving target processing scheme for clutter suppression and moving target imaging and location for a high-resolution wide-swath SAR system. Finally, we locate the well-focused moving targets at the stationary scene image without any disturbing artifacts. The simulated and real data are used to validate the effectiveness of our proposed method.

This paper presents the design, implementation, and validation tests of a C-band analog-front-end (AFE) for the frequency modulated continuous wave (FMCW) radar. The system was designed to be used in various radar applications, including short rage mode, synthetic aperture radar (SAR) and moving target indication (MTI) mode. The AFE presented here was based on commercial off-the-shelf radio frequency components, and designed as a homodyne system, so the final applications were based on the FMCW radar. Validation tests and experiments were carried out in the laboratory and in open-air environments. The authors present tests of the AFE, including MTI and SAR trials, conducted using a ground moving platform (a car) and an airborne platform (a small aircraft). The results are discussed with the prospect of future work and further improvements in mind.

This paper investigates the problem of ground vehicle tracking with ground-moving target indicator (GMTI) radar. In practice, the movement of ground vehicles may involve several different maneuvering types (acceleration, deceleration, standstill, etc.). Consequently, the GMTI radar may lose measurements when the radial velocity of the ground vehicle is below a threshold, i.e., falling into the Doppler blind region. In this paper, to incorporate the information gathered from normal measurements and knowledge on the Doppler blindness constraint, we develop an enhanced particle filtering method for which the importance distributions are inspired by a recent noise-related Doppler blind (NRDB) filtering algorithm for GMTI tracking. Specifically, when constructing the importance distributions, the proposed particle filter takes the advantages of the efficient NRDB algorithm by applying the extended Kalman filter and its generalization for interval-censored measurements. In addition, the linearization and Gaussian approximations in the NRDB algorithm are corrected by the weighting process of the developed filtering method to achieve a more accurate GMTI tracking performance. The simulation results show that the proposed method substantially outperforms the existing methods for the GMTI tracking problem.

To detect moving targets in a distributed multiple-input multiple output (MIMO) radars, it is preferable to null the clutter rather than whiten the clutter. This is mainly because clutter nulling approaches do not require range training data to form a sample covariance estimate of clutter in each transmit-receive pair, especially in the distributed MIMO radars with non-homogeneous clutter. Moreover, geometry diversity of the distributed MIMO helps improve moving target detection since for a given target velocity, different transmit-receive pairs produce different Doppler frequencies that are less likely to be all small and reside in the clutter nulling region. Based on these facts, the authors consider a subspace model for clutter and derive a uniformly most powerful invariant (UMPI) detector as an optimum invariant test. In this case, analytical expressions for calculating the false alarm and detection probabilities for Swerling 0 and Swerling 1 target models are derived in the closed-forms. Moreover, theoretical and numerical analyses of the proposed UMPI test are represented for several scenarios. More importantly, the simulation results show that the proposed subspace-based UMPI detector can attain the predetermined false alarm probability and superior detection performance in the presence of correlated clutter. Finally, authors consider a situation in which perfect waveform separations at the local receivers are no longer valid, and see the detection performance degradation of the proposed optimal detector designed for ideal waveform separations.

This paper proposes a novel ground vehicle tracking method using an airborne ground moving target indicator radar where the surrounding geographic information is considered to determine vehicle’s movement type as well as constrain its positions. Multiple state models corresponding to different movement modes are applied to represent the vehicle’s behaviour within different terrain conditions. Based on geographic conditions and multiple state models, a constrained variable structure multiple model particle filter algorithm aided by particle swarm optimisation is proposed. Compared with the traditional multiple model particle filtering schemes, the proposed algorithm utilises a particle swarm optimisation technique for the particle filter which generates more effective particles and generated particles are constrained into the feasible geographic region. Numerical simulation results in a realistic environment show that the proposed method achieves better tracking performance compared with current state-of-the-art ones for manoeuvring vehicle tracking.

Accessibility of a fast and accurate multichannel synthetic aperture radar raw data generator of stationary clutter and moving targets has high importance, especially in the application of ground moving target indication. In this paper, a fast four-stage algorithm for generating the raw data of each channel stationary clutter and moving targets, has been proposed respectively in the frequency and the hybrid time–frequency domain. Using this simulator, in different conditions in terms of target motion speed, acceleration and direction, for each of the channels, after generating the raw data, its final image has been extracted by the range-Doppler algorithm. Then, using clutter suppression techniques such as DPCA, ATI and hybrid DPCA–ATI, the multichannel SAR final image has been obtained in ideal and nonideal conditions. Finally, the obtained images of the first channel have been studied using the extracted formulas for predicting the effects of target motion parameters on the SAR images as well as analyzing the multichannel SAR final image. The results show that the proposed algorithm for generating the raw data of each channel stationary clutter and moving targets has better performance in terms of speed and accuracy than the other existing simulators and the proposed multichannel SAR simulation method has high quality.

Random and unwanted fluctuations, which perturb the phase of an ideal reference sinusoidal signal, may cause significant performance degradation in radar systems exploiting coherent integration techniques. To quantify the resulting performance loss, we develop a fast-time/slow-time data matrix radar signal representation, modeling the undesired phase fluctuations via multivariate circular distributions and describing the phase noise power spectral density (PSD) through a composite power-law model. Hence, we accurately predict the performance degradation experienced by moving target indication (MTI) algorithms for clutter cancellation, providing a closed form expression for the improvement factor I. The subsequent analysis shows that phase noise affects I directly through its characteristic function (CF). Additionally, I shares a robust behavior with respect to the actual phase noise multivariate circular distribution, as long as the phase noise PSD correctly represents the available measurements.

The authors address the problem of slow target detection in heterogeneous clutter through dimensionality reduction. Traditional approaches of implementing the space-time adaptive processing (STAP) require a large number of training data to estimate the clutter covariance matrix. To address the issue of limited training data especially in the heterogeneous scenarios, they propose a novel thinned STAP through selecting an optimum subset of antenna-pulse pairs that achieves the maximum output signal-to-clutter-plus-noise ratio. The proposed strategy utilises a new parameter, named spatial spectrum correlation coefficient, to analytically characterise the effect of space-time configuration on STAP performance and reduce the dimensionality of traditional STAP. Two algorithms are proposed to solve the antenna-pulse selection problem. The effectiveness of the proposed strategy is confirmed by extensive simulation results, especially by utilising the multi-channel airborne radar measurement data set.

Multiple-input multiple-output synthetic aperture radar (MIMO SAR) has drawn wide attention for its increased degrees of freedom (DOFs) compared to the traditional multi-channel SAR (MC-SAR) system. The MIMO SAR system, which makes each antenna illuminate one subswath, can widen the range swath without decreasing PRF. One of the foremost tasks involved in the MIMO SAR system is the waveform design at the transmitter. Moreover, at the receiver, how to accurately separate the auto-correlation signal from the cross-correlation interferences is the other important issue. In this paper, an orthogonal frequency division multiplexing (OFDM) chirp signal designing method is firstly proposed, which is based on low cross-correlation interferences and good peak-to-sidelobe ratio (PSLR) rules. Using the designed OFDM chirp signals, the focused signal of MIMO SAR system matched the robust principal component analysis (RPCA) basic model. The moving target (sparse signal), clutter (low-rank signal) and noise (noise signal) can be separated directly. Hence, RPCA based method can be employed for ground moving target indication (GMTI) with no need for extracting the matched signal or suppressing the unmatched interferences. Extensive simulations demonstrate the effectiveness of the waveform designing method and GMTI with RPCA based method for MIMO SAR system.

In traditional filtering methods, clutter is often assumed to obey a uniform distribution over the entire monitoring area. For many sensors, however, clutter may concentrate in target-containing regions. Under this condition, the performance of the traditional multi-target tracking filter can be degraded. In an effort to solve this problem, this paper proposes an improved algorithm based on a Gaussian Mixture probability hypothesis density GM-PHD filter to deal with state-dependent clutter. First, the relationship between state and clutter is modeled using the uniform distribution centered on the target state. Then, the clutter intensity is calculated according to the distribution of clutter in the whole monitoring area and is used to update the filter. The simulation results show that the improved filter can track targets' trajectories more effectively in an environment of state-dependent clutter than the standard GM-PHD filter.

In this paper, a novel method for traffic monitoring using a spaceborne dual-platform synthetic aperture radar system is presented. The chosen along-track baseline between both platforms needs to be on the order of ten to several tens of kilometers, resulting in a time lag of several seconds between the target observations. With the proposed method, the true geographical positions, the velocities, and the moving directions of the detected moving targets can be estimated with high accuracy. The method has been verified and evaluated using experimental data acquired with the German TerraSAR-X/TanDEM-X radar satellite formation. The results are compared with ground truth reference data.

This paper proposes a novel simultaneous monostatic and bistatic ground moving target indication (GMTI) mode for improved target detection and imaging capability. The mode uses an airborne multichannel radar system and a stationary transmitter. Both systems transmit simultaneously on adjacent frequency bands, and the airborne multichannel system receives both its monostatic echoes and the bistatic returns. Geometrical diversity between the monostatic and bistatic measurements enhances moving target detection capabilities. Moreover, for movers detected in both data sets, an estimation of the target velocity vector (i.e., velocity and direction of motion) can be performed. By simply extracting a single-channel data set, this also allows correct focusing of moving targets both in monostatic and bistatic data sets, if synthetic aperture radar GMTI capability is required. Consequently, situational awareness over the observed area is greatly improved. The effectiveness of the proposed technique is analyzed both from a theoretical point of view and by means of an ad hoc experiment conducted by the Fraunhofer Institute for High Frequency Physics and Radar Techniques (FHR) in fall 2013.

In this paper, we first develop a novel array self-calibration method for estimating array gain-phase errors by computing the clutter subspace from the radar system parameters and using the clutter data in space-time adaptive processing (STAP). The proposed algorithm is shown to perform well even in nonhomogeneous clutter, and it can improve the performance of existing STAP algorithms, such as the clutter subspace-based method in the presence of array gain-phase errors.We also develop a two-stage STAP approach for suppressing nonhomogeneous clutter in the presence of model errors in addition to array gain-phase errors. In our two-stage STAP approach, the first stage explores the clutter subspace calculated from the radar system parameters to suppress the main clutter. The second stage employs the conventional method, such as the partially adaptive sample matrix inversion STAP method, to remove any residual clutter. Numerical results illustrate the benefits of the array self-calibration method and the effectiveness of the two-stage STAP method. Finally, the performance of the three STAP methods is compared via the well-known MCARM data set. The results further confirm that there is an improvement in performance when using array self-calibration together with the two-stage STAP method.

In this paper, shape-parameter-dependent matched filter (MF) detectors are proposed for moving target detection in K-distributed clutter, which are specified by a single parameter α [0, 1], the α-MF detectors for short. The α-MF detectors include the MF and normalized matched filter (NMF) detectors as special examples with α = 0 and 1. The parameter α can be chosen to match clutter characteristics. An empirical formula is given that the optimal parameter α equals the number of integrated pulses divided by it plus the shape parameter of K-distributed clutter. It is proved that the α-MF detectors are constant false alarm rate (CFAR) with respect to the scale parameter, clutter covariance matrix, and Doppler steering vector. The properties of adaptive α-MF (α-AMF) detectors are discussed. For K-distributed clutter, the optimal α-MF detectors are superior to the MF and NMF detectors and are comparable with the optimal K-distributed detectors (OKDs) of high computational cost. Finally, real high-resolution sea clutter data are available to verify the proposed detectors. The optimal α-AMF detectors under K-distributed clutter model are competitive in performance with the adaptive generalized likelihood ratio test linear threshold detectors (GLRT-LTDs) that are optimal for the compound-Gaussian clutter with the inverse Gamma texture.

High-frequency surface wave radars (HFSWRs) have been used to monitor moving vessel targets within large areas of the coastal ocean. Moreover, a dual-frequency HFSWR can overcome the negative effect of sea clutter on vessel target detection. As only the information of point targets are used in the traditional point association method of dual-frequency HFSWR, it is difficult to select proper threshold values to obtain correct association and fusion results, which will increase the false alarm or miss rate. In this study, it is found that a moving vessel target is shown as an area target rather than a point target in range-Doppler (R-D) image. First, from the characteristic analyses of a vessel target in R-D fusion image, it is concluded that the two constant false alarm rate points of a single vessel target should locate within the same area target in the fusion image when it is detected by both frequencies. Then a new detection method of dual-frequency HFSWR was proposed on a fusion R-D image level. Finally, the proposed method is validated with the measured HFSWR data, and the results show that it can obtain more reliable association results than the traditional point association method.

To overcome the shortcomings of the conventional moving target detection method in single-channel synthetic aperture radar (SAR), this study proposes a novel method for moving target detection in single-channel multi-band SAR. In this method, according to the detectable velocity in different bands, the genetic algorithm is applied to optimise the choice of radar band parameters, for example, wavelength and pulse repetition frequency. With the obtained optimal parameter combinations, the multi-band SAR system could achieve the highest velocity detection rate and the maximal bands utilisation rate. Then, moving target detection, imaging and location could be accomplished with the radon transform and the deramp method. Finally, simulation results are presented to demonstrate the effectiveness and feasibility of the proposed method.

Long synthetic aperture time can improve the imaging quality of a ground moving target, whereas a moving target may be severely smeared in the cross-range image due to the range migration and the Doppler frequency migration. In this paper, the effects of the third-order Doppler broadening and Doppler ambiguity of a fast-moving target are considered. To address these issues, a novel motion parameter estimation method named high-order time-chirp rate transform (HTRT) is proposed, and then a new synthetic aperture radar (SAR) imaging method based on Radon-HTRT (RHTRT) for a ground moving target is developed. The major contributions are as follows: 1) The proposed SAR imaging method can eliminate the Doppler ambiguity effect. 2) The proposed method can realize longer time coherent integration than Radon-Fourier transform (RFT) and Radon-fractional Fourier transform (RFRFT) methods. 3) The proposed method is computationally efficient since HTRT can obtain the motion parameters of a moving target via performing the 2-dimensional (2-D) fast Fourier transform (FFT). Both the simulated and real data processing results show that the proposed method can finely image a ground moving target in a high signal-to-clutter and noise ratio (SCNR) environment.

This paper examines moving target detection in distributed multi-input multi-output radar with sensors placed on moving platforms. Unlike previous works which were focused on stationary platforms, we consider explicitly the effects of platform motion, which exacerbate the location-induced clutter non-homogeneity inherent in such systems and thus make the problem significantly more challenging. Two new detectors are proposed. The first is a sparsity based detector which, by exploiting a sparse representation of the clutter in the Doppler domain, adaptively estimates from the test signal the clutter subspace, which is in general distinct for different transmit/receive pairs and, moreover, may spread over the entire Doppler bandwidth. The second is a fully adaptive parametric detector which employs a parametric autoregressive clutter model and offers joint model order selection, clutter estimation/mitigation, and target detection in an integrated and fully adaptive process. Both detectors are developed within the generalized likelihood ratio test (GLRT) framework, obviating the need for training signals that are indispensable for conventional detectors but are difficult to obtain in practice due to clutter non-homogeneity. Numerical results indicate that the proposed training-free detectors offer improved detection performance over covariance matrix based detectors when the latter have a moderate amount of training signals.

Airborne radar arrays oriented toward any direction other than sidelooking cause the range dependence of clutter. It is difficult to handle this range dependence problem in the presence of range ambiguity. Frequency diverse array (FDA) employs a small frequency increment across the array elements, which induces the spatial frequency variant with respect to slant range. Thus, it provides extra degrees-of-freedom in range domain. In this paper, a novel framework of space-time adaptive processing (STAP) radar is established with FDA as the transmit array. In the FDA-STAP radar, the range ambiguous clutter can be discriminated in spatial frequency domain. This is due to the fact that the spatial frequencies of the clutters from different slant ranges are distinguishable even though the range ambiguous clutter is within the same range bin. Simultaneously, the FDA-STAP radar induces secondary range dependence of clutter. To alleviate the secondary range dependence of clutter, a secondary range dependence compensation (SRDC) method is proposed for two cases: 1) the target is assumed in the unambiguous range region and 2) the target is assumed in the ambiguous range region. After the range ambiguous clutter is separated in the spatial frequency domain by using the proposed SRDC method, the traditional clutter compensation approach is applied to further align the spectrum distribution of clutter. Our simulation results demonstrate the superiority of the proposed approach in clutter suppression under range ambiguous clutter scenarios.

High-frequency surface wave radar (HFSWR) has been successfully developed for early warning, especially for monitoring the moving vessels. However, it can be blinded by space spread clutter (SSC) which can cover a wide looking directions, such as sea clutter and ionospheric clutter in HFSWR. A few methods have made great efforts for SSC cancellation, but these efforts make no sense for the clutter in main-lobe that is a long-standing problem. In this paper, a spread clutter estimated canceller (SCEC) based on single notch space filter has been developed for SSC cancellation to cancel the clutter in the main-lobe with the idea of estimating the SSC in the looking direction through the ones in side-lobes. The feasibility of the idea has been described and demonstrated by the results of simulations and measured data which make great significance for target detection and tracking.

The use of spaceborne synthetic aperture radar (SAR) for ground moving target indication (GMTI) has recently attracted a great deal of interest. In this study, an efficient imaging algorithm for spaceborne SAR/GMTI systems is proposed. The proposed imaging algorithm first implements the secondary range compression and the range cell migration correction in the two-dimensional frequency domain and then the azimuth compression in the range Doppler domain. The proposed algorithm can finely focus the stationary scene and coarsely focus multiple slow-moving targets simultaneously without a priori knowledge of targets’ motion parameters and position parameters. Moreover, the proposed algorithm is interpolation free and can be efficiently implemented by using only complex multiplications and fast Fourier transforms. These advantages make the proposed algorithm useful in practice. Experimental results from both simulated data and real data validate the proposed algorithm.

An approach is examined for reducing false alarms during detection of human targets via radar surveillance from moving platforms. The ability to distinguish between human target and clutter discrete micro-Doppler signatures is quantified using simulated and real data collected using the DORC X-band wideband experimental airborne radar. Signal processing methodologies to extract key target signature characteristics are discussed along with separability of target classes and classifier performance.