Moving Target Indication Radar Research Articles

A ViSAR Shadow-Detection Algorithm Based on LRSD Combined Trajectory Region Extraction

Shadow detection is a new method for video synthetic aperture radar moving target indication (ViSAR-GMTI). The shadow formed by the target occlusion will reflect its real position, preventing the defocusing or offset of the moving target from making it difficult to identify the target during imaging. To achieve high-precision shadow detection, this paper proposes a video SAR moving target shadow-detection algorithm based on low-rank sparse decomposition combined with trajectory area extraction. Based on the low-rank sparse decomposition (LRSD) model, the algorithm creates a new decomposition framework combined with total variation (TV) regularization and coherence suppression items to improve the decomposition effect, and a global constraint is constructed to suppress interference using feature operators. In addition, it cooperates with the double threshold trajectory segmentation and error trajectory elimination method to further improve the detection performance. Finally, an experiment was carried out based on the video SAR data released by Sandia National Laboratory (SNL); the results prove the effectiveness of the proposed method, and the detection performance of the method is proved by comparative experiments.

Range Ambiguous Clutter Suppression Approach With Elevation Time Diverse Array

When range ambiguity and range dependence coexist in forward-looking ground moving target indication radar, the traditional space-time adaptive processing (STAP) approaches may fail to work. To cope with this issue, a range-ambiguous clutter suppression approach based on elevation time diverse array (TDA) radar system and azimuth phase coding technique is presented in this article. Due to the advantages of frequency scanning, the elevation TDA radar is able to cover all directions by transmitting a single time-shifted waveform. Therefore, a frequency bandpass filter is devised for each single range bin to achieve the mainlobe echo extraction procedure. In the sequel, the azimuth phase decoding and Doppler filtering are performed to deal with the residual sidelobe ambiguous energy. With two-dimensional filtering, one can extract the unambiguous signals for each range region independently. Then, the traditional clutter compensation and STAP methods are employed to suppress the ground clutter. Finally, the effectiveness of the proposed framework in resolving range ambiguity is verified by the numerical simulation experiments.

Simulation of Moving Target Indication Radar System Based on VisSim/Comm Application

Moving target indication (MTI) is mainly designed to detect moving targets and while unmoving targets are filtered out. It focuses on the technique of the modern stationary target indication (STI), by using directly the signal details to determine the reflecting object’s mechanical properties, after that it becomes easier to find moving or non-moving targets. This paper presents the simulation design of the MTI radar system. The main purpose of this design is to help students in understanding the radar system subject and help teachers to explain this subject in a simpler approach. Both students and teachers need to know how the signals inside the MTI radar processor are working and how they are generated and related to each other. This paper introduces a method of simulation of MTI radar signals including, A-scope radar display, transmitted and returned radar pulses with constant and multiple PRF, n-delay line cancellers.

Shipborne moving target indicator radar versus incoming sea-skimming missile mathematical modeling and simulation

Anti-ship missiles are a primary threat to naval targets because sea-skimming tactics enable the missile to approach the vessel while being obscured by sea clutter. In order to counteract this serious danger, the ship’s radar employs moving target indicator processing in order to detect and subsequently start the tracking of the incoming missile. In this paper, we set up a relevant simulation in order to examine the moving target indicator process for a naval scenario at horizon range which gives the greatest counter-reaction time, which usually is for very small grazing angles in a sea cluttered environment. Finally, the results of the simulator are beneficial to radar designers and educational professionals.

Syntactic Enhancement to VSIMM for Roadmap Based Anomalous Trajectory Detection: A Natural Language Processing Approach

Syntactic tracking aims to classify a target's spatio-temporal trajectory by using natural language processing models. This paper proposes constrained stochastic context-free grammar (CSCFG) models for target trajectories confined to a roadmap. We present a particle filtering algorithm that exploits the CSCFG model structure to estimate the target's trajectory. This metalevel algorithm operates in conjunction with a base-level target tracking algorithm. Extensive numerical results using simulated ground moving target indicator radar measurements show useful improvement in both trajectory classification and target state (both coordinates and velocity) estimation.

A new Gaussian mixture method with exactly exploiting the negative information for GMTI radar tracking in a low-observable environment

This paper investigates the problem of ground vehicle tracking with a 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 when it stops, i.e. falling into the Doppler blind region. Besides, there will be false alarms in low-observable environments where there exist high noises interferences. In this paper, we develop a novel algorithm for the GMTI tracking in a low-observable environment with false alarms while exactly incorporating the ‘negative information’ (i.e., the target is likely to stop when no measurements are recorded) based on the Bayesian inference framework. For the Bayesian inference implementation, the Gaussian mixture approximation method is adopted to approximate related distributions, while different filtering algorithms (including both extended Kalman filter and its generalization for interval-censored measurements) are applied for updating the Gaussian mixture components. Target state estimation can be directly obtained through the Gaussian mixture model for the GMTI tracking at every time instance. We have compared the developed method with other state-of-the-art ones and the simulation results show that the proposed method substantially outperforms the existing methods for the GMTI tracking problem.

Filters that mitigate waveform modulation of radar clutter

Moving target indication (MTI) radars will require modified signal processing to be able to employ pulse-to-pulse agile waveforms. The main problem with conventional processing is that matched-filter pulse compression leads to range-sidelobe modulation (RSM) of clutter. Due to the fact that range sidelobes differ on each pulse, clutter energy that leaks into these sidelobes cannot be cancelled by subsequent processing. This study derives a figure-of-merit for pulse-compression filters that quantifies the signal-to-interference-plus-noise ratio (SINR) loss that results from RSM. Optimising this figure-of-merit results in sets of filters that mitigate RSM by producing matched sidelobes. The proposed filters are compared to low-sidelobe mismatch filters designed separately for each pulse. Simulation results indicate that an MTI radar could achieve a 5–10 dB improvement in SINR by using the proposed filters instead of low-sidelobe filters.

An improved multiple model particle filtering approach for manoeuvring target tracking using airborne GMTI with geographic information

This paper proposes a 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 in different terrain conditions. Based on geographic conditions and multiple state models, a constrained variable structure multiple model particle filter algorithm is proposed. Compared with the traditional multiple model particle filtering schemes, the proposed algorithm utilises a particle swarm optimisation technique 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.

Ground target tracking with RCS estimation based on signal strength measurements

The discrimination of closely spaced targets is a major challenge in the ground target-tracking domain based on measurements of airborne ground moving target indication radar. Being a standard output of modern radar systems, the measured signal strength of a radar detection can be used to estimate the characteristic mean radar cross section (RCS) of a ground target, which is then used as additional target attribute information to improve the tracking performance in situations with closely spaced targets. For this method to work, the fluctuations of the target RCS are assumed to follow the analytically tractable Swerling-I and Swerling-III cases. In this work, the RCS estimation scheme originally developed for air targets [1, 2] is generalized to ground-moving objects and then implemented into the Gaussian mixture cardinalized probability hypothesis density filter. The performance of the resulting algorithm is analyzed based on single and multiple-target simulation scenarios. In the latter case, a modified version of the optimal subpattern assignment metric that also accounts for labeling errors is used.

Analytical expressions for space-time adaptive processing (STAP) performance

Space-time adaptive processing (STAP) is a family of algorithms frequently employed in surface moving target indication radar systems to enable detection of moving objects in the presence of fixed (i.e., nonmoving) clutter. In this paper, two new closed-form expressions that quantify the loss associated with the STAP notch centered on clutter are derived in terms of system parameters of interest. Although there are many excellent reports, books, and papers focused on STAP, a simple yet accurate approximation for the STAP notch has not previously appeared. It is also shown that a new, accurate approximation for the important STAP metric known as minimum detectable velocity may be derived from the STAP notch expression. Furthermore, accurate expressions are derived that predict when “aperture-limited” STAP performance may be obtained. This work provides the first analytical, unifying connection between these STAP metrics. As they are implemented in compact closed-form expressions, the new results are attractive for system design. The significant computational benefits associated with the new results can be very advantageous in trade studies or large simulations in which STAP performance estimates must be computed thousands of times. It is expected that this work will be of great use to both readers with a background in STAP and readers new to this field. For readers with STAP experience, this work provides a new understanding of the drivers of STAP performance and of the interconnections between STAP performance measures. For readers new to the STAP field, this work removes the mystery associated with STAP by providing intuition into how STAP works. With accurate, analytical expressions, system engineers can now implement accurate predictions of STAP performance without the necessity of constructing patch-based analysis tools that estimate STAP performance by computing the notch associated with hundreds to thousands of clutter patches.

A subspace‐based channel calibration algorithm for geosynchronous satellite‐airborne bistatic multi‐channel radars

Ground moving target indication based on geosynchronous (GEO) satellite-airborne (SA) bistatic radar can keep a certain area under continuous surveillance, and thus has important military application values. However, a channel error will affect the clutter suppression performance of the bistatic multi-channel moving target indication radar. And it is found that the channel calibration algorithm used in the long sequence monostatic side-looking synthetic aperture radar, is not applicable to the long sequence translational variant bistatic radar. An improved subspace-based channel calibration algorithm is proposed in this study to deal with channel errors of the GEO SA bistatic multi-channel radar. The algorithm utilises the clutter as calibrated sources, and estimates channel error parameters by minimising the projections of the calibrated sources onto the noise subspace. As the channel errors are estimated from the data, the proposed algorithm does not need any extra calibration sources. Also, the proposed algorithm does not have the limitation of the traditional array self-calibration algorithms, which may have non-unique estimations under the condition of linear arrays. Besides, it avoids finding the inversion of large-size matrix and calculating with any iteration, and hence it is efficient. Moreover, the proposed algorithm can estimate channel errors that vary with azimuth. Simulations are performed to validate the proposed algorithm.

Performance Analysis of Ground Moving Target Detection for Airborne MIMO Radar

The performance of a Ground Moving Target Indication (GMTI) radar is determined by the interference level of the clutter to a large extent. With the longer illumination and the larger aperture, Multiple-Input, Multiple-Output (MIMO) radar greatly enhances the ability to detect the slow-moving target. This paper compares MIMO radar with conventional phased-array radar in terms of signal to clutter and noise ratio (SCNR) performance. Moreover, the paper proves the fact that the improvement of SCNR with MIMO techniques is not only limited in the antenna configuration of the side-looking but also non-sidelooking array-antenna. The analysis of mianlobe clutter and sidelobe clutter levels can greatly simplify the computation of the total clutter. Finally, the experimental simulation is been given.

High Resolution Angle-Doppler Imaging for MTI Radar

To reduce the need for training data or for accurate prior knowledge of the clutter statistics in space-time adaptive processing (STAP), we consider high resolution angle-Doppler imaging by processing each range bin of interest independently. Specifically, we use a weighted least-squares-based iterative adaptive approach (IAA) to form angle-Doppler images of both clutter and targets for each range bin of interest. The resulting angle-Doppler images can be used with localized detection approaches for moving target indication (MTI). We show via numerical examples that the robust and nonparametric IAA algorithm can be used to enhance the MTI performance significantly as compared with existing approaches.

Linear prediction of range-dependent inverse covariance matrix (PICM) sequences

Knowledge of the true clutter covariance matrix is required for optimum space–time adaptive processing (STAP). In practise, this matrix is not known and must be estimated from training data. For bistatic ground moving target indication radar, the clutter Doppler frequency depends on range for all array geometries. This range dependency leads to problems in clutter suppression through STAP techniques. In this paper, we propose a new training strategy for STAP by using linear prediction techniques (least squares estimation) to obtain an estimate of the inverse covariance matrix. We present the issues associated with applying linear prediction theory to the range-dependent inverse covariance matrix in bistatic airborne radar systems. Performance measures are compared against conventional STAP techniques in terms of improvement factor loss plots.

Multiple Phase Center Performance of Reflector Antennas Using a Dual Mode Horn

A new application for reflector antennas is proposed and developed. Using the aperture theory, a phase center on the reflector aperture is determined and shown that, its location is dependent on the field distribution. The proposed concept is, initially, verified by using a symmetric reflector. It is shown that the phase center is located at the aperture geometric center, when the reflector is illuminated symmetrically about its principal planes. Then, a dual mode feed, employing TE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sub> and TM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">01</sub> modes, is used for generating different reflector illuminations, and causing displacement of its phase center. The concept is then extended to offset reflectors, and the influence of the reflector geometry on the phase center displacement and other reflector electrical parameters is investigated. Based on the established feed radiation pattern requirements, a feed horn is designed using circular waveguide that can propagate both modes. By modifying the amplitude and phase of the modes in the horn, a controlled asymmetric reflector aperture field is achieved. A prototype feed horn is fabricated and tested for its dual mode radiation patterns. The results are in good agreement with simulations. The reflector phase center properties are then investigated, by using the designed feed. A reflector-feed assembly, with its dual phase center capability, was developed for improving the performance of the ground moving target indicator radars. The concept allows the conversion of a single reflector to two or more reflectors, simply by modifying the mode excitation amplitudes and phases, in the feed alone

The moving target detector

The paper presents a brief biographical note about the moving target detector (MTD) radar, the evolution from multiple antenna surveillance radar up to the designs to overcome the limitations of the existing moving target indicator radar

Prediction of inverse covariance matrix (PICM) sequences for STAP

In this letter, we study issues associated with applying least-squares estimation to predict the inverse covariance matrix in bistatic airborne radar systems. For the bistatic ground moving target indication radar, the clutter Doppler frequency depends on the range for all array geometries. This range dependency leads to problems in clutter suppression through space-time adaptive processing (STAP) techniques. This paper proposes a new method of obtaining an estimate of the inverse covariance matrix using linear prediction techniques. Simulation results show a significant improvement in processor performance as compared to conventional STAP methods.

Knowledge-Aided STAP Processing for Ground Moving Target Indication Radar Using Multilook Data

Knowledge-aided space-time adaptive processing (KASTAP) using multiple coherent processing interval (CPI) radar data is described. The approach is based on forming earth-based clutter reflectivity maps to provide improved knowledge of clutter statistics in nonhomogeneous terrain environments. The maps are utilized to calculate predicted clutter covariance matrices as a function of range. Using a data set provided under the DARPA knowledge-aided sensor signal processing and expert reasoning (KASSPER) Program, predicted distributed clutter statistics are compared to measured statistics to verify the accuracy of the approach. Robust STAP weight vectors are calculated using a technique that combines covariance tapering, adaptive estimation of gain and phase corrections, knowledge-aided prewhitening, and eigenvalue rescaling. Techniques to suppress large discrete returns, expected in urban areas, are also described. Several performance metrics are presented, including signal-to-interference-plus-noise ratio (SINR) loss, target detections and false alarms, receiver operating characteristic (ROC) curves, and tracking performance. The results show more than an order of magnitude reduction in false alarm density when compared to standard STAP processing.

Training strategies for joint domain localised-space–time adaptive processing in a bistatic environment

Optimum space–time adaptive processing (STAP) requires knowledge of the true interference covariance matrix. In practice, this matrix is not known and must be estimated from training data, which must be target free and statistically homogeneous with respect to the range gate under test. These conditions are often not satisfied, which degrades the detection performance. Particularly for bistatic ground moving target indication radar, the clutter Doppler frequency depends on range for all array geometries. This range dependency leads to problems in clutter suppression through STAP techniques. The main aim in this paper is to access the relative merits of several strategies associated with STAP weight training for bistatic airborne radar applications. In particular, the issues associated with applying reduced-dimension conventional STAP and in-the-gate processing are studied. Simulation results show that, for a bistatic radar environment, in-the-gate processing approach performs better than conventional STAP approaches.

Clutter-limited detection performance of multi-channel conformal arrays

Conformal arrays possess certain desirable characteristics for deployment on unmanned aerial vehicles and other payload-limited platforms: aerodynamic design, minimal payload weight, increased field of view without the need for cumbersome mechanical couplings and rotary joints, and ease of integration with diverse sensor functions. This paper examines the adaptive detection performance potential of aerospace moving target indication radar employing a conformal antenna array. We consider four candidate conformal designs taking the following shapes: belly-mounted canoe, airfoil, cone and chined radome. Furthermore, we compare results to side- and forward-looking planar arrays. We find that array nonlinearity and orientation induce clutter non-stationarity; this non-stationary behavior adversely affects space–time adaptive processing implementations necessary for effective clutter suppression. We find that additional adaptive losses exceed 12 dB as a result of conformal geometry-induced clutter non-stationarity. Several mitigating solutions to non-stationarity are given.