Moving Target Indication Technique Research Articles

A Priori Knowledge Based Ground Moving Target Indication Technique Applied to Distributed Spaceborne SAR System

Through formation flying, the distributed spaceborne SAR(synthetic aperture radar) system can increase the number of spatial degree of freedoms (DOFs) and provide flexible multi-baselines for SAR-GMTI (ground moving target indication), which improves the system performance. This paper proposes an a priori knowledge-based adaptive clutter cancellation and moving target detection technique applied to the distributed spaceborne SAR-GMTI systems. Firstly, the adaptive clutter cancellation technique is exploited to suppress the ground clutter. A priori knowledge, such as road network information, is integrated to the adaptive clutter cancellation processor to reduce any moving target steering vector mismatch. Secondly, adaptive matched filter (AMF) and adaptive beamformer orthogonal rejection test (ABORT) are exploited as adaptive detection techniques for moving target detection. Due to the dense road network, the moving target steering vector estimation may be ambiguous for the different position and orientation of the roads. The multiple hypothesis testing (MHT) technique is proposed to detect the moving targets and resolve the potential ambiguities. A scheme is exploited to detect, classify, and relocate the moving targets. Finally, simulation experiments and performance analysis have demonstrated the effectiveness and robustness of the proposed technique.

Reduction of False Alarm Rate in SAR-MTI Based on Weighted Kurtosis

Moving target indication (MTI) is considered one of the most important applications of synthetic aperture radar (SAR) in military operations and traffic monitoring. Although many studies have focused on detectors based on the statistical clutter models, a mismatch between the measured data and the applied statistical model often leads to unreliable MTI results, particularly in terms of false alarm rates. To reduce the number of false alarms, we propose an efficient MTI scheme consisting of conventional MTI techniques (the displaced phase center antenna (DPCA) and the interferogram's magnitude and phase (IMP) methods), which operate in dual-receive antenna (DRA) mode in an SAR system. These techniques are coupled with a new discrimination stage based on a new detection metric-weighted kurtosis. Using simulated and real measured data from TerraSAR-X, the proposed MTI scheme demonstrates robust performance in the reduction of false alarm rates with only a slight increase in the computation time compared with conventional schemes.

Doppler beam sharpening/ground moving target indication techniques based on space–time adaptive processing for airborne active phased array fire‐control radar

Doppler beam sharpening/ground moving target indication techniques based on space–time adaptive processing for airborne active phased array fire‐control radar

On the Application of Digital Moving Target Indication Techniques to Short-Range FMCW Radar Data

In this paper, we describe three digital moving target indication (MTI) and moving target segmentation techniques (based on target speed) and apply them to short-range frequency-modulated continuous wave (FMCW) radar data. The described approaches are applicable to many short-range radar sensors. In particular, we focus on FMCW radar, which are ubiquitous in numerous applications, including gesture recognition radar, automotive radar, and imaging radar. The three digital MTI filtering methods explored are background subtraction, finite impulse response (FIR) filtering, and infinite impulse response (IIR) filtering. Each of the methods is implemented in the time domain for simpler logic implementation. We apply the MTI methods on data sets gathered using a C-band FMCW radar in both a short-range, direct line-of-sight scenario and a complex cluttered through wall radar scenario. Based on the analyses, it is shown that each of the MTI techniques are extremely effective when deployed in the right scenario. Background subtraction is found to be well suited for slow-moving targets. FIR and IIR filtering techniques provide the simplest, one-step processes for moving target segmentation.

Modeling Fast Boat Motion Impact on Satellite SAR MTI Systems

This letter analyzes the problems of imaging small and fast boats with synthetic aperture radar (SAR), which may experience severe defocusing and/or smearing. This situation is encountered, inter alia , in ocean traffic monitoring and surveillance applications where the detection of small and fast vessels is of great interest. The target modeling and impact of moving vessels in SAR images are presented and discussed using simulated results. A matched filter bank is used before applying moving target indication techniques over multichannel SAR images, refocusing the target signal in the azimuth domain and enhancing the detectability of these types of vessels.

LOW COST X BAND RADAR SYSTEM FOR MULTIPLE TARGET DETECTION

Radar based object detection systems have been in place for about a century serving various purposes like detection of ships aircrafts, spacecraft, navigation, reading weather formations and terrain mapping. With the advent of better packing technologies and signal conditioning circuits radars have become compact and affordable thus offering comparatively low cost solutions than other systems based on imaging. This paper describes an X band Radar System for multiple target detection that can work as an effective solution for obstacle detection and collision avoidance system to be used in automated braking systems. This radar system is capable of detecting multiple targets from the clutter encountered by the radar signals within a range of 20 metres. The paper discusses various moving target indicator techniques employed for multiple target detection and speed of the object in real time. This is particularly useful in automotive industries where the low cost motion sensors are needed in variety of applications which are necessary to ensure the safety of the passengers. To ensure a good signal to noise ratio we have used non coherent integration so that the radar signal is recognizable among the clutter. Moving Target Indicator techniques are used primarily to detect the number of objects in the range of the radar. This is carried out along with adaptive constant false alarm thresholding technique so that the object can be detected among the clutter and noise accurately. An Arduino Uno is used for detecting the objects and speed after processing on the non-coherently integrated pulse.

Radar Detection of Moving Targets Behind Corners

Detection of moving objects concealed behind a concrete wall corner has been demonstrated, using Doppler-based techniques with a stepped-frequency radar centered at 10 GHz, in a reduced-scale model of a street scenario. Micro-Doppler signatures have been traced in the return from a human target, both for walking and for breathing. Separate material measurements of the reflection and transmission of the concrete in the wall have showed that wall reflections are the dominating wave propagation mechanism for producing target detections, while wave components transmitted through the walls could be neglected. Weaker detections have been made of target returns via diffraction in the wall corner. A simple and fast algorithm for the detection and generation of detection tracks in down range has been developed, based on moving target indication technique.

IMPLEMENTATION OF MTI RADARS USING NEURAL NETWORK

The purpose of moving target indicator (MTI) radars is to reject signals from fixed unwanted targets, such as buildings, hills, and trees, and retain signals for detection of moving targets such as aircraft, ships, missiles,..etc. There are many conventional MTI techniques such as, the adaptive pulse canceller, doppler filter banks, and fast Fourier transform (FFT) filter banks. Each technique suffers certain considerable limitation. This paper utilizes a neural network approach to implement a system that can be used to detect moving targets in severely cluttered environment. A three-layer neural network has been trained to perform as moving target indicator. The back-propagation training algorithm has been used to train the proposed network. Significant performance improvements have been achieved as compared with the conventional fast Fourier transform method.