Moving target indication

About: Moving target indication is a research topic. Over the lifetime, 2653 publications have been published within this topic receiving 32435 citations.

Application of clutter reduction techniques for detection of metallic and low dielectric target behind the brick wall by stepped frequency continuous wave radar in ultra-wideband range

Abstract: A study of clutter reduction techniques for detection of metallic and non-metallic (low dielectric constant) targets behind a brick wall with the help of ultra-wideband (UWB) through wall imaging system is presented. It is known that sometimes the clutter level is comparable to the level of target reflection that makes it difficult to detect the target correctly. Detection of low dielectric constant materials becomes more difficult due to low reflection from such targets. Therefore there is a need to analyse various clutter removal techniques and check the performance of these techniques for enhancement of target signal-to-clutter ratio. For this purpose, an UWB stepped frequency wave radar is indigenously assembled with the use of vector network analyser, which works in the frequency range of 3.95–5.85 GHz. An experiment is carried out for detection of metal as well as Teflon (low dielectric constant) targets with the application of clutter reduction techniques. The authors have considered statistical-based techniques like singular value decomposition, principle component analysis, factor analysis and independent component analysis (ICA) for clutter removal. It is observed that the signal-to-clutter ratio for metal target detection is quite enhanced by all the four techniques, whereas only ICA is able to enhance the signal-to-clutter ratio for a low dielectric constant target like Teflon.

Manoeuvring target detection in over-the-horizon radar using adaptive clutter rejection and adaptive chirplet transform

Abstract: In over-the-horizon radar (OTHR) systems, the signal-to-clutter ratio (SCR) used for moving target detection is very low. For slowly moving targets such as ships, the SCR is typically from 2 50 dB to 2 60 dB and their Doppler frequencies are close to that of the clutter. For manoeuvring targets, such as aircraft and missiles, the Doppler frequencies are time-varying when a long integration time is considered. When a target does not move uniformly, the Fourier transform based target detection techniques, including super-resolution spectrum techniques, may fail to work appropriately. In such situations, the Doppler signatures are time-varying and, therefore, time - frequency analysis techniques can be used for manoeuvring target detection. In addition, clutter rejection is also required for target detection due to the low SCR. The existing adaptive clutter rejection algorithms combine clutter rejection with spectrum analysis methods, which usually assume uniformly moving target (i.e. sinusoidal Doppler signature) models. An adaptive clutter reject algorithm is proposed together with the adaptive chirplet transform technique for manoeuvring target detection in a multipath environment. Simulation results using a simulated manoeuvring target signal with received raw OTHR clutter data show that targets with SCR below 2 50 dB can be detected by using the proposed algorithm.

Cramer-Rao bounds for target angle and Doppler estimation with space-time adaptive processing radar

Abstract: Space-time adaptive processing (STAP) can substantially improve airborne radar performance in environments with interference (clutter and/or jamming). This paper considers target parameter estimation with the STAP radar. The maximum-likelihood estimator for target angle and Doppler is reviewed. Cramer-Rao bounds for target angle and Doppler estimation accuracy are derived for an arbitrary interference scenario. These bounds show that in clutter, angle accuracy depends on the target Doppler and vice-versa. They are useful for quantifying the best-case degradation in estimator accuracy due to interference, and for determining the fractions of the Doppler space and coverage sector over which a specified level of accuracy can be achieved.

GMTI Tracking via the Gaussian Mixture Cardinalized Probability Hypothesis Density Filter

Abstract: The cardinalized probability hypothesis density (CPHD) filter is a recursive Bayesian algorithm for estimating multiple target states with a varying target number in clutter. In particular the Gaussian mixture variant (GMCPHD), which provides closed-form prediction and update equations for the filter in the case of linear Gaussian systems, is a candidate for real time multi-target tracking. The following three issues are addressed. First we show the equivalence between the GMCPHD filter and the standard multi hypothesis tracker (MHT) in the case of a single target. Second by using a Gaussian sum approach, we extend the GMCPHD filter to incorporate digital road maps for road constrained targets. The use of such external information leads to more precise tracks and faster and more reliable target number estimates. Third we model the effect of Doppler blindness by a target state-dependent detection probability, which leads to a more stable target-number estimation in the case of low-Doppler targets.

Ground target tracking with STAP radar

Abstract: The problem of tracking ground moving targets by a moving radar (airborne, spaceborne) is addressed. Tracking of low Doppler targets within a strong clutter background is of special interest. The motion of the radar platform induces a spreading of the clutter Doppler spectrum so that low Doppler target echoes may be buried in the clutter band. Detection of such targets can be much alleviated by space–time adaptive processing (STAP), which implicitly compensates for the Doppler spread effect caused by the platform motion. Even if STAP is applied, low Doppler targets can be masked by the clutter notch. This physical phenomenon is frequently observed and results in a series of missing detections which may seriously degrade the tracking performance. A new sensor model adapted to STAP is proposed and its benefits to tracking well-separated targets are discussed. By exploiting a priori information on the sensor specific clutter notch, the model in particular provides a more appropriate treatment of missing detections. In this context, the minimum detectable velocity (MDV) proves to be an important sensor parameter, explicitly entering into ground moving target indication (GMTI) tracking.