A simulation framework for testing GMTI trackers

A common problem in ground moving target indication (GMTI) radar is detecting a target with even a large radar cross section (RCS) when its line-of-sight velocity falls below the minimum detectable velocity (MDV) for that radar system. In a cooperative scenario, a target may employ a tagging device, which can shift or spread its Doppler signature to become more detectable. In this paper we develop a bound on the minimum modulatable RCS of a passive (non-amplifying) device needed to be detectable as a function of radar and mode parameters, and compute this bound for specific GMTI and SAR radar modes.

Moving targets localization is an important challenge in current synthetic aperture radar (SAR) — ground moving target indication (GMTI) system. The along track interferometry (ATI) method is usually used for GMTI in SAR, however, which suffers from ambiguous parameters estimation due to wrapped phase and less robust to estimation errors (of system parameters and motion parameters of the moving targets). Focusing on these, we propose a new neural network based method to improve the moving targets localization performance. After analyzing the main error source of the conventional ATI method, we utilize an error back propagation (BP) neural network to replace the processing after range compression in the ATI method. And the proposed method is proved effective by the experiments, and it performs much better than the conventional ATI method in localization accuracy, efficiency and robustness.

Knowing the statistical characteristics of the radar cross-section (RCS) of man-made, or cultural clutter, is crucial to the success of clutter mitigation, radar target detection algorithms, and radar system requirements in urban environments. Open literature studies regarding the statistical nature of cultural clutter focus primarily on radar probability models or limited experimental data analysis of specific locations and frequencies. This paper seeks to expand the existing body of work on cultural clutter RCS statistics at Ku-band for ground moving target indication (GMTI) and synthetic aperture radar (SAR) applications. We examine the normalized RCS probability distributions of cultural clutter in several urban scenes, across aspect and elevation angle, for vertical transmit/receive (VV) polarizations, and at diverse resolutions, using experimental data collected at Ku-band. We further describe frequency and RCS strength statistics of clutter discretes per unit area to understand system demands on radars operating in urban environments in this band.

This paper studies the range equation modeling of a ground moving target for multichannel medium-Earth-orbit (MEO) synthetic aperture radar (SAR) ground moving target indication (GMTI), an issue which is challenging to tackle due to the non-linear motion of the radar platform and the Earth rotation. In the paper, the coordinates of the multichannel MEO SAR and the target, as well as the target’s range equation with respect to each channel, are developed. Moreover, an expression of concise form is derived for the target’s quadratic-approximated range equation, which will benefit the design of GMTI methods. Furthermore, theoretical analyses are conducted to reveal the dependency between the accuracy of the quadratic-approximated range equation and the parameters of the radar and the target. Numerical simulations are carried out to investigate the influence of the quadratic approximation of the range equation on the GMTI performance and to figure out the quadratic-approximated range equation’s scope of application.

This paper presents a study on signal modeling for airborne multichannel circular stripmap synthetic aperture radar (CSSAR)-ground moving target indication (GMTI) systems with relatively high resolution. High resolution is an important trend of synthetic aperture radar (SAR), which means higher accuracy requirement of range history and longer synthetic aperture time. It's known that targets are probably experience accelerations as the synthetic aperture time increases. Therefore, compared with the systems of lower resolution, the target signal modeling of high-resolution systems becomes more challenging. Besides, the coupling relationship among a target's motion and position parameters becomes more complicated due to the existence of accelerations. In this paper, a fourth-order range history model of a moving target is developed. Moreover, the target signal model is established, and the along-track interferometric (ATI) phase history is derived to reveal the coupling relationship among a target's motion and position parameters.

The short-range clutter has serious range dependence for forward looking airborne radar (FLAR). As a result, when a radar detects moving targets on far range gates, the ambiguous short-range clutter degrades the performance of ground moving target indication (GMTI) obtained by spacetime adaptive processing (STAP). The elevation three dimensional adaptive processing can well mitigate the problem, but, the computational load of the method is too heavy to be applied in real scenarios. A robust elevation pre-filtering algorithm is proposed. This method does not need adaptive processing on the elevation elements in a planar-array antenna. Therefore, the computational load can be reduced greatly. This method suppresses the ambiguous short-range clutter by multi-elevation angles restriction and the cancellation weight on the clutter has a deep and wide notch. Moreover, the method is robust to the elevation angle errors. Simulation results show the validity of the method.

The aim of syntactic tracking is to classify spatio-temporal patterns of a target's motion using natural language processing models. In this paper, we generalize earlier work by considering a constrained stochastic context free grammar (CSCFG) for modeling patterns confined to a roadmap. The constrained grammar facilitates modeling specific directions and road names in a roadmap. We present a novel particle filtering algorithm that exploits the CSCFG model for estimating the target's patterns. This meta-level algorithm operates in conjunction with a base-level tracking algorithm. Extensive numerical results using simulated ground moving target indicator (GMTI) radar measurements show substantial improvement in target tracking accuracy.

The design and implementation of a multiple model nonlinear filter (MMNLF) for ground target tracking using ground moving target indicator (GMTI) radar measurements is described. Like the well-known interacting multiple model Kalman filter (IMMKF), the MMNLF is based on the theory of hybrid stochastic systems. However, since it models the probability distribution for the target in a region, rather than just the distribution's first and second moments, a nonlinear filter is able to capture more fine-grained detail of the target motion and requires fewer models than typical IMMKF implementations. This is illustrated here with a two-model MMNLF in which one motion model incorporates terrain constraints while the second is a nearly constant velocity (CV) model. Another feature of the MMNLF is that it enables incorporation of prethresholded measurements. To implement the filter, the target state conditional probability density is discretized on a set of moving grids and recursively updated with sensor measurements via Bayes' formula. The conditional density is time updated between sensor measurements using alternating direction implicit (ADI) finite difference methods, generalized for this hybrid application. In simulation testing against low signal-to-interference-plus-noise ratio (SINR) targets, the MMNLF is able to maintain track in situations where single model filters based on either of the component models or filters that use thresholded data fail. Potential applications of this work include detection and tracking of foliage-obscured moving targets.

Spatio-Temporal Adaptive Processing (STAP) technique has been widely applied to mitigate the effects of strong interference (joint effects of clutter and other electronic counter measures(ECM)). Limited channel and reduced rank STAP techniques are alternatively proposed in the literature to address the computational issues present in fully adaptive STAP. This paper investigates performance of two channel ΣΔ (SigmaDelta)-STAP for detecting multiple targets in Ground Moving Target Indication (GMTI) scenario in the presence of clutter. Clutter is simulated from an airborne platform operating at 10 GHz and a pulse repetition frequency of 5 kHz. First, a single ground moving target is detected effectively using the Σ Δ-STAP. Further, this algorithm is applied to detect two targets moving with 7.5 m/s and 15 m/s velocities. In addition, five target scenario is considered and the Σ Δ-STAP algorithm successfully detected these five targets,

This paper formulates and solves a sequential detection problem that involves the mutual information (stochastic observability) of a Gaussian process observed in noise with missing measurements. The main result is that the optimal decision is characterized by a monotone policy on the partially ordered set of positive definite covariance matrices. This monotone structure implies that numerically efficient algorithms can be designed to estimate and implement monotone parametrized decision policies.The sequential detection problem is motivated by applications in radar scheduling where the aim is to maintain the mutual information of all targets within a specified bound. We illustrate the problem formulation and performance of monotone parametrized policies via numerical examples in fly-by and persistent-surveillance applications involving a GMTI (Ground Moving Target Indicator) radar.

This work focuses on two challenging types of problems related to quality assessment and comparison of thematic maps generated from remote sensing (RS) images when little or no ground truth knowledge is available. These problems occur when: (1) competing thematic maps, generated from the same input RS image, assumed to be available, must be compared, but no ground truth knowledge is found to assess the accuracy of the mapping problem at hand, and (2) the generalization capability of competing classifiers must be estimated and compared when the small/unrepresentative ground truth problem affects the RS inductive learning application at hand. Specifically focused on badly posed image classification tasks, this paper presents an original data-driven (i.e., unsupervised) thematic map quality assessment (DAMA) strategy complementary (not alternative) in nature to traditional supervised map accuracy assessment techniques, driven by the expensive and error-prone digitization of ground truth knowledge. To compensate for the lack of supervised regions of interest, DAMA generates so-called multiple reference cluster maps from several blocks of the input RS image that are clustered separately. Due to the unsupervised (i.e., subjective) nature (ill-posedness) of data clustering, DAMA provides no (absolute) map accuracy measure. Rather, DAMA's map quality indexes are to be considered unsupervised (i.e., subjective) relative estimates of labeling and segmentation consistency between every competing map at hand and the set of multiple reference cluster maps. In two badly posed RS image mapping experiments, DAMA's map quality measures are proven to be: (1) useful in the relative comparison of competing mapping systems; (2) consistent with theoretical expectations; and (3) in line with mapping quality criteria adopted by expert photointerpreters. Documented limitations of DAMA are that it is intrinsically heuristic due to the subjective nature of the clustering problem, and like any evaluation measure, it cannot be injective.

The radar cross section (RCS) of a target reflects its ability to backscatter incident energy and is generally related to its geometrical complexity. This radiated energy allows one to obtain several different points of the target, generally labeled as the scattering centers. Most of the existing techniques used to detect and recognize a target depended on these particular points and are labeled as the scattering-center model. This model, while only an approximation, is conceptually simple, and provides a sparse abstraction of the actual target for numerous radar applications. The conventional radar image generated with the Fourier transform is preferred over model techniques if all the scattering centers can be resolved. Accurate estimates of the locations of the scattering centers are given by the peaks in the radar image. Furthermore, the amplitudes of the peaks the contribution of the scattering centers to the total backscattered field. The estimation of the geometrical features of a target is not necessarily possible if the location of these centers are not known. When analysing the RCS of a target, it may be possible to select some ranges of the signal in respect with the observation angle /spl theta/ that are relevant for the target's geometrical features. The application of wavelet transform to the RCS interpretation brings significant additional information that yields an eventual determination of the target size.

In this paper, we propose and investigate a model based on asymptotic methods, designed to compute the radar cross section (RCS) of complex targets. A combined method of physical optics, geometrical optics, and the equivalent current method is applied to establish an effective backscattering analysis procedure. The scattering model includes shadowing effects, multiple bounce, and diffraction by edges. This model is used for RCS estimation and to generate inverse synthetic aperture radar (ISAR) raw data for imaging applications. We review the theoretical aspects and describe the proposed model and experimental setup in detail. Numerical results are provided and compared with the experimental results obtained in the anechoic chamber of ENSTA Bretagne to validate the approach through the computation of the RCS for canonical objects and a generic boat. Finally, a parallelepiped representation of the RCS is presented to be used for ISAR imagery, and the first results concerning the ISAR imaging of a generic boat are provided.

In this paper we analyze the power budget and calculate signal to noise ratio (SNR) and radar cross section (RCS) for different ground targets using a forward scattering radar (FSR) system, which consists of a transmitter mounted on a satellite of GPS and a receiver located on the Earth's surface. The FSR is a specific bistatic radar system where the bistatic angle is near 180o, and the target is located near the transmitter-receiver baseline. Theoretical and numerical calculations of SNR and RCS are presented for different ground targets when the GPS L1 signal is transmitted by satellites. The SNR and RCS are also estimated by using real records of the GPS signal shadows from different targets. The obtained numerical and experimental results are analyzed and discussed.

The assessment of process mining techniques using real-life data is often compromised by the lack of ground truth knowledge, the presence of non-essential outliers in system behavior and recording errors in event logs. Using synthetically generated data could leverage ground truth for better evaluation. Existing log generation tools inject noise directly into the logs, which does not capture many typical behavioral deviations. Furthermore, the link between the model and the log, which is needed for later assessment, becomes lost. We propose a ground-truth approach for generating process data from existing or synthetic initial process models, whether automatically generated or hand-made. This approach incorporates patterns of behavioral deviations and recording errors to produce a synthetic yet realistic deviating model and imperfect event log. These, together with the initial model, are required to assess process mining techniques based on ground truth knowledge. We demonstrate this approach to create datasets of synthetic process data for three processes, one of which we used in a conformance checking use case, focusing on the assessment of (relaxed) systemic alignments to expose and explain deviations in modeled and recorded behavior. Our results show that this approach, unlike traditional methods, provides detailed insights into the strengths and weaknesses of process mining techniques, both quantitatively and qualitatively.