Point Target Assumption Research Articles

Integrated Sensing and Communications for V2I Networks: Dynamic Predictive Beamforming for Extended Vehicle Targets

We investigate sensing-assisted beamforming for vehicle-to-infrastructure (V2I) communication by exploiting integrated sensing and communications (ISAC) functionalities at the roadside unit (RSU). The RSU deploys a massive multi-input-multi-output (mMIMO) array at mmWave. The pencil-sharp mMIMO beams and fine range-resolution implicate that the point-target assumption is impractical, as the vehicle’s geometry becomes essential. Therefore, the communication receiver (CR) may never lie in the beam, even when the vehicle is accurately tracked. To tackle this problem, we consider the extended target with two novel schemes. For the first scheme, the beamwidth is adjusted in real-time to cover the entire vehicle, followed by an extended Kalman filter to predict and track the position of CR according to resolved scatterers. An upgraded scheme is proposed by splitting each transmission block into two stages. The first stage is exploited for ISAC with a wide beam. Based on the sensed results at the first stage, the second stage is dedicated to communication with a pencil-sharp beam, yielding significant communication improvements. We reveal the inherent tradeoff between the two stages in terms of their durations, and develop an optimal allocation strategy that maximizes the average achievable rate. Finally, simulations verify the superiorities of proposed schemes over state-of-the-art methods.

Aspect dependent-based ghost suppression for extended targets in through-the-wall radar imaging under compressive sensing framework

Several approaches have been proposed to suppress multipath ghost in through-the-wall radar imaging (TWRI). One classical approach, called Aspect Dependent (AD), exploits locations of ghosts in the images without demanding prior knowledge of the reflecting geometry. This operation strategy makes the method superior over multipath exploitation-based approaches. However, the AD method assumes a point target that emulates unreal environment. Therefore, reconstructing extended targets with this method leads to incorrect scene interpretation. This work proposes a ghost suppression method for extended targets based on the AD feature that exploits duo sub-apertures. Firstly, we evaluate the best suppression method using a performance metric called relative clutter peak. Next, the evaluated method is extended to encompass a target extent during sub-images reconstruction. Following this strategy, an effective image fusion method suitable for extended targets is proposed. The method considers pixel neighborhood to effectively recover the given extended target. Simulation results show that, when signal-to-noise ratio is 20dB, the proposed method significantly improves signal-to-clutter ratio and relative clutter peak by 8.8% and 23.8%, respectively, relative to the existing AD based methods under point target assumption.

Multiple detection joint integrated track splitting for multiple extended target tracking

The extended target tracking problem, which occurs due to a high-resolution sensor resolving multiple scattering points of a target, is considered in this paper. Typical target tracking algorithms which use the point target assumption yield poor performances in extended target tracking scenarios, especially in the presence of closely spaced targets. This performance degeneration is mainly caused by a number of measurements that are shared among tracks when the extended targets are closely spaced. In order to achieve robust tracking performances in multiple extended target tracking environments, a novel tracker, named multiple detection joint integrated track splitting (MD-JITS), is proposed here. This tracker incorporates the measurement cells and the random matrix into a target existence based multitarget track splitting structure. In MD-JITS, each track builds a type of tree, and each branch of the tree is a track component corresponding to a sequence of data associations for multiple scans. The track kinematic state and extension state are obtained on the basis of the track components. Then, the proposed algorithm is tested using both Monte Carlo simulations and a real video surveillance application to demonstrate the effectiveness.

Linear multitarget integrated probabilistic data association for multiple detection target tracking

The point target assumption, which suggests that a target can generate at most one measurement at a time, is used in typical target tracking algorithms. However, in many practical applications, multiple scattering points of a target can be resolved using a high-resolution sensor, which gives rise to the multiple detection problem. The typical algorithms with the point target assumption are not eligible for multiple detection tracking environments. The multiple detection joint integrated probabilistic data association algorithm is designed to solve the multiple detection multitarget tracking problem. However, the computational complexity of this algorithm grows exponentially with the number of tracks and measurement cells. Here, multiple detection linear multitarget integrated probabilistic data association is proposed to enhance computational efficiency by introducing the modulated clutter measurement density, which takes into account the contributions of clutter as well as other targets of each measurement cell. The computational complexity of the proposed algorithm is linear in the number of tracks and the number of measurement cells. Simulation results verify the applicability and efficiency of the proposed algorithm in multiple detection multitarget tracking scenarios.

Comparison of ambiguity function of eigenwaveform to wideband and pulsed radar waveforms: a comprehensive tutorial

The authors present a comprehensive research tutorial on the ambiguity function (AF) of eigenwaveform for extended targets compared to AFs of common radar waveforms (e.g. wideband and pulsed waveforms). They present new findings of AF properties that contradict classical AF results for the point target assumption. It is shown that the AF properties (peak and volume) for an extended target are not constant thereby contradicting AF properties of waveforms for a point target. They investigate corresponding AFs and note many advantages (or few disadvantages) of using eigenwaveform compared to classical wideband and rectangular-pulsed waveforms. They investigate unambiguous range, range resolution, Doppler resolution, and detection probability of various waveforms for insightful engineering trade-offs. For illustration, they utilise two extended target models to show that these parameters are not only functions of the transmit waveform but also of the nature of the target. They use both single-pulse and pulse-train waveforms to produce AFs to illustrate the effect on Doppler and range ambiguities and resolutions. Finally, they investigate range-Doppler map (RDM) applications of the traditional waveforms and compared them with eigenwaveform RDM. They conclude that the eigenwaveform is superior in probability of detection and Doppler considerations compared to wideband and rectangular waveforms.

Moving Target Imaging and Ghost Mitigation in Through-the-Wall Sensing Application

Human is one kind of the most interesting targets in through-the-wall imaging. In high-resolution imaging applications, human is no longer a point target. Therefore, the previous signal models constructed by point target assumption cannot accurately describe real characteristics of EM propagation. We construct the signal models based on extended target theory in this paper. Compared with previous works, the main contributions are as follows. Firstly, human is considered as an extended target. The expressions of target scattering and wall reflections are derived. Secondly, target scattering is no longer isotropic in new model. A new kind of ghost problem which is caused by target obscuring in EM propagation is discovered and exploited. Thirdly, to improve image quality in moving target imaging, an efficient approach which adopts CFAR, clustering method, and spatial geometry relationship is proposed to remove the ghosts. The derived models are shown to agree with synthetic and experimental results. And the efficiency of proposed method is also validated, which illuminates that the ghosts are efficiently mitigated and the image quality is significantly improved.

Target range estimation based on a non‐homogenous Poisson process model

In this study, the author analyses target range estimation errors in matched filtering-based detection performed in high range resolution (HRR) radars. Conventional radar signal processors use point target detectors, where extended target responses are put through a point detection process by windowing and thresholding. The author demonstrates through simulations that the performance of degradation under the point target assumption can be significant for HRR radars, where targets extend across several detection cells. The author modelled the reflections for stationary and moving extended target scenarios by using three target signal models (TSMs). A non-homogenous Poisson process (NHPP) is provided to model the signal at the output of the target detector, which includes reflections from targets and clutter. The corresponding maximum likelihood (ML) estimator is derived as a range estimation technique. The author simulated a target detection process and made comparisons between the classical- and NHPP-based peak estimator performances for each of the TSMs. Furthermore, the ML estimation (MLE) algorithm is extended for multiple targets. The author demonstrates that the ML estimator significantly reduces target range estimation errors compared with the classical point target estimators.

Experimental analysis of various estimators for target range estimation in high resolution radars

The author analyses the target range estimation errors in pulse compression high-resolution radars (HRRs). Conventional radar signal processors assume a point target model in matched filtering-based detection and tracking. The author demonstrates through simulations that the performance degradation, under the point target assumption, can be significant for HRRs, where targets extend across several detection cells. Also, the author models events of backscatters from illuminated target and clutter as a non-homogenous Poisson process (NHPP). The corresponding maximum likelihood, maximum a posteriori, minimum mean square error and minimum mean absolute error estimators are derived for range estimation. Typical target detection process has been simulated and the performance of NHPP-based estimators has been compared to that of the most commonly used peak estimators, namely, the maximum point and interpolation peak estimators. Simulation results show that the proposed estimators significantly reduce target range estimation errors in HRRs.

An Alternative Model for Target Position Estimation in Radar Processors

In this paper, we analyze the target position estimation errors induced by conventional radar signal processors, which assume a point-target model in matched filtering-based detection and tracking. As we demonstrated through simulations, the performance degradation under the point-target assumption can be significant for high-resolution radars, where targets extend across several detection cells. One of the main contributions of this paper is to provide an alternative model for reflections from extended targets and clutter. We model the events of backscatters from illuminated targets and clutter as a nonhomogenous Poisson process. The corresponding maximum likelihood estimator and the Cramer-Rao lower bound have been derived. Typical target detection process has been simulated and the validity of the model has been verified by using the Kullback-Leibler distance. It has been shown that the new method significantly reduces target position estimation errors compared to the peak picking method.

Track-before-detect algorithm for tracking extended targets

For certain types of sensor-target configurations, a point target model or approach is not suitable and the physical extent of the target is accounted for during processing. An extended target track-before-detect (TBD) algorithm is presented and the performance is compared to an algorithm based on the point target assumption. Simulations illustrate the gain in performance obtained by using the extended target model where a particle filter is used for the TBD implementation.

Theoretical considerations for the use of microbubbles as point targets for phase aberration correction

Bubbles can be produced by vaporization of perfluorocarbon droplets of a few μm diameter. These bubbles can reach up to 100 μm in diameter and their backscatter is calculated to be more than 10 dB above that of several organ tissues. At these sizes and for diagnostic frequencies (2–8 MHz), bubbles can be approximated by the nonrigid sphere scattering solution employed here. This presentation concerns the bubble size and its implications on the backscatter amplitude and the phase error introduced in diagnostic ultrasound when assuming that the bubble acts as a point target for phase aberration correction. The phase error is the difference between the phase at each location along the receiving aperture relative to that at the aperture center, compared with the same relative phase for a perfect point target. Evaluations were made of the phase error with respect to a range of transducer f-numbers (0.5–2.0) for a specific bubble size (30 μm radius) and at certain frequencies (2–8 MHz). For example, at 5 MHz the phase error introduced by the point target assumption is maximally 5 deg, while the phase error of breast tissue scattering is around 160 deg. [Work supported by PHS Grant No. R01HL54201 from the National Heart, Lung, and Blood Institute.]

A new wideband spread target maximum likelihood estimator for blood velocity estimation. I. Theory

The derivation and theoretical evaluation of new wideband maximum-likelihood strategies for the estimation of blood velocity using acoustic signals are presented. A model for the received signal from blood scatterers, using a train of short wideband pulses, is described. Evaluation of the autocorrelation of the signal based on this model shows that the magnitude, periodicity, and phase of the autocorrelation are affected by the mean scatterer velocity and the presence of a velocity spread target. New velocity estimators are then derived that exploit the effect of the scatterer velocity on both the signal delay and the shift in frequency. The wideband range spread estimator is derived using a statistical model of the target. Based on the point target assumption, a simpler wideband maximum-likelihood estimator is also obtained. These new estimation strategies are analyzed for their local and global performance. Evaluation of the Cramer-Rao bound shows that the bound on the estimator variance is reduced using these estimators, in comparison with narrowband strategies. In order to study global accuracy, the expected estimator output is evaluated, and it is determined that the width of the mainlobe is reduced. In addition, it is shown that the height of subsidiary velocity peaks is reduced through the use of these new estimators.