Moving target localization using distributed dual-frequency radars and sparse reconstruction

Recent progress in bistatic radar techniques can be used to improve performances of classical monostatic radar. A prominent limitation of coherent radar is its inability to measure the complete velocity vector (magnitude and direction) of a detected target. A single coherent detection can provide range-rate only. At least two detections, separated in time, are needed to estimate the target's velocity vector. This study discusses how the velocity vector can be determined by two simultaneous detections spaced in distance. The second detection is obtained by an auxiliary distant bistatic coherent receiver; an approach proposed in the 1990s to enhance meteorological radar. Being a very simple case of a distributed radar system allows for a simple demonstration of how to calculate the target's position and velocity vector and how to analyse the estimation accuracy, including geometric dilution of precision plots of the velocity error. Also discussed are two methods to identify correct data association when more than one target is detected.

We propose seven velocity-estimation methods for fast moving targets beyond the Nyquist-limit velocity for pulse Doppler radars. The Doppler-shift frequency beyond the Nyquist limit, which equals onehalf the pulse-repetition frequency, cannot be correctly measured as the frequency is folded back within the Nyquist interval. Our methods use pulse compression with divided reference signals of varying center frequencies to detect the difference among the Doppler-shift frequencies of the pulse-compressed signals and increase sensitivity through signal integration, all without modifying the transmitted signals. These methods are either grid-search-type or Newton-type methods, each possessing distinct characteristics. The Cramér-Rao lower bounds on the variance of estimated velocity are derived, and the upper bounds on the probability of correct ambiguity resolution are shown. The results of our numerical simulation indicate that our methods can estimate target velocity beyond the Nyquistlimit velocity and that the method of independently estimating the Doppler frequencies at both end parts of the signal bandwidth and estimating the target velocity from the difference exhibits a lower decrease than the other methods regarding the success probability of Doppler ambiguity resolution even for high target velocities. We also confirmed that the upper bounds closely match the probability of corresponding methods for when the target velocity is lower than several times the Nyquist velocity and the signal-tonoise ratio on the range-Doppler map is above approximately 20 dB.

Dual-frequency radar is a preferred solution for moving target localization because of its low complexity and cost. Although several studies for dual-frequency radar are addressed in the literature, all of those methods are designed for the synchronous mode. In this letter, a new localization method based on phase compensation is proposed for dual-frequency radar in the asynchronous mode. Compared with the synchronous mode, the proposed method is suitable for most commercial short-range radars with the single local oscillator architecture and can provide a cost-effective solution. Moreover, both the theoretical variance and Cramer–Rao lower bound are derived for the performance analysis in the asynchronous mode. Computer simulations were conducted to verify the validity of the proposed method.

With the wide spread availability and the favourable Doppler resolution, the frequency modulation (FM) commercial radio signals have become attractive for passive radar applications. Passive radar networks using multiple illuminators of opportunity and multichannel receivers have been shown to offer significant performance improvement owing to their advantage of signal and spatial diversities. In this study, the authors compute the joint Cramer–Rao lower bound (CRLB) for the target parameter (delay and Doppler) estimation error utilising FM commercial radio signals as illuminators of opportunity for passive radar network systems, where the non-coherent and coherent processing scenarios are considered. The numerical simulations are provided to show that the joint CRLB is not only a function of the transmitted waveforms but also of the relative geometry between the target and the passive radar networks for both non-coherent and coherent cases. The expressions for joint CRLB are an important performance metric for target parameter estimation in FM-based passive radar networks.

Optimized power allocation among the transmitters in MIMO radar networks has a potential to conserve a significant amount of energy without loss of target-estimation performance. In this paper, we develop power allocation strategies for joint target position and velocity estimation in a distributed MIMO radar network. Transmitting power optimization algorithms are proposed for predetermined parameter estimation accuracy requirements in terms of the equivalent Fisher information matrix (EFIM). These algorithms aim to minimize the power budget while guaranteeing the estimation accuracy with or without the presence of parameter uncertainties. Numerical results show that the proposed algorithms can achieve appreciable energy savings.

The Gaussian noise model and estimators based on least squares (LS) are widely used in target localisation with distributed multiple‐input multiple‐output (MIMO) radar because of their computational efficiency. However, the accuracy of existing LS‐based target localisation algorithms deteriorates sharply in the presence of outliers in the measurements. Thus, a robust solution is developed based on the ‐norm minimisation criterion and iteratively reweighted least squares (IRLS) for locating a moving target with impulse noise using the angle of arrival (AOA), time delay (TD), and Doppler shift (DS) measurements. First, the AOA, TD, and DS measurement noise models are developed based on the α‐stable distribution. Then, the localisation problem is transformed into an ‐norm minimisation problem by linearising the AOA, TD, and DS measurement equations. Finally, the ‐norm minimisation problem is solved using an IRLS method to obtain the target position and estimate the velocity. Moreover, the optimum of the norm order (p) and the Cramér–Rao lower bound for the target position and velocity estimation are derived under α‐stable distributed measurement noise. The simulation results demonstrate that the developed algorithm offers higher accurascy and robustness than the existing ones in the presence of measurement outliers.

Intercepting a moving target requires a prediction of the target's future motion. This extrapolation could be achieved using sensed parameters of the target motion, e.g., its position and velocity. However, the accuracy of the prediction would be improved if subjects were also able to incorporate the statistical properties of the target's motion, accumulated as they watched the target move. The present experiments were designed to test for this possibility. Subjects intercepted a target moving on the screen of a computer monitor by sliding their extended finger along the monitor's surface. Along any of the six possible target paths, target speed could be governed by one of three possible rules: constant speed, a power law relation between speed and curvature, or the trajectory resulting from a sum of sinusoids. A go signal was given to initiate interception and was always presented when the target had the same speed, irrespective of the law of motion. The dependence of the initial direction of finger motion on the target's law of motion was examined. This direction did not depend on the speed profile of the target, contrary to the hypothesis. However, finger direction could be well predicted by assuming that target location was extrapolated using target velocity and that the amount of extrapolation depended on the distance from the finger to the target. Subsequent analysis showed that the same model of target motion was also used for on-line, visually mediated corrections of finger movement when the motion was initially misdirected.

Inverse synthetic aperture radar (ISAR) imaging is one of the important high- resolution radar applications for moving target detection and identification. The radial velocity, radial acceleration, and acceleration rate of maneuvering targets can cause range cell migration and Doppler variation in the received signals, which will degrade the imaging quality. Therefore, these parameters should be estimated and compensated before imaging. In low signal-to-noise ratio (SNR) conditions, the conventional motion parameter estimation and compensation algorithms for maneuvering targets do not work properly. To effectively detect and identify the maneuvering targets in low SNR conditions, a new radar imaging algorithm is proposed. The major contributions are as follows: 1) a novel algorithm for envelope migration correction is proposed with adjacent cross correlation function; 2) the target's motion parameters are estimated with the operations of Keystone transform and time-frequency transform to the adjacent cross correlation; 3) a theoretical analysis of the impact of motion parameters on the envelope migration is made, then cross-range phase estimation with the adjacent cross function is presented; 4) based on the estimated motion parameters of targets, a new algorithm for high-resolution ISAR imaging is developed.

Stepped frequency waveform (SFW) has been widely utilized in millimetre‐wave radars, achieving high‐resolution range profiles without expanding the radar's instantaneous bandwidth. Nevertheless, the inherent large time‐bandwidth product associated with SFW results in significant ranging errors and energy dispersion, impeding its effectiveness in detecting high‐speed targets. Spurred by this limitation, this study introduces an innovative velocity estimation method that employs the fractional Fourier transform (FrFT) to overcome these drawbacks. Specifically, by harnessing the Doppler signature of a moving target, which appears as a chirp signal with a frequency rate directly proportional to the target's velocity, FrFT provides precise velocity measurements. Following the velocity estimation, a compensation process is implemented using the derived metrics, after which the inverse fast Fourier transform locates the target accurately. Furthermore, an iterative algorithm based on the golden section search technique has been developed to enhance the computational efficiency of determining the optimal order for the FrFT. The validity of the proposed method is confirmed through simulation data, demonstrating that the developed approach can accurately estimate the velocity of high‐speed targets with a notably reduced computational complexity.

Multistatic active sonar systems involve the transmission and reception of multiple probing sequences and can achieve significantly enhanced performance of target detection and localization through exploiting spatial diversity. This paper mainly focuses on two signal processing aspects of such systems, namely, enhanced range-Doppler imaging and improved target parameter estimation. The main contributions of this paper are (1) a hybrid dense-sparse method is proposed to generate range-Doppler images with both low sidelobe levels and high accuracy; (2) a generalized K-Means clustering (GKC) method for target association is developed to associate the range measurements from different transmitter-receiver pairs, which is actually a range fitting procedure; (3) the extended invariance principle-based weighted least-squares method is developed for accurate target position and velocity estimation. The effectiveness of the proposed multistatic active sonar signal processing techniques is verified using numerical examples.

The micro-Doppler (m-D) provides valuable information for the motion parameters extraction and the target recognition of space targets. To address the issue of estimating the motion parameters of precession warhead targets, a new method based on the m-D spectrum of the top and the bottom of the cone is proposed in this paper. In this method, the m-D features of the cone target are firstly extracted by calculating the first-order moments of the time-frequency distribution of the echo signal. Then, the motion parameters of the target are roughly estimated by the Fourier transformation of the m-D curve. Based on the rough estimation, the search method is employed to estimate the motion parameters of the cone target precisely. The validity of the proposed method is verified by the analysis data.

Fil: Roberti, Flavio. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - San Juan. Instituto de Automatica. Universidad Nacional de San Juan. Facultad de Ingenieria. Instituto de Automatica; Argentina

By using high resolution range information from multiple radar stations in monostatic or bistatic modes, the position estimates can be highly improved compared with conventional measurements from a single radar. In this paper, fundamental geometry relationships and the possible accuracy of target position and velocity estimates are analyzed when data from several radars are combined. The velocity error is proportional to the wavelength and the Doppler resolution, which is limited by the available time of measurement. The estimation error increases when the radars are close, or when the target is close to the radar level surface. For ground stations, the accuracy in estimated target altitude is usually significantly lower than the position data in the horizontal plane. Examples and simulation results are presented, displaying the performance of three radars working in bistatic and monostatic modes. The analysis is generalized to an arbitrary number of radar stations.

Moving target localization and tracking are of great interest for rescue, surveillance and security operations. The radar signal processing procedure providing the target position estimation consists of such phases of signal processing as raw radar data pre-processing, background subtraction, detection, time of arrival estimation, localization and tracking. In this paper, we will focus on the localization phase where we assume that for the target localization UWB radar equipped by one transmitting and N receiving antennas is used. Following this intention, the accuracy of the target localization provided by the least-squares method, the spherical-interpolation method and the Taylor series method applied within localization phase will be compared based on processing of radar signals obtained from real target tracking by UWB radar. The acquired results confirm that the Taylor series method can provide the best accuracy of the target positioning.

This paper presents new radar algorithm for detecting moving targets and measuring target velocities. The algorithm is based on calculating spectrum estimates by using the signals reflected from the adjacent range bins. Statistical hypothesis checking is fulfilled on the difference between the adjacent spectra. The decision-making on distinction between different spectral components is used for estimating velocity of the target. Efficiency of the algorithm is investigated by using statistical modeling. The algorithm is checked by processing a real radar data containing a signal on the background of reflections from rain.