Target Radial Velocity Research Articles

SAR Ground Moving Target Indication via Cross-Track Interferometry for a Forward-Looking Array

In this paper, an effective synthetic aperture radar ground moving target indication (SAR-GMTI) method is proposed based on cross-track interferometry only, by combining the squint-looking and a 2-D cruciform forward-looking array. First, for a multiple-channel SAR with an arbitrarily array configuration and an arbitrary-looking angle, the 2-D response of a ground moving target, including its phase, amplitude, and position, is derived in the complex image domain via back-projection algorithm. Second, an interferometry phase sensitivity function is defined to evaluate the interferometry properties of an arbitrarily baseline with a closed-form expression, from which the GMTI and altitude-estimating abilities of the conventional along-track baseline and cross-track baseline can be verified. Besides, it is shown that the interferometry phases of the upright cross-track and straightforward along-track baselines are only sensitive to the target's height and velocity, respectively. Furthermore, the interferometry phase of the level cross-track baseline can be sensitive to both target's radial velocity and height when the squint-looking angle is large. Therefore, to solve the coupled phases of velocity and height of the moving target in a real fluctuating terrain, a cruciform cross-track baseline with five receiving channels is further proposed for a squint-looking SAR, by which the height, radial velocity, and location of both fixed and moving targets can be estimated, simultaneously. This means that the SAR-GMTI can be effectively accomplished by cross-track interferometry only without along-track baseline. Finally, some results of numerical experiments are provided to demonstrate the effectiveness of the proposed method.

Image-Based Target Detection and Radial Velocity Estimation Methods for Multichannel SAR-GMTI

In order to enhance the performance of spaceborne synthetic aperture radar–ground moving target indication (SAR-GMTI) systems, multichannel systems with large and preferably nonuniform baselines are required. In this paper, SAR-GMTI algorithms for multichannel SAR systems, which we call multichannel displaced phase center antenna (DPCA), multichannel along track interferometry (ATI), and multichannel DPCA-ATI, are presented. Multichannel DPCA is a deterministic algorithm for clutter and azimuth ambiguity suppression. It successfully suppresses not only uniform azimuth ambiguities but also nonuniform isolated ones, since it does not require uniform clutter covariance assumption as adaptive algorithms do. Multichannel ATI and multichannel DPCA-ATI are the algorithms for target radial velocity estimation. Both of them reduce the target radial velocity ambiguities, which arise with the long baseline systems, by exploiting the multiple receive channel signals. And multichannel DPCA-ATI further achieves robust performance to clutter influence by suppressing the clutter and the azimuth ambiguity in advance. The performances of the proposed algorithms are shown through airborne Ku-band three-channel SAR experiments. It is shown that the multichannel DPCA suppresses strong azimuth ambiguity up to more than 20 dB, and the accuracy of the radial velocity estimation of the multichannel DPCA-ATI is on the order of 0.1 m/s. Furthermore, statistical performance analysis is presented to discuss the potential performance on the spaceborne system.

Long-CPI Multichannel SAR-Based Ground Moving Target Indication

The detection of ground moving targets with arbitrary linear motion from an airborne multichannel radar via a long coherent processing interval is considered. The technique maximizes the target signal energy in synthetic aperture radar (SAR) images by matching to the target's linear motion profile. A reparameterization of the target's linear motion is developed that decouples the array processing from the image formation process. An explicit approach to forming SAR images focused with respect to a particular along-track velocity is presented. The ground clutter is canceled via an adaptive array technique, which also yields an estimate of the target radial velocity. Because the algorithm generates estimates of the target's along-track and cross-track velocity components, a unique determination of the target's motion parameters is possible. The approach is derived and investigated via simulated point spread functions. Algorithm performance is demonstrated on the GOTCHA SAR ground moving target indication data set.

Adaptive sequential detection of target trajectory using decision statistics of pips at the unknown signal-to-noise ratio

Adaptive algorithms of multialternative sequential detection of target trajectory using the upper and lower thresholds and employing decision statistics of pips at the unknown signal-to-noise ratio (SNR) have been derived on the basis of the sequential criterion of simple complement. The application of lower thresholds made it possible to implement the procedure of discarding unsuccessful hypotheses. An adaptive two-alternative sequential algorithm of target track detection was developed using decision statistics of pips with SNR estimation by the criterion of error mean square minimum. The statistical simulation was used to analyze the algorithms for the case of target track detection on the basis of data of the surveillance radar that measures the range and radial velocity of target.

Detection and measurement of coherent-pulse signals

Algorithms of joint detection and measurement of coherent-pulse signals with linear adjustment of parameters have been synthesized; these algorithms make it possible to unambiguously measure the radial velocity of target in the specified range while preserving an unambiguous measurement of the range. A functional block diagram of the detecting-and-measuring device was proposed. The computer simulation was used to carry out a comparative analysis of the synthesized and known algorithms of detection and measurement.

Performance comparison and assessment of displaced phase center antenna and along-track interferometry techniques used in synthetic aperture radar-ground moving target indication

Displaced phase center antenna (DPCA) and along-track interferometry (ATI) are the two popular techniques used to determine synthetic aperture radar-ground moving target indication fields, and studies have shown that the combinations of these techniques can improve the target detection performance. However, a crucial problem is how to combine the two techniques, which requires a complete analysis and comparison of the individual techniques. Generally, it is well known that the performances of these techniques are closely related to clutter and noise. A detailed comparison of the detection performance of ATI and DPCA is presented, together with an assessment developed by theoretical analysis and simulations. The results show that the ATI is limited mainly by the clutter and noise, while DPCA is limited mainly by channel imbalance and noise. The ATI’s main drawback is its high false alarm rate, and DPCA is more sensitive to the channel imbalance. In most cases, DPCA is better than ATI, but for a high clutter-to-noise ratio, low signal-to-clutter power ratio, and channel imbalance, ATI has a better performance than DPCA. The real data experiments verify the theoretical findings. Meanwhile, the effects of target radial velocity, incidence angle, transmission bandwidth, and terrain type on the performance of the two detection approaches are also investigated.

New chirp sequence radar waveform

The general requirement in the automotive radar application is to measure the target range R and radial velocity v <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> simultaneously and unambiguously with high accuracy and resolution even in multitarget situations, which is a matter of the appropriate waveform design. Based on a single continuous wave chirp transmit signal, target range R and radial velocity v <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> cannot be measured in an unambiguous way. Therefore a so-called multiple frequency shift keying (MFSK) transmit signal was developed, which is applied to measure target range and radial velocity separately and simultaneously. In this case the radar measurement is based on a frequency and additionally on a phase measurement, which suffers from a lower estimation accuracy compared with a pure frequency measurement. This MFSK waveform can therefore be improved and outperformed by a chirp sequences waveform. Each chirp signal has in this case very short time duration T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">chirp</sub> . Therefore the measured beat frequency f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> is dominated by target range R and is less influenced by the radial velocity v <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> . The range and radial velocity estimation is based on two separate frequency measurements with high accuracy in both cases. Classical chirp sequence waveforms suffer from possible ambiguities in the velocity measurement. It is the objective of this paper to modify the classical chirp sequence to get an unambiguous velocity measurement even in multitarget situations.

Sequential detection of target trajectory tracking loss using the decision statistics of pips

A sequential algorithm of detection of the tracking loss of target trajectory based on the cumulative sums method has been obtained using the values of decision statistics of pips obtained during the primary processing of radar data. In this case the probabilistic data combining method is used for the estimation of moving target parameters at small values of the signal-to-noise ratio. The obtained algorithm was analyzed by using the statistical simulation of a model case of the target tracking with the data of surveillance radar measuring the range and radial velocity of target. The description of decision statistics of target and noise pips involved the use of the Rice and exponential distribution laws, respectively.

Two-stage multiscan detection of target using the “l/n - d” criterion with identification of pips by the maximum of decision statistic

Algorithms of two-stage multiscan detection of target based on the incoherent accumulation using the “l/n - d” criterion have been obtained. The primary detection is performed at the first stage with sufficiently high probability of false alarm. At the second stage the tracking task is solved by using the “strongest neighbor” criterion and the incoherent interscan accumulation of decision statistics is performed. Expressions for probabilities of the true detection of target and false alarm of the algorithm using the “3/5 - 2” criterion were obtained. This algorithm was analyzed by using the statistical simulation for a case of multiscan target detection from data of the radar measuring the range and radial velocity of target.

Moving target inference with bayesian models in SAR imagery

This work combines the physical, kinematic, and statistical properties of targets, clutter, and sensor calibration as manifested in multi-channel SAR imagery into a unified Bayesian structure that simultaneously estimates (a) clutter distributions and nuisance parameters and (b) target signatures required for detection/inference. A Monte Carlo estimate of the posterior distribution is provided that infers the model parameters directly from the data with little tuning of algorithm parameters. Per- formance is demonstrated on both measured/synthetic wide-area datasets. Index Terms—synthetic aperture radar, moving target detec- tion, low-rank, hierarchical Bayesian models I. I NTRODUCTION This work provides an algorithm for inference in multi- antenna and multi-pass synthetic aperture radar (SAR) im- agery. Inference can mean many different things in this frame- work, including detection of moving targets, estimation of the underlying clutter distribution, estimation of the target radial velocity, and classification of pixels. To this end, the output of the proposed algorithm is an estimated posterior distribution over the variables in our model. This posterior distribution is estimated through Markov Chain Monte Carlo (MCMC) techniques. Subsequently, the inference tasks listed above are performed by appropriately using the posterior distribution. For example, detection can be done by thresholding the posterior probability that a target exists at any given location. Recently, there has been great interest by Wright et al. (1), Lin et al. (2), Candes et al. (3) and Ding et al. (4) in the so- called robust principal component analysis (RPCA) problem that decomposes high-dimensional signals as

Detection and tracking of a moving target using SAR images with the particle filter-based track-before-detect algorithm.

A novel approach to detecting and tracking a moving target using synthetic aperture radar (SAR) images is proposed in this paper. Achieved with the particle filter (PF) based track-before-detect (TBD) algorithm, the approach is capable of detecting and tracking the low signal-to-noise ratio (SNR) moving target with SAR systems, which the traditional track-after-detect (TAD) approach is inadequate for. By incorporating the signal model of the SAR moving target into the algorithm, the ambiguity in target azimuth position and radial velocity is resolved while tracking, which leads directly to the true estimation. With the sub-area substituted for the whole area to calculate the likelihood ratio and a pertinent choice of the number of particles, the computational efficiency is improved with little loss in the detection and tracking performance. The feasibility of the approach is validated and the performance is evaluated with Monte Carlo trials. It is demonstrated that the proposed approach is capable to detect and track a moving target with SNR as low as 7 dB, and outperforms the traditional TAD approach when the SNR is below 14 dB.

Sequential detection of target trajectory using the decision statistics of pips

Algorithms of target trajectory detection using the likelihood functions of the pip with maximum decision statistic and all the pips in the tracking strobe have been synthesized on the basis of the Wald sequential criterion. These algorithms were analyzed using the statistic simulation for the case of detecting the target trajectory from data of surveillance radar measuring the range and radial velocity of target.

Robust Radial Velocity Estimation of Moving Targets Based on Adaptive Data Reconstruction and Subspace Projection Algorithm

In practice, inevitable image coregistration error and channel phase mismatch will significantly degrade the estimation performance of the target radial velocity in the ground moving target indication processing with multichannel synthetic aperture radar (SAR) systems. To overcome this problem, a new radial velocity estimation method using the subspace projection (SP) algorithm with adaptive data reconstruction is proposed in this letter. Based on the joint-pixel signal model, the Wiener weight vector is used to reconstruct the multichannel data vector of the pixel containing the moving target. Then, the SP algorithm is adopted to deal with the radial velocity estimation with the reconstructed single “snapshot” data. The validity and robustness are verified by both simulations and real SAR data experiments.

The Research of Velocity Compensation Method Based on Range-Profile Function

In the high-resolution radar imaging system, step-frequency radar signal is very important in getting the high-resolution range-profile, which can be used for target’s detection and recognition. However, target radial velocity will produce many problems, therefore, movingtargets imaging is a bottleneck for step-frequency radar signal. In this paper, an effective motion compensation methods is presented, which is based on the function of Range-profile Contrast. The numeric simulation indicates that this method is effective, which can estimate the target’s velocity in real-time, and it is easy to be used in engineering project. After target’s motion compensation, the high-resolution range-profile will be much better than that is used to be, which can be used for the detection, recognition and ranging of moving targets..

A State Space Method for Estimating the Translational Radial Velocity of Ballistic Targets

为了分析弹道中段飞行目标的微动分量，需要对雷达回波中的高速平动分量进行准确估计和补偿。该文建立了目标高速平动和微动共同作用下的超宽带信号模型，提出了一种基于状态空间处理的平动径向速度高精度估计方法，该方法可以有效抑制微动分量对平动径向速度估计的影响，估计精度优于传统方法。同时包含高速平动分量和微动分量的超宽带信号模型，也可直接用于后续的微动参数提取处理。仿真结果验证了文中信号模型与径向速度估计方法的有效性。

High‐frequency radar aircraft detection method based on neural networks and time‐frequency algorithm

Aircraft detection is an important application of Wuhan Ionosonde Sounding System (WISS), which recently has been developed by the Ionospheric Laboratory of Wuhan University. Since the ionosphere varies temporally and spatially, severe multipath effects are produced, which jeopardise the characteristic quantities extracting of targets from the recorded data. To solve the above problems and further identify the targets from the fuzzy signals, this study presents a neural networks and time-frequency-based algorithm. By neural networks, the characteristic quantities of targets are extracted from the recorded data, and then, the Doppler spectrum of target signals is computed to determine the radial velocity of targets. Moreover, with the help of time-frequency analysis, the radial velocity variability in time domain can be identified, which finally leads to the identification of the type of the targets. Simulations using the recorded data of the WISS show that the type of the targets is aircraft and 90.9% accurate recognition of aircraft targets can be achieved.

Fast Two-Dimensional CFAR Procedure

An FMCW radar system, which transmits a sequence of rapid chirp signals, is considered to measure target range and radial velocity simultaneously even in multitarget situations. The main outcome of the coherent processing procedure applied to the received echo signal is the two-dimensional range-Doppler-matrix (RDM), which is the basis for an adaptive constant false alarm rate (CFAR) target detection technique. The well-known one-dimensional cell averaging (CA) CFAR procedure suffers from masking effects in all multitarget situations and is additionally very limited in the proper choice of the reference window length. In contrast the ordered statistic (OS) CFAR is very robust in multitarget situations but requires a high computation power. Therefore two-dimensional CFAR procedure based on a combination of OS and CA CFAR is proposed. In this case any masking effects in multitarget situations can be avoided without increasing the computation complexity. In addition the size of the reference window can be adjusted in order to optimize the detection performance.

Experimental Evaluation of the Range–Doppler Coupling on HF Surface Wave Radars

High-frequency surface wave radar (HFSWR) is used in oceanography to monitor surface wind waves and currents and, more recently, to detect ships in maritime surveillance. The radar accuracy is affected by range-Doppler coupling, which yields a displacement in the measured range proportional to the target radial velocity, i.e., the Doppler shift in the returned pulse. Although in oceanography this effect is usually not accounted for, its relevance grows in ship detection. In this letter, we present the results of two experimental data sets showing displacements in the HFSWR range measurements of up to 300 m and confirming the theoretical analysis. Furthermore, we show that the correction based on theoretical arguments, achieved by the statistical correlation between the range and Doppler measurements, provides remarkable improvement in the radar accuracy.

Joint estimation of moving target reflectivity and velocity via AT-InSAR systems based on complex interferometric data

In this paper a technique for the joint estimation of the complex reflectivity and of the radial velocity of ground moving targets, by means of Along-Track Interferometric Synthetic Aperture Radar (AT-InSAR) systems, is presented. It is essentially based on statistical estimation techniques and exploits the complex interferometric SAR images, contrarily to other conventional AT-InSAR methods, which estimate the target radial velocity exploiting only the interferometric phases. The performances of the method are evaluated both for single baseline and for dual-baseline satellite systems, in terms of Cramer Rao Lower Bounds and of Root Mean Square Errors obtained in numerical experiments, with reference to all the target parameters to be estimated, which are the amplitude and phase of the target reflectivity and the target radial velocity. It is shown that for single baseline systems the obtained radial velocity estimation accuracy is comparable with the one achievable with AT-InSAR methods exploiting phase-only data, and is very high for high values of signal to clutter ratio. For dual-baseline systems, instead, the use of amplitude and phase data allows a performance significantly better than the one achievable exploiting phase-only data. Moreover, in the dual baseline case, high velocity estimation accuracy can be obtained also with lower signal to clutter ratios.

On Refining Polynomial Phase Signal Parameter Estimates

Newton algorithms are commonly used in the final “refinement” stage of parameter estimation for sinusoids and higher order phase polynomial signals. Such parameter estimation scenarios arise commonly in radar applications where the radial target velocity (which is possibly time-varying) must be estimated. The author and coworkers have previously proposed an elegant and efficient alternative to a Newton gradient search type algorithm based on filtering and phase unwrapping. A statistical and computational analysis of this filtering/phase unwrapping method is presented here. The analysis shows the algorithm to be computationally efficient and much less sensitive to the accuracy of the initial guesses for the parameters than a Newton algorithm. A first-order statistical analysis of the filtering/phase unwrapping algorithm is performed, and guidelines are derived for the required accuracy of the initial estimates. Simulations are presented to confirm the analysis.