Low-angle Targets Research Articles

Accurate Low-Angle Tracking Using Successive Interference Cancelation and Single-Tone Frequency Estimation

This paper tackles the low-angle target tracking problem caused by surface-reflected multipaths through successive interference cancelation (SIC) and single-tone frequency (STF) estimation. The coherent specular multipath component in the received radar signal is canceled through SIC, after which the angle-of-arrival (AoA) of the direct path is accurately estimated by applying STF estimation to the interference-suppressed signal. To further improve estimation accuracy, we develop an iterative process where SIC and STF are sequentially repeated. Drawing on the AoA of the direct path estimated using STF, a more accurate AoA of the specular path is obtained by exploiting their geometrical relationship between the two paths, which is then used to suppress the specular multipath in the received radar signal more precisely during the next SIC process. Computer simulations verify that the proposed angle estimation method using SIC and STF (AE-SICSTF) outperforms conventional angle estimation methods. Furthermore, while computational complexity analysis shows that the complexity of each iteration in AE-SICSTF is comparable to that in three-dimensional beam-domain maximum likelihood, it is also found that the former requires less than 10 iterations to converge the root mean square error performance, resulting in considerably less computing time than the refined maximum likelihood and root-MUSIC algorithms.

Low-Angle Target Tracking in Sea Surface Multipath Using Convolutional Neural Networks

Multipath interference while tracking sea-skimming targets can significantly distort the estimated height of the target. If accounted for however, this interference can be used to obtain more accurate estimates. In this study we accomplish this with a convolutional neural network (CNN) used as a parameter estimator. The performance of this network is compared with maximum likelihood and least squares methods. We found that the CNN performs well in comparison to these methods with only a fraction of the computations.

A Novel DOA Estimation for Low-Elevation Target Method Based on Multiscattering Center Equivalent Model

The multipath in very high-frequency (VHF)radar will reduce the direction-of-arrival (DOA) estimation accuracy for the low-angle target following. Since the target and multipath signals have strong coherence in the spatial domain, it is challenging to distinguish them accurately. Especially in some terrain complex regions, various scattering media cause the multipath echo exhibit the multi-channel and nonuniform energy distribution features. Therefore, the single multipath DOA estimation methods will mismatch the actual signal, resulting in inaccurate DOA estimation. The current letter presents a new DOA estimation approach using a multiple scattering center model in complex terrain to solve the mentioned issue. In the presented method, multiple multipath is analogous to a single one based on the multipath signal’s spatial coherence. The equivalent model represents the DOA estimation problem with a parametric optimization problem, and the accurate estimation results can be attained using the Newton optimization approach. Compared with the conventional sparse reconstruction method, the presented method is not restricted by the Restricted Isometry Property (RIP), and employs a robust parameter estimation method to solve the angle super-resolution problem under the low signal-to-noise ratio (SNR). The experimental results reflect that the presented model and approach can efficiently promote the DOA estimation precision and calculational performance compared to conventional DOA estimation approaches.

Array Scheduling With Power and Bandwidth Allocation for Simultaneous Multibeam Tracking Low-Angle Targets in a VHF-MIMO Radar

The very high frequency (VHF) radar suffers from the multipath interference and false alarms when tracking low-angle targets. The co-located multiple-input multiple-output (MIMO) radar technique can mitigate these adverse effects with waveform diversity. However, the limited resources restrict the potential of the VHF-MIMO radar. In this paper, an array scheduling with power and bandwidth allocation (ASPBA) strategy is proposed for the VHF-MIMO radar tracking low-angle targets. The predicted posterior Cramér-Rao lower bound (PCRLB) under the multipath interference and measurement origin uncertainty (MOU) effects is derived to serve as the optimization metric. The measurement amplitude information (AI) is incorporated into the PCRLB to facilitate target detection and tracking. It is shown that the ASPBA problem is a mixed integer programming and NP-hard problem, where the array, power, and bandwidth allocation variables are both coupled in the objective and constraints. An efficient three-stage-based solution is proposed for problem-solving. The approximated relationship among three variables is derived using Hölder's inequality, and an auxiliary variable is introduced to describe the array contribution. Thereby, the array scheduling is determined by a heuristic rounding algorithm, and the power and bandwidth allocation are achieved using the approximated relationship. Simulation results confirm the effectiveness and efficiency of the proposed ASPBA strategy, compared with state-of-the-art algorithms. It is also shown that the target height is a main factor that influences the resource allocation results in the low-angle tracking scenario.

Beam resource scheduling for a VHF-MIMO radar network in low-angle tracking

The low-angle tracking in multipath interference is a challenging problem for the Very High Frequency (VHF) radar. The colocated Multi-Input Multi-Output (MIMO) technique can remedy such a defect. In this paper, a Joint Beam-Target Assignment and Power Allocation (JBTAPA) strategy is proposed for the VHF-MIMO radar network tracking low-angle targets. The core of the JBTAPA strategy is to improve the worst tracking accuracy among multiple targets by assigning appropriate beams to targets and allocating the power resource in each beam using the feedback information in the tracking cycle. Taking into account the transmit multipath and receive multipath, we derive the Cramér-Rao Lower Bound (CRLB) on angle estimate, which is then incorporated in the Predicted Conditional CRLB (PC-CRLB). A more accurate and consistent lower bound is provided as the optimization metric since the PC-CRLB is based on the most recently realized measurements. A two-stage-based technique is proposed to solve the JBTAPA problem, which is originally NP-hard. Simulation results verify the effectiveness and efficiency of the proposed method. The results also imply that the target reflectivity plays one of the important roles in resource allocation.

Low-Elevation Target DOA Estimation Based on Multi-Scattering Center Equivalent Model

In very-high-frequency (VHF) radar, the direction-of-arrival (DOA) estimation performance of low-angle targets tracking is strongly affected by the multipath phenomenon. Especially in the complex terrain conditions, the multipath echo comes from a region where the different scattering media make the multipath echo show the characteristics of multi-channel and uneven energy distribution. In this case, the received signal mismatches with the signal model, which leads to performance degradation and even failure of the traditional DOA algorithm. To deal with this problem, the authors propose a new signal model based on multiple scattering center. A multipath signal equivalent model is deduced and analyzed using multipath vector synthesis. Subsequently, the fitness function is established based on the equivalent model, and the target elevation angle is estimated by particle swarm optimization (PSO) algorithm. Simulation results and real data analysis show that the proposed model and algorithm can effectively improve the DOA estimation accuracy of low elevation target under complex terrain and less snapshot condition.

Target Height Measurement under Complex Multipath Interferences without Exact Knowledge on the Propagation Environment

This paper investigates the direction-of-arrival (DOA) estimation-based target localization problem using an array radar under complex multipath propagation scenarios. Prevalent methods may suffer from performance degradation due to the deterministic signal model mismatch, especially when the exact knowledge of a propagation environment is unavailable. To cope with this problem, we first establish an improved signal model of multipath propagation for low-angle target localization scenarios, where the dynamic nature of convoluted interferences induced by complex terrain reflections is taken into account. Subsequently, an iterative implementation-based target localization algorithm with the improved propagation model is proposed to eliminate the detrimental effect of coherent interferences on target localization performance. Compared to existing works, the proposed algorithm can maintain satisfactory estimation performance in terms of target location parameters, even in severe multipath interference conditions, where the decorrelation preprocessing and accurate knowledge about the multipath propagation environment are not required. Both simulation and experimental results demonstrate the effectiveness of the proposed propagation model and localization algorithm.

An efficient radar-target assignment and power allocation strategy for low-angle tracking in the MIMO-multisite radar system

How to utilize limited resources effectively in a multiple-input multiple-output (MIMO) radar network plays a critical role in its tracking capability. In this paper, a joint radar target assignment and power allocation (JRTAPA) strategy is proposed for the MIMO-multisite radar system (MIMO-MSRS) tracking low-angle targets. The MIMO-MSRS integrates the distributed MIMO radar architecture and the digital beamforming (DBF) technique, and hence better tracking performance in low-angle tracking is achievable. The mechanism of JRTAPA strategy is to allocate the limited beam and power resources using the prior target information feedback from the tracking recursive cycle. The location posterior Cramér-Rao lower bound (PCRLB) under the multipath interference (MI) is derived and adopted as the constraint metric. Considering the specific tracking accuracy requirement of each target, a quality of service (QoS)-based optimization model is established to minimize the total power consumption. An efficient two-stage Karush–Kuhn–Tucker (KKT) condition-based solver is proposed for solving such a nonconvex optimization model. The simulation results confirm the superiority of the proposed JRTAPA strategy over the joint scheduling and power allocation (JSPA) strategy, the target assignment and resource optimization (JTARO) strategy, as well as the exhaustive search-based algorithm. The results also reveal that the target geometry and tracking accuracy requirements have important effects on resource allocation.

A method of Robust low-angle target height and compound reflection coefficient joint estimation

It is always a challenging issue for radar systems to estimate the height of a low-angle target in the multipath propagation environment. The highly deterministic maximum likelihood estimator has a high accuracy, but the errors of the ground reflection coefficient and the reflecting surface height have serious influence on the method. In this paper, a robust estimation method with less computation burden is proposed based on the compound reflection coefficient multipath model for low-angle targets. The compound reflection coefficient is estimated from the received data of the array and then a one-dimension generalized steering vector is constructed to estimate the target height. The algorithm is robust to the reflecting surface height error and the ground reflection coefficient error. Finally, the experiment and simulation results demonstrate the validity of the proposed method.

Low Elevation Angle Estimation with Range Super-Resolution in Wideband Radar.

Height detection of a low elevation angle target is crucial in radar applications. Due to the presence of the multiple path reflections, elevation angle estimation is difficult with conventional narrowband radar waveforms. The reflection ground area parameters are especially hard to obtain for modeling. In this paper, we proposed a wideband, low elevation angle estimator based on range super-resolution, achieving a high robustness to variations in reflection coefficients. A relaxation (RELAX) algorithm was applied as the range super-resolution algorithm to separate the direct target echo and the reflected signal thanks to the relatively abundant frequency diversity. The grazing angle was obtained by synthesizing the steering vector of the direct signal and the range structure relationship between the two signal components. Theoretical analysis and simulation results confirmed the improved behavior of the proposed robust estimator in contrast to other conventional algorithms.

An edge computing framework based on OFDM radar for low grazing angle target tracking

In this paper, a novel adaptive algorithm to detect and track targets with low grazing angle is addressed. For this purpose, an orthogonal frequency division multiplexing (OFDM) radar signal is employed through an edge computing framework over the radar platform. However, detecting the targets in the low grazing angle area is a great challenge due to severe multipath reflection effects. The Earth’s curvature geometry model is presented as the multipath propagation model. Based on the fact that the different scattering centers of a target resonate at different frequencies, we use the optimized OFDM waveform and propose a novel target tracking procedure for low grazing angle target tracking scenarios. The obtained results show that using an OFDM radar waveform provides a more uniform detection coverage in the presence of multipath propagation such that this will fill in the nulls. Finally, simulations are used to compare the performance of the proposed OFDM waveform with the conventional equal-power, the generalized likelihood ratio (GLR)-based and single-carrier waveforms.

Estimation of Direction of Arrival by Time Reversal for Low-Angle Targets

In a low-angle target parameter estimation scenario, the backscattered signals from targets are often distorted due to clutter and multipath, which significantly degrades the performance of direction-of-arrival (DOA) estimation. In general, the backscattered multipaths are modeled as coherent signals with respect to the direct path. Decorrelation algorithms such as spatial smoothing and matrix reconstruction can mitigate the effect of multipath. However, most of these methods will perform poorly or even fail for solving parameter estimation problem of low-angle targets in complex terrain where there are rich multipath scatterings. This paper presents a novel method that combines time reversal (TR) technique, coherent signal-subspace method (CSM), and multiple signal classification algorithm to perform DOA estimation in a low-angle target scenario. The TR technique takes advantage of multipath echoes recorded by a sensor array and adaptively adjusts TR probing waveforms to increase the signal-to-noise ratio in a rich scattering environment. Moreover, the CSM method compresses the energy of a signal into a predefined subspace to exploit the full time-bandwidth product of signal sources and cope with coherent wavefronts. As a result, the proposed new DOA estimation algorithm outperforms significantly the conventional methods. The Cramer–Rao bounds analysis and numerical simulations commendably validate the accuracy and robustness of the proposed method.

Low angle estimation for MIMO radar with arbitrary array structures

ABSTRACTLow angle target estimation is always a difficult problem for radar elevation estimation. Multiple-input multiple-output (MIMO) radar can increase array degree of freedom and resolution, and improve the low angle target measurement precision. At the same time, the computation complexity of the MIMO radar is also increasing, especially for non-uniform linear array (ULA) configuration in which some fast algorithms cannot be used. In this paper, a fast low angle target estimation method is proposed for monostatic MIMO radar with arbitrary array configurations. With the array interpolation and the quasi-spatial-smoothing technique, the MIMO steering vector can be transformed into a virtual low dimension (typical similar size as the number of array elements) ULA steering vector. Then, the polynomial rooting algorithm is used to estimate the elevation of the low angle target. The simulation results of the proposed algorithm are presented and the performances are investigated and analysed.

Adaptive hybrid method for low‐angle target tracking in multipath

Tracking low-angle targets is a serious problem in radar systems because of the multipath phenomenon. When a signal is reflected from the ground surface, due to the low altitude of the target, it enters the radar main beam and seriously reduces its performance. The authors propose a novel adaptive hybrid method based on the complex angle (CA) method and the array monopulse system. They present a way to reduce the ambiguity of the CA method, and then, eliminate the multipath effect by using beamforming and provide an accurate estimate of the elevation angle. The simulation results show the performance of this method in tracking low-angle targets.

Projection Techniques for Altitude Estimation Over Complex Multipath Condition-Based VHF Radar

In very high-frequency (VHF) radar, the performance of low-angle target tracking is strongly affected by multipath phenomenon, especially in the case of complex terrain where exact information of reflection paths is unknown. To deal with this problem, a practical multipath signal model for VHF radar in complex terrain is presented, where both curved earth geometry and actual ground surface reflection coefficient are considered. Then, a novel altitude estimation technique based on alternating projection is proposed, where the motion or Doppler shift of a target is neglected during the observation time of one coherent processing interval. The technique applies to vertical antenna arrays which perform an interferometric estimation of the target elevation angle. Simulation results and real data analysis show that the target tracking accuracy can be efficiently improved by using the proposed algorithm under both simple and complex multipath scenarios.

Height Measurement of Low-Angle Target Using MIMO Radar Under Multipath Interference

For a very high frequency radar, multiple reflection paths, i.e., multipaths, of target echoes may cause severe errors in elevation estimation of low-angle targets, especially in the case of complex terrain where exact information of reflection paths is unknown. To deal with this problem, a practical multipath signal model for multiple-input multiple-out (MIMO) radar in complex terrain is first presented, where both transmitted multipaths and received multipaths are considered. A novel altitude estimation technique based on rank-1 constraint and sparse representation is then proposed. Simulation results show that the target localization accuracy can be efficiently improved by using our proposed technique under both simple and complex multipath scenarios.

Spatial Differencing Reconstruction Based Low-Angle DOA Estimation with MIMO Radar

Due to the multipath effect, the direction of arrival (DOA) estimation performance for low-angle targets decreases greatly. To overcome this problem, this paper proposes a spatial differencing reconstruction based DOA estimation algorithm by using the received signal model of multiple input multiple output (MIMO) radar. Combining with the spatial diversity of MIMO radar, the proposed method can first use the multipath echo power to select the best signal. Then, a spatial differencing based iterative scheme is developed to reduce the effect of additive noise, resulting in a better estimation performance for low-angle targets. Simulation results show that the proposed method has better advantages in suppressing noise and improving estimation accuracy.

Low‐angle target tracking using frequency‐agile refined maximum likelihood algorithm

An Low-angle target tracking problem is investigated via the refined maximum likelihood (RML) algorithm. The results of the RML algorithm reveal that increasing the operating frequency of radar does not always reduce the mean-squared error (MSE) of angle estimate and thus an appropriate selection of the operating frequency can improve the angle estimation accuracy. Here, a frequency-agile RML algorithm is proposed, which adaptively adjusts the operating frequency during target tracking to minimize the MSEs of angle estimate. Theoretical analysis and simulation are made to verify the effectiveness of the frequency-agile RML algorithm.

DOA estimation algorithms for low‐angle targets with MIMO radar

The direction of arrival estimation algorithm for low-angle targets with MIMO radar is discussed, and a novel peaks searching algorithm according to the multipath received signal model is derived. The proposed algorithm, which uses the multipath echo signal power and MIMO radar spatial diversity, can avoid the effects of complex multipath transmitter variable and targets glint. It is illustrated that the algorithm has a better performance with the increase of multipath variable or the transmit antennas. Simulation results verify the usefulness of the algorithm.

Altitude measurement of low‐angle target in complex terrain for very high‐frequency radar

For low-angle targets, the performance of altitude measurement is affected by multipath phenomenon. Especially, for complex terrain case, the main problem is due to the fact that the multipath signal is perturbed by irregular reflector, which leads to the mismatch of the steering vector, and thus degrades the performance of or even fails the existing methods. To deal with this problem, the authors propose a new perturbational multipath signal model, where perturbation caused by complex terrain is considered as the gain and phase errors of the steering vector of the multipath signal. With the spatial sparsity of the incident signals, the sparse Bayesian learning technique is adopted to estimate the perturbation and direction of arrival (DOA) iteratively. The computer simulation results show that their algorithm is able to estimate the effect of perturbation with high precision and to enhance the DOA accuracy compared with existing algorithms. Furthermore, real data analysis validates the efficiency of altitude measurement in practice. Finally, the proposed perturbational multipath signal model is also applicable to other situations where complex multipath phenomenon is not negligible.