This paper discusses the problem of low-elevation target height estimation for multiple-input multiple-output (MIMO) radar in multipath environments. The beamspace compresses the data and is ideal for reducing the computational burden of elevation estimation. To obtain the height parameter of the target accurately, we propose a height estimation method based on a beamspace joint alternating iterative (BJAI) algorithm in MIMO radar. This method mainly converts the reduced-dimensional MIMO radar element space data into beamspace data and whitens them to improve the reliability. Then, a simplified model is used to obtain the initial value of the elevation, and we combine the reflection coefficient and the target elevation angle for alternate estimation. Finally, we calculate the target height using the obtained elevation information. Simulation results verify that the proposed algorithm has high estimation accuracy and strong robustness.

Abstract In this paper, attention is devoted to the analysis of the detection threshold based on the multi‐frame integration in heavy‐tailed clutter for the radar with high resolution and even smaller grazing angle. The closed‐form expressions of both the probability of the detection and the probability of false alarm for the heavy‐tailed clutter background, which can be used for the theoretical analysis of constant false alarm rate (CFAR) detectors, are derived with the multi‐frame integration technique. Accordingly, an improved CFAR detector is designed to work well with the presence of target‐like outliers in the heavy‐tailed clutter. In addition, the proposed CFAR detector is capable to alleviate the masking‐effect resorting to the additive feedback operation when a target is large enough to cross several cells in multi‐target case. The theoretical analysis and numerical simulations demonstrate that the proposed CFAR detector based on multi‐frame integration can improve the signal‐to‐clutter rate of the targets exhibiting better performance than ones based on single frame in heavy‐tailed clutter background. It is validated from the simulations that the proposed CFAR detector with additive feedback operation can deal with masking‐effect for large target occupying several cells.

To avoid Doppler ambiguity, pulse Doppler radar may operate on a high pulse repetition frequency (PRF). The use of a high PRF can, however, lead to range ambiguity in many cases. At present, the major efficient solution to solve range ambiguity is based on a waveform design scheme. It adds complexity to a radar system. However, the traditional multiple-PRF-based scheme is difficult to be applied in multiple targets because of unknown correspondence between the target range and measured range, especially using the Chinese remainder theorem (CRT) algorithm. We make a study of the CRT algorithm for multiple targets when the residue set contains noise error. In this paper, we present a symmetry polynomial aided CRT algorithm to effectively achieve range estimation of multiple targets when the measured ranges are overlapped with noise error. A closed-form and robust CRT algorithm for single target and the Aitken acceleration algorithm for finding roots of a polynomial equation are used to decrease the computational complexity of the proposed algorithm.

Low-angle estimation for very high frequency (VHF) radar is a difficult problem due to the multipath effect in the radar field, especially in complex scenarios where the reflection condition is unknown. To deal with this problem, we propose an algorithm of target height and multipath attenuation joint estimation. The amplitude of the surface reflection coefficient is estimated by the characteristic of the data itself, and it is assumed that there is no reflected signal when the amplitude is very small. The phase of the surface reflection coefficient and the phase difference between the direct and reflected signals are searched as the same part, and this represents the multipath phase attenuation. The Cramer-Rao bound of the proposed algorithm is also derived. Finally, computer simulations and real data processing results show that the proposed algorithm has good estimation performance under complex scenarios and works well with only one snapshot.

This letter discusses the elevation angle estimation problem for a scene containing a direct and a reflected signal. Beamspace processing is an excellent candidate to reduce the computational burden of elevation angle estimation without affecting the multipath phase that contains the reflection coefficient phase and the phase difference between the direct and the reflected signals. In addition, the multipath phase is a function of the target elevation angle. With these considerations, we solve the multipath phase problem without searching within 3D beamspace data and therefore obtain the target elevation angle with a very low computational burden. Under the same prior information, the proposed algorithm manages an estimation accuracy similar to the ones requiring a search under the condition of a certain signal-to-noise ratio. Our evaluation results demonstrate the suitability of the proposed algorithm for engineering applications.

In traditional target tracking methods, the angle error and range error are often measured by the empirical value, while observation noise is a constant. In this paper, the angle error and range error are analyzed. They are influenced by the signal-to-noise ratio (SNR). Therefore, a model related to SNR has been established, in which the SNR information is applied for target tracking. Combined with an advanced nonlinear filter method, the extended Kalman filter method based on the SNR model (SNR-EKF) and the unscented Kalman filter method based on the SNR model (SNR-UKF) are proposed. There is little difference between the SNR-EKF and SNR-UKF methods in position precision, but the SNR-EKF method has advantages in computation time and the SNR-UKF method has advantages in velocity precision. Simulation results show that target tracking methods based on the SNR model can greatly improve the tracking performance compared with traditional tracking methods. The target tracking accuracy and convergence speed of the proposed methods have significant improvements.

Multiple-input multiple-output radar has many advantages over the phased-array radar system due to the waveform diversity, one of which is the greater flexibility to design the transmit beampattern. Hence, waveform set design for transmit beampattern has become an attractive topic, and many methods have been proposed in recent years. However, previous methods cannot synthesize the waveforms with constant-envelope and easy-generation properties. In this letter, we propose to design a set of correlated linear frequency modulation (LFM) waveforms to solve this problem. First, the covariance matrix of the LFM waveform set is analyzed, and the formulation of the transmit beampattern is obtained accordingly. Since the transmit beampattern is mainly affected by the frequency steps and initial phases of the LFM waveforms, the correlated LFM waveform set design problems are formulated by optimizing these parameters for different beampatterns. The resulting problems are solved by adopting the constrained nonlinear optimization, and the LFM waveforms are obtained consequently. The designed waveforms have the properties of constant-envelope and easy generation, and can match the desired transmit beampattern properly. Simulation results demonstrate the superiority of our proposed method.

The orthogonal frequency-division multiplexing (OFDM) chirp waveform has attracted much attention due to its high range resolution, low peak-to-average ratio, and large time-bandwidth product. In its application to multiple-input multiple-output radar, the correlation property of multiple OFDM chirp waveforms should be considered primarily for good detection performance. The simulation results show that high sidelobes exist in correlation functions of the conventional waveforms. In this letter, the reason for high correlation sidelobes is explored first, which is the equal subchirp durations and the same subcarrier bandwidth. Second, two new OFDM chirp waveform diversity design schemes are proposed to suppress the correlation interference. Via designing the various subchirp durations or subcarrier bandwidths specially, the high sidelobes are depressed and the correlation property is improved. Both simulation results and comparisons verify the effectiveness of the proposed methods.

We propose a low complexity robust beamforming method for the general-rank signal model, to combat against mismatches of the desired signal array response and the received signal covariance matrix. The proposed beamformer not only considers the norm bounded uncertainties in the desired and received signal covariance matrices, but also includes an additional positive semidefinite constraint on the desired signal covariance matrix. Based on the worst-case performance optimization criterion, a computationally simple closed-form weight vector is obtained. Simulation results verify the validity and robustness of the proposed beamforming method.

In inverse synthetic aperture radar (ISAR) imaging, a target is usually regarded as consist of a few strong (specular) scatterers and the distribution of these strong scatterers is sparse in the imaging volume. In this paper, we propose to incorporate the sparse signal recovery method in 3D multiple-input multiple-output radar imaging algorithm. Sequential order one negative exponential (SOONE) function, which forms homotopy between 1 and 0 norms, is proposed to measure the sparsity. Gradient projection is used to solve a constrained nonconvex SOONE function minimization problem and recover the sparse signal. However, while the gradient projection method is computationally simple, it is not robust when a matrix in the algorithm is ill conditioned. We thus further propose using diagonal loading and singular value decomposition methods to improve the robustness of the algorithm. In order to handle targets with large flat surfaces, a combined amplitude and total-variation objective function is also proposed to regularize the shapes of the flat surfaces. Simulation results show that the proposed gradient projection of SOONE function method is better than orthogonal matching pursuit, CoSaMp, l1-magic, Bayesian method with Laplace prior, smoothed l0 method, and l1-ls in high SNR cases for recovery of ± 1 random spikes sparse signal. The quality of the simulated 3D images and real data ISAR images obtained using the new method is better than that of the conventional correlation method and minimum l2 norm method, and competitive to the aforementioned sparse signal recovery algorithms.