In the application of system identification, not only the output but also the input of the system may be corrupted by noise, which is often characterized by the errors-in-variables (EIV) model. To identify such systems, a gradient-descent total least-squares (GD-TLS) and a maximum total correntropy (MTC) algorithms were proposed. In some scenarios, the weight vector of the unknown system may be sparse, e.g., the echo path in acoustic echo cancelation (AEC). Employing TLS or MTC to estimate such systems may result in slow convergence rate, since they assign the same gain to the update of each weight and therefore cannot make use of the sparsity feature of the system to accelerate convergence. To address the above problem, this article proposes a uniform optimization model for deriving proportionate total adaptive filtering algorithms, and then two proportionate total adaptive filtering algorithms are developed, namely, the proportionate total normalized least mean square (PTNLMS) algorithm for Gaussian noise disturbance and the proportionate MTC (PMTC) algorithm for impulsive noise interference, which are both derived by utilizing the method of Lagrange multipliers. Moreover, this article also makes a steady-state performance analysis of the two proposed algorithms. Simulations are performed to demonstrate the superior performance of the two proposed algorithms and to test the accuracy of the theory on the steady-state performance analysis.

Millimeter-wave (mmw) radar is indispensable for intelligent transportation systems (ITS), which can monitor traffic conditions in all weathers. An end-to-end simulation method for mmw radar monitoring and identification at traffic intersections is proposed in this paper. In this method, a virtual intersection scenario model is constructed, and the scattering coefficient of the target is calculated using the bidirectional analytical ray tracing (BART) algorithm. Combined with the generation of time-domain waveforms, the operation of frequency-domain convolution is simplified by inverse Fourier transform, and the echo signals received by the sparse array are simulated. After raw signal processing, point cloud images containing target position information and range-Doppler map (RDM) containing target state feature are obtained. The performance of mmw radar in detecting the specific location information of the target is evaluated by analyzing point cloud images. In addition, a self-defined convolutional neural network is introduced to evaluate the object recognition performance of the RDM. After the training of the neural network, the classification accuracy of this method for four types of vehicle targets can reach 92%.

A 3-D security imaging experiment at 24 GHz is demonstrated with a 1-D sparse multi-input multi-output (MIMO) array. The MIMO array is an 8 transmission/16 reception arc array to achieve real-aperture imaging along the vertical dimension. It is time-switching multiplexed with a low-cost frequency modulated continuous wave transceiver working at 22-26 GHz. A calibration procedure is proposed to calibrate the channel imbalance across the MIMO array. The experiment is conducted on humans moving on a cart, where we take advantage of the linear motion of humans to form inverse synthetic aperture along the horizontal dimension. To track the motion of humans, a 3-D depth camera is used as an auxiliary sensor to capture the rough position of the target to aid synthetic aperture radar imaging. The back-projection imaging algorithm is implemented on a graphics processing unit for quasi-real-time operations. Finally, experiments are conducted with a real human with concealed objects and a preliminary automatic object detection algorithm based on convolutional neural networks is developed and evaluated on real data.

In comparison with the conventional radar altimeter, synthetic aperture radar altimeter (SRAL) is developed with better along‐track spatial resolution and height precision. When SRAL improves the performance of height measurement, it also complicates the radar system designing, signal processing, data analyzing, and system testing. The SRAL raw signal is the result of the interaction between the transmitted signal and the complex terrain surfaces. Generally, the SRAL is used in ocean applications since the PDF can be derived from different ocean surfaces. However, the actual acquired SRAL waveforms can be from complex and variable surface, which may include mountains, cities, forests, lakes, and other types of terrain surfaces. These waveforms contain information about the surfaces of the Earth although difficult to analyze. This paper focuses on the study of the raw signal simulation of SRAL for complex terrain surfaces including land surfaces. The principle of radar altimeter, the electromagnetic characteristics of land targets, and the SRAL signal processing are analyzed. The geometric models are extracted from digital elevation model, and the scattering characteristics of the terrain surface are established with three scattering models. Finally, three cases of terrain surfaces, namely, ice surface, mountain area, and urban area, are simulated and validated against actual SRAL signals measured by the Sentinel‐3 SRAL sensor.

High-fidelity deception jamming can seriously mislead Synthetic Aperture Radar (SAR) image interpretation and target detection, which is difficult to identify or eliminate through traditional anti-jamming methods. Based on the Range-Doppler Algorithm (RDA), an anti-jamming method for SAR by using joint waveform modulation and azimuth mismatched filtering is proposed in this paper. The signal model of SAR echo after pulse compression with deception jamming is derived from the radar detection and jamming characteristics. A multi-objective cost function is introduced to suppress the jamming level and keep the real target information, parameterized with the waveform initial phase and the azimuth mismatched filter coefficients, which is optimized using the second-order Taylor expansion approximation and the alternating direction multiplier method (ADMM). The performance of this proposed method is evaluated through the convergence analysis and anti-jamming experiments on the point target and the distributed target scenarios.

Through the development of Synthetic Aperture Radar (SAR) technology, it is now possible to observe dynamic processes on the earth with fine temporal resolution by forming SAR time series. Nonetheless, such sequential images remain difficult to interpret due to the speckle effect. Despeckling them is further complicated by outliers caused by abrupt changes in weather conditions or the appearance of objects. In spite of the fact that many state-of-the-art methods can achieve excellent filtering performances over stable areas, they often result in artifacts in those areas where outliers existed at the time of acquisition. To simultaneously mitigate the speckle noise and extract outliers, we propose a novel SAR time series despeckling method based on nonlocal total variation regularized robust principle component analysis, which is termed SAR-NL-TVRPCA. By comparing it to other state-of-the-art methods, the effectiveness of its despeckling has been validated in real data experiments. Furthermore, the extracted outliers can provide insight into abrupt changes occurring throughout the observation period, which provides byproducts for further analysis.