The detection performances of MIMO radar can be improved by waveform diversity technology. The waveform diversity gain depends on the correlation side-lobe level of the orthogonal waveform set. Generally, peak side-lobe level (PSL) and integrated side-lobe level (ISL) are two most important performance metrics of the correlation side-lobe level. Few existing orthogonal waveform set design algorithms can minimize PSL and ISL simultaneously. To tackle this problem, this paper first proposes a correlation side-lobe performance metric in the form of p -norm, denoted by p -ISL. Then, in order to minimize the p -ISL, this paper develops a p -MM algorithm based on Majorization-Minimization (MM) framework, which transforms the complex p -order polynomial minimization problem into a series of low-order simple optimization problems. Numerical results show that the p -MM algorithm can effectively suppress the correlation side-lobe level and strike a good balance between the PSL and ISL metrics. Compared with the best PSL optimization algorithm based on primal dual method, the p -MM obtains about the same PSL and lower ISL values. Compared with Multi-CAN, MM-Corr, ISL-New and the other state-of-the-art ISL minimization algorithms, the p -MM obtains slightly higher ISL and much lower PSL. Briefly, the p -MM is able to obtain almost the best PSL and relatively low ISL values.

Low-cost Global Navigation Satellite System (GNSS) receivers and monocular cameras are widely used in daily activities. The complementary nature of these two devices is ideal for outdoor navigation. In this paper, we investigate the integration of GNSS and monocular camera measurements in a simultaneous localization and mapping system. The proposed system first aligns the coordinates between two sensors. Subsequently, the measurements are fused by an optimization-based scheme. Our system can function in real-time and obtain the absolute position, scale, and attitude of the vehicle. It achieves a high accuracy without a preset map and also has the capability to work with a preset map. The system can easily be extended to create other forms of maps or for other types of cameras. Experimental results on a popular public dataset are presented to validate the performance of the proposed system.

Interrupted sampling repeater jamming (ISRJ), falls within the category of intrapulse coherent interference. Leveraging undersampling principles, ISRJ generates multiple false target peaks on the range profile, thus interfering with the detection and tracking of genuine targets. To address this issue, this paper presents a method called Waveform-Domain Adaptive Matched Filtering (WD-AMF) for ISRJ suppression. Initially, recognizing ISRJ's partial matching characteristics, we introduce an extended domain, namely the waveform domain, into the matched filtering process. This extension allows us to analyze the local characteristics of the interference signal and the components of the authentic target echo signal. Based on these characteristics, we define an adaptive threshold function in the waveform domain to distinguish between valid and invalid integration elements, which does not rely on prior information about ISRJ. Additionally, we introduce the Interacting Multiple Model-Kalman Filtering (IMM-KF) algorithm for state estimation of waveform domain signals and establish an estimated state space related to valid integration elements to mitigate the effects of noise. Furthermore, we introduce waveform domain coherent processing methods over slow time to reduce clutter interference. Lastly, while suppressing invalid integration elements in the waveform domain signal, we retain valid integration elements and supplement the corresponding length of integration elements from the estimated state space. This integration yields a range profile devoid of false targets. Through simulations and realworld experiments, we demonstrate the remarkable capability of the proposed method in ISRJ suppression, surpassing other methods even in the absence of a pre-existing ISRJ model.

The false targets generated by interrupted sampling repeater jamming (ISRJ) bring serious threats to radar target detection and imaging. Transmitted waveform design with joint processing has become a promising way to deal with them. In this letter, the ambiguity function (AF) of integral power frequency modulated (PFM) waveform is derived. Meanwhile, the time-frequency characteristic of ISRJ signal received by the radar is analyzed on the range-Doppler plane. In consideration of the range-Doppler coupling characteristic, an improved Doppler filtering processing method is proposed to suppress the jamming false targets. Simulation results demonstrate that the selected PFM waveform with proposed joint processing method can effectively suppress the false targets resulting from ISRJ.

In recent years, the utilization of In-Band Full-Duplex (IBFD) technology has gained considerable attention as an effective solution to address limited spectrum resources. However, achieving an IBFD system necessitates the utilization of both passive suppression and active cancellation techniques to completely eliminate strong self-interference (SI) signals in the local receiver. This research paper aims to tackle this challenge by introducing the Multi-Domain Intelligent Cancellation (MDIC) method, which integrates analog-domain and digital-domain cancellation techniques through comprehensive nonlinear system modeling. Moreover, this study presents an extensive analysis of the underlying principles of MDIC, while evaluating the limits of SI suppression performance under the assumption of perfect channel estimation and no phase noise. The channel parameter estimation is conducted using the time-domain least squares method, without considering the effects of Gaussian white noise and phase noise. To enhance the performance of SI cancellation (SIC), a deep learning network (DLN) is introduced in the digital domain. Simulation results demonstrate that the proposed MDIC method achieves a substantial SIC performance of approximately 60 dB, which closely approaches the theoretical limit. Furthermore, experimental data indicates that the proposed deep learning approach exhibits superior SIC performance and broader adaptability to broadband signals compared to traditional methods.

The intelligent noise jamming, known as Smart Noise-Interrupted Sampling Repeater Jamming (SN-ISRJ), constitutes a coherent disturbance with suppressive characteristics, engineered to attenuate radar detection performance. The study focuses on anti-SN-ISRJ techniques within the waveform domain, primarily leveraging signal processing methodologies specific to the waveform domain. We illustrate that when utilizing the Waveform Domain Complementary Signal Set (WDCSS) as the transmission waveform, the waveform response function of SN-ISRJ also manifests waveform domain complementarity. Concurrently, we establish that the cumulative waveform coherence function (CWCF) of SN-ISRJ is locally piecewise nonlinear. Moreover, we demonstrate that in the presence of SN-ISRJ, the CWCF of the the received signal constitutes the unique non-zero linear function at the target location. To extract linear components and suppress nonlinear elements in the waveform domain, we introduce Bernoulli filtering within the waveform domain. This procedure, in conjunction with matched filtering, gives rise to a comprehensive signal processing algorithm: Waveform Domain Bernoulli Matched Filtering (WD-BMF). Simulation results substantiate that the proposed WD-BMF attains full suppression of SN-ISRJ without dependence on jamming prior information, preserving the energy of the target echo. It showcases superior adaptive anti-jamming capabilities when compared to conventional methods.