As the use of radar and radar jammers increases, a radar device is likely to face interference from jammers or other radar devices. Traditional phase-coded pulse compression radar devices are widely used, but these tools struggle to overcome jamming and mutual interference. To solve this problem, we propose a novel chaotic-encrypted pulse compression radar signal based on radar identity (ID). Each radar has its own ID, which is encrypted with different chaotic binary sequences in every pulse period. The ambiguity function calculated for the coded radar signal is thumbtack-shaped, indicating that the signal has a good resolution. The received signal is used to range and decrypt in two channels: the range channel and the radar ID channel. The signals of the two channels are analyzed separately. Analyses of anti-barrage jamming and anti-mutual interference show that both channels perform well in terms of antijamming, while the antijamming ability is influenced by the processing gain, bit error rate (BER) and correlation function. In addition, the dual-channel antijamming method further improves the radar antijamming ability. The simulation result verifies the strong antijamming ability and high range resolution of the proposed radar signal, and the proposed antijamming method performs much better than the traditional phase-coded pulse compression radar signal in the antijamming scenario.

Currently, frequency-modulated continuous-wave (FMCW) proximity sensors are widely used. However, they suffer from a serious sweep jamming problem, which significantly reduces the ranging performance. To improve its anti-jamming capability, this paper analyzed the response mechanism of a proximity sensor with the existence of real target echo signals and sweep jamming, respectively. Then, a multi-channel harmonic timing sequence detection method, using the spectrum components’ distribution difference between the real echo signals and sweep jamming, is proposed. Moreover, a novel fast Fourier transform (FFT)-based implementation was employed to extract multi-channel harmonic information. Compared with the traditional band-pass filter (BPF) implementation, this novel realization scheme only computes FFT once, in each transmission cycle, which significantly reduced hardware resource consumption and improved the real-time performance of the proximity sensors. The proposed method was implemented and proved to be feasible through the numerical simulations and prototype experiments. The results showed that the proximity sensor utilizing the proposed method had better anti-sweep jamming capability and ranging performance.

Radio frequency interference (RFI) significantly hampers the target detection performance of frequency-modulated continuous-wave radar. To address the problem and maintain the target echo signal, this paper proposes a priori assumption on the interference component nature in the radar received signal, as well as a method for interference estimation and mitigation via time–frequency analysis. The solution employs Fourier synchrosqueezed transform to implement the radar’s beat signal transformation from time domain to time–frequency domain, thus converting the interference mitigation to the task of time–frequency distribution image restoration. The solution proposes the use of image processing based on the dual-tree complex wavelet transform and combines it with the spatial domain-based approach, thereby establishing a dual-domain fusion interference filter for time–frequency distribution images. This paper also presents a convolutional neural network model of structurally improved UNet++, which serves as the interference estimator. The proposed solution demonstrated its capability against various forms of RFI through the simulation experiment and showed a superior interference mitigation performance over other CNN model-based approaches.

In this paper, a resource allocation problem is formulated to maximize the throughput of LTE vehicular user equipments (VUEs) in both licensed and unlicensed frequency bands under constraints of reliability and latency for vehicular communications as well as the Quality of Service (QoS) for WiFi network based on a network system with coexisting VUEs and WiFi user equipments (WUEs). In the system, the VUEs are able to access to the unlicensed frequency bands and the interference among the VUEs and WUEs are mitigated by the listen-before-talk (LBT) scheme or the duty cycle scheme. Due to the mixed integer nonlinear programming (MINLP) objective in the optimization problem, the problem is hard to be solved directly. Instead, we solve the problem by two steps. The number of VUEs offioaded to unlicensed frequency bands as well as the time factor for duty cycle scheme is firstly determined, and then, the optimization problem is converted into a convex optimization problem, which is solved by the proposed Lagrange Duality Method (LDM). Numerical results show the efficiency of our proposed application scenario compared to case with only LTE system or only WiFi system. Moreover, compared to traditional Greedy algorithm, the proposed algorithm performs better in terms of throughput with a guaranteed QoS of WUEs.

In this paper, an improved two-dimensional (2-D) direction of arrival (DOA) estimation algorithm for L-shaped nested arrays is proposed. Unlike the approach for a classical nested array, which use the auto-correlation matrix (ACM) to increase the degrees of freedom (DOF), we utilize the cross-correlation matrix (CCM) of different sub-arrays to generate two long consecutive virtual arrays. These acquire a large number of DOF without redundant elements and eliminate noise effects. Furthermore, we reconstruct the CCM based on the singular value decomposition (SVD) operation in order to reduce the perturbation of noise for small numbers of samples. To cope with the matrix rank deficiency of the virtual arrays, we construct the full rank equivalent covariance matrices by using the output and its conjugate vector of virtual arrays. The unitary estimation of signal parameters via rotational invariance technique (ESPRIT) is then performed on the covariance matrices to obtain the DOA of incident signals with low computational complexity. Finally, angle pairing is achieved by deriving the equivalent signal vector of the virtual arrays using the estimated angles. Numerical simulation results show that the proposed algorithm not only provides more accurate 2-D DOA estimation performance with low complexity, but also achieves angle estimation for small numbers of samples compared to existing similar methods.

Vortex electromagnetic waves are conveniently produced by a uniform circular array (UCA). The mutual coupling matrix (MCM) of UCAs presents rotational symmetry, which can be employed to cut down the parameters in the circuit model. The impedance matrix also does not rely on a specific port. This work uses the rotational symmetry to expand the MCM into orbital angular momentum (OAM) mode domain. The OAM domain phase sequence is derived by the Discrete Fourier Transform (DFT) of the first row of the MCM. The connection between the number of OAM modes and excitation of elements is described. Furthermore, an improved propagator method (PM) for OAM based decoupled two-dimensional (2-D) angle estimation is proposed. This method employs original steering vector of OAM waves to process the receiving signal, which avoids error caused by the Bessel function approximation. The estimation error is also reduced by redefining the propagator matrix. In terms of computational complexity, the proposed method avoids spectral search and element level eigenvalue decomposition (EVD). The results demonstrate that the coupling is compensated effectively by the OAM mode domain MCM. The error of proposed method is less than 1 degree under the condition of snr = 20dB and 1000 snapshots. The computational complexity is reduced by 1 to 2 orders of magnitude compared to the origin propagator method. The method is also suitable for one-dimensional estimation scenarios.