Single snapshot direction of arrival (DOA) estimation gains traction in automotive MIMO radar. Gridless methods based on atomic norm show superiority in single snapshot DOA estimation. However, the atomic norm is a convex relaxation of the atomic l0 norm, which leads to resolution limitations. To avoid the disadvantage of resolution limitation, we propose a multi-objective DOA estimation model with atomic l0 norm and measurement errors as optimization objectives. It can estimate the angle and the number of sources simultaneously and has the advantage of directly exploiting sparsity through the atomic l0 norm. Then, we design a cooperative co-evolution crayfish optimization algorithm (CO3) to solve this model. The algorithm contains two innovations, one of which is the proposal of a new multi-population cooperative co-evolutionary decomposition strategy that efficiently decomposes a multi-objective DOA estimation model into multiple single-objective problems without having to consider the fitness allocation problem. Each single-objective problem is then solved using a crayfish optimization algorithm. The other is to propose a variable-length neighbor-hood orthogonal crossover operator to carry out the work of information exchange between populations, which can effectively speed up the convergence of the algorithm. Simulation results and actual data verify the superiority of the method in this paper in terms of source number selection and DOA estimation. © 2025 IEEE.

The L1-SVD is widely used to solve direction of arrival (DOA) estimation problem in sparse manner. However, due to the influence of the coherent environment often caused by multipath propagation in practical applications, the noise immunity and estimation accuracy of the traditional L1-SVD algorithm deteriorate. In this paper, to improve its performance in correlated environment, we propose a new method called L1-SSD. We introduce spatial smoothing processing into the singular value decomposition (SVD) process and complete the DOA estimation by using the new spatial smoothing decomposition (SSD) with l1-norm minimization. The hardware experimental results in real coherent environment verify that the L1-SSD algorithm can have higher estimation accuracy and better noise immunity than the traditional L1-SVD algorithm with slightly faster computation speed. © 2024 IEEE.

The trimmed lasso is a new nonconvex regularized approach for sparse signal recovery, which shows better estimation results in the single measurement vector problem. In this paper, we define the penalty term of the trimmed lasso in the multiple measurement vector model by £2,1-norm. We then present an approach to apply the trimmed lasso to on-grid based direction-of-arrival estimation problems by the alternating directions method of multipliers (ADMM) and majorization-minimization algorithm, which change the nonconvex objective to the convex weighted problem. Numerical simulations show that the proposed method improves the performance over £2,1 minimization algorithm. © 2023 IEEE.

The fixed-dictionary based gridded frequency sparse representation in random demodulation systems faces the grid mismatch problem, which may lead to severe degradation of the compression algorithm to the extent that high precision signal parameters and signal reconstruction cannot be obtained. Gridless compressed sensing introduces the concept of atomic norm, which solves the grid problem caused by spectrum discretization and greatly improves the accuracy of signal frequency estimation. However, in practice, solving large-scale meshless compressed sensing problems directly can consume a lot of time and storage resources. In this paper, we borrow the idea of segmented random sampling to compress the signal by generating segmented pseudo-random sequences in a random demodulation system. Then each segment of the compressed sampled signal is reconstructed and frequency estimated by atomic norm separately, and the obtained frequency estimates of several segments are averaged to obtain the frequency estimates of the original signal. The algorithm can achieve a recovery signal-to-noise ratio of 68 dB for a signal with 256 points per segment, and the recovery accuracy is only related to the number of points within the segment, independent of the number of segments. The algorithm reduces the computational complexity and improves the solution size and the accuracy of frequency estimation. © 2023 IEEE.

Radar target simulators have been widely used in accelerating radar performance testing. The real-time performance and signal quality of the echo signal are important criteria for evaluating the target simulator, and SFDR is an important indicator for evaluating the signal quality. The traditional target simulator adopts the form of target playback with poor real-time performance, and cannot effectively suppress spurs in the baseband signal processing, which affects the signal quality. Therefore, this paper introduces a FPGA-based low-spur real-time target echo simulator. That suppress phase spurs by generating m sequences, realizing real-time simulation using link deterministic delay and FPGA parallel structure. The high-signal-quality real-time echo generated by this device can make up for the defects of the traditional simulator and achieve high-precision and high-quality radar testing. Simulations validate that the proposed simulator can achieve real time echo simulation with high SFDR.
© 2020 IEEE.

Radar target simulators have been widely used in accelerating radar performance testing. The real-time performance and signal quality of the echo signal are important criteria for evaluating the target simulator, and SFDR is an important indicator for evaluating the signal quality. The traditional target simulator adopts the form of target playback with poor real-time performance, and cannot effectively suppress spurs in the baseband signal processing, which affects the signal quality. Therefore, this paper introduces a FPGA-based low-spur real-time target echo simulator. That suppress phase spurs by generating m sequences, realizing real-time simulation using link deterministic delay and FPGA parallel structure. The high-signal-quality real-time echo generated by this device can make up for the defects of the traditional simulator and achieve high-precision and high-quality radar testing. Simulations validate that the proposed simulator can achieve real time echo simulation with high SFDR. © 2020 IEEE.

Modulated Wideband Converter (MWC) is an attractive system implementing analog to information conversion (AIC) of multiband signals at sub-Nyquist rate, which is based on the emerging theory of Compressed Sensing (CS). Frequency mixing with periodic sequence is a pivotal step. However, random waveforms constructing the mixing sequences make the MWC has high complexity. To reduce the complexity, in this letter, we present a novel Diagonal Remainder Matrix based AIC (DRM-AIC). DRM-AIC has only one non-zero element in each sequence, which is similar to the sample-and-hold sampling and each sequence is produced by the delay of a base sequence. We obtain the non-uniform delay between sequences by a simple remainder function plus uniform sampling interval. This special structure makes the observation matrix of DRM-AIC consists of diagonal matrix and a submatrix of a Vandermonde matrix. The rationality and superiority of the observation matrix are verified by theoretical analysis. Simulation results show that DRM-AIC has better reconstruction performance than MWC.
© 2020 IEEE.