Peak Sidelobe Research Articles

Improved Sparrow Search Algorithm for Rectangular Planar Array Synthesis

To optimize sparse rectangular planar array antennas under multiple constraints, including array aperture, number of elements, and minimum element spacing, an improved sparrow search algorithm (ISSA) is proposed. Initially, a position distribution matrix is generated using the Blackman window weighting method. The sparrow search algorithm (SSA) is then enhanced by incorporating Kent mapping for population initialization, which improves the initial population’s diversity. Additionally, the strategy for updating the discoverer’s position integrates elements from the sine cosine algorithm (SCA), along with a nonlinear sine learning factor, thereby enhancing global search capability. Finally, a crossover strategy is embedded into the SSA to refine the optimization accuracy by improving the search methodology used by the vigilantes. To verify the efficacy of our approach, we carried out simulation experiments. The results demonstrate that this method significantly enhances the performance of the array antenna by reducing the peak sidelobe level and minimizing the zero-trap depth. These findings validate the reliability and effectiveness of the suggested method.

Non-uniformly Spaced Antenna Arrays with Overlapped Elements Constraint

In the literature, inter-element spacing antenna design methods have been widely discussed and presented as an alternative approach to element excitation amplitude and/or phase control methods that may be relied upon to achieve the required array pattern shapes. However, methods associated with non-uniformly distributed elements suffer from the element overlap problem, where some of the optimized element locations may overlap each other and cause changes in the overall array aperture length. Practically, these element overlaps cannot be implemented, due to the physical antenna element size, without omitting some of them. Consequently, the overall performance of the antenna array is degraded. Further, degradation may occur when considering phased arrays with scanned main beams. In this paper, we first illustrate the effect of the problem of overlapped element locations and then we propose two approaches based on the genetic algorithm to optimize non-uniformly spaced arrays with overlapped element locations, while simultaneously preserving the array's directivity. To solve the problem of overlapping and to determine the physical array element size, the minimum element-spacing constraints are incorporated in a simple way in the proposed approaches. Thus, the time required to perform optimization-related computations is greatly reduced. Simulation results confirm the effectiveness of the two proposed solutions, where the probability of the elements overlapping has been reduced to zero under specific conditions related to the locations of the some of the elements, while the peak sidelobe levels were always kept below -15 dB and directivity was maintained, to the extent possible, at the level of that of standard uniformly spaced arrays.

LPI radar waveform design based on complementary phase and discrete chaotic frequency joint coding

In order to reduce the probability of radar radiation signals being detected by enemy passive detection systems, this paper proposes a phase and frequency joint coding low intercept Radar waveform design method. Based on the linear frequency modulation signal, this method uses complementary binary code and chaotic sequence to phase encode the intra-pulse modulation. Code and frequency coding. The numerical simulation results show that the designed waveform exhibits pseudo-random characteristics in the time-frequency domain, and the low recognition performance is improved; The signal has an extremely low peak sidelobe level after matched filtering, showing excellent low intercept performance; its three-dimensional ambiguity function diagram presents an ideal “graph”. Fishing type”, with good distance, speed resolution and anti-interference characteristics.

Photonics-assisted high-precision free space RF synchronization technology

In this paper, a high-precision frequency synchronization scheme in free space based on microwave photonic architecture is proposed, which can be used between multi-nodes in relative motion or in a motionless state. The reference frequency is modulated to high frequency through microwave photon up-conversion, and the reference signal is recovered at the receiver through photonics self-mixing technology. The experiment over free space in the laboratory in 4m, shows that the performance of Allen variance is s under the noise floor is s, and it is better than that of the electrical generation method with the Allen variance is s. The impact of system signal-to-noise ratio (SNR) on frequency stability is analyzed, and the experimental results are consistent with the theoretical value. At the same time, a functional experiment based on free space RF synchronization is conducted, compared to the direct synchronization method, the phase degradation of this method is only 0.25 rad, and the degradation of peak side lobe ratio (PLSR) of pulse compression loss is 0.29 dB. The good, coherent result further verifies the correctness of this synchronization method.

Orthogonal Waveform Design for MIMO Radar Using Deep Learning

Abstract This paper explores the design of orthogonal waveforms for multiple-input multiple-output (MIMO) radar. We combine the peak sidelobe level (PSL) with the omnidirectional beampattern matching as the optimization objective. To address this challenging non-linear and non-convex problem, we propose an unsupervised optimization network based on residual network and attention mechanism. This approach harnesses the potent nonlinear fitting capabilities of neural networks to address the complex optimization problem. Simulation results indicate that the waveforms designed by the algorithm proposed in this paper exhibit better orthogonality than those designed by the traditional algorithm.

Anti-Spectral Interference Waveform Design Based on High-Order Norm Optimized Autocorrelation Sidelobes Properties

This paper introduces a robust waveform design method aimed at reducing the impact of electromagnetic interference in radar systems, thereby enhancing target detection accuracy. We propose utilizing a high-order p-norm to characterize the peak sidelobe level (PSL) of the waveform. Additionally, the method incorporates spectral zero-trapping within known interfering frequency bands to mitigate interference effects. A unified optimization objective function is developed to ensure optimal correlation properties of waveforms for dual-use in radar and communication systems. By employing the AdamW algorithm for dynamic adjustment of the iteration factor, combined with a gradient descent search, this method refines both the autocorrelation of the waveform and its resilience to known disturbances. Experimental results demonstrate that our approach significantly improves autocorrelation performance over randomly generated initial waveforms. Moreover, the introduction of spectral zero-trapping notably enhances interference suppression in targeted frequency bands, thereby boosting overall signal performance. Our method effectively balances interference rejection with the minimization of sidelobe levels, offering a pragmatic waveform solution for complex radar environments.

Optically Delaying a Radio Frequency–Linear Frequency-Modulated (RF-LFM) Pulse Using Kerr Comb Carriers and Off-the-Shelf Concatenation of a Linearly Chirped Fiber Bragg Grating and a Chirped-and-Sampled Fiber Bragg Grating

We demonstrate a low latency delay of a radio frequency (RF)–linear frequency-modulated (LFM) pulse by modulating it onto optical carriers from a Kerr comb and sending the signal through a concatenation of off-the-shelf linearly chirped fiber Bragg gratings (LC-FBGs) and chirped-and-sampled FBG (CS-FBG). We characterize the frequency response and latency of the LC-FBG and CS-FBG. Then, experimentally, the LFM pulse performance is characterized by measuring the peak sidelobe level (PSL) at the output of the tunable delay system. The experiment, performed with an LFM pulse of 1 GHz bandwidth at a 10 GHz center frequency, shows a PSL better than 34.4 dB, attesting to the high quality of the buffer RF transfer function. Thus, the proposed optical memory buffer architecture, utilizing compact devices based on a Kerr comb and FBGs, offers several benefits for delaying LFM pulses, including (i) a larger tunable delay range, (ii) low latency, (iii) wide bandwidth, and (iv) high PSL.

Non-uniform optical phased array based on dual-adaption genetic algorithm improved by chaos sequence

To address the issue of diminishing peak side lobe levels (PSLL) in non-uniform optical phased array (OPA) as steering angle increases, we introduce a dual-adaption genetic algorithm (DAGA) based on chaos sequence to optimize the antenna array layout. By leveraging chaos mapping traversal and randomness, this approach enhances the diversity of the initial antenna layout, thereby improving the algorithm's capacity to escape local optima. Concurrently, we also reduce background noise during the array steering process to enhance PSLL at large steering angle. In this study, we optimize non-uniform antenna arrays with 64-, 128-, and 256-channels. The results demonstrate that at a steering angle of 80°, the PSLL is improved by 2.18, 2.81, and 3.59 dB, respectively, compared to the conventional genetic algorithm (GA). Notably, for a 256-channel array, the PSLL can be optimized to an impressive -18.46 dB using this method. To our knowledge, this represents the lowest PSLL achieved for an equivalent number of channels. Our research offers valuable insights for the practical implementation of OPAs as transmitters in chip-scale frequency modulated continuous wave (FMCW) light detection and ranging (LiDAR) systems.

A Novel Waveform Optimization Method for Orthogonal-Frequency Multiple-Input Multiple-Output Radar Based on Dual-Channel Neural Networks.

The orthogonal frequency-division multiplexing (OFDM) mode with a linear frequency modulation (LFM) signal as the baseband waveform has been widely studied and applied in multiple-input multiple-output (MIMO) radar systems. However, its high sidelobe levels after pulse compression affect the target detection of radar systems. For this paper, theoretical analysis was performed, to investigate the causes of high sidelobe levels in OFDM-LFM waveforms, and a novel waveform optimization design method based on deep neural networks is proposed. This method utilizes the classic ResNeXt network to construct dual-channel neural networks, and a new loss function is employed to design the phase and bandwidth of the OFDM-LFM waveforms. Meanwhile, the optimization factor is exploited, to address the optimization problem of the peak sidelobe levels (PSLs) and integral sidelobe levels (ISLs). Our numerical results verified the correctness of the theoretical analysis and the effectiveness of the proposed method. The designed OFDM-LFM waveforms exhibited outstanding performance in pulse compression and improved the detection performance of the radar.

Synthesis of sparse rectangular planar arrays with weight function and improved grey wolf optimization algorithm

AbstractThe present article proposes a novel hybrid approach for addressing the synthesis problem of rectangular planar arrays under multiple constraints, through joint optimization of the weight function and the improved grey wolf optimization (IGWO) algorithm. Firstly, the grey wolf optimization (GWO) algorithm is improved by using tent chaotic mapping, nonlinear convergence factor, dominant wolf dynamic belief strategy, and opposition‐based learning strategy to increase the population diversity and the ability to jump out of the local optimum. Secondly, the array elements are weighted using the weight function to generate the position distribution matrix, which reduces the thinned matrix optimization time and improves the optimization efficiency. Finally, the position distribution matrix is used to generate the thinned array, and the IGWO algorithm is applied to perform the sparse optimization with multiple constraints. The effectiveness of the method is verified through numerical simulation and full‐wave simulation experiments, demonstrating its capability to enhance array antenna performance and reduce peak sidelobe level (PSLL). These experimental results hold significant engineering implications and provide valuable references for addressing the array distribution problem under multiple constraints.

Sparse TFM imaging of different-scale phased array based on the DWSO algorithm

ABSTRACT Using sparse matrix for total focusing method (TFM) imaging can improve computational efficiency in non-destructive testing (NDT). In sparse matrix design, binary particle swarm optimisation (BPSO) and genetic algorithm (GA) often fall into local optimal solution, and the computational efficiency decreases significantly with the expansion of array scale. In this paper, a discrete war strategy optimisation (DWSO) is proposed to realise sparse array ultrasonic imaging. This method uses equidistant scatter mapping to real-number encode the array position and limit the search range. Then, the fitness function is constructed with low side lobe peak and narrow main lobe width to obtain the optimal array element distribution. Experiments on standard test block demonstrate the DWSO algorithm has a narrower main lobe width and lower side lobe peaks than BPSO and GA. The imaging time of the sparse array constructed by this algorithm is reduced by more than 65% compared with the full array. As array scale grows, the running time variation of the proposed method is reduced by 73.2% and 77.3% compared with GA and BPSO, which has good adaptability and provides theoretical support for real-time defect detection.

Multi-layered medium ultrasonic phased array sparse TFM imaging based on self-adaptive differential evolution algorithm

Abstract Multilayer Composite material structures have been widely used in modern engineering fields. However, defects within these materials can adversely affect mechanical properties. Ultrasonic phased array total focusing method (TFM) imaging has advantages of high precision and dynamic focusing over the entire range, achieving significant progress in homogeneous medium detection. However, heavy computational burdens of multilayer structures lead to inefficient imaging. To address this issue, a sparse-TFM imaging algorithm using ultrasonic phased arrays suitable for multilayer media is proposed in this paper. This method constructs a fitness function with constraints such as main lobe width and sidelobe peak. Its objective is to obtain the distribution of sparse array element positions using an self-adaptive differential evolution algorithm. Subsequently, the delay time of each array element in multilayer media sparse TFM is calculated using the root mean square (RMS) principle and combined with amplitude weighting, the method corrects the imaging results. Compared with the Ray-based full-matrix capture and TFM method (Ray-based FMC/TFM), the RMS-based full-matrix capture and TFM (RMS-based FMC/TFM), and the phase shift method, the experimental and simulation results demonstrate that the proposed method significantly reduces the imaging data volume, improves computational efficiency, and maintains quantitative errors within 0.2 mm.

Synthesis of Non-Uniform Spiral Antenna with Low Peak Sidelobe Level Using Enhanced Harris Hawks Optimization Algorithm

Synthesis of Non-Uniform Spiral Antenna with Low Peak Sidelobe Level Using Enhanced Harris Hawks Optimization Algorithm

Minimum peak sidelobe ratio filter for MIMO radar via convex optimization

Multiple input multiple output (MIMO) radar retains transmitting freedom by transmitting multiple waveforms simultaneously. However, due to the cross-correlation between waveforms, the performance of range sidelobes is degraded. So, low sidelobes techniques are especially demanded in MIMO radar. This article proposes a mismatched filtering method for minimizing MIMO radar’s peak sidelobe level ratio (PSLR). In this method, we formulate the problem as a convex optimization (CO) problem and thus ensure its solution is globally optimal. To apply mismatched filters to MIMO radar, we introduce the traditional strategy and propose a new one. Instead of attempting to minimize all auto-correlations and cross-correlations of all waveforms as in the traditional strategy, our proposed strategy directly optimizes the mismatched filter of the synthesis signal for each considered direction. To verify the effectiveness of our method, numerical simulations are carried out. Firstly, the PSLR effects of those two strategies are compared. The results indicate that our proposed strategy dramatically improves the PSLR performance of MIMO radar compared to the traditional one. Then, the influences of several parameters on the PLSR performance are revealed, which indicates that our method is flexible enough to make trade-offs between PSLR and other parameters.

Planar array sparse distribution based on T-2GWO algorithm

Abstract This paper introduces an approach termed T-2GWO (Tent chaos-dual channel Grey Wolf Optimization) algorithm designed for achieving sparse distribution within constrained position ranges for planar arrays. The initial population is generated by using the Tent Chaos algorithm, modeling individual coordinate positions within the population as a two-channel matrix. Subsequently, the Grey Wolf Algorithm is employed for optimization purposes. The algorithm has verified its effectiveness under the assumption of uniform excitation and can obtain the minimum peak side lobe level, reaching peak side lobe levels of -25.78 dB and - 26.15 dB in the azimuth and pitch directions. Compared with the genetic algorithm, the reduction is 3.53 dB and 10.32 dB respectively. At the same time, better main beam radiation can be obtained.

Sparse Reconstruction-Based Joint Signal Processing for MIMO-OFDM-IM Integrated Radar and Communication Systems

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) technology is widely used in integrated radar and communication systems (IRCSs). Moreover, index modulation (IM) is a reliable OFDM transmission scheme in the field of communication, which transmits information by arranging several distinguishable constellations. In this paper, we propose a sparse reconstruction-based joint signal processing scheme for integrated MIMO-OFDM-IM systems. Combining the advantages of MIMO and OFDM-IM technologies, the integrated MIMO-OFDM-IM signal design is realized through the reasonable allocation of bits and subcarriers, resulting in better intercarrier interference (ICI) resistance and a higher transmission efficiency. Taking advantage of the sparseness of OFDM-IM, an improved target parameter estimation method based on sparse signal reconstruction is explored to eliminate the influence of empty subcarriers on the matched filtering at the receiver side. In addition, an improved sequential Monte Carlo signal detection method is introduced to realize the efficient detection of communication signals. The simulation results show that the proposed integrated system is 5 dB lower in the peak sidelobe ratio (PSLR) and 1.5 ×105 lower in the number of complex multiplications than the latest MIMO-OFDM system and can achieve almost the same parameter estimation performance. With the same spectral efficiency, it has a lower bit error rate (BER) than existing methods.

An efficient position optimization method based on improved genetic algorithm and machine learning for sparse array

Sparse array antennas are significant in communication and radar systems due to the advantages of high resolution, low complexity, and immunity to interference. In this letter, a novel array synthesis method based on an improved genetic algorithm (IGA) is proposed to optimize the antenna position in the sparse array. Different from the traditional generic methods with high computational complexity, a method combined with Gaussian process regression (GPR) prediction is proposed to optimize the antenna position with low computational complexity. The proposed method can minimize the peak sidelobe level (PSLL) during the beam scanning procedures. Moreover, the minimum antenna spacing constraint is also considered to ensure a physically achievable solution. Simulation results show that the proposed method can achieve a lower PSLL with fewer iterations and provide a global optimization solution for large array synthesis compared with existing methods. Furthermore, a prototype of the sparse array principle optimized by IGA is designed. Measurement results indicate that the optimized sparse array has good synthesis performance in high frequency.

Hybridization of almost difference sets and DFT technique for concentric circular antenna array thinning

This paper introduces a hybrid methodology for minimizing the peak sidelobe level (PSLL) in concentric circular antenna arrays (CCAAs). Traditionally, the almost difference set (ADS) approach has been utilized for determining active element positions, but its effectiveness in PSLL reduction is limited. To address this limitation, the proposed methodology integrates the ADS approach with the discrete Fourier transform (DFT) technique. This integration optimizes excitation amplitudes for activated elements within the ADS-based thinned array, resulting in significant PSLL reduction. Simulation results across various case studies demonstrate that the hybrid ADS-DFT method achieves lower PSLL values than existing thinning techniques while maintaining high computational efficiency. Additionally, electromagnetic (EM) simulations validate the efficiency of the proposed method, considering mutual coupling effects.

A Modified Frequency Nonlinear Chirp Scaling Algorithm for High-Speed High-Squint Synthetic Aperture Radar with Curved Trajectory

The imaging of high-speed high-squint synthetic aperture radar (HSHS-SAR), which is mounted on maneuvering platforms with curved trajectory, is a challenging task due to the existence of 3-D acceleration and the azimuth spatial variability of range migration and Doppler parameters. Although existing imaging algorithms based on linear range walk correction (LRWC) and nonlinear chirp scaling (NCS) can reduce the range–azimuth coupling of the frequency spectrum (FS) and the spatial variability of the Doppler parameter to some extent, they become invalid as the squint angle, speed, and resolution increase. Additionally, most of them ignore the effect of acceleration phase calibration (APC) on NCS, which should not be neglected as resolution increases. For these issues, a modified frequency nonlinear chirp scaling (MFNCS) algorithm is proposed in this paper. The proposed MFNCS algorithm mainly includes the following aspects. First, a more accurate approximation of range model (MAARM) is established to improve the accuracy of the instantaneous slant range history. Second, a preprocessing of the proposed algorithm based on the first range compression, LRWC, and a spatial-invariant APC (SIVAPC) is implemented to eliminate most of the effects of high-squint angle and 3-D acceleration on the FS. Third, a spatial-variant APC (SVAPC) is performed to remove azimuth spatial variability introduced by 3-D acceleration, and the range focusing is accomplished by the bulk range cell migration correction (BRCMC) and extended secondary range compression (ESRC). Fourth, the azimuth-dependent characteristics evaluation based on LRWC, SIVAPC, and SVAPC is completed to derive the MFNCS algorithm with fifth-order chirp scaling function for azimuth compression. Consequently, the final image is focused on the range time and azimuth frequency domain. The experimental simulation results verify the effectiveness of the proposed algorithm. With a curved trajectory, HSHS-SAR imaging is carried out at a 50° geometric squint angle and 500 m × 500 m imaging width. The integrated sidelobe ratio and peak sidelobe ratio of the point targets at the scenario edges approach the theoretical values, and the range-azimuth resolution is 1.5 m × 3.0 m.

Two-phase optimization of binary sequences with low peak sidelobe level value

The search for binary sequences with low peak sidelobe level value represents a formidable computational problem. To locate better sequences for this problem, we designed a stochastic algorithm that uses two fitness functions. In these fitness functions, the value of the autocorrelation function has a different impact on the final fitness value. It is defined with the value of the exponent over the autocorrelation function values. Each function is used in the corresponding optimization phase, and the optimization process switches between these two phases until the stopping condition is satisfied. The proposed algorithm was implemented using the compute unified device architecture and therefore allowed us to exploit the computational power of graphics processing units. This algorithm was tested on sequences with lengths L=2m−1, for 14≤m≤20. From the obtained results it is evident that the two-phase optimization improved the efficiency of the algorithm significantly, the solver speed is increased significantly by using graphics processing units, new-best known solutions were achieved, and the achieved peak sidelobe level values were significantly less than L.