Low Autocorrelation Research Articles

On the Cryptanalysis of a Simplified AES Using a Hybrid Binary Grey Wolf Optimization

Cryptosystem cryptanalysis is regarded as an NP-Hard task in modern cryptography. Due to block ciphers that are part of a modern cipher and have nonlinearity and low autocorrelation in their structure, traditional techniques and brute-force attacks suffer from breaking the key presented in traditional techniques, and brute-force attacks against modern cipher S-AES (simplified-advanced encryption standard) are complex. Thus, developing robust and reliable optimization with high searching capability is essential. Motivated by this, this paper attempts to present a novel binary hybridization algorithm based on the mathematical procedures of the grey wolf optimizer (GWO) and particle swarm optimization (PSO), named BPSOGWO, to deal with the cryptanalysis of (S-AES). The proposed BPSOGWO employs a known plaintext attack that requires only one pair of plaintext–ciphertext pairs instead of other strategies that require more pairs (i.e., it reduces the number of messages needed in an attack, and secret information such as plaintext-ciphertext pairs cannot be obtained easily). The comprehensive and statistical results indicate that the BPSOGWO is more accurate and provides superior results compared to other peers, where it improved the cryptanalysis accurateness of S-AES by 82.5%, 84.79%, and 79.6% compared to PSO, GA, and ACO, respectively. Furthermore, the proposed BPSOGWO retrieves the optimal key with a significant reduction in search space compared to a brute-force attack. Experiments show that combining the suggested fitness function with HPSOGWO resulted in a 109-fold reduction in the search space. In cryptanalysis, this is a significant factor. The results prove that BPSOGWO is a promising and effective alternative to attack the key employed in the S-AES cipher.

A Smartphone-Enabled Imaging Device for Chromotropic Acid-Based Measurement of Nitrate in Soil Samples.

In this study, a novel chromotropic acid-based color development method was proposed for quick estimation of soil nitrate (NO3-). The method utilized a 3D printed device integrated with the rear-end camera of a smartphone and a stand-alone application called SMART NP. By analyzing the mean Value (V) component of the sample's image, the SMART NP provides instant predictions of soil NO3- levels. The limit of detection was calculated as 0.1 mg L-1 with a sensitivity of 0.26 mg L-1. The device showed a % bias of 0.9% and a precision of 1.95%, indicating its reliability. Additionally, the device-predicted soil NO3- data, combined with kriging interpolation, showcased spatial variability in soil NO3- levels at the regional level. The study employed a Gaussian model of variogram for kriging, and the high Nugget/Sill ratio indicated low spatial autocorrelation, emphasizing the impact of management factors on the spatial distribution of soil NO3- content in the study area. Overall, the imaging device, along with geostatistical interpolation, provided a comprehensive solution for the rapid assessment of spatial variability in soil NO3-content.

Efficient binary sequence set designs for MIMO PMCW radar

Multiple-input multiple-output (MIMO) phase-modulated continuous wave (PMCW) radar systems have major advantages for mutual-interference mitigation over their linear frequency-modulated continuous wave (LFMCW) counterparts. To attain a virtual array length of M×M̃ with M transmit and M̃ receive antennas, a probing sequence set with both low auto-correlation sidelobes and low cross-correlations is needed for MIMO PMCW radar. To reduce the hardware cost of sequence generation, binary probing sequence (BPS) sets are desirable. We present herein four efficient computational algorithms to generate BPS sets with desirable properties, and arbitrary sequence lengths and arbitrary number of sequences in the set are allowed. When initialized by random BPS sets, they can be used to obtain many different BPS sets with similar properties, i.e., with large diversity. Moreover, fast Fourier transform (FFT) are exploited, making the algorithms computationally efficient. They can be used to design long BPS sets that are hardly possible using existing methods in the literature. The convergence properties and the relationships among the proposed algorithms are also analyzed under a unified framework. Numerical examples are included to illustrate the effectiveness of our designs.

How to better estimate bunch number at vineyard level?

Despite the extensive use of sampling to estimate the average number of grape bunches per vine, there is no clearly established sampling protocol that can be used as a reference when performing these estimations. Each practitioner therefore has their own sampling protocol. This study characterised the effect of differences between sampling protocols in terms of estimation errors. The goal was to identify the most efficient practices that will improve the early estimation of an important yield component: average bunch number. First, the appropriateness of including non-productive vines (i.e., dead and missing vines) in the sampling protocol was tested; the objective was to determine whether it is relevant to estimate two yield components simultaneously. Second, sampling protocols with sampling sites of varying size were compared to determine how the spatial distribution of observations and potential spatial autocorrelation affect estimation error. Third, a new confidence interval for estimation error was determined to express expected error as a percentage. It aimed at designing a new tool for finding the best sample size in an operational context. Tests were performed on two vineyards in the South of France, in which the number of bunches per vine had been exhaustively determined on all the plants before flowering. The results show that the simultaneous estimation of number of bunches and proportion of dead and missing vines increased the estimation errors by a factor of 2. Despite the low spatial autocorrelation of bunch number, the results show that the observation must be spread across at least 2 or 3 sampling sites to reduce estimation errors. Finally, the confidence intervals expressed as a percentage were validated and used to define an adequate sample size based on a compromise between the expected precision and the variability observed in the first measurements.

Climate–Growth Relationships in Laurus azorica—A Dominant Tree in the Azorean Laurel Forest

Forests on oceanic islands, such as the Azores archipelago, enable interesting dendroclimatic research, given their pronounced climatic gradients over short geographical distances, despite the less pronounced seasonality. The Lauraceae play an essential ecological role in Macaronesian natural forests. An example is Laurus azorica (Seub.) Franco, a relevant species given its high frequency and physiognomic dominance in Azorean laurel forests. This study aims to quantify climate–growth relationships in L. azorica using a dendroecological approach. We sampled four stands at São Miguel and two stands at Terceira islands, for a total of 206 trees. Following standard dendrochronological methods and rigorous sample selection procedures, we obtained relatively low rbar values and high temporal autocorrelation. Using a stepwise Random Forest analysis followed by Generalized Linear Models calculation, we found prominent effects of present and previous year temperature, but a low precipitation signal on growth rings, with some model variation between stands. Our results agreed with previous observations for broad-leaved species with diffuse porous wood, contributing to increase the baseline dendroecological knowledge about Azorean forests. Due to the high levels of within- and between-stand variation, and to refine the climatic signal analysis, complementary approaches should be explored in the future.

Evaluation of Geological Hazard Susceptibility Based on the Regional Division Information Value Method

The traditional susceptibility evaluation of geological hazards usually comprises a global susceptibility evaluation of the entire study area but ignores the differences between the local areas caused by spatial non-stationarity. In view of this, the geographically weighted regression model (GWR) was used to divide the study area at regional scale. Seven local areas were obtained with low spatial auto-correlation of each evaluation factor. Additionally, 11 evaluation factors, including the aspect, elevation, curvature, ground roughness, relief amplitude, slope, lithology, distance from the fault, height of the cut slope, multiyear average rainfall and the normalized difference vegetation index (NDVI) were selected to establish the evaluation index system of the geological hazard susceptibility. The Pearson coefficient was used to remove the evaluation factors with high correlation. The global and seven local areas were evaluated for susceptibility using the information value model and the global and regional division susceptibility evaluation results were obtained. The results show that the regional division information value model had better prediction performance (AUC = 0.893) and better accuracy. This model adequately considers the influence of the geological hazard impact factors in the different local areas on geological hazard susceptibility and weakens the influence of some factors that have higher influence in the global model but lower influence in local areas on the evaluation results. Therefore, the use of the regional division information value model for susceptibility evaluation is more consistent with the actual situation in the study area and is more suitable for guiding risk management and hazard prevention and mitigation.

Predistortion for Very Low Pulse-Compression Sidelobes in Solid-State Meteorological Radar

In this paper, we address the problem of combating the effect of the nonlinearities of the transmit-receive chain in solid-state radars, which are typically generated by the power amplifier in transmission. This issue is of particular importance in meteorological radar applications, as they require transmit pulses featuring very low autocorrelation sidelobes, which are significantly raised due to nonlinearities. In this work, we describe an adaptive predistortion design method based on an iterative approach in the spectral domain. The proposed method has been validated by means of hardware using a customized software radio and driving an amplifier into saturation. Two different ideal, highly performing pulse compression waveforms, expressly designed for weather radar applications, were sent to its input. The experimental results demonstrate the benefits of the proposed approach to design the digital predistortion module.

An Unambiguity and Anti-Range Eclipse Method for PD Radar Using Biphase Coded Signals

Target detection is an important research content in the radar field. At present, efforts are being made to optimize the precision of detection information. In this paper, we use the high pulse repetition frequency (HPRF) transmission method and orthogonal biphase coded signals in each pulse to avoid velocity ambiguity and range ambiguity of radar detection. In addition, We also apply Walsh matrix and genetic algorithm (GA) to generate satisfying orthogonal biphase coded signals with low auto-correlation sidelobe peak and cross-correlation peak, which make the results more accurate. In a radar receiver, data rearrangement of echo signals is performed, and then pulse compression and moving target detection (MTD) are utilized to get the final velocity and range information of a target without velocity ambiguity and range ambiguity. Besides, a small transmitting pulse time width is adopted to reduce the working blind area, and two different high pulse repetition frequencies (HPRFs) are adopted to solve the problem of range eclipse. Simulation results finally prove the effectiveness and feasibility of the proposed method.

Computational Search of Long Skew-symmetric Binary Sequences with High Merit Factors

In this paper, we present a computational search for best-known merit factors of longer binary sequences with an odd length. Finding low autocorrelation binary sequences with optimal or suboptimal merit factors is a very difficult optimization problem. An improved version of the heuristic algorithm is presented and tackled to search for aperiodic binary sequences with good autocorrelation properties. High-performance computations with the execution of our stochastic algorithmto search skew-symmetric binary sequences with high merit factors. After experimental work, as results, we present new binary sequences with odd lengths between 201 and 303 that are skew-symmetric and have the merit factor F greater than 8.5. Moreover, an example of a binary sequence having F > 8 has been found for all odd lengths between 201 and 303. The longest binary sequence with F > 9 found to date is of length 255.

Selection of random coefficients in ordered response models: a framework to detect heterogeneity in household surveys

This paper develops a Bayesian method to detect heterogeneity in the relationship between covariates and the outcome in models with ordered responses. To this end, we construct an efficient Markov chain Monte Carlo algorithm for a hierarchical Bayesian model that selects random coefficients in ordered models. This method extends an approach for selecting random coefficients in linear mixed models into the ordered setting by adding two enhancements that are relevant to the latter category of models. First, we construct steps to efficiently estimate cut-points by addressing identification and ordering constraints. Second, we develop a framework to evaluate marginal effects that combine the fixed and random effects of each covariate. The marginal effects additionally allow for model uncertainty by averaging across models visited by the selection algorithm. Simulation studies demonstrate that this method detects random effects when they are present, estimates parameters accurately and efficiently samples from the posterior with low autocorrelations across successive draws. On applying this method on data from the survey of consumer expectations, we find clear support for the presence of household-level heterogeneity in relationships between demographic variables, and current as well as expected financial conditions.

Exploring links between climatic predictability and the evolution of within- and transgenerational plasticity.

In variable environments, phenotypic plasticity can increase fitness by providing tight environment-phenotype matching. However, adaptive plasticity is expected to evolve only when the future selective environment can be predicted based on the prevailing conditions. That is, the juvenile environment should be predictive of the adult environment (within-generation plasticity) or the parental environment should be predictive of the offspring environment (transgenerational plasticity). Moreover, the environmental predictability can also shape transient responses such as stress response in an adaptive direction. Here, we test links between environmental predictability and the evolution of adaptive plasticity by combining time series analyses and a common garden experiment using temperature as a stressor in a temperate butterfly (Melitaea cinxia). Time series analyses revealed that across season fluctuations in temperature over 48 years are overall predictable. However, within the growing season, temperature fluctuations showed high heterogeneity across years with low autocorrelations and the timing of temperature peaks were asynchronous. Most life-history traits showed strong within-generation plasticity for temperature and traits such as body size and growth rate broke the temperature-size rule. Evidence for transgenerational plasticity, however, was weak and detected for only two traits each in an adaptive and non-adaptive direction. We suggest that the low predictability of temperature fluctuations within the growing season likely disfavors the evolution of adaptive transgenerational plasticity but instead favors strong within-generation plasticity.

Peak Side Lobe Reduction analysis of NLFM and Improved NLFM Radar signal

Signal design is an important component for good performance of radar systems. Here, the problem of determining a good radar signal with the objective of minimizing autocorrelation sidelobes is addressed. Linear Frequency Modulated signal (LFM) is frequently used pulse compression waveform. But using LFM makes it necessary to use weighting techniques in order to reduce the side lobes which considerably decrease the SNR. At the radar receiver, the LFM signal results in first side lobe of -13dB corresponding to the main lobe. This may reduce the resultant SNR usually by 1 or 2dB. In this paper, an attempt is done to design a radar signal to attain low autocorrelation Peak Side Lobe Ratio (PSLR) which exhibits high SNR and high range resolution. In order to maintain high SNR and to get suppressed side lobes of the signal, one can use Non-Linear Frequency Modulation (NLFM). A NLFM signal is designed for different time sweeps called as Improved NLFM. In Improved NLFM the total time sweep and bandwidth are divided into Two Stage and Tri-Stage sweeps NLFM. The quality of the overall best radar signal is assessed through the parameter PSLR for different values of BT. From this analysis, it is found that the tri stage NLFM gives the low PSLR up to -17.62dB. Simulation results shows the reduction in the sidelobe level from LFM to NLFM and in Improved two and Tri-Stage NLFM.

Spatial-temporal variations and influencing factors of air quality in China's major cities during COVID-19 lockdown.

To control the spread of COVID-19, the Chinese government announced a "lockdown" policy, and the citizens' activities were restricted. This study selected three standard air quality indexes, AQI, PM2.5, and PM10, of 2017-2021 in 40 major cities in six regions in China to analyze their changes, spatial-temporal distributions, and socioeconomic influencing factors. Compared with 2019, the values of AQI, PM2.5, and PM10 decreased, and the days with AQI levels "AQI ≤ 100" increased during the "lockdown" in 2020. Due to different degrees of industrialization, the concentration of air pollutants shows significant regional characteristics. The AQI values before and after the "lockdown" in 2020 show significant spatial autocorrelation, and the cities' AQI values in the north present high autocorrelation, and the cities in the south are in low autocorrelation. From the data at the national level, carbon emission intensity (CEI), per capita energy consumption (PEC), per capita GDP (PCG), industrialization rate (IR), and proportion of construction value added (PCVA) have the greatest impact on AQI. This study gives regulators confidence that if the government implements regionalized air quality improvement policies according to the characteristics of each region in China and reasonably plans socioeconomic activities, it is expected to improve China's air quality sustainably.

Evolution Mode, Influencing Factors, and Socioeconomic Value of Urban Industrial Land Management in China

(1) Background: Accurate measurement of the matching relationship between urban industrial land change and economic growth is of great value for industrialized and re-industrialized countries to perform land resource management in territorial spatial planning. (2) Methods: Based on the combination of the Boston Consulting Group matrix, Geodetector, and decoupling model, we constructed a new method integrating “model evolution + driving mechanism + performance evaluation + policy design” in this paper, and conducted an empirical study on the economic value of urban industrial land management in the Yangtze River Delta. (3) Results: The evolution modes of urban industrial land in the Yangtze River Delta are divided into four types: stars, cows, dogs, and question, distributed in structures ranging from an “olive” shape to a “pyramid” shape, with high spatial heterogeneity and agglomeration and low autocorrelation. The government demand led by driving economic growth and making large cities bigger is the key factor driving the change in urban industrial land and the influence of transportation infrastructure and the business environment has remained stable for a long time. The mechanisms of industrialization, globalization, and innovation are becoming increasingly complicated. Industrial land change and value-added growth in most cities have long been in a state of strong and weak decoupling, with progressive decoupling occurring alongside the unchanged stage and regressive decoupling. The government outperforms the market in terms of urban industrial land management, and the degradation of the synergy between urban industrial land and corporate assets emerges as a new threat to sustainable and high-quality development of the region. (4) Conclusions: This paper establishes a technical framework for zoning management and classification governance of urban industrial land to divide the Yangtze River Delta into reduction-oriented transformation policy zoning, incremental high-quality development zoning, incremental synchronous growth zoning, and reduction and upgrading development zoning. It also proposes an adaptive land supply governance strategy for quantitative and qualitative control, providing a basis for territorial spatial planning and land resource management.

M-Ary Character-Based Sequences of Length pq With Low Autocorrelation

Using elementary properties of primitive multiplicative characters we derive the autocorrelation of a unimodular <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> -ary character-based sequence of length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$pq$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> are distinct odd primes. Subsequently: (1) we identify three new sets of twin-prime quadriphase sequences with the largest autocorrelation sidelobe magnitude equal to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sqrt {5}$ </tex-math></inline-formula> or 3, (2) we show that the smallest maximum autocorrelation sidelobe magnitude of an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> -ary character-based sequence, when <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4|M$ </tex-math></inline-formula> , either decreases with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> and tends to 2, when <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6\nmid M$ </tex-math></inline-formula> , or is equal to 2, when <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6|M$ </tex-math></inline-formula> , and (3), we show that the smallest maximum autocorrelation sidelobe magnitude of a full-alphabet <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> -ary character-based sequence, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M&gt;2$ </tex-math></inline-formula> , is greater or equal to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sqrt {3}$ </tex-math></inline-formula> , and, in particular, is equal to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sqrt {3}$ </tex-math></inline-formula> for the sextic phase sequence.

Counteracting the dynamic degradation of high-dimensional digital chaotic systems via a stochastic jump mechanism

To analyze the periodic distribution and state-mapping structures of high-dimensional digital chaotic systems in fixed-point arithmetic domain. A chaotic cycle-finding algorithm (CCFA) can be designed to precisely calculate the period length of the orbit of digital chaos under different finite calculation accuracy. Furthermore, to test the versatility and effectiveness of our proposed algorithm, the periodic distribution and security of the digital Sprott D system are analyzed in detail. Numerical simulation results show that the chaotic system have potential security risks, such as short period and low sequence complexity. In addition, we put forward a new resistance method based on automatic cycle detection and random jump of chaotic trajectories for the objective of enhancing the dynamical properties of high-dimensional digital chaotic systems. This scheme can construct a new chaotic model with many desirable properties, such as long period, high state-space utilization, low autocorrelation, and good ergodicity.

Some new classes of quaternary sequences with low autocorrelation property via two binary cyclotomic sequences

Some new classes of quaternary sequences with low autocorrelation property via two binary cyclotomic sequences

Seasonal forecasting of lake water quality and algal bloom risk using a continuous Gaussian Bayesian network

Abstract. Freshwater management is challenging, and advance warning that poor water quality was likely, a season ahead, could allow for preventative measures to be put in place. To this end, we developed a Bayesian network (BN) for seasonal lake water quality prediction. BNs have become popular in recent years, but the vast majority are discrete. Here, we developed a Gaussian Bayesian network (GBN), a simple class of continuous BN. The aim was to forecast, in spring, mean total phosphorus (TP) and chlorophyll a (chl a) concentration, mean water colour, and maximum cyanobacteria biovolume for the upcoming growing season (May–October) in Vansjø, a shallow nutrient-rich lake in southeastern Norway. To develop the model, we first identified controls on interannual variability in seasonally aggregated water quality. These variables were then included in a GBN, and conditional probability densities were fit using observations (≤39 years). GBN predictions had R2 values of 0.37 (chl a) to 0.75 (colour) and classification errors of 32 % (TP) to 17 % (cyanobacteria). For all but lake colour, including weather variables did not improve the predictive performance (assessed through cross-validation). Overall, we found the GBN approach to be well suited to seasonal water quality forecasting. It was straightforward to produce probabilistic predictions, including the probability of exceeding management-relevant thresholds. The GBN could be sensibly parameterised using only the observed data, despite the small dataset. Developing a comparable discrete BN was much more subjective and time-consuming. Although low interannual variability and high temporal autocorrelation in the study lake meant the GBN performed only slightly better than a seasonal naïve forecast (where the forecasted value is simply the value observed the previous growing season), we believe that the forecasting approach presented here could be particularly useful in areas with higher sensitivity to catchment nutrient delivery and seasonal climate and for forecasting at shorter (daily or monthly) timescales. Despite the parametric constraints of GBNs, their simplicity, together with the relative accessibility of BN software with GBN handling, means they are a good first choice for BN development with continuous variables.

Computationally Efficient Long Binary Sequence Designs With Low Autocorrelation Sidelobes

The design of binary sequences with low autocorrelation sidelobes is critically important to diverse applications, including radar and sonar as well as communications. Many computational methods have been proposed recently to design binary sequences through optimizing the integrated sidelobe level (ISL), which is a commonly used optimization metric. However, due to the high computational complexities, few of them can be used for long binary sequence designs, which are needed in, for example, low-cost commercial automotive radar systems. To improve the performance of the recently introduced fast Fourier transform (FFT) based CANARY algorithm for long aperiodic binary sequence designs, we introduce a new penalty function into the ISL metric and also propose a new framework of double-loop iterations. These new ideas are also extended to design long periodic binary sequences with low autocorrelation sidelobes and long aperiodic and periodic binary sequences with low correlation zones. The associated cost functions are still optimized efficiently via FFT-based iterations, making the proposed algorithms computationally efficient and, hence, suitable for long binary sequence designs. Moreover, the convergence properties and the computational complexities of these algorithms are analyzed. Numerical examples are provided to demonstrate the effectiveness of the proposed algorithms.

A Multi-Objective Quantum Genetic Algorithm for MIMO Radar Waveform Design

Aiming at maximizing waveform diversity gain when designing a phase-coded multiple-input multiple-output (MIMO) radar waveform set, it is desirable that all waveforms are orthogonal to each other. Hence, the lowest possible peak cross-correlation ratio (PCCR) is expected. Meanwhile, low peak auto-correlation side-lobe ratio (PASR) is needed for good detection performance. However, it is difficult to obtain a closed form solution to the waveform set from the expected values of the PASR and PCCR. In this paper, the waveform set design problem is modeled as a multi-objective, NP-hard constrained optimization problem. Unlike conventional approaches that design the waveform set through optimizing a weighted sum objective function, the proposed optimization model evaluates the performance of multi-objective functions based on Pareto level and obtains a set of Pareto non-dominated solutions. That means that the MIMO radar system can trade off each objective function for different requirements. To solve this problem, this paper presents a multi-objective quantum genetic algorithm (MoQGA) based on the framework of quantum genetic algorithm. A new population update strategy for the MoQGA is designed based on the proposed model. Compared to the state-of-the-art methods, like BiST and Multi-CAN, the PASR and PCCR metrics of the waveform set are 0.95–3.91 dB lower with the parameters of the numerical simulation. The MoQGA is able to minimize PASR and PCCR of the MIMO radar waveform set simultaneously.