Pub Date : 2024-11-11DOI: 10.1109/TSP.2024.3495696
Runhao Shi;Daniel P. Palomar
Strongly Adaptive meta-algorithms (SA-meta) are popular in online portfolio selection due to their resilience in adversarial environments and adaptability to market changes. However, their application is often limited by high variance in errors, stemming from calculations over small intervals with limited observations. To address this limitation, we introduce the Strongly Adaptive Optimistic Follow-the-Regularized-Leader (SAOFTRL), an advanced framework that integrates the Optimistic Follow-the-Regularized-Leader (OFTRL) strategy into SA-meta algorithms to stabilize performance. SAOFTRL is distinguished by its novel regret bound, which provides a theoretical guarantee of worst-case performance in challenging scenarios. Additionally, we reimagine SAOFTRL within a mean-variance portfolio (MVP) framework, enhanced with shrinkage estimators and adaptive rolling windows, thereby ensuring reliable average-case performance. For practical deployment, we present an efficient SAOFTRL implementation utilizing the Successive Convex Approximation (SCA) method. Empirical evaluations demonstrate SAOFTRL's superior performance and expedited convergence when compared to existing benchmarks, confirming its effectiveness and efficiency in dynamic market conditions.
{"title":"SAOFTRL: A Novel Adaptive Algorithmic Framework for Enhancing Online Portfolio Selection","authors":"Runhao Shi;Daniel P. Palomar","doi":"10.1109/TSP.2024.3495696","DOIUrl":"10.1109/TSP.2024.3495696","url":null,"abstract":"Strongly Adaptive meta-algorithms (SA-meta) are popular in online portfolio selection due to their resilience in adversarial environments and adaptability to market changes. However, their application is often limited by high variance in errors, stemming from calculations over small intervals with limited observations. To address this limitation, we introduce the Strongly Adaptive Optimistic Follow-the-Regularized-Leader (SAOFTRL), an advanced framework that integrates the Optimistic Follow-the-Regularized-Leader (OFTRL) strategy into SA-meta algorithms to stabilize performance. SAOFTRL is distinguished by its novel regret bound, which provides a theoretical guarantee of worst-case performance in challenging scenarios. Additionally, we reimagine SAOFTRL within a mean-variance portfolio (MVP) framework, enhanced with shrinkage estimators and adaptive rolling windows, thereby ensuring reliable average-case performance. For practical deployment, we present an efficient SAOFTRL implementation utilizing the Successive Convex Approximation (SCA) method. Empirical evaluations demonstrate SAOFTRL's superior performance and expedited convergence when compared to existing benchmarks, confirming its effectiveness and efficiency in dynamic market conditions.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"5291-5305"},"PeriodicalIF":4.6,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142599330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-07DOI: 10.1109/tsp.2024.3493603
Jakob Möderl, Erik Leitinger, Franz Pernkopf, Klaus Witrisal
{"title":"Variational Inference of Structured Line Spectra Exploiting Group-Sparsity","authors":"Jakob Möderl, Erik Leitinger, Franz Pernkopf, Klaus Witrisal","doi":"10.1109/tsp.2024.3493603","DOIUrl":"https://doi.org/10.1109/tsp.2024.3493603","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"6 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142597757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-06DOI: 10.1109/TSP.2024.3492692
Tingting Zhang;Sergiy A. Vorobyov;Feng Xu
We present a novel slow-time transmit beamspace (TB) multiple-input multiple-output (MIMO) technique for L-shaped array radar with uniform linear subarrays to estimate target parameters including 2-dimensional (2-D) directions of arrival (DOA) and unambiguous velocity. Doppler division multiple access (DDMA) approach, as a type of slow-time waveform achieving waveform orthogonality across multiple pulses within a coherent processing interval, disperses the transmit energy over the entire spatial region, suffering from beam-shape loss. Moreover, Doppler spectrum division, which is necessary for transmit channel separation prior to parameter estimation, leads to the loss of crucial information for velocity disambiguation. To optimize transmit energy distribution, slow-time TB technique is proposed to focus the energy within a desired spatial region. Unlike DDMA approach, slow-time TB technique divides the entire Doppler spectrum into more subbands than the number of transmit antenna elements to narrow down the beam mainlobe intervals between adjacent beams formed by DDMA modulation vectors. As a result, more beams are incorporated into the region of interest, and slow-time TB radar can direct transmit energy to the region of interest by properly selecting the DDMA modulation vectors whose beams are directed there. To resolve velocity ambiguity, tensor signal modeling, by storing measurements in a tensor without Doppler spectrum division, is used. Parameter estimation is then addressed using canonical polyadic decomposition (CPD), and the performance of slow-time TB L-shaped MIMO radar is shown to be improved as compared to DDMA MIMO techniques. Simulations are conducted to validate the proposed method.
我们为带有均匀线性子阵列的 L 形阵列雷达提出了一种新型慢时发射波束空间（TB）多输入多输出（MIMO）技术，用于估计目标参数，包括二维（2-D）到达方向（DOA）和明确的速度。多普勒频分多址（DDMA）方法作为一种慢时波形，可在一个相干处理间隔内通过多个脉冲实现波形正交，但会将发射能量分散到整个空间区域，从而造成波束形状损失。此外，多普勒频谱划分对于参数估计前的发射信道分离十分必要，但却会导致速度消歧的关键信息丢失。为了优化发射能量分布，提出了慢速 TB 技术，将能量集中在所需的空间区域内。与 DDMA 方法不同，慢时 TB 技术将整个多普勒频谱划分为比发射天线元件数量更多的子带，以缩小由 DDMA 调制矢量形成的相邻波束之间的波束主间隔。因此，更多波束被纳入感兴趣区域，慢时 TB 雷达可通过适当选择波束指向感兴趣区域的 DDMA 调制矢量，将发射能量导向感兴趣区域。为了解决速度模糊性问题，采用了张量信号建模，将测量数据存储在张量中，而不进行多普勒频谱划分。然后使用规范多义分解（CPD）进行参数估计，结果表明，与 DDMA MIMO 技术相比，慢时 TB L 型 MIMO 雷达的性能有所提高。仿真验证了所提出的方法。
{"title":"Transmit Energy Focusing for Parameter Estimation in Slow-Time Transmit Beamspace L-Shaped MIMO Radar","authors":"Tingting Zhang;Sergiy A. Vorobyov;Feng Xu","doi":"10.1109/TSP.2024.3492692","DOIUrl":"10.1109/TSP.2024.3492692","url":null,"abstract":"We present a novel slow-time transmit beamspace (TB) multiple-input multiple-output (MIMO) technique for L-shaped array radar with uniform linear subarrays to estimate target parameters including 2-dimensional (2-D) directions of arrival (DOA) and unambiguous velocity. Doppler division multiple access (DDMA) approach, as a type of slow-time waveform achieving waveform orthogonality across multiple pulses within a coherent processing interval, disperses the transmit energy over the entire spatial region, suffering from beam-shape loss. Moreover, Doppler spectrum division, which is necessary for transmit channel separation prior to parameter estimation, leads to the loss of crucial information for velocity disambiguation. To optimize transmit energy distribution, slow-time TB technique is proposed to focus the energy within a desired spatial region. Unlike DDMA approach, slow-time TB technique divides the entire Doppler spectrum into more subbands than the number of transmit antenna elements to narrow down the beam mainlobe intervals between adjacent beams formed by DDMA modulation vectors. As a result, more beams are incorporated into the region of interest, and slow-time TB radar can direct transmit energy to the region of interest by properly selecting the DDMA modulation vectors whose beams are directed there. To resolve velocity ambiguity, tensor signal modeling, by storing measurements in a tensor without Doppler spectrum division, is used. Parameter estimation is then addressed using canonical polyadic decomposition (CPD), and the performance of slow-time TB L-shaped MIMO radar is shown to be improved as compared to DDMA MIMO techniques. Simulations are conducted to validate the proposed method.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"5228-5243"},"PeriodicalIF":4.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10746379","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Polarization Diversity Detection and Localization of a Target With Energy Spillover","authors":"Naixin Kang, Weijian Liu, Jun Liu, Chengpeng Hao, Xiaotao Huang, Zheran Shang","doi":"10.1109/tsp.2024.3490844","DOIUrl":"https://doi.org/10.1109/tsp.2024.3490844","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"33 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-05DOI: 10.1109/TSP.2024.3491899
Yumeng Zhang;Sundar Aditya;Bruno Clerckx
Orthogonal frequency division multiplexing (OFDM) has been widely adopted in dual-function radar-communication (DFRC) systems. However, with random communication symbols (CS) embedded in the DFRC waveform, the transmit signal has a random ambiguity function that affects the radar's delay-Doppler estimation performance, which has not been well explored. This paper addresses this gap by first characterizing the outlier probability (OP) – the probability of incorrectly estimating a target's (on-grid) delay-Doppler bin – in OFDM DFRC for any given CS realization. This subsequently motivates the OFDM DFRC waveform design problem of minimizing the OP w.r.t the CS probability distribution (i.e., the input distribution ). Conditioned on the CSs, the OP only depends on the CS magnitudes. Hence, we consider the following two schemes for the above optimization: CSs with (1) constant magnitude input distribution (phase shift keying), and (2) variable magnitude input distribution (Gaussian). For (1), minimizing the OP reduces to the familiar power allocation design across OFDM's subcarriers and symbols, with uniform power allocation across OFDM subcarriers and a windowed power allocation across OFDM symbols being near-optimal. For (2), the mean and variance of the Gaussian distribution at each subcarrier is optimized, with an additional communication constraint to avoid the zero-variance solution where no CSs are carried. We observe that subcarriers with strong communication channels feature a large variance (favour communications) while the others are characterized by a large mean (favour radar). However, the overall power allocation (i.e., the sum of the squared mean and variance) across the OFDM subcarriers and symbols is similar to (1). Simulations for (2) show that while random CS magnitudes benefit communications, they degrade radar performance, but this can be mitigated using our optimized input distribution.
{"title":"Input Distribution Optimization in OFDM Dual-Function Radar-Communication Systems","authors":"Yumeng Zhang;Sundar Aditya;Bruno Clerckx","doi":"10.1109/TSP.2024.3491899","DOIUrl":"10.1109/TSP.2024.3491899","url":null,"abstract":"Orthogonal frequency division multiplexing (OFDM) has been widely adopted in dual-function radar-communication (DFRC) systems. However, with random communication symbols (CS) embedded in the DFRC waveform, the transmit signal has a random ambiguity function that affects the radar's delay-Doppler estimation performance, which has not been well explored. This paper addresses this gap by first characterizing the outlier probability (OP) – the probability of incorrectly estimating a target's (on-grid) delay-Doppler bin – in OFDM DFRC for any given CS realization. This subsequently motivates the OFDM DFRC waveform design problem of minimizing the OP w.r.t the CS probability distribution (i.e., the \u0000<italic>input distribution</i>\u0000). Conditioned on the CSs, the OP only depends on the CS magnitudes. Hence, we consider the following two schemes for the above optimization: CSs with (1) constant magnitude input distribution (phase shift keying), and (2) variable magnitude input distribution (Gaussian). For (1), minimizing the OP reduces to the familiar power allocation design across OFDM's subcarriers and symbols, with uniform power allocation across OFDM subcarriers and a \u0000<italic>windowed</i>\u0000 power allocation across OFDM symbols being near-optimal. For (2), the mean and variance of the Gaussian distribution at each subcarrier is optimized, with an additional communication constraint to avoid the zero-variance solution where no CSs are carried. We observe that subcarriers with strong communication channels feature a large variance (favour communications) while the others are characterized by a large mean (favour radar). However, the overall power allocation (i.e., the sum of the squared mean and variance) across the OFDM subcarriers and symbols is similar to (1). Simulations for (2) show that while random CS magnitudes benefit communications, they degrade radar performance, but this can be mitigated using our optimized input distribution.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"5258-5273"},"PeriodicalIF":4.6,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142588772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-05DOI: 10.1109/TSP.2024.3491993
Seyed Mohammad Azimi-Abarghouyi;Lav R. Varshney
This paper introduces a federated learning framework that enables over-the-air computation via digital communications, using a new joint source-channel coding scheme. Without relying on channel state information at devices, this scheme employs lattice codes to both quantize model parameters and exploit interference from the devices. We propose a novel receiver structure at the server, designed to reliably decode an integer combination of the quantized model parameters as a lattice point for the purpose of aggregation. We present a mathematical approach to derive a convergence bound for the proposed scheme and offer design remarks. In this context, we suggest an aggregation metric and a corresponding algorithm to determine effective integer coefficients for the aggregation in each communication round. Our results illustrate that, regardless of channel dynamics and data heterogeneity, our scheme consistently delivers superior learning accuracy across various parameters and markedly surpasses other over-the-air methodologies.
{"title":"Compute-Update Federated Learning: A Lattice Coding Approach","authors":"Seyed Mohammad Azimi-Abarghouyi;Lav R. Varshney","doi":"10.1109/TSP.2024.3491993","DOIUrl":"10.1109/TSP.2024.3491993","url":null,"abstract":"This paper introduces a federated learning framework that enables over-the-air computation via digital communications, using a new joint source-channel coding scheme. Without relying on channel state information at devices, this scheme employs lattice codes to both quantize model parameters and exploit interference from the devices. We propose a novel receiver structure at the server, designed to reliably decode an integer combination of the quantized model parameters as a lattice point for the purpose of aggregation. We present a mathematical approach to derive a convergence bound for the proposed scheme and offer design remarks. In this context, we suggest an aggregation metric and a corresponding algorithm to determine effective integer coefficients for the aggregation in each communication round. Our results illustrate that, regardless of channel dynamics and data heterogeneity, our scheme consistently delivers superior learning accuracy across various parameters and markedly surpasses other over-the-air methodologies.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"5213-5227"},"PeriodicalIF":4.6,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10742892","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142588773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-05DOI: 10.1109/tsp.2024.3492165
Ege C. Kaya, Abolfazl Hashemi
{"title":"Localized Distributional Robustness in Submodular Multi-Task Subset Selection","authors":"Ege C. Kaya, Abolfazl Hashemi","doi":"10.1109/tsp.2024.3492165","DOIUrl":"https://doi.org/10.1109/tsp.2024.3492165","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"80 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142588775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-04DOI: 10.1109/TSP.2024.3490838
Zhiduo Ji;Cailian Chen;Xinping Guan
Distributed sensing is a key process for acquiring system state information in the network environments of industrial cyber-physical system (ICPS). Considering the unknown complex industrial system models, the intelligent methods for distributed sensing are received extensive attention. In most existing works, the system observability is assumed strictly first to obtain complete sensing information for subsequent state estimation. But with the expansion of industrial monitoring network scale, the observability requirement is increasingly difficult to be satisfied in advance. Therefore, a new distributed intelligent sensing method with guaranteed observability is proposed for ICPS in this paper. Specifically, a distributed learning mechanism based on field level data is designed to dynamically approximate the distributed sensing process. Then, the learning weight complete update condition is provided to actively guarantee the observability, and the novel convex-set construction approach is proposed to handle the non-convex property of this condition. Besides, the learning convergence speed and error bound are analyzed in detail. Finally, the proposed method is applied into the industrial hot rolling laminar cooling process based on the established simulation system. Compared with state-of-the-art methods in distributed intelligent sensing, the proposed method can actively reduce the sensing cost while improving the sensing performance with guaranteed observability. An average overall improvement of 24.1% in the normalized sensing performance and selection number of sensing terminals is achieved, which provides a solution for the upgrade of intelligent sensing of key processes in similar ICPS.
{"title":"Observability Guaranteed Distributed Intelligent Sensing for Industrial Cyber-Physical System","authors":"Zhiduo Ji;Cailian Chen;Xinping Guan","doi":"10.1109/TSP.2024.3490838","DOIUrl":"10.1109/TSP.2024.3490838","url":null,"abstract":"Distributed sensing is a key process for acquiring system state information in the network environments of industrial cyber-physical system (ICPS). Considering the unknown complex industrial system models, the intelligent methods for distributed sensing are received extensive attention. In most existing works, the system observability is assumed strictly first to obtain complete sensing information for subsequent state estimation. But with the expansion of industrial monitoring network scale, the observability requirement is increasingly difficult to be satisfied in advance. Therefore, a new distributed intelligent sensing method with guaranteed observability is proposed for ICPS in this paper. Specifically, a distributed learning mechanism based on field level data is designed to dynamically approximate the distributed sensing process. Then, the learning weight complete update condition is provided to actively guarantee the observability, and the novel convex-set construction approach is proposed to handle the non-convex property of this condition. Besides, the learning convergence speed and error bound are analyzed in detail. Finally, the proposed method is applied into the industrial hot rolling laminar cooling process based on the established simulation system. Compared with state-of-the-art methods in distributed intelligent sensing, the proposed method can actively reduce the sensing cost while improving the sensing performance with guaranteed observability. An average overall improvement of 24.1% in the normalized sensing performance and selection number of sensing terminals is achieved, which provides a solution for the upgrade of intelligent sensing of key processes in similar ICPS.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"5198-5212"},"PeriodicalIF":4.6,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142580020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-04DOI: 10.1109/TSP.2024.3484582
Yu-Hang Xiao;David Ramírez;Lei Huang;Xiao Peng Li;Hing Cheung So
One-bit sampling has emerged as a promising technique in multiple-input multiple-output (MIMO) radar systems due to its ability to significantly reduce data volume, hardware complexity, and power consumption. Nevertheless, current detection methods have not adequately addressed the impact of colored noise, which is frequently encountered in real scenarios. In this paper, we present a novel detection method that accounts for colored noise in MIMO radar systems. Specifically, we derive Rao's test by computing the derivative of the likelihood function with respect to the target reflectivity parameter and the Fisher information matrix, resulting in a detector that takes the form of a weighted matched filter. To ensure constant false alarm rate (CFAR), we also consider noise covariance uncertainty and examine its effect on the probability of false alarm. The detection probability is also studied analytically. Simulation results demonstrate that the proposed detector provides considerable performance gains in the presence of colored noise.
在多输入多输出（MIMO）雷达系统中，单比特采样已成为一种很有前途的技术，因为它能显著减少数据量、硬件复杂性和功耗。然而，目前的检测方法还没有充分解决实际场景中经常遇到的彩色噪声的影响。在本文中，我们提出了一种在 MIMO 雷达系统中考虑彩色噪声的新型检测方法。具体来说，我们通过计算与目标反射率参数和费舍尔信息矩阵相关的似然函数导数来推导 Rao 检验，从而得到一种加权匹配滤波器形式的检测器。为了确保恒定的误报率（CFAR），我们还考虑了噪声协方差的不确定性，并研究了其对误报概率的影响。我们还对检测概率进行了分析研究。仿真结果表明，在存在彩色噪声的情况下，所提出的检测器能提供相当大的性能提升。
{"title":"One-Bit Target Detection in Colocated MIMO Radar With Colored Background Noise","authors":"Yu-Hang Xiao;David Ramírez;Lei Huang;Xiao Peng Li;Hing Cheung So","doi":"10.1109/TSP.2024.3484582","DOIUrl":"10.1109/TSP.2024.3484582","url":null,"abstract":"One-bit sampling has emerged as a promising technique in multiple-input multiple-output (MIMO) radar systems due to its ability to significantly reduce data volume, hardware complexity, and power consumption. Nevertheless, current detection methods have not adequately addressed the impact of colored noise, which is frequently encountered in real scenarios. In this paper, we present a novel detection method that accounts for colored noise in MIMO radar systems. Specifically, we derive Rao's test by computing the derivative of the likelihood function with respect to the target reflectivity parameter and the Fisher information matrix, resulting in a detector that takes the form of a weighted matched filter. To ensure constant false alarm rate (CFAR), we also consider noise covariance uncertainty and examine its effect on the probability of false alarm. The detection probability is also studied analytically. Simulation results demonstrate that the proposed detector provides considerable performance gains in the presence of colored noise.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"5274-5290"},"PeriodicalIF":4.6,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142580308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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