Human Activity Recognition (HAR) such as fall detection has become increasingly critical due to the aging population, necessitating effective monitoring systems to prevent serious injuries and fatalities associated with falls. This study focuses on fine-tuning the Vision Transformer (ViT) model specifically for HAR using radar-based Time-Doppler signatures. Unlike traditional image datasets, these signals present unique challenges due to their non-visual nature and the high degree of similarity among various activities. Directly fine-tuning the ViT with all parameters proves suboptimal for this application. To address this challenge, we propose a novel approach that employs Low-Rank Adaptation (LoRA) fine-tuning in the weight space to facilitate knowledge transfer from pre-trained ViT models. Additionally, to extract fine-grained features, we enhance feature representation through the integration of a serial-parallel adapter in the feature space. Our innovative joint fine-tuning method, tailored for radar-based Time-Doppler signatures, significantly improves HAR accuracy, surpassing existing state-of-the-art methodologies in this domain. Our code is released at https://github.com/wangyijunlyy/SelaFD.

Underwater object detection is crucial for marine exploration but faces challenges like low image quality, severe object overlap, and detection of small, diverse targets. This paper presents a novel model, Global and Edge Information Enhancement Underwater Object Detection model (GEE-UOD), designed to address these issues and improve detection accuracy. The Global and Edge Information Enhancement Module (GEI-EM) extracts global and edge features via multi-branch convolution, improving feature robustness. Additionally, the integration of GLSA, ELA attention mechanisms, and the BiFPN network boosts multi-scale feature extraction and fusion. To balance accuracy and efficiency, a shared convolution detection head is introduced, reducing model parameters and computational cost. Experimental results on the RUOD dataset show that GEE-UOD achieves an mAP of 86.9%, demonstrating its superior performance and potential for advancing underwater object detection.

Millimeter-wave radar has attracted increasing attention for respiratory monitoring due to its non-contact operation and privacy-preserving characteristics. Nevertheless, extracting fine-grained respiratory waveforms from non-stationary radar signals remains highly challenging, as these signals are frequently contaminated by various interferences, most notably aperiodic body micromotion. The spectral components of such interference often overlap with the respiratory frequency band and typically exhibit power levels that significantly exceed the target signal. This letter introduces the Gated Attention Wavelet Network (GAWNet), an interpretable framework that integrates deep learning with physical priors by operating on radar phase information in the wavelet domain. GAWNet leverages a two-stage suppression strategy: first, a Temporal Gated Attention (TGA) encoder combines convolutional gating and self-attention to achieve initial interference reduction; second, a Frequency Gated Attention (FGA) decoder provides further refinement by transforming wavelet coefficients to the frequency domain for precise filtering. The clean respiratory waveform is then reconstructed using an Inverse Discrete Wavelet Transform (IDWT). Extensive experiments with data from 12 subjects demonstrate that GAWNet consistently outperforms state-of-the-art models and exhibits robust generalization capability.

In this letter, we present a novel strategy for designing zero-redundancy arrays exploiting the concept of the sum-difference coarray. This approach aims to eliminate redundant lags between the difference and sum coarrays, resulting in a more efficient array design. Consequently, we introduce two new sparse array configurations, named Zero-Redundancy Sparse Array (ZRSA-I and ZRSA-II), for direction-of-arrival (DOA) estimation. These arrays have been proven to possess the zero-redundancy property and offer more uniform degrees of freedom (uDOFs) compared to many existing sparse arrays. Furthermore, we propose the Sum-Difference Translation Invariance Criteria (SDTIC) to mitigate the mutual coupling (MC) effects in the arrays. Theoretical propositions and simulation results confirm the exceptional performance of the proposed ZRSAs, highlighting their potential for enhancing angle resolution and estimation accuracy.

Simultaneous measurements of multi-target vital signs have been quite challenging for radar-based non-contact vital signs monitoring. This paper presents a novel cumulative phase gradient approach for accurate estimates of multi-target respirations without either range or beam discrimination in a single-input single-output (SISO) radar sensor. The signal model of multi-target respiration is first provided, and then a cumulative phase gradient approach is developed to accurately estimate multi-target respirations from the observed mixtures by exploiting the underlying periodicity of the respiration waveforms. The proposed method provides average estimated error rates around 3% in the experiment with closely located three human targets within range resolution, and demonstrates its significant superiorities over other methods.

This paper considers exploiting the time difference of arrival (TDOA) measurements from a ultra wideband (UWB) indoor positioning system to locate a moving point target. In indoor environments, measured TDOAs are subject to large errors due to multipath and/or non-line-of-sight (NLOS) propagation. Besides, they are nonlinearly related to the target position. This paper presents an enhanced two-step approach to achieve robust TDOA indoor tracking. Similar to the existing method, the first-step of the new algorithm preprocesses the raw TDOAs to mitigate the effect of large TDOA errors while its second step applies a recursively bounded grid-based filter (RBGF) to achieve target position tracking. To improve performance, in this work, the possible target position area, which is explicitly obtained by the RGBF, is fed back to the first-step such that a constrained TDOA measurement preprocessing is now performed. Extensive simulation results show that the newly proposed scheme offers better TDOA estimation and indoor target positioning accuracy over the original method and other benchmark algorithms.

The technologies of vital signs detection have been proven of great use while it is still limited by several challenges. One of the major challenges in vital signs detection is strong interferences, such as multiple targets in continuous wave radar system and random body movement (RBM), which significantly degrade the accuracy of the measurement. In this paper, a 77GHz linear frequency modulated continuous-wave (LFMCW) radar system is investigated to mitigate multiple-targets interferences. Furthermore, a novel regional hidden Markov model (RHMM) is proposed to acquire accurate estimates of the respiration rate (RR) and heart rate (HR) by exploiting the underlying slow-variant characteristics of these vital signs in the RBM environment. Experiments demonstrate the error rates of the proposed method are less than 9% for RR and less than 3% for HR in the multi-targets RBM environment.

Multi-task compressive sensing is a framework that, by leveraging the useful information contained in multiple tasks, significantly reduces the number of measurements required for sparse signal recovery and achieves improved sparse reconstruction performance of all tasks. In this paper, a novel multi-task adaptive matching pursuit (MT-AMP) algorithm based on a hierarchical Bayesian model is proposed with the exploitation of both the group structure across different tasks and the intra-group correlation, yielding an effective means to simultaneously perform sparse recovery as well as learn the statistical inter-task and intra-group relationships. Experimental results using both synthetic data and real data sets demonstrate the superiorities of the proposed method over existing state-of-the-art algorithms.

Conventional space-time adaptive processing suffers from the requirement of a large number of secondary samples. In this paper, a novel method is proposed to accurately estimate the clutter covariance matrix based on a small number of secondary samples, by exploiting the common clutter support across nearby range cells in the angle-Doppler domain. By taking advantage of the intrinsic sparsity of the clutter in the angle-Doppler domain, the recently developed sparse Bayesian learning technique is employed for high-resolution clutter profile estimation. The proposed method does not require the independent and identically distributed secondary sample assumption, and the required number of secondary data samples can be significantly reduced. In addition, we propose a sparse reconstruction-based approach to acquire the 2-D motion parameters of moving targets, by exploiting their group sparsity in the velocity domain in the multistatic passive radar systems. Simulation results verify the effectiveness of the proposed algorithm.

For spatially distributed targets encountered in radar and sonar applications, direct application of subspace-based methods usually do not lead to an accurate estimation of the direction and angular extent of the signal arrivals. If the spatial distribution of the targets can be parameterized with a known model a priori, the direction-of-arrival (DOA) estimation problems can be simplified as parameter estimation problems. However, these methods do not apply when the targets are not parameterizable. Motivated by this fact, we propose an effective approach for the DOA estimation of nonparametric spatially extended targets. In the proposed approach, the spatially extended targets are modeled as a continuous sparse structure, which are effectively estimated using the Bayesian compressive sensing techniques based on a paired spike-and-slab prior accounting for the angular target spread. In particular, the problem is examined under a collocated multiple-input multiple-output (MIMO) radar platform. Signal transmission at multiple coprime transmit frequencies are also considered to achieve increased degrees-of-freedom. The group sparsity of the targets across different frequencies is exploited to achieve improved DOA estimation performance.

In this paper, we propose a novel structured compressive sensing algorithm based on non-parametric Bayesian framework for the reconstruction of sparse entries with a continuous structure. A paired spike-and-slab prior is first employed to impose signal sparsity. A logistic Gaussian kernel model, which involves the logistic model and location-dependent Gaussian kernel, is then proposed to encourage the underlying structure of a sparse signal. A closed-form and analytical posterior inference is carried out in a Gibbs sampling scheme. Simulation results demonstrate that the proposed algorithm outperforms existing state-of-the-art sparse Bayesian learning algorithms.

This paper presents a low noise chopper-stabilized instrumentation amplifier (IA) for EEG recording. The current-reuse technology is adopted to reduce noise of the IA and the regulated cascode circuit is used to boost the open-loop gain of the amplifier. A ripple reduction loop (RRL) based on a novel output-resistance-tuning technology is proposed to reduce the intrinsic offset of the IA and reduce the output ripple of the chopper amplifier. Simulation results show the high-pass cutoff frequency of 0.15Hz, the input referred noise of 0.7μVRMS(BW=100Hz) and a NEF of 2.83. The attenuation to the chopping ripple is about 35dB with the novel output-resistance-tuning based RRL. The proposed IA achieves an average CMRR of 110dB, average PSRR of 105dB and average input-impedance of 1.5GΩ from Monte Carlo analysis. The overall IA consumes only 1.1μA current at a 1.2V supply.

In this letter, we propose a multi-task compressive sensing algorithm for the reconstruction of clustered sparse entries based on hierarchical Bayesian framework. By extending a paired spike-and-slab prior to a general multi-task model, the proposed algorithm has the capability of modeling both inter-task and intra-task dependencies of the observation data. The latter is achieved by imposing a clustered prior on non-zero entries and finds applications in radar where targets exhibit spatial extent. Simulation results verify that the proposed algorithm outperforms state-of-the-art group sparse Bayesian learning algorithms.

An effective complex multitask Bayesian compressive sensing (CMT-BCS) algorithm is proposed to recover sparse or group sparse complex signals. The existing multitask Bayesian compressive sensing (MT-CS) algorithm is powerful in recovering multiple real-valued sparse solutions. However, a large class of sensing problems deal with complex values. A simple approach, which decomposes a complex value into independent real and imaginary components, does not take into account the group sparsity of these two components and thus yields poor recovery performance. In this paper, we first introduce the CMT-BCS algorithm that jointly treats the real and imaginary components, and then derive a fast and accurate algorithm for the estimation of the prior parameters by solving a surrogate convex function. The proposed CMT-BCS algorithm achieves effective complex sparse signal recovery and outperforms MT-CS and complex group Lasso.

A Field-Programmable Analog Array (FPAA) architecture designed for intelligent sensory application is presented, which consists of high performance and high flexible Configurable Analog Blocks (CABs). The CAB is developed to realize both continuous-time and discrete-time circuits for achieving optimal performance in different applications. In addition to employ coarse-grained reconfigurable CAB in FPAA, a fine-grained reconfigurable amplifier in the CAB is utilized to maximize programmability and flexibility. The precision of the analog processing is enhanced by employing fat-tree interconnection network topology to minimize the number of switches used in FPAA and using correlated double sampling (CDS) techniques to suppress the offset and noise. The FPAA is designed and implemented in SMIC 0.18μm CMOS process with a 3.3 V supply voltage. An instrumental amplifier and a capacitive sensor signal readout circuit are taken as application examples. The relative precision and dynamic range of the analog processing are 97.6% and 119dB respectively.

A new way of looking at Loffeld's bistatic formula (LBF) is presented for tandem configuration in this letter. The factors that affect the precision of the spectrum are obtained through the comparison with another analytical one. It has been proved that the cosine of the half bistatic angle plays a more important role respect to the other factor, which is whether the baseline to range ratio is equal to the tangent of the half bistatic angle or not. As long as the cosine of the half bistatic angle is very close to one, the LBF spectrum is of high quality, and it does not have a direct influence by the length of the baseline (baseline-to-range ratio) or the size of the squint angle. The factors that affect the precision of the spectrum are discussed in detail through simulated experiments.

In an ultra low voltage hearing aid front-end system on chip, a novel Current Mode Feed-Forward Gain Control (CMFGC) technique is presented. Compared with the conventional automatic gain control (AGC), CMFGC significantly reduces the total harmonic distortion (THD) without worsening other performances by digitally controlling the passive resistor array. To attain the digital control codes according to extremely weak signal from the microphone, a novel high precision current mode rectifier is proposed. A prototype chip has been designed based on a 0.13μm standard CMOS process and tested with 0.8V supply voltage. The measurement results show that the THD of the system is between 0.02% and 0.06% at the output level of 500mVp-p. In addition, the typical input referred noise within the system's bandwidth is 4μVrmsand the typical power consumption of the complete system is 40μW.

A bistatic range migration algorithm (RMA) based on an exact analytical bistatic point-target (PT) spectrum in the tandem configuration is proposed in this letter. For the conventional geometry-based bistatic formula method, the derived spectrum is only a quasi-analytical one because a variable called half-quasi-bistatic angle (HQBA) is not exactly analytically expressed. The key step of the proposed algorithm is to deduce an analytical HQBA in the tandem configuration, and thus, an exact analytical closed-form PT spectrum is acquired. Based on this analytical spectrum, a bistatic RMA is presented. It is demonstrated that this algorithm can handle the bistatic tandem configuration with extremely large baseline/range ratio and can also be applied to wide-swath imaging.

A novel approach to motion parameter estimation with low pulse repetition frequency (PRF) sampling based on compressed sensing (CS) theory is introduced. As is known to us, when PRF is less than the Doppler spectrum bandwidth, moving targets suffer both Doppler centroid frequency ambiguity and Doppler spectrum ambiguity. Under this condition, the traditional parameter estimation method in the Doppler domain is out of action. The key of this letter converts motion parameter estimation in the synthetic aperture radar system with low PRF sampling into solving an optimization equation based on CS theory. Because moving targets in the scene can be regarded as sparse signals after clutter cancellation, an optimization algorithm based on CS theory is proposed to reconstruct sparse signals and meanwhile estimate the along-track velocities and azimuth positions of moving targets. Considering the fact that range cell migration of moving targets is not subject to PRF limitations, Radon transform is adopted to obtain unambiguous across-track velocities and range positions. Results on simulation and real data are provided to show the effectiveness of this method.

This paper deals with an omega-K algorithm for focusing bistatic synthetic aperture radar (SAR) data. The key of the proposed algorithm is the derivation of a frequency mapping function by using Neo's method of series reversion. The registration of the focused image onto the ground plane is discussed in detail, which is based on a conclusion that the targets whose instantaneous Doppler frequencies at zero azimuth time are the same as that of the reference target at zero azimuth time will all be focused at the reference target's azimuth position in the focused image. The range invariance region size, which the proposed algorithm can process, is also determined in this paper. The proposed algorithm can handle extreme bistatic configurations with wide apertures and large squint angles. The effectiveness of the proposed algorithm is confirmed by simulation results. Although developed for the azimuth-invariant case, the proposed algorithm can be readily extended to the azimuth-variant case, as long as we divide the SAR data into blocks in the azimuth direction so that each block of SAR data can be assumed quasi-stationary in the azimuth direction.