The iterative adaptive approach (IAA) has been shown to suppress sidelobes of strong targets effectively to the level of the noise, thereby unmasking weak targets nearby and yielding substantial sensitivity improvement over traditional pulse compression techniques. Unfortunately, heavy computational complexity is required to solve problems, including range cell migration, Doppler frequency migration, and high sidelobes encountered by multiple targets with high speed when adopting existing methods. As such, this article proposes a blind speed compensation (BSC)-based fast iterative adaptive approach. The proposed method first performs BSC-based coherent integration to concentrate most of the target energy within a small region in range-velocity domain effectively, which is helpful for the design of the adaptive filter with reduced dimensionality in the subsequent sidelobe suppression processing. Then, a partitioned IAA processing is applied on the data selected by a processing window centered around the target trajectory in BSC-based coherent integration result to correct residual migrations and suppress sidelobes. Both the BSC-based coherent integration preprocessing and the piecewise adaptive filtering can improve the computational efficiency with tolerable performance loss. Numerical simulations and experimental results illustrate that the proposed method enables efficient implementation schemes, resulting in low computational complexity while maintaining the performance benefit of sidelobe suppression and migration correction for fast moving targets.

In range-Doppler imaging radars, the sidelobes of strong scatterers may mask weak scatterers in the matched filter outputs. Adaptive pulse compression and iterative adaptive approach pioneer a class of iterative filtering algorithms with remarkable sidelobe suppression performance. These algorithms formulate a multivariate linear model (MLM) by taking the complex scattering coefficients in all range-Doppler cells as its parameters, and solve for these parameters iteratively. As the MLM is potentially overparameterized, they incur significantly high computational costs. To address this issue, we propose a computationally efficient iterative filtering approach named iterative sidelobe suppression via progressive model expansion (PME-ISS) in this article. It adaptively formulates a series of progressively expanding MLMs based on the estimated distribution of scatterer cells (DSC), which refers to range-Doppler cells occupied by scatterers, and estimates the complex scattering coefficients of these scatterers iteratively. To ensure the compactness of the MLMs, we propose a DSC estimation method composed of identifying potential scatterer cells and removing range-Doppler cells not occupied by scatterers. A specific implementation algorithm of PME-ISS is derived, and its computational cost analysis is provided. Simulations demonstrate a computational cost reduction of multiple orders of magnitude compared with existing iterative filtering algorithms without sacrificing sidelobe suppression performance.

The sidelobe suppression performance of existing iterative adaptive filtering methods severely deteriorates in the presence of range-Doppler-straddling targets. To resolve this issue, a straddling-robust iterative adaptive filtering (SR-IAF) algorithm is proposed. It incorporates the range-Doppler-straddling and intrapulse Doppler effects into the received signal model and compensates the straddling mismatch iteratively using the estimated straddling offsets. To further improve the capability of SR-IAF for sidelobe suppression in scenarios including range-spread targets, a modified SR-IAF (MSR-IAF) algorithm is then proposed. In this algorithm, the coarse positions, the lengths, and the number of range-spread targets are first determined according to the SR-IAF outputs, then the straddling offsets of range-spread targets are refined based on the minimum error criterion, and finally, SR-IAF is again performed with the estimated range-Doppler image and straddling offsets as prior knowledge to further subdue sidelobe levels of range-spread targets effectively. Simulation results show that the proposed SR-IAF method is capable of recovering the range-Doppler images with well-suppressed sidelobes for point straddling targets, resulting in successful detection of weak targets located near strong targets. Compared with SR-IAF, MSR-IAF can further improve the performance of sidelobe suppression for range-spread targets with straddling, as demonstrated by the simulated and the experimental results.

The performance of sidelobe suppression methods based on on-grid signal models deteriorates significantly when target parameters deviate from the predefined discrete parameter grids. To address this issue, we propose a backtracking dichotomous-least squares (BD-LS) algorithm based on the minimum residual energy criterion. BD-LS iteratively estimates the parameters of targets whose powers are higher than the threshold used for stopping the iteration, in the descending order of the target powers. During each iteration, the coarse position of the strongest target is first estimated based on the 2-D matched filtering results of the residual signal, and then the straddling offset of the target is estimated according to the minimum residual energy criterion and the dichotomous iterative strategy. After the straddling offsets of all targets are accurately estimated, the complex amplitudes of targets are simultaneously estimated by using the LS algorithm. To mitigate the impact of target interference on the estimation of straddling offsets, multiple backtracking refinements are adopted. In each backtracking refinement processing, the straddling offsets of all the targets above a given threshold are sequentially refined by using the obtained straddling offset estimates of targets as prior information. Simulation results show that BD-LS can effectively suppress the sidelobes of targets with range-Doppler-straddling and accurately estimate the straddling offsets and powers of targets. Compared to the straddling-robust iterative adaptive filtering algorithm, BD-LS provides better sidelobe suppression performance and more accurate estimation of the straddling offsets and powers of targets with lower computational complexity in multitarget scenarios. The effectiveness of the proposed method is demonstrated by both simulated and real data.

During the imaging time, range migration (RM), velocity ambiguity, and range-velocity sidelobes may occur for multiple closely-spaced moving targets, which may result in image smeared. To mitigate the impact of these factors on image focusing, this paper proposes a modified iterative adaptive filtering method based on coherent integration outputs. It first employs Keystone transform with each possible velocity ambiguity number to correct RM and applies fast Fourier transform-based integration to focus target energy within the mainlobe. Subsequently, the coherent integration outputs are divided into distinct processing windows. The data within each processing window are sequentially employed as inputs for the modified iterative adaptive filtering to effectively suppress sidelobes including blind speed sidelobes and retrieve targets with low computational complexity. The adoption of a threshold criterion and the exploitation of covariance matrix structure facilitate a further reduction in computational complexity. Both simulation and experiment results validate the performance of the proposed method.

Detection of multiple closely spaced targets with range-Doppler (RD) migration is a challenging issue for radars, because range cell migration (RCM) and Doppler frequency migration (DFM) during the coherent processing interval (CPI), as well as high sidelobes of strong targets, may deteriorate the performance of target detection and parameter estimation. To realize migration correction and sidelobe suppression simultaneously, a joint iterative adaptive approach (IAA) based on RD processing outputs (RD-JIAA) is first proposed in this article. The input data of RD-JIAA are selected within a small processing window centered around the response peak trajectory in range-velocity domain obtained by the RD processing. Compared with IAA and wideband IAA (WIAA), RD-JIAA has low computational burden. Some instructive suggestions on the selection of processing window sizes are presented considering that most of the target energy should be included in the processing window. Then, a fast implementation, namely, RD-JIAA based on the signal sparsity (RD-SJIAA), is presented to further improve the computational efficiency with tolerable performance loss. Both RD-JIAA and RD-SJIAA are able to utilize the structure relationships between covariance matrices of adjacent range cells to reduce the computational complexity. Finally, the performance of the proposed methods is evaluated by numerical examples.

Random frequency and pulse repetition interval agile (RFPA) radars, distinguished by their superior electronic counter-countermeasure capability, show great potential in numerous applications. However, the wide distribution of distant sidelobes along the range dimension limits their practical application. The recently proposed multitimeslot wide-gap frequency-hopping sequence paired with a low-pass filter (LPF) in the receiver offers a different perspective for the distant sidelobe suppression of RFPA radars. Nevertheless, energy leakage from adjacent pulses' echoes within and outside the subband of each pulse's echo limits its performance. While the reduction of the former energy leakage has been addressed in one of our previous works, the reduction of the latter poses a challenge to the design of the receiving filter. Reducing the latter energy leakage by simply increasing the LPF's stopband attenuation extends its impulse response, which, in turn, increases the span of the near sidelobes of RFPA radars. The subsequent processing for near sidelobe suppression, such as the iterative adaptive approach based on matched filter outputs, also becomes computationally more costly. To address this issue, we formulate an optimal finite impulse response distant sidelobe suppression filter (FIR-DSSF) design problem for random multitimeslot wide-gap frequency-hopping and pulse repetition interval agile (RMWFPA) radars. The optimization objective is to maximize the signal-to-distant-sidelobe-related-interference ratio (SDIR) in the samples related to one range-velocity cell. By deriving a lower bound of the SDIR, this optimization problem is relaxed into a generalized Rayleigh quotient maximization problem independent of the probing scene. Then, we give its closed-form solution. Simulations demonstrate superior distant sidelobe suppression performance for RMWFPA radars with the optimized FIR-DSSFs without significantly increasing the span of the near sidelobes.

Direct-sequence spread spectrum (DSSS) systems have found wide applications in space missions. The possibility for the receiver to operate at a low symbol energy to noise density ratio ($E_{S} / N_{0}$) enabled by error correction coding makes carrier tracking the new processing bottleneck. Robust carrier tracking at low $E_{S} / N_{0}$ often involves a frequency-locked loop (FLL), whose transient and steady-state error strongly depend on the noise performance of the frequency discriminator. However, the presence of symbol transitions in the received signal poses a challenge to the frequency discriminator. Despite the availability of various cross-symbol discrimination approaches, most of them assume known symbol transition boundaries and neglect symbol transitions within each coherent integration interval. Without symbol synchronization, the risk of trespassing a symbol transition boundary limits the coherent integration time, thereby restricting the signal-to-noise ratio (SNR) of the coherent integration results. Consequently, the frequency discriminators suffer greater SNR loss from subsequent non-linear processing in low $E_{S} / N_{0}$ scenarios, resulting in a degradation in noise performance. To address this issue, this paper proposes a cross-symbol differential correlation frequency discriminator. The proposed discriminator employs an approximate maximum likelihood (ML) approach to jointly discriminate the frequency offset and detect the unknown symbol transition and transition boundaries. Additionally, it utilizes an overlapping secondary integration to extend the coherent integration time without reducing the discrimination range. Numerical simulations are presented to verify its advantage over conventional frequency discriminators.

Random frequency and pulse repetition interval agile (RFPA) signals have excellent anti-jamming ability and achieve low probability of intercept (LPI), making them promising for applications in radar systems. However, their matched filter (MF) outputs suffer from random range-velocity sidelobes. In the range dimension, these sidelobes can be classified into two categories: the distant sidelobe floor spread out beyond the minimum interval between pulses, and the near sidelobe plateau confined within about one pulsewidth. These sidelobes seriously degrade the target detection capability of RFPA signals. To address this issue, we propose the concept of a multi-timeslot wide-gap frequency-hopping sequence (multi-timeslot WGFHS) and use it in the design of RFPA signals. In doing so, the distant sidelobes are easily suppressed with a simple low-pass filter (LPF) in the receiver if the parameters of the multi-timeslot WGFHS are chosen properly, while the remaining near sidelobes are suppressed by the iterative adaptive approach based on matched filter outputs (MF-IAA). Simulation results show that the proposed method can effectively suppress the sidelobes of RFPA signals, accurately recover the range-velocity images, and successfully detect weak targets in the presence of strong targets or clutter.

The research on DSAs position estimation method has become a research hotspot in recent decades. However, its random sparse characteristics lead to extremely narrow beam main lobe, so the target search is very difficult. Against these issues, a DSAs target estimation method based on beam feature matching is proposed. The method performs matching search for array beam feature information to realize target estimation. This method effectively reduces the computational complexity compared with existing methods. The simulation results show that the proposed method can reduced the computational complexity effectively for large-scale DSAs.

Recently, a two-dimensional joint iterative adaptive filtering (2-D JIAF) has been proposed to address the masking effect of large targets on adjacent small targets in multi-target scenarios. However, its perfor-mance is impaired for the cases with fast moving targets due to intrapulse Doppler mismatch. In this paper, we consider the intrapulse Doppler shifts in filtering design and propose a robust iterative adap-tive filtering algorithm based on matched filter outputs, termed robust iterative adaptive filtering against intrapulse Doppler shifts (RIAF-IDS), to suppress the range sidelobes induced by intrapulse Doppler mis-match and improve the filtering performance. The proposed algorithm can jointly suppress range-Doppler sidelobes and obtain accurate estimation of range-Doppler image even in cases with fast moving targets. The derivation of RIAF-IDS is provided in detail, and its filtering performance is validated through several simulations, which demonstrate that RIAF-IDS has superior Doppler tolerance over 2-D JIAF at the cost of more computation. (c) 2023 Elsevier B.V. All rights reserved.

Existing cross-correlation mitigation algorithms based on the minimum mean square error (MMSE) criterion can effectively suppress multiple access interference (MAI) but suffer from high complexity and modeling grid mismatch. In this work, we propose a cell-straddling robust-fast cross-correlation mitigation (CSR-FCCM) algorithm with two improvements. First, CSR-FCCM combines the 2-D joint iterative adaptive filtering with interference cancellation, which significantly reduces the number and computational cost of complex amplitude MMSE filters. Second, two discriminators are designed to estimate the straddling offset of delay and frequency for the direct sequence spread spectrum signal. The mismatch problem can be ameliorated by substituting estimated straddling offsets into the signal model, which further improves the MAI mitigation effect. The effectiveness of the CSR-FCCM is verified by simulations using 1023- and 63-length gold codes. Simulation results show that CSR-FCCM has a better MAI mitigation performance and a lower complexity than the open-loop MAI mitigation algorithms, including 2-D jointly iterative adaptive filter and RSR-APC.

This article presents a computationally efficient iterative adaptive approach based on range–Doppler matched filter outputs for sidelobe suppression and range–Doppler imaging. A sidelobe suppression scheme, named as dimension reduction based fast iterative adaptive approach (DR-FIAA), is designed by adopting a small processing window on range–Doppler matched filter outputs to eliminate the masking of weak targets by strong targets nearby with low computational complexity. Two specific methods are proposed under this scheme, namely synchronous FIAA (SY-FIAA) and sequential FIAA (SE-FIAA). Compared to SY-FIAA, SE-FIAA has lower computational complexity at the cost of some performance loss. Based on the structure relationships among covariance matrices, further reduced computational complexity can be achieved by SY-FIAA and SE-FIAA. Numerical examples for different scenarios are included to demonstrate the effectiveness of the proposed designs.

To enable next-generation connected autonomous vehicles (CAVs), the future Vehicle-to-everything (V2X) network is expected to provide centimeter-accurate localization service while attaining low-latency transmissions in high-mobility environments. Nevertheless, these unprecedented requirements are far beyond the capabilities of 5G vehicular networks. Given the above evolution trend, a natural idea is thus to design a joint system architecture that combines both communications and sensing subsystems. To this end, research efforts toward integrated sensing and communications (ISAC) for the V2X network are well underway. It is our belief that ISAC should facilitate both sensing and communication via a single system in a spectrum-/energy-/cost-efficient way. Moreover, it can also improve the performance of both functionalities with mutual assistance, which is also essential to enable CAV's mission-critical services for 6G and beyond V2X. In this article, we first provide a brief historical overview of V2X and ISAC. In particular, we analyze the forces driving the usage of ISAC in V2X. Then we introduce three ISAC design schemes based on their underlying systems. We also survey state-of-the-art enabling technologies by reviewing recent developments of ISAC-assisted beamforming technologies in vehicular networks. Finally, we shed light on some potential challenges and research directions.

Accurate and efficient foliage penetration (FOPEN) target recognition plays a vital role in many mission-critical applications, ranging from civilian to surveillance and military. Recently, device-free sensing (DFS), as an emerging technique, has gained great popularity because it requires no dedicated equipment other than wireless transceivers. Although some DFS-based approaches have been successfully applied in foliage environments, they are vulnerable to climate dynamics and heavily rely on relabeling large amounts of new data when the weather is altered. To address this issue, a convolutional neural network (CNN)-based weather adaptive target recognition network (WATRNet) is proposed in this article. Specifically, a lightweight weather conditional normalization (WCN) module is embedded atop each convolutional block to encode inputs under different weather conditions into a shared latent feature space. Under an end-to-end learning manner, the proposed WATRNet first learns knowledge from sufficient labeled data under a certain weather condition to achieve a precise classifier. When applying this model under another weather condition, only the WCN module needs to be retrained using limited new labeled samples to learn weather-invariant features, while the rest convolutional parameters in WATRNet are frozen. Consequently, the domain discrepancy caused by climate variations can be adaptively mitigated with as few relabeled data as possible. Comprehensive evaluations are carried out on a real FOPEN dataset collected under four different weather conditions. Experimental results verify that the presented method can achieve over 90% accuracy, even when it implements from a normal weather condition to another severe weather condition with only small amounts of training samples.

Adaptive pulse compression (APC) has been proposed based on the minimum mean square error (MMSE) criterion to effectively suppress range sidelobes of strong targets and retrieve all targets in multi-target scenarios. However, MMSE-based algorithms suffer from deteriorated performance in the presence of targets with range-straddling due to modelling mismatch. This degradation can be partially compensated by modified MMSE-based algorithms. To further suppress sidelobes when range straddling occurs, we propose a modified APC algorithm robust to targets with range-straddling, namely range-straddling-robust APC (RSR-APC). We first establish the signal model for targets with range-straddling and then derive the expressions of the MMSE filter based on matched filter outputs and the range-straddling offsets based on an improved Rife algorithm. Simulation results show that the proposed algorithm can suppress sidelobes of targets with range-straddling and improve estimation accuracy of target positions in different scenarios.

Device-free sensing (DFS) is an emerging technology that empowers wireless communication systems with the ability for not only data communication but also smart sensing. By taking advantage of machine-learning technologies, DFS transforms traditional wireless communication networks into intelligent context-aware networks and will open the doors for a myriad of promising 6G-enabled Internet of Things (IoT) applications, ranging from smart home to smart buildings. Although significant progress has been made for human activity recognition at a single location by leveraging this technology, performance at multiple locations has not been fully explored. As far as multilocation activity sensing is concerned, the performance is compromised along with the change of locations and labor-intensive annotation works caused by multilocation. To tackle this issue, an activity decomposition network (ActNet) is presented to decompose the activity information directly from input samples by using the training data from different locations together. Instead of dealing with different locations separately, our ActNet can assemble data from different locations together for training to mitigate the data limitation issue caused by a single location. To achieve this, a multiple-input–multiple-output (MIMO)-orthogonal frequency-division multiplexing (OFDM) technology-based prototype system is utilized to collect data samples at 24 different locations in a cluttered office environment. Especially, for each location, only ten samples of each activity are used for training. Experiments demonstrate that the average classification accuracy is 94.6% across all locations with ensured robustness produced by our method.

A novel parameter estimator of polynomial phase signals (PPSs) is proposed. The proposed approach unwraps a spectrum phase to obtain the group delay (GD) for parameter estimation. For PPSs with monotonic instantaneous frequency (IF) laws, which are referred to as strictly monotonic PPSs (SMPPSs), the GD and IF are inverses of each other. According to this property, we can perform polynomial regression on the GD instead of the IF to estimate parameters. However, the proposed method cannot be applied to general PPSs without monotonic IF laws. We prove that linear frequency modulation signals can be used to transform general PPSs into SMPPSs. In this manner, the proposed strategy is easily extended to general PPSs. Finally, the obtained results are refined by employing the O'Shea refinement strategy to achieve the Cramer-Rao lower bound. Simulation results show that the proposed technique has a lower estimation threshold and is less complex than the existing methods for high-order PPSs. (C) 2021 Elsevier B.V. All rights reserved.

Random frequency and pulse repetition interval (PRI) agile (RFPA) signals bring excellent performance of electronic counter-countermeasures to radar systems and have been received considerable attention in recent years. However, the research on their ambiguity function (AF) is not comprehensive. In this article, the analytical expressions of the AF expectation and variance are given. According to the expressions, the direct relationships between the key metrics of the AF and the waveform parameters of RFPA signals are specified. The results in this article are verified by Monte Carlo simulations and provide some insights into RFPA waveform design.