Targets Detection and Moving Parameters Estimation Based on Intra-pulse Doppler Compensation

In this thesis, we cope with the fundamental mathematical and physical problems of the space-time-Doppler UWB system design for fading channels: multi-input multi-output (MIMO) wireless communication with adaptive filtering. The estimation algorithm from equalizer providing an accurate parameters estimate is investigated. UWB radio is a fast emerging technology with uniquely attractively features inviting major advances in wireless communications, networking, radar, imaging, and positioning systems. By its rule-making proposal in 2002, the Federal Communications Commission (FCC) in the United States essentially unleashed huge new bandwidth (3.6-10.6 GHz) at the noise floor, where UWB radios overlaying coexistence RF systems cerate using low-power ultra-short information bearing pulses. Right now, there are three basic types of UWB: multiband OFDM UWB (MB-OFDM UWB), impulse-UWB (I-UWB), and direct sequence UWB (DS-UWB). We focus on I-UWB and MB-OFDM-UWB, and compare their performance. A simple means for spreading the spectrum of low duty cycle pulse trains is time hopping (TH), with pulse position modulation (PPM) for data modulation at the rate of many pulses per data bit. The main contribution of this thesis for I-UWB is the design of leading edge detectors, coherent correlation detector (CCD), and pilot performance enhancement compared with different Rayleigh and Ricean fading conditions. For MB-OFDM UWB, we have considered the performance analysis and design criteria of MIMO OFDM UWB systems for high data-rate wireless transmission. The signal processing of frame detection, time synchronization, frequency synchronization, channel estimation, synchronization tracking using pilot subcarriers and 2-D MIMO detection algorithms is discussed. The main contributions of this thesis are 1) Compared with receiver employing the Maximum Likelihood (ML) detector, the receiver employing a low-complexity linear Minimum Mean Square Error (MMSE) demodulator can perform a limited performance loss in spatially uncorrelated channels. 2) Due to the operations of spreading and dispreading combined with MMSE-based frequency domain equalization, the adverse effects of the low-SNR subcarriers on the average BER performance are potentially improved. This is a direct consequence of spreading, because even if the signal corresponding to a specific chip is obliterated by a deep frequency domain channel fade, after dispreading these effects are spread over the Walsh-Hadamard Transform (WHT) length. Hence there is a high chance of still recovering all the partially affected subcarrier symbols without errors. 3) As a test case, the OFDM-based 200 Mb/s MB-UWB network (IEEE 802.15.3a) are considered, but the simulation results are shown above section. 4) The simulation channel can be easily applied to NOS and NLOS situation with WHT transformation from Simulink model. The fading channels of UWB with low/high signal to noise ratio (SNR), multipath effects, and multiuser interference may introduce large errors in location. The location engine performs the following tasks: █ Data fusion techniques: Different data fusion techniques are implemented using Time Of Arrival (TOA), Angle Of Arrival (AOA), or a combination of both. █ Channel modeling: A multipath, multiuser channel environment is created that models path loss, shadowing, Rayleigh fading, and Doppler frequency effects. █ Parameter estimation: TOA and AOA estimation algorithms are implemented as part of the location finding engine. Different variations of algorithms are implemented for performance and comparison purposes. █ Configuring the physical layer for different wireless networks. Among the programmable parameters are spreading factor, packet size, training length, constellation type, modulation technique, carrier frequency, level of transmitted signal power, and the number of antennas at the transmitter for both sides. █ Configuring the mobile user conditions. The wireless channel models depend on the Doppler frequencies present in the environment. The Doppler frequency depends on the mobile speed and carrier frequencies as input and generates a Rayleigh fading channel with the U-shape Doppler spectrum. █ Configuring the environmental parameters and network geographical structure. One of the factors affecting the performance of the wireless location system is the environment type (e.g., bad urban, urban, suburban, or rural area). For example, in a bad urban area with many blocking objects and buildings, non-line of sight (NLOS) effects plays an important role in the estimation accuracy.

In this paper, we propose a novel integrated sensing and backscatter communication (ISABC) system in which each moving target is attached with a backscatter device (BD) to facilitate simultaneous target identification and parameter estimation. When the base station (BS) transmits signals to its desired user, each BD attached to the target transmits the target identification information to the BS via backscatter communication, which concurrently enhances the echo signal strength. The BS needs to detect the BD symbols and to estimate the target parameters using the echoes. This task, however, is challenging due to the coupling between the BD symbols and the target parameters. To address this issue, we propose a novel iterative detection and estimation (IDE) framework, which involves the following two processes alternately: 1) Utilizing a modified maximum likelihood (ML) estimator to perform parameter estimation with the detected BD symbols; 2) Employing the ML detector to detect the BD symbols with the estimated target parameters. Since the inter-carrier interference (ICI) of the OFDM signal is independent of the BD symbols, but contains the delay and Doppler shift information, we develop a target parameter initialization method using such ICI component to improve the performance of the proposed IDE scheme. Moreover, the closed-form Miller-Chang bound is derived to demonstrate the theoretical performance for the target parameter estimation. Finally, simulation results are provided to validate the effectiveness of the proposed designs.

To meet the urgent demand of low-observable moving target detection in complex environments, a novel method of Frequency Diverse Array (FDA) radar signal processing method based on Space-Rang-Doppler Focusing (SRDF) is proposed in this paper. The current development status of the FDA radar, the design of the array structure, beamforming, and joint estimation of distance and angle are systematically reviewed. The extra degrees of freedom provided by FDA radar are fully utilizsed, which include the Degrees Of Freedom (DOFs) of the transmitted waveform, the location of array elements, correlation of beam azimuth and distance, and the long dwell time, which are also the DOFs in joint spatial (angle), distance, and frequency (Doppler) dimensions. Simulation results show that the proposed method has the potential of improving target detection and parameter estimation for weak moving targets in complex environments and has broad application prospects in clutter and interference suppression, moving target refinement, etc..

An important standard to measure the effectiveness of radar acquisition systems is the accuracy of target parameter estimation. To solve the estimation problem, the echo model of moving targets is established and the FRactional Fourier Transform (FRFT) is subsequently used to estimate the acceleration; further, data fusion is used to raise estimation accuracy. Finally, Range Cell Migration (RCM) and Doppler ambiguity are solved by using the Keystone transform and the ambiguity channels based on the estimated acceleration. The simulation results show high accuracy, complexity, and noise robustness.

HighlightsWhat are the main findings?This paper presents a high-order phase compensation and parameter estimation algorithm designed for maneuvering targets, which effectively balances detection performance and computational efficiency.The proposed method achieves precise compensation of high-order range migrations without the need for exhaustive parameter search, thereby circumventing the blind speed sidelobe effect. Futhermore, the incorporation of a time-delay variable significantly improves the anti-noise capability of the approach.What is the implication of the main finding?This algorithm introduces a novel motion compensation technique to address the challenges of range migration, Doppler frequency migration, and Doppler ambiguity in detecting weak maneuvering targets. It achieves precise range migration compensation with enhanced accuracy and robustness, forming a vital preprocessing step for high-quality SAR imaging.The algorithm supports simultaneous multi-target processing and holds promising application potential in fields such as airborne radar detection, maritime moving target indication, cooperative observation by UAV formations, and other fields.This study focuses on the key challenges in detecting and estimating motion parameters of ground maneuvering targets for airborne radar sensors. The complex unknown motion states of the ground maneuvering target, including velocity, acceleration, and jerk, result in range migrations (RMs) and Doppler frequency migrations (DFMs). These effects severely degrade the long-time coherent accumulation performance of the airborne radar, thereby limiting the reliable detection and precise parameter estimation of maneuvering targets. To address this issue, a new detection and motion parameter estimation method based on the range frequency reversal transform (RFRT) and searching Lv’s distribution (SLVD), i.e., RFRT-SLVD, is proposed. Specifically, the third-order RM (TRM) and quadratic DFM (QDFM) are considered. The proposed method operates as follows: First, RMs are eliminated simultaneously via the RFRT operation, which multiplies the echo by its reversed data in the range frequency and slow-time domains, leveraging the symmetric equal-interval sampling property of the range frequency. Subsequently, a phase compensation function (PCF) related to the jerk is constructed to compensate the QDFM. Finally, the LVD is performed to remove residual DFMs and achieve effective signal energy accumulation. Additionally, the case of a fast-moving target with Doppler ambiguity is analyzed, and a method for estimating three motion parameters is provided. A key advantage of the proposed technique is its ability to directly compensate the RMs without requiring prior knowledge of the maneuvering target, while also avoiding the blind speed sidelobe (BSSL) effect. In comparison with existing algorithms, RFRT-SLVD achieves a balanced trade-off between parameter estimation performance and computational efficiency. Numerical analyses and experiments are conducted to validate the method, assessing its detection capability for ground maneuvering targets, Doppler ambiguity resolution in parameter estimation, computational complexity, and method applicability in multi-target scenarios.

To avoid the disadvantages of the active radar which utilizes its own transmitter to emit electromagnetic radiations, passive radars use the signals readily available in the environment and can provide superior capabilities of stealth target detection, low probability of intercept, low cost, and robustness. This paper investigates the joint target parameter (delay and Doppler) estimation performance for a frequency modulation (FM)‐based distributed passive radar network (DPRN) system with antenna arrays. The DPRN system consists of multiple FM‐based illuminators of opportunity and multiple radar receivers, which are placed on moving platforms. First, we consider the scenario where the target state parameters are unknown, the maximum likelihood estimator is developed, and the log‐likelihood ratio of the received signal for a complex Gaussian extended target is derived. Then, the Cramér‐Rao lower bounds (CRLBs) on the Cartesian coordinates of target position and velocity are computed for a DPRN system with MT FM‐based transmitters of Q antenna elements and MR multichannel receivers of P antenna elements. Finally, numerical examples demonstrate that grouping the receiving antenna elements into properly sized arrays can reduce estimation errors. It is also shown that the joint CRLB is a function of signal‐to‐noise ratio, the number of receiving antenna elements, the properties of the transmitted FM waveform, and the relative geometry between the target and the DPRN architecture. The analytically closed‐form expressions for CRLB are an important performance metric in that they enable the optimal placement of radar receivers to improve the target parameter estimation performance.

Target detection, parameter estimation, and tracking capabilities of a radar system can be significantly enhanced by deploying a distributed network. In this article, we consider a network without a central coordinator, wherein only neighboring nodes share limited target related information. We demonstrate that target detection and estimation of range, Doppler, and angle parameters can be significantly improved with the distributed network compared to the localized single-node system. The nodes that observe low signal-to-noise ratio (SNR) values can improve their detection and estimation capabilities considerably by cooperating with others that observe high SNR values. However, when nodes observing low SNR begin to have adverse effects on the global detection performance, these nodes should then be confined to passive sensing, yielding resources to the better performing nodes. We propose a deep learning approach with long-short term memory (LSTM) networks so that each node can compensate neighboring nodes’ observations with respect to its own. Several methodologies for addressing challenges, such as joint data association and parameter estimation for efficient multitarget tracking, distributed detection, and estimation in cluttered radar environments, optimizing network resource allocations, increasing algorithm robustness against channel impairments, and implementing synchronization approaches, are discussed for the distributed radar networks.

This paper addresses the coherent integration problem for detecting a maneuvering target with complex motions, involving range migration (i.e., range walk and range curvature) and Doppler frequency migration (DFM) within the coherent processing interval. A new method based on adjacent cross correlation function (ACCF) and fractional Fourier transform (FRFT) is proposed, where the ACCF operation is firstly employed to remove the range migration (RM) and reduce the order of DFM. After that, the FRFT is applied to achieve the coherent accumulation. Several simulation experiments are provided to demonstrate the effectiveness.

This study focuses on the rotating target imaging and parameter estimation with narrowband radar echoes, which is essential for radar target recognition. First, a two-dimensional (2D) imaging model with narrowband echoes is established in this paper, and two images of the target are formed on the velocity-acceleration plane at two neighboring coherent processing intervals (CPIs). Then, the rotating velocity (RV) is proposed to be estimated by utilizing the relationship between the positions of the scattering centers among two images. Finally, the target image is rescaled to the range-cross-range plane with the estimated rotational parameter. The validity of the proposed approach is confirmed using numerical simulations.

Integrated sensing and massive multiple-input-multiple-output (MIMO) communication (mMIMO-ISAC) at terahertz (THz) bands can provide vast spatial degrees of freedom and abundant bandwidth resources. However, the employment of a massive number of antennas will pose prominent challenges to both target sensing and channel training in THz-mMIMO-ISAC. In this article, our goal is to integrate the target sensing functionality into the channel estimation stage and develop a channel-training-aided target sensing framework to facilitate the efficient resource sharing of THz-mMIMO-ISAC. Specifically, by exploiting the sparse characteristics of THz mMIMO channels, we build up the intrinsic connection between the channel parameters and the target parameters in angular, delay, and Doppler dimensions. Then, we propose a shared channel training pattern accommodating the hybrid architecture constraints of THz transceiver. Both the channel estimation and the target sensing can be formulated as two structured tensor decomposition problems and then concurrently addressed at the UE and BS sides, respectively. Next, we propose a tensor-based parameter estimation algorithm to acquire the target and channel parameters, where the associated angles of arrival/departure, time delays, Doppler shifts, and coefficients can be extracted from the estimated factor matrices. In addition, we present the detailed derivation of the Cramér-Rao bound (CRB) for the considered parameter estimation problem in THz-mMIMO-ISAC. Numerical results demonstrate that the proposed algorithm can achieve the target parameters estimation performance close to their corresponding CRB, and recover the high-dimensional THz mMIMO channels with substantially reduced training overhead.

Long-time coherent integration can remarkably improve the detection and motion parameter estimation ability of radar for maneuvering targets. Unfortunately, the linear range migration, quadratic range migration (QRM), and Doppler frequency migration (DFM) within the coherent processing interval seriously degrade the target detection and parameter estimation performance. In this regard, a novel coherent integration approach based on frequency autocorrelation function (FAF) and Lv’s distribution (LVD), i.e., FAF–LVD, is proposed in this paper. The QRM is firstly removed by the second-order keystone transform. Then, the FAF with a variable delay is introduced to remove the DFM, followed by the scaled Fourier transform to achieve coherent integration. Finally, the acceleration and velocity are estimated simultaneously by LVD. Analysis demonstrates that FAF–LVD could be efficiently implemented via complex multiplications, the fast Fourier transform (FFT) and inverse FFT. Detailed comparisons with several representative methods indicate that FAF–LVD achieves a better balance among computational complexity, detection ability, and parameter estimation performance. Extensive simulations are presented to validate the effectiveness of FAF–LVD method.

The target motion parameter estimation is an important index to measure the performance of radar system. Accurate estimation of acceleration parameter has important influence on improving the detection and tracking performance of high maneuvering target. When radar transmits a signal with fixed frequency, the maneuvering target echo signal can be approximated as linear frequency modulation signal. Therefore, the acceleration parameter estimation of maneuvering target is transformed into the parameter estimation of linear frequency modulation signal. In this paper, a novel method for acceleration parameter estimation of maneuvering target is proposed based on the compressed sensing theory. The linear frequency modulation signal has sparse characteristic in fractional Fourier transform domain and compressed sensing theory asserts that we can employ nonadaptive linear projections to preserve the structure of the signal and reconstruct the signal with these projections using an optimization process. Theoretical analysis and experimental results verify that the method can effectively solve the precision problem of parameter estimation caused by the large sampling rate in the traditional signal processing method, and effectively reduce the data storage and transmission pressure in radar signal processing.

The vortex ElectroMagnetic (EM) wave has a unique spiral phase wavefront. The rotational Doppler effect, induced by the lateral micromotion of a target is expected to provide a new mode of radar target detection. Under the illumination of vortex EM waves, the micromotion of a cone-shaped target periodically modulates the instantaneous frequency of the radar echo; this can effectively reflect the geometric characteristics and micromotion parameters of a cone-shaped target. This paper focuses on the estimation of cone-shaped target parameters under forward-looking radar conditions. First, based on the principle of vortex EM wave target rotational Doppler detection, a mathematical equation describing the vortex EM wave of a cone-shaped target echo is derived, and an echo rotational Doppler model is established. Second, a method for cone-shaped target parameter estimation under forward-looking conditions is proposed. Using the two-dimensional rotational Doppler information of the scattering points at the top and bottom of a cone-shaped target, the micromotion and geometric parameters of the target can be effectively estimated. The simulation results verify the effectiveness and robustness of the method proposed in this paper.

This article presents a short-range cooperative radar system in the 77-GHz band for the estimation of kinematic parameters, such as position, velocity, and acceleration of a target. The proposed system uses two FMCW radar sensors that are operated incoherently and only depend on a common trigger for coarse synchronization. The resulting frequency offset and phase offset in the beat spectrum of the radar sensors are eliminated through calibration with a known active reference target. For easy detection of the reference target in the beat spectrum, a novel interchirp modulation method is proposed. The sampled data of a single receive channel of only one of the sensors of the system allow for a complete estimation of the kinematic target parameters. The theoretical analysis of incoherent FMCW radar systems is carried out, and the constraints and signal processing algorithms are derived from the results. The theory and operating principle of the proposed system are verified through measurements in an anechoic chamber. The accuracies for the position, velocity, and direction of movement (DoM) estimation are better than 10 mm, 0.03 m/s, and 1.4°, respectively.

Target detection in hyperspectral images (HSI) is an important technique and many target detection algorithms have been developed in recent years. The most widely detection algorithms by the original spectral characteristics may lack the ability of target signal enhancement and background suppression. This paper presents an efficient algorithm for detecting hyperspectral targets based on fractional Fourier transform (FrFT). Firstly, fractional Fourier transform primary search is used as preprocessing to obtain the better intermediate domain features with complementary characteristics between the original reflection spectrum and the Fourier transform domain. Secondly, fractional Fourier transform secondary search and constrained energy minimization (FrFT-CEM) was adopted to find an optimal fractional order to distinguish the target from the background. The proposed method has been proved to be superior in two real hyperspectral data sets.