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In this paper, we consider the adaptive detection with multiple-input multiple-output (MIMO) radar in the presence of compound-Gaussian clutter with a limited number of secondary data set. We assume that multiple a-priori spectral models for the clutter are available, and model the actual clutter inverse covariance structure as a combination of these available a-priori models. In this framework, a sequential optimization algorithm is first presented to estimate the unknown parameters. Then, an approximate generalized likelihood ratio test (GLRT) is developed by exploiting the obtained estimates. Finally, we evaluate the capabilities of the proposed detector against compound-Gaussian clutter as well as its superiority with respect to some existing techniques with few secondary data support.

This paper considers the problem of estimating a moving target for Multiple Input Multiple Output (MIMO) waveform Sky-Wave Over The Horizon Radar (OTHR), in which the target is often smeared by the ionosphere disturbance. We start from signal model of the MIMO waveform OTHR, derive the smeared echo model. Based on the echo model, we propose a blind identification algorithm to estimate the target's velocity in MIMO waveform OTHR. Unlike the estimation methods for classic MIMO radar, the proposed algorithm considers the coincident spread effect from the ionosphere disturbance, which is often inevitable in OTHRs. The performances of the proposed algorithm is evaluated by numerical simulations.

In conventional radar tracking, kinematic measurements such as range, bearing and Doppler are usually used in target tracking methods. It has been demonstrated that the tracking performance can be further improved by utilizing more information. With regard to polarimetric radar system where polarization information (PI) is available, PI thus should also be exploited to ensure robustness of tracking performance. This paper considers the target tracking of polarimetric radar by incorporating PI into the probabilistic data association filter (PDAF). Firstly, a generalized structure of PI aided PDAF (PDAFPI) method is given. It is suitable for different radar models and different data association algorithms. Secondly, for the polarimetric monostatic radar, two versions of PDAFPI method are proposed for a single-target tracking. Specifically, the first version is the PDAFPI algorithm in optimum case (O-PDAFPI) where the polarimetric characteristics of target and noise covariances are known as a prior. The later one is a fully adaptive PDAFPI algorithm suitable for the suboptimum cases (S-PDAFPI) where the polarimetric parameters are unknown. Simulation results show that the proposed algorithms can effectively improve the tracking performance and track more reliably both non-maneuvering and maneuvering targets.

Tracking target in high range resolution (HRR) radar systems via dynamic programming based track-before-detect (DP-TBD) is considered in this paper. The radar clutter is modeled as compound-Gaussian process, which can be interpreted as a Rayleigh process (the so-called speckle) whose mean square value is a slowly varying random process (the socalled texture), and the target is modeled as distributed target. Generalized likelihood ratio test (GLRT) based DP-TBD strategy is proposed in this paper without the a prior knowledge of texture. The performance of the proposed GLRT based DP-TBD strategy is analyzed via various simulations. Simulation results show that the proposed GLRT based DP-TBD strategy can implement the target tracking in HRR radar systems efficiently.

In this paper, we focus on the performance improvement of dynamic programming based track-before-detect (DP-TBD) in heterogeneous Rayleigh background, in which case the surveillance scene is divided into two parts with separate independent and identically distributed Rayleigh probability density functions. Original DP-TBD (DP-TBD using signal amplitude scoring function) suffers significant performance loss under this condition. Optimal DP-TBD (DP-TBD using log-likelihood ratio (LLR) scoring function) usually can not be obtained since the clutter edge position and clutter statistics are unknown. To overcome these challenges, we propose an improved DP-TBD strategy, which applies a data pre-processing step to the measurement frame before DP processing, the clutter is then suppressed. Simulation results show that the proposed DP-TBD outperforms the original DP-TBD, the false tracks is reduced over 80%, and the performance loss is less than 0.5dB in comparison with the optimal DP-TBD.

In this paper, we consider the adaptive detection with multiple-input multiple-output (MIMO) radar in compound-Gaussian clutter. The covariance matrices of the primary and the secondary data share a common structure but different power levels (textures). A Bayesian framework is exploited where both the textures and the structure are assumed to be random. Precisely, the textures follow inverse Gamma distribution and the structure is drawn from an inverse complex Wishart distribution. In this framework, the generalized likelihood ratio test (GLRT) is derived. Finally, we evaluate the capabilities of the proposed detector against compound-Gaussian clutter as well as their superiority with respect to some existing techniques.

This paper addresses the tracking problem using the short state sequences in time series (which is called plot-sequences in this paper) obtained from multi-frame track-before-detect (TBD). Some TBD methods, e.g., dynamic programming (DP), maximum likelihood probabilistic data association (ML-PDA), histogram probabilistic multihypothesis tracker (H-PMHT) and Hough transform (HT) based TBD, are batch processing methods whose outputs are plot-sequences which are neither filtered target trajectories nor traditional point detections. In radar systems, traditional tracking algorithms are generally suitable to process detected point plots, which are inappropriate for the plot-sequences. Additionally, many TBD methods are grid based method, which perform the search in the discretized state space. Therefore the estimation accuracy suffers at least half a grid loss. Besides, the missing reports and false alarms in TBD detections will also deprave the tracking results especially when the signal-to-noise ratio (SNR) is low. Thus, a tracker which can combine the plot-sequences into continuous target trajectories that have higher estimation accuracy is needed. In this paper, a nonlinear and non-Gaussian measurement model to describe the plot-sequences of DP based TBD is formulated, then a particle filtering (PF) algorithm for target tracking using the plot-sequences as the input measurements to estimate the target states recursively is developed. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.

This paper considers the imaging problem of a target hiding behind multiple non-uniform walls using through-the-wall radar (TWR). We propose a new wall model in which the relative permittivity of the wall changes along the direction of wall thickness, and propose the integrations-based wall compensation algorithm based on the wall model. Firstly, the wall is subdivided into many small layer, when the thickness of each layer is small enough each small layer can be seen as uniform layer; then figure out propagation delay of electromagnetic wave in the wall in the integral form; and then calculate out the focusing delay and imaging the scene subsequently. Finally the simulation proves the reliability of this algorithm.

This paper mainly considers the tracking problem for multiple targets hidden behind walls using through-wall imaging (TWI) radar, and proposes an efficient multiple target tracking algorithm. Specifically, first, we present a fast wall compensation methods, based on time minimization plus look-up table technique, to obtain a high quality image. Second, the locations of the targets are extracted by two-dimensional low-pass elliptic filter and image erosion operation. Third, the measurement-to-track data association accuracy is improved through integrating nearest-neighbour(NN) data association method with amplitude information. Finally, targets' estimation state and error covariance matrix are updated by interacting multiple model (IMM) estimation algorithms. The proposed algorithm is evaluated by the real radar data.

A sensor networks comprise a large number of sensor nodes collaboratively collecting information, carrying out simple computation with their onboard processing capabilities. In recent years, sensor selection has become an important problem in managing sensor networks. Facts have shown that effective sensor selection benefits much to the performance of the sensor networks, such as target tracking. Among many works about sensor selection problems, a criterion to select sensors based on information theory is proposed in a recent paper. However the criterion in their work is suitable for only some special cases because of the stringent constraints. In this paper, we propose a novel general criterion with the consideration of correlation in the measurement errors from different radars. Based on theoretical analysis we define a matrix norm (∥R 1 + R 2 ∥ 2 ) comprised of different measurement error covariance matrices as the measure to select radars. Meanwhile, Kalman filter combined with Covariance-Intersection (CI) information fusion is adopted to deal with the tracking process considering the measurement errors are correlated or not. Simulations have shown that the criterion proposed in this paper is applicable for a more general sensor networks and the tracking performance is also improved with the consideration of the correlated observations from different resources.

This paper considers the problem of detecting and estimating a scattering signal with unknown location and length in multiple measurement, which is often arisen in signal processing society, e.g., identifying a seismic wave measured by the observation stations around the earthquake hypocenter and detecting a distributed target in unknown occurring range cells with High Resolution (HR) Multiple-Input Multiple-Output (MIMO) radars. We propose the novel algorithm, which is consisted of an recursive signal energy accumulated process and a novel Generalized Likelihood Ratio Test (GLRT). Unlike the classic method which detects the signal and estimate it's location and length in each measurement independently, the proposed algorithm makes it equivalent to a random walking process, then estimates the moving velocity during the recursive process and obtains the Maximum Likelihood Estimations (MLEs) of the unknown signal length through a Dynamic Programming (DP) method. Meanwhile, the decision result and the estimations output at the same time. The performances of the proposed algorithm are evaluated by numerical simulations.

The fluctuating target tracking problem through the use of dynamic programming based track-before-detect (DP-TBD) is considered in this paper. The target is modelled by the well-known Swerling family of target amplitude fluctuation models in order to capture the effect of radar cross-section changes that a target would present to radar over time. For each of the target fluctuation models, the complex likelihood ratio (CLLR), which utilizes the target amplitude fluctuation information together with the phase information, is derived and used in the integration process of DP-TBD. Simulation results show a significant gain in detection and tracking performance through accurately modeling the target amplitude fluctuations.

This paper considers the coherent integration problem for a maneuvering target with jerk motion, involving range migration (RM) and Doppler frequency migration (DFM) within the coherent integration period. A fast non-searching method based on adjacent cross correlation function (ACCF) is proposed to realize the coherent accumulation, where two adjacent cross correlation operations are employed to remove the RM and reduce the order of DFM. Then the coherent integration is obtained via Fourier transform. The advantage of the presented method is that it can achieve the coherent integration without any searching procedure and thus the computational cost is reduced significantly. Finally, simulations are provided to demonstrate the effectiveness.

The adaptive multi-pulse compression (AMPC) has been shown to successfully suppress the range-doppler sidelobes at the cost of heavy computation load. The fast adaptive multi-pulse compression (FAMPC) algorithm reduces the computation complexity of the AMPC via dimensionality reduction while it suffers noticeable performance degradation. This paper proposes a modified adaptive multi-pulse compression (MAMPC) algorithm that uses a two-stage processing scheme and implements with the gain-constraint-APC (GCAPC). The proposed method has almost identical estimation performance to the AMPC while maintains the same order of computation load as that of the FAMPC.

This paper considers an effective electronic counter-countermeasures (ECCM) technique against velocity deception jamming in multi-target scenario for radar system. The essence of the technique is outlined based on the variations of initial phases of the transmitted pulses in pulse repetition interval (PRI) domain. With the optimized waveform, the false targets generate notches around the true targets in frequency domain. We formulate the multi-target, multi-jamming signal model and derive the cost function for the optimizing waveform design. In addition, we propose a modified Newton method to solve the optimizing problem. Simulations are presented to demonstrate the effectiveness of the proposed method in solving the ECCM optimizing problem for multi-target scenarios.

In this paper the radar detection and tracking of targets in sea clutter is examined through the use of particle filter (PF). The clutter is modeled by the K-distribution, which is usually used to model radar sea clutter returns received from small grazing angle or high resolution radars. Two target models are considered, i.e. non-fluctuating target model and Swerling type 1 target model, to capture the effect of radar cross-section changes that a target would present to the radar over time. For the above two target models, the probability density functions of clutter-only and clutter-plus-target case are given, and then the likelihood ratios (LLRs) are derived and used in the filtering process of PF. The performance is examined using various simulations.

This paper addresses the problem of computational efficiency with respect to Dynamic Programming (DP) based Track-Before-Detect (TBD). Generally, DP-TBD is a grid-based method that estimates target trajectory by means of searching all the physically admissible paths in a finite discrete state space. However, its computational complexity increases nonlinearly as the state space expands with size or dimension, which heavily restricts its applications for many realistic scenarios, e.g., radar target detection. To alleviate this problem, a fast two-step implementation of DP-TBD is proposed in this work. In the first step, to reduce the computational costs, target states are roughly estimated by searching a discrete grid with larger cell size. In the second step, an accurate search is performed only on the parts of the state space indicated by the results in the first step with reasonable computational cost. Additionally, the proposed algorithm is also suitable for the surveillance radar scenarios. Simulation results show that the proposed algorithm can effectively improve the computational efficiency of DP-TBD with limited performance degradation.

This paper considers an effective signaling scheme to combat the range repeat jammer for radar system. Robustness against jammer is achieved by designing different phase-coded pulses with weighted auto- and cross- correlation in a coherent processing interval (CPI). In this way, the matched filter output forms notches around the true target so that the true target can be indicated clearly. In the same time the jammer is suppressed for the good cross-correlation property between pulses. To obtain the desired waveforms, we formulate the penalty function and propose the pattern search (PS) method to optimize it. Finally, we present simulations to demonstrate the effectiveness of the proposed method.

In this paper, a new system of integrated radar and communication which bases on stepped frequency continuous waveform is put forward. Specifically, firstly, the binary communication data are modulated into stepped frequency continuous waveform through BPSK modulation, which can make the communication data hide in the radar waveform. Secondly, we provide a signal separate method which can divide the radar signal and communication signal in the baseband mixed signal. So this integrated system can transmit binary data precisely with little influence on the radar sensing performance. Finally, we evaluate the radar sensing performance and wireless communication performance via simulation results. The results show that the proposed integrated system has a lower SER meanwhile guaranteeing the radar sensing performance.