Multistatic pseudolinear target motion analysis using hybrid measurements

This paper deals with the issue of ground moving target imaging and motion parameter estimation with an airborne dual-channel circular stripmap synthetic aperture radar (CSSAR) system. Although several methods of ground moving target motion parameter estimation have been proposed for the conventional airborne linear stripmap SAR, they cannot be applied to airborne CSSAR because the range history of a ground moving target for airborne CSSAR is different than that for airborne linear stripmap SAR. In this paper, the moving target’s range history for airborne dual-channel CSSAR and the target signal model after the displaced phase center antenna processing are derived, and a new ground moving target imaging and motion parameter estimation algorithm is developed. In this algorithm, the estimation of baseband Doppler centroid and its compensation are first performed. Then focusing is implemented in the 2-D frequency domain via phase multiplication, and the target is focused in the SAR image without azimuth displacement due to the compensation of the Doppler shift caused by its motion. Finally, the target’s motion parameters are estimated with its Doppler parameters and its position in the SAR image. Numerical simulations are conducted to validate the derived range history and the performance of the proposed algorithm.

The synthetic-wideband and pulse repetition frequency jittering radar systems are receiving increasing attention due to the relatively low construction costs and good anti-jamming performance. However, the inter-pulse random hopping of the carrier frequency and pulse repetition frequency introduces challenges to the long-time coherent integration and motion parameter estimation of the high-speed weak targets for these radar systems. A modified generalized Radon Fourier transform for weak target detection and motion parameter estimation is proposed. The proposed method can achieve full coherent integration gain for the high-speed weak target and has a good parameter estimation accuracy. After the target motion has been well compensated, the well-focused high-resolution range profile of the target can be obtained by synthetic-wideband processing. The effectiveness of the proposed method is verified by Monte Carlo simulations. In addition, experimental data collected using an X-band radar system are processed and analyzed to illustrate the practical performance of the proposed method.

We present a new method for estimating the motion parameters of a target from its inverse synthetic aperture radar (ISAR) signature. This method uses the phase of the target's echo transfer function to calculate a focal quality indicator while avoiding two-dimensional Fourier processing. The focal quality indicator reaches the global minimum of a parametric motion surface when the phase is compensated with the target's actual motion parameters. The presence of an absolute minimum without local minima guarantees that the estimated motion parameters are an accurate representation of the target's motion and allows the use of a simple search procedure. Polynomial fitting is incorporated to the new method to improve the robustness by reducing estimation errors due to the finite order of the parametric motion model.

The airborne coherent multi-input multi-output (MIMO) radar benefiting from coherent integration (including intra-channel integration and inter-channel integration) of multi-channel echoes can obtain superior detection performance for high-speed maneuvering targets. However, due to the coupling motion characteristic between multiple airborne platforms and high-speed targets, the range walk (RW) and Doppler walk (DW) will occur within the intra-channel integration. Besides, the inter-channel signals also exist envelope and phase differences, which will lead to the difficulty in multi-channel integration. We make contributions towards addressing these limitations. On the one hand, we establish a three-dimensional (3-D) geometrical model for high-speed maneuvering target detection with airborne coherent MIMO radar and derive the mathematical expression of radar echoes, while the generalized Radon-Fourier transform (GRFT) is utilized to simultaneously correct the RW and DW for acquiring intra-channel integration. On the other hand, we analyze the properties of GRFT outputs and propose a GRFT-domain integration method to achieve the multi-channel coherent integration. More specifically, the proposed method first constructs a set of coupling equations for the estimation of target motion parameters (i.e., position, velocity and acceleration), then, the target's motion parameters can be estimated by Newton-Raphson method and solving the linear equations. Second, in order to settle the envelope and phase differences between multi-channel echoes, the envelope alignment and phase compensation functions of inter-channel echoes are constituted based on the estimated parameters. Finally, the coherent accumulation of multi-channel echoes can be implemented. Numerical simulations demonstrate the effectiveness of the proposed method.

Efficient matched processing for localisation of a moving acoustic source

A unified treatment of several least squares (LS) algorithms is presented for bearings-only tracking of a target moving at constant acceleration. The close link between the maximum likelihood (ML) estimator and other nonlinear and "linearized" LS algorithms is explored under the assumption of Gaussian bearing noise. In this context, a new asymptotically unbiased closed-form instrumental variables (IV) algorithm is derived. Reduced-bias total least squares (TLS) and constrained TLS (CTLS) algorithms are developed. The equivalence of the ML algorithm to the structured TLS (STLS) algorithm is established. Simulation examples are provided to demonstrate the improved performance of the IV and TLS estimators vis-a-vis the pseudolinear estimator.

This paper presents a study on signal modeling for airborne multichannel circular stripmap synthetic aperture radar (CSSAR)-ground moving target indication (GMTI) systems with relatively high resolution. High resolution is an important trend of synthetic aperture radar (SAR), which means higher accuracy requirement of range history and longer synthetic aperture time. It's known that targets are probably experience accelerations as the synthetic aperture time increases. Therefore, compared with the systems of lower resolution, the target signal modeling of high-resolution systems becomes more challenging. Besides, the coupling relationship among a target's motion and position parameters becomes more complicated due to the existence of accelerations. In this paper, a fourth-order range history model of a moving target is developed. Moreover, the target signal model is established, and the along-track interferometric (ATI) phase history is derived to reveal the coupling relationship among a target's motion and position parameters.

In radar detection, long-time coherent integration (LTCI) is widely used to enhance the detection performance of high-speed targets. However, its effectiveness is fundamentally limited by motion-induced range migration (RM) and Doppler frequency migration (DFM), which cause significant degradation in coherent integration performance. To address this issue, this paper proposes a novel method for high-speed target detection and parameter estimation based on the range frequency reversing transform, Lv’s distribution, and frequency-domain derampkeystone transform (RFRT-LVD-FDDKT). First, the RFRT algorithm is applied to correct RM and extract the echo signal along the slow-time dimension. Then, the LVD algorithm is used to estimate the target’s acceleration and to compensate for the RM and DFM that it induces. Finally, the FDDKT algorithm is used to estimate the target’s velocity and further corrects RM, thereby achieving coherent integration. Detailed comparisons show that, compared to the RFRT and second-order Wigner-Ville Distribution (RFRT-SoWVD) algorithm, the proposed RFRT-LVD-FDDKT algorithm improves detection ability by approximately 4 dB while maintaining comparable computational complexity. Moreover, the effectiveness of the proposed method is validated using real radar data.

Multi-channel synthetic aperture radar (SAR) systems is widely used in ground motion target indication and motion parameter estimation. This paper presents a new unambiguous speed estimation algorithm for multi-channel SAR systems. This paper presents a novel unambiguous velocity estimation algorithm for multichannel SAR systems. The proposed velocity estimation algorithm is a multistage procedure: the first stage is to generate a group of multichannel data with the reduced PRF; in the second stage, adaptive clutter suppression and velocity estimation using spatial spectral processing technique are applied for the two groups of multichannel data to calculate two ambiguity velocities for each moving target. Finally, the unambiguous radial velocity can be estimated using the two ambiguity velocities. The effectiveness of the proposed unambiguous velocity estimation approach is verified by simulated multichannel SAR data.

This paper considers the coherent integration problem for maneuvering target detection and motion parameters estimation, involving range migration (RM) and Doppler frequency migration (DFM) within the coherent pulse interval. A fast non-searching method based on adjacent cross correlation function (ACCF) and Lv's distribution (LVD) is proposed, where the adjacent correlation operation is first employed to remove the RM and reduce the order of DFM. After that, LVD is applied to realize the coherent integration, target detection and parameters estimation. In addition, at the cost of some performance loss, another fast method via ACCF iteratively is also introduced to further reduce the computational complexity and obtain the motion parameters estimation. The proposed two methods are fast in that they can be easily implemented by using complex multiplications, the fast Fourier transform (FFT) and inverse FFT (IFFT). Compared with the existing methods, the presented algorithms can obtain the motion parameters estimation without any searching procedure and can achieve a good balance between the computational cost and the detection ability as well as parameters estimation performance. Finally, several simulation experiments are provided to demonstrate the effectiveness.

Inverse synthetic aperture radar (ISAR) imaging is one of the important high- resolution radar applications for moving target detection and identification. The radial velocity, radial acceleration, and acceleration rate of maneuvering targets can cause range cell migration and Doppler variation in the received signals, which will degrade the imaging quality. Therefore, these parameters should be estimated and compensated before imaging. In low signal-to-noise ratio (SNR) conditions, the conventional motion parameter estimation and compensation algorithms for maneuvering targets do not work properly. To effectively detect and identify the maneuvering targets in low SNR conditions, a new radar imaging algorithm is proposed. The major contributions are as follows: 1) a novel algorithm for envelope migration correction is proposed with adjacent cross correlation function; 2) the target's motion parameters are estimated with the operations of Keystone transform and time-frequency transform to the adjacent cross correlation; 3) a theoretical analysis of the impact of motion parameters on the envelope migration is made, then cross-range phase estimation with the adjacent cross function is presented; 4) based on the estimated motion parameters of targets, a new algorithm for high-resolution ISAR imaging is developed.

In underwater, the bearings-only passive target tracking can be done with many available estimators by supplying the noisy bearing measurements from a passive sonar as inputs. The problem here is, all the estimators fail to work in the abnormal conditions which includes the situation of missed bearings, the situation of spurious bearings and the situation in which the noise is non-Gaussian. The algorithm developed in this paper will take care of the above mentioned problems. The main step involved in the algorithm includes preprocessing of the noisy bearing measurements and then supply the preprocessed data along with the variance as input to the advanced nonlinear filter like Modified Gain Bearings-Only Extended Kalman Filter (MGBEKF) to find the target motion parameters. Preprocessing stage involves reducing the amplitude of the noise in the measurements, converting the nongaussian noise to Gaussian and generating the substitute for the missed and spurious bearings using Pseudo Linear Estimator (PLE).

An attractive and challenging problem in source localisation is to locate a target in three‐dimensional (3D) space using a single station. To satisfy the observability requirements and achieve higher accuracy, the authors draw on the idea of the inverse synthetic aperture radar for single‐station localisation, which leverages the mobility of target and a time serial measurements of angle of arrival (AoA) and time difference of arrival (TDoA). A closed‐form pseudo‐linear estimator (PLE) is developed to estimate both 3D position and velocity of mobile target through the linearisation of AoA–TDoA measurement equations. Furthermore, to suppress the large bias of PLE caused by the correlation of measurement noise, the authors propose a superior bias‐reduced estimator (BRE), which imposes a quadratic constraint to minimise the noise correlation term. They prove that BRE is asymptotically efficient, attaining the Cramér‐Rao lower bound (CRLB) over the moderate noise region. Extensive simulations show that both bias and mean square error of BRE are well predicted by theoretical analysis. Most importantly, in comparison with both PLE and two traditional bias reduction methods, namely weighted total least squares and weighted instrumental variables, BRE can approach the CRLB over a wider noise region and maintain a lower bias.

This letter considers the coherent integration problem for a maneuvering target, involving range migration (RM) and Doppler frequency migration (DFM) within one coherent pulse interval. A new coherent integration method, known as Radon-Lv's distribution (RLVD), is proposed. It can not only eliminate the RM effect via jointly searching in the target's motion parameters space, but also remove the DFM and achieve the coherent integration via Lv's distribution (LVD). Finally, several simulations are provided to demonstrate the effectiveness. The results show that for detection ability, the proposed method is superior to the moving target detection (MTD), Radon-Fourier transform (RFT), and Radon-fractional Fourier transform (RFRFT) under low signal-to-noise-ratio (SNR) environment.

The observability for bearings-only target motion analysis is a very important problem. The bearings-only system is to be said observable if and only if the target motion parameters can be uniquely determined by noise-free bearing measurements. The problem of unobservability for hearing-only target tracking system is discussed in this paper based on the target that travels in the 2-dimentional space with a constant acceleration. Utilizing Gramm rule and rank of matrix, a new proposition that the bearing-only locating and tracking system remains unsolvable prior to an observer maneuver is presented and proved. By proving the proposition, it is shown that for certain type of observer movement the estimation process remains unobservable, even if the target moves with a constant accelerate. The work done in this paper is a valuable study in solving the bearings-only target motion analysis.