Thayananthan Thayaparan

Separation of target rigid body and micro-doppler effects in ISAR imaging

Micro-Doppler (m-D) effect is caused by moving parts of the radar target. It can cover rigid parts of a target and degrade the inverse synthetic aperture radar (ISAR) image. Separation of the patterns caused by stationary parts of the target from those caused by moving (rotating or vibrating) parts is the topic of this paper. Two techniques for separation of the rigid part from the rotating parts have been proposed. The first technique is based on time-frequency (TF) representation with sliding window and order statistics techniques. The first step in this technique is recognition of rigid parts in the range/cross-range plane. In the second step, reviewed TF representation and order statistics setup are employed to obtain signals caused by moving parts. The second technique can be applied in the case of very emphatic m-D effect. In the first step the rotating parts are recognized, based on the inverse Radon transform (RT). After masking these patterns, a radar image with the rigid body reflection can be obtained. The proposed methods are illustrated by examples

Micro-Doppler-based target detection and feature extraction in indoor and outdoor environments

Abstract In many cases, a target or a structure on a target may have micro-motions, such as vibrations or rotations. Micro-motions of structures on a target may introduce frequency modulation on the returned radar signal and generate sidebands on the Doppler frequency shift of the targets body. The modulation due to micro-motion is called the micro-Doppler (m-D) phenomenon. In this paper, we present an effective quadratic time–frequency S-method-based approach in conjunction with the Viterbi algorithm to extract m-D features. For target recognition applications, mainly those in military surveillance and reconnaissance operations, m-D features have to be extracted quickly so that they can be used for real-time target identification. The S-method is computationally simple, requiring only slight modifications to the existing Fourier transform-based algorithm. The effectiveness of the S-method in extracting m-D features is demonstrated through the application to indoor and outdoor experimental data sets such as rotating fan and human gait. The Viterbi algorithm for the instantaneous frequency estimation is used to enhance the weak human m-D features in relatively high noise environments. As such, this paper contributes additional experimental m-D data and analysis, which should help in developing a better picture of the human gait m-D research and its applications to indoor and outdoor imaging and automatic gait recognition systems.

Signal Decomposition by Using the S-Method With Application to the Analysis of HF Radar Signals in Sea-Clutter

This paper presents a new approach to the time-frequency signal analysis and synthesis, using the eigenvalue decomposition method. It is based on the S-method, the time-frequency representation that can produce a distribution equal or close to a sum of the Wigner distributions of individual signal components. The new time-frequency signal decomposition method is evaluated on the simulated and experimental high-frequency surface-wave radar (HFSWR) data. Results demonstrate that it provides an effective way for analyzing and detecting maneuvering air targets with significant velocity changes, including target signal separation from the heavy clutter. The analysis shows that this method can provide additional insight into the interpretation and processing of radar signals, with respect to the traditional Fourier transform based methods currently used by the HFSWRs. The proposed method could also be used in other signal processing applications

A Multiple-Detection Joint Probabilistic Data Association Filter

Most conventional target tracking algorithms assume that a target can generate at most one measurement per scan. However, there are tracking problems where this assumption is not valid. For example, multiple detections from a target in a scan can arise due to multipath propagation effects as in the over-the-horizon radar (OTHR). A conventional multitarget tracking algorithm will fail in these scenarios, since it cannot handle multiple target-originated measurements per scan. The Joint Probabilistic Data Association Filter (JPDAF) uses multiple measurements from a single target per scan through a weighted measurement-to-track association. However, its fundamental assumption is still one-to-one. In order to rectify this shortcoming, this paper proposes a new algorithm, called the Multiple-Detection Joint Probabilistic Data Association Filter (MD-JPDAF) for multitarget tracking, which is capable of handling multiple detections from targets per scan in the presence of clutter and missed detection. The multiple-detection pattern, which can account for many-to-one measurement set-to-track association rather than one-to-one measurement-to-track association, is used to generate multiple detection association events. The proposed algorithm exploits all the available information from measurements by combinatorial association of events that are formed to handle the possibility of multiple measurements per scan originating from a target. The MD-JPDAF is applied to a multitarget tracking scenario with an OTHR, where multiple detections occur due to different propagation paths as a result of scattering from different ionospheric layers. Experimental results show that multiple-detection pattern based probabilistic data association improves the state estimation accuracy. Furthermore, the tracking performance of the proposed filter is compared against the Posterior Cramér-Rao Lower Bound (PCRLB), which is explicitly derived for the multiple-detection scenario with a single target.

Adaptive local polynomial fourier transform in ISAR

The adaptive local polynomial Fourier transform is employed for improvement of the ISAR images in complex reflector geometry cases, as well as in cases of fast maneuvering targets. It has been shown that this simple technique can produce significantly improved results with a relatively modest calculation burden. Two forms of the adaptive LPFT are proposed. Adaptive parameter in the first form is calculated for each radar chirp. Additional refinement is performed by using information from the adjacent chirps. The second technique is based on determination of the adaptive parameter for different parts of the radar image. Numerical analysis demonstrates accuracy of the proposed techniques.

Micro-doppler signal estimation for vibrating and rotating targets

Vibrations or rotations of a target or structures on a target give rise to the micro-Doppler effect of the reflected waveforms. In this paper, we analyze micro- Doppler signals in the joint time-frequency plane using commonly applied distributions and time-frequency transforms. Performance is evaluated based on achieved resolution and reduced artifacts. The latter is significant in microDoppler analysis, as the signals have sinusoidal timefrequency (TF) signatures. This presents a challenge for the already established distributions, as they have to suppress the cross as well as the auto artifacts. We propose a new decomposition of basis functions, given the apriori knowledge of the time varying nature of the radar returns. This approach can be cast as an application of waveform diversity. The new decomposition is free from any artifacts and provides improved estimates of the target micro-Doppler signature.

Micro-Doppler Removal in the Radar Imaging Analysis

The micro-Doppler (m-D) effect is caused by fast moving reflectors. This effect may significantly decrease the readability of the inverse synthetic aperture radar/synthetic aperture radar (ISAR/SAR) images. An L-statistics-based method for m-D effects removal is proposed. The L-statistics approach is performed on the spectrogram, while the rigid body signal synthesis is done in the complex time-frequency (TF) domain. The proposed method is very simple to use and produces better results than the other TF-based approaches. In addition to being capable of separating the rigid body and the m-D parts, this approach is robust to the noise influence. It may also separate close rigid body points, which are not separated in the original radar image. In the numerical implementation of this approach for radar imaging, the computational efficiency is further improved by using two thresholds. The first threshold determines whether there is a target signal in a range cell, while the second threshold determines whether there are m-D effects in this range cell. These thresholds could significantly decrease the computation time in real-time applications. The theory is illustrated by examples.

Time-frequency-based instantaneous frequency estimation of sparse signals from incomplete set of samples

The estimation of time-varying instantaneous frequency (IF) for monocomponent signals with an incomplete set of samples is considered. A suitable time-frequency distribution (TFD) reduces the non-stationary signal into a local sinusoid over the lag variable prior to the Fourier transform. Accordingly, the observed spectral content becomes sparse and suitable for compressive sensing reconstruction in the case of missing samples. Although the local bilinear or higher order auto-correlation functions will increase the number of the missing samples, the analysis shows that an accurate IF estimation can be achieved even if we deal with only few samples, as long as the auto-correlation function is properly chosen to coincide with the signals phase non-linearity. In addition, by employing the sparse signal reconstruction algorithms, ideal time-frequency representations are obtained. The presented theory is illustrated on several examples dealing with different auto-correlation functions and corresponding TFDs.

Autofocusing of SAR images based on parameters estimated from the PHAF

The local polynomial Fourier transform (LPFT) based algorithm for auto-focusing SAR images has recently been proposed by the authors. It produces a well focused image of moving targets, without defocusing stationary targets or inducing undesired cross-terms. The drawback of this algorithm is its high computational burden caused by the large number of elements in the set of used chirp-rates. We propose an algorithm with decreased number of elements used for the LPFT-based SAR imaging. The product high-order ambiguity function (PHAF) is applied to estimate parameters of a radar signal. The estimated chirp-rate is used as an initial value for forming the set of chirp-rates. The proposed algorithm has significantly smaller set of chirp-rate values (tens comparing to several hundreds or thousands used in the previous algorithm version). In this manner, the calculation complexity is significantly reduced. The proposed procedure is fully automated, meaning that it follows the change of motion parameters. In addition, our procedure considers the third-order phase compensation.

Integrated track initialization and maintenance in heavy clutter using probabilistic data association

Target tracking in high clutter or low signal-to-noise ratio (SNR) environments is an important topic and still a challenging task. Joint Maximum Likelihood Probabilistic Data Association (JML-PDA) is a well-known batch method for initializing the tracks of very low observable (VLO) targets in heavy clutter environments. On the other hand, the Joint Probabilistic Data Association (JPDA) algorithm, which is commonly used for recursive track maintenance, lacks track initialization capability. In this paper, we propose a Combined JML-PDA and JPDA (CJML-PDA) algorithm to automatically initialize and maintain the tracks. This combined approach seamlessly shares information between the initialization and maintenance stages of the tracker. In contrast, in other batch-recursive approaches the initialization and maintenance algorithms operate rather independent of each other. The effectiveness of the proposed algorithm is demonstrated on a heavy clutter scenario and its performance is tested on closely-spaced (but resolved) targets with association ambiguity using angle-only measurements.