KEYWORDS: Detection and tracking algorithms, Sensors, Target detection, Automatic tracking, Evolutionary algorithms, Distance measurement, Signal to noise ratio, Motion models, Error analysis, Monte Carlo methods
Target tracking in high clutter or low signal-to-noise environments presents many challenges to tracking systems.
Joint Maximum Likelihood estimator combined with Probabilistic Data Association (JML-PDA) is a well-known
parameter estimation solution for the initialization of tracks of very low observable and low signal-to-noise-ratio
targets in higher clutter environments. On the other hand, the Joint Probabilistic Data Association (JPDA)
algorithm, which is commonly used for track maintenance, lacks automatic track initialization capability. This
paper presents an algorithm to automatically initialize and maintain tracks using an integrated JPDA and
JML-PDA approach that seamlessly shares information on existing tracks between the JML-PDA (used for
initialization) and JPDA (used for maintenance) components. The motivation is to share information between
the maintenance and initialization stages of the tracker, that are always on-going, so as to enable the tracking of
an unknown number of targets using the JPDA approach in heavy clutter. The effectiveness of the new algorithm
is demonstrated on a heavy clutter scenario and its performance is tested on negibouring targets with association
ambiguity using angle-only measurements.
A new design of a noise radar system is proposed with capabilities to measure and acquire the radar signature of various targets. The proposed system can cover a noise bandwidth of near DC to 30 GHz. The noise radar signature measurements were conducted for selective targets like spheres and carpenter squares with and without dielectric bodies for a noise band of 400MHz-3000MHz. The bandwidth of operation was limited by the multiplier and the antennae used. The measured results of the target signatures were verified with the simulation results.
This paper addresses the issue of interference
suppression in noise radars. The proposed methods can
be divided into non-parametric and parametric ones. The
considered non-parametric methods are based on linear
time-frequency (TF) tools, namely the short-time Fourier
transform (STFT) and local polynomial Fourier transform
(LPFT). The STFT is the simplest TF method, but, due to
the resolution problem, it performs poorly with highly nonstationary
interferences. The LPFT resolves the resolution
problem, however at the cost of increased complexity. In
parametric methods, the phase of interference is locally
approximated by a polynomial, which is motivated by the
Weierstrass's theorem. Using the phase approximation, the
corrupted received signal is demodulated and successively
filtered. Two methods for polynomial phase approximation
are considered, the high-order ambiguity function (HAF)
and product high-order ambiguity function (PHAF). The
method based on the HAF is computationally efficient;
however, it suffers from the identifiability problem when
multicomponent signals are considered. The identifiability
problem can be resolved using the PHAF.
The detection and identification of concealed weapons is an extremely hard problem due to the weak signature
of the target buried within the much stronger signal from the human body. This paper furthers the automatic
detection and identification of concealed weapons by proposing the use of an effective approach to obtain
the resonant frequencies in a measurement. The technique, based on Matrix Pencil, a scheme for model
based parameter estimation also provides amplitude information, hence providing a level of confidence in
the results. Of specific interest is the fact that Matrix Pencil is based on a singular value decomposition,
making the scheme robust against noise.
KEYWORDS: Sensors, Signal detection, Target detection, Fourier transforms, Signal to noise ratio, Radar sensor technology, Current controlled current source, Radar, Time-frequency analysis
In this paper, we present a time-frequency-based detection scheme for the
high-frequency surface-wave radar (HFSWR) for the detection of maneuvering
air targets in the presence of strong sea-clutter. The performance of the
proposed method is evaluated using both synthetic and experimental data. In
addition, the proposed time-frequency detection scheme is examined in detail
with different signal-to-noise ratio and various examples are considered.
The time-frequency-based detection method is then compared with the
Fourier-based detector. Results clearly demonstrate that the
time-frequency-based detector can significantly improve the detection
performance of the HFSWR and add considerable physical insight over what can
be achieved by conventional Fourier-based detector currently used by HFSWRs.
These results distinctly suggest that the Fourier-based detector is optimal
for stationary signals, whereas the Time-Frequency-based detector is optimal
for non-stationary signals.
We present an effective quadratic time-frequency S-method based approach in
conjunction with the Viterbi algorithm to extract m-D features. 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 micro-Doppler
features in relatively high noise environments. As such, this paper
contributes additional experimental micro-Doppler data and analysis, which
should help in developing a better picture of the human gait micro-Doppler
research and its applications to indoor and outdoor imaging and automatic
gait recognition systems.
KEYWORDS: Signal to noise ratio, Time-frequency analysis, Radar, Image fusion, Signal processing, Transform theory, Data fusion, Interference (communication), Wavelets, Signal detection
We describe a new fusion method for time-frequency distribution (TFD) that increases the ability to detect and classify time-varying signals while suppressing signal-dependent artifacts and noise. This is achieved by applying a least-squares algorithm to estimate the second-order approximation Volterra series coefficients of the outputs of selected TFDs. These coefficients are used for the fusion of the selected TFDs and generate a new TFD. The proposed fusion method is compared with four other fusion methods in terms of resolution and signal-to-noise ratio (SNR) in the time-frequency (TF) plane. Five representative TFDs are fused to generate a new TFD and their performances are analyzed. The results show that the new fusion method considerably increases sharpness (resolution) and strength (SNR) of the signal in the TF plane and, furthermore, achieves better signal description over other fusion methods and the traditional TFDs.
This paper studies ionospheric clutter conditions and compares ionosonde measurements in the mid-latitude and arctic regions to determine the most favourable conditions for HFSWR surveillance for surface vessels and low-altitude air targets. The best time to perform HFSWR surveillance is between approximately 06:00-15:00 UT and 20:00-00:00 UT. During these hours, the number of days that sporadic-E interference occurs in a month and the range of frequencies reflected is minimized compared to other times of the day. Of the sites considered, Resolute Bay is the most favourable site for HFSWR surveillance in the summer since sporadic-E interference occurs least often, resulting in reduced signal interference. Similarly, Eureka is the preferred site during the winter months. In addition, the ionosphere at Eureka generally reflects the lowest range of maximum frequencies (~4 - 8 MHz), again resulting in less clutter interference. In all the observations, polar cap sites Eureka and Resolute Bay yield results that are less prone to sporadic-E interference than the mid-latitude site Cambridge Bay.
When target motion is confined to a two-dimensional plane during coherent processing intervals, the adaptive joint time-frequency algorithm is shown to be an effective method for achieving rotational motion compensation in ISAR imaging. We illustrate the algorithm using both simulated and measured experimental radar data sets. The results show that the adaptive joint time-frequency algorithm performed very well in achieving a focused image of the target. Results also demonstrate that adaptive joint time-frequency techniques can significantly improve the distorted ISAR image over what can be achieved by conventional Fourier transform methods when the rotational motion of the target is confined to a two-dimensional plane. This study also adds insight into the distortion mechanisms that affect the ISAR images of a target in motion.
This paper presents a new concept for Time-Frequency estimation, which is based on algorithmic fusion. It is shown that algorithmic fusion increases considerably the detectability of signals while suppresses artifacts and noise. The paper reviews a sample of representative Time-Frequency algorithms. Their performance is studied from a qualitative and quantitative point of view. For simplicity, we have considered the Mean-Squared Error (MSE) as a measure of performance in quantitative performance evaluation studies. The algorithmic fusion is presented using a self adaptive signal and noise dependent or independent approach, while the fusion is performed using the first two terms of the Volterra Series expansion. Simplistic algorithmic fusion methods on time-frequency results (e.g. weighted averaging or weighted multiplication), are special cases of the proposed fusion technique. Experimental results are presented from simulated and real High Resolution (HR)-SAR data. Real HR-SAR data were used from the experiments performed by the Defence Research Establishment (DRDC)-Ottawa.
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