Page 209 - DCAP208_Management Support Systems

Management Support Systems

Notes 12.2.8 Detection and Tracking of Moving Targets

The moving target detection and track methods here are “track before detect” methods. They
correlate sensor data versus time and location, based on the nature of actual tracks. The track
statistics are “learned” based on Artificial Neural Network (ANN) training with prior real or
simulated data. Effects of different clutter backgrounds are partially compensated based on
space-time-adaptive processing of the sensor inputs, and further compensated based on the
ANN training. Specific processing structures are adapted to the target track statistics and sensor
characteristics of interest. Fusion of data over multiple wavelengths and sensors is also supported.
Compared to conventional fixed matched filter techniques, these methods have been shown to
reduce false alarm rates by up to a factor of 1000 based on simulated SBIRS data for very weak
ICBM targets against cloud and nuclear backgrounds, with photon, quantization, and thermal
noise, and sensor jitter included.

Examples of the backgrounds, and processing results, are given below.
Figure 12.14: (a) Raw input backgrounds with weak targets included, (b) Detected target
sequence at the ANN processing output, post-detection tracking not included

Source: http://tralvex.com/pub/nap/#CoEvolution of Neural Networks for Control of Pursuit & Evasion
The methods are designed to overcome the weaknesses of other advanced track-before-detect
methods, such as 3+-D (space, time, etc.) matched filtering, dynamic programming (DP), and
multi-hypothesis tracking (MHT). Loosely speaking, 3+-D matched filtering requires too many
filters in practice for long-term track correlation; DP cannot realistically exploit the
non-Markovian nature of real tracks, and strong targets mask out weak targets; and MHT cannot
support the low pre-detection thresholds required for very weak targets in high clutter. They
have developed and tested versions of the above (and other) methods in their research, as well
as Kalman-filter probabilistic data association (KF/PDA) methods, which they use for
post-detection tracking.
Space-time-adaptive methods are used to deal with correlated, non-stationary, non-Gaussian
clutter, followed by a multi-stage filter sequence and soft-thresholding units that combine
current and prior sensor data, plus feed back of prior outputs, to estimate the probability of
target presence. The details are optimized by adaptive “training” over very large data sets, and
special methods are used to maximize the efficiency of this training.

12.2.9 Real-time Target Identification for Security Applications

The system localises and tracks peoples’ faces as they move through a scene. It integrates the
following techniques:
Motion detection

Tracking people based upon motion
Tracking faces using an appearance model

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