Optimal signal acquisition technique and system

A feedback control system utilizing optimal signal acquisition for an auttic real-time sensing system. A sensor generates a continuous train of sensor output signals which are sampled. A histogram is determined from that sample and processed utilizing a digital transinformation technique. A feedback signal is achieved that is used to adjust a control signal of the sensor.

BACKGROUND OF THE INVENTION 
1. Field of the Invention 
The field of the invention disclosed herein is directed to signal 
processing and more specifically to optimal signal acquisition and 
especially pertaining to automatic control of real-time sensing systems. 
2. DESCRIPTION OF RELATED PRIOR ART 
The prior art offers several definitions and methods of "optimal" signal 
acquisition and associated automatic sensor gain control. For analog 
signal reception or recording, the popularly accepted approach has been to 
roughly maximize the received signal power while avoiding saturation. This 
technique is typically performed either by a human operator or built-in 
AGC (automatic gain control) circuit defined below. In the case of digital 
signal acquisition (quantization) the conventional approach is known as 
"minimum mean-square-error" quantization first formally documented by Joel 
Max in his 1961 paper "Quantizing for Minimum Distortion". In this 
approach, a human operator or AGC circuit controls an analog signal level 
while a uniform n-bit level digitizer quantizes the signal into discrete 
samples. 
There are significant problems with the prior art that preclude extraction 
of the optimal amount of information from a signal. Although a human 
operator is ultimately the best judge for optimal signal acquisition for 
himself, he is often not the best judge for another human observer or for 
a machine vision device. In addition, "man-in-the-loop" approaches prevent 
real-time optimization for signal acquisition. Although real time, typical 
AGC circuits simply adjust the gain to ensure a near-constant average 
signal intensity. The "minimum-mean square error" approach, limits 
quantization error effects, but for most signal distributions does not 
ensure the maximum signal information content. In fact, none of these 
aforementioned approaches in the prior art are optimal in the 
information-theoretic sense and none address the case where the signal is 
corrupted by noise. 
SUMMARY OF THE INVENTION 
The objective of the present invention is to provide a technique and 
apparatus for signal acquisition that maximizes the actual information 
received in a noise-limited channel. 
The invention disclosed herein is directed to a method of optimal signal 
acquisition utilizing a feedback control system for an automatic real-time 
sensing system. A continuous train of sensor output signals are sampled 
which are used to determine histogram data. The data is processed 
utilizing a digital transinformation technique to output a feedback signal 
for interactive adjustment of the control signal of the system. A 
subprocessing step may be used which restricts processing to one or more 
subsets of the data, performed by an in it place automatic target 
recognizer in the preferred embodiment.

PREFERRED EMBODIMENTS 
The preferred embodiment will now be discussed with reference to the 
drawing figures. FIG. 1 depicts the block diagram for the method and 
feedback system utilizing optimal signal acquisition for an automatic 
real-time sensing system. Sensor 10 is a real-time device that accepts 
information which is processed and includes output signal means for 
generation of a continuous train of sensor output signals. The output 
signals which may be either digital, analog or a combination thereof are a 
function of a control signal of sensor 10. Digital output signals are 
sampled by a down-sampling means and then clocked out of digital port 12 
and then on through line 13 as a digital signal sample. Analog signals are 
put into an appropriate format by such means as an RS-170 video and output 
to analog port 14. Analog sampling and A/D converting means 16 accepts the 
analog signal from port 14 through line 15 so as to sample the analog 
signal and convert that sample as a digital signal sample through line 17. 
Histogram means 18 accepts the digital signal samples from line 13 and/or 
17. The sample that has been determined is a statistically significant 
number of samples collected for use by histogram means 18. Histogram means 
18 functions to determine histogram data from the sample. The output from 
histogram means 18 is fed through interface bus 19 to processing means 100 
which utilizes a digital transinformation technique to process the 
histogram data from which there is an output of a feedback signal as a 
digital signal. 
The processing step by which processing means 100 determines a feedback 
signal involves establishing the entropy of the histogram data and then 
using a digital transinformation (DTI) technique. The entropy of the 
histogram data is determined by normalization of the data over the entire 
data range. DTI is the subtracting of a real-time approximation of a 
determined noise entropy of said data from the determined entropy of the 
histogram sample according to the following equation: 
##EQU1## 
where p(yj) is the normalized histogram probability of the yj 
k is the gain 
a and b are the quantizer limits 
.sigma.n is the sensor noise standard deviation (rms) 
Hn(k) is the noise entropy=.SIGMA.P(ni)logP(ni) where P(ni) is the noise 
distribution function. 
A subprocessing step may also be present which restricts the processing to 
one or more subsets of the histogram data to produce unique optimal 
control signals for each of the subsets processed. All subsets of the data 
can then be processed to produce an optimal composite signal. 
The feedback signal is delivered to processing output ports 101 and 104 
which are then utilized by a control adjusting means to adjust the control 
signal for digital control port 103 and analog control port 108 
respectively. For digital control, processor control signals are delivered 
out of port 101 via line 102 to a digital control port 103 where they are 
interpreted by means within the sensor and converted to an appropriate 
control setting. For analog control, processor control signals are 
delivered out of port 104 via line 105 to a D/A converter means 106 which 
converts the digital control signal to an analog signal which is set via 
line 107 and by adjusting means 109 adjusts the analog control 108. It is 
understood that the invention is not limited to the sensing of a 
particular part of the electromagnetic spectrum or to a particular control 
parameter. 
FIG. 2 depicts the block diagram of the preferred embodiment for a digital 
sensor. FIG. 20 digitizes the incoming signal at the detector array 21 to 
m bits, converts those bits to n bits (n&lt;m) through an adjustable look-up 
table 22, and outputs the n bits via the digital output port 23. To arrive 
at the appropriate m-to-n look-up table values, a statistically 
significant period of m-bit data is first fed 24 directly to histogram 
means 25, where the m-bit histogram is determined and passed through 
interface 26 to processor 27. Here the histogram is normalized over the 
total number of samples as previously discussed to produce a 2.sup.m set 
probability distribution, but, in addition, this distribution is 
subdivided into a 2.sup.n equi-probability intervals or levels. In this 
fashion, the first of the 2.sup.n output levels is set at the first of the 
2.sup.m levels whose preceding probability contributions add to 
1/(2.sup.n); the second level is set such that the next interval's 
contributions sum to 1/(2.sup.n), etc. These levels form the look-up table 
or conversion map (non-linear function) that is then passed via line 28 to 
sensor 20 and stored. The output n bits converted via the table 22 will 
then be at maximum entropy. 
FIG. 3 depicts the preferred embodiment of the present invention which will 
be discussed in conjunction with the operation of the present invention. 
A continuous flow of signals 30, such as light photons from a scene, enter 
a sensor 31, are converted to corresponding analog voltage signals at a 
detector 32, and amplified by an adjustable gain 33. These signals are 
either sampled and clocked out of a digital port 34, and put into an 
appropriate format by a RS-170 video and output to an analog port 35. In 
the digital case, a statistically significant number of signal samples are 
fed directly to a real-time histogrammer 37; in the analog case, the 
signal is first piped through a uniform n-bit quantizer 36 and a 
statistically significant number of samples collected before being passed 
to histogrammer 37. Histogram data is then sent on to control 
microprocessor 38 which determines the associated probability 
distribution, P(yi), and calculates the digital transinformation as 
discussed below. 
If the sensor gain function is precisely known, the microprocessor computes 
directly the gain setting that produces the maximum DTI value; by using 
the first obtained distribution (the original P(yi)) as an approximation 
of the analog distribution, it computes new distributions and noise 
entropy for all possible known gain settings (subject to a programmed gain 
resolution) and outputs a digital gain control signal corresponding to the 
gain of max DTI. If the gain function is not well described, 
microprocessor 38 sends out a control signal incrementally varying the 
gain, first in an arbitrary direction (up or down), and subsequently in a 
direction that tends to increase the next computed value of DTI. 
If desired, an approximation of the gain k is obtained for calculating 
Hn(k) by examining the resultant increase in standard deviation of the yi. 
For example, if the initial DTI from an eight-bit system was 5.500 bits, 
the first gain adjustment might be an incremental gain increase and result 
in a new, reduced DTI of 5.42 bits; then the next gain adjustment command 
would be to decrease the gain. This process would continue iteratively, 
until microprocessor gain adjustments in either direction no longer 
resulted in increased TI. For the digital sensor, microprocessor gain 
control signals are delivered to an output port 39 and passed on to a 
sensor input port 300 where they are interpreted by sensor software and 
converted to an appropriate gain setting. In the analog sensor case, gain 
control signals are delivered to another digital output port 301 and sent 
to a pulse train generator/amplifier 302. The resultant pulse train is 
then clocked out to drive a gain potentiometer stepper-motor 303, thereby 
adjusting the sensor analog gain control 304. 
In FIG. 3 there is also shown an automatic target recognition (ATR) control 
as subprocessing means 305. Subprocessing means 305 can be made to 
interrupt the control microprocessor 38 via an interface bus 306 to inform 
it of a Region-of-Interest (ROI) it wants considered for local DTI 
maximization (i.e. a rectangular region of an image containing a "hot 
spot"). Microprocessor 38 then restricts the focus of the real-time 
histogrammer 37 to samples from the ROI only (via another interface bus 
307), and the control loop continues sensor adjustment as before until the 
local TI is maximized. In this way, an ATR could analyze many ROIs in an 
image at maximum local entropy in a relatively short period of time. For 
imaging sensors like state-of-the-art FLIRS with individually adjustable 
detector gains, each detector IFOV (Individual Field-of-View) can be 
viewed as a ROI; in this manner a composite image can be formed with DTI 
locally optimized throughout the image. 
FIGS. 4 and 5 are an indicative histogram and ATR image output 
respectively, from the prior art, where no post-acquisition noise 
rejection was performed. FIGS. 6 and 7 are the histogram and image output 
respectively utilizing the present invention viewing the same scene. 
A TRAPIX PLUS high performance virtual image processor was used to obtain 
what is shown in FIGS. 6 and 7, with specific components including model 
KAD8-3 triple 8-bit digitizer and model KVPP pixel processor (with 12-bit 
hardware histogrammer) all made by Recognition Concepts, Inc., Nev. As is 
clearly seen from the results, there is a marked increase in information 
obtained that is the result from the present invention. 
Industrial applicability of this invention includes but is not limited to: 
Audio (such as music digitization), Electrical (such as feedback detector 
loops), Optical (such as photography, VCR), Image acquisition (such as 
machine vision for ATR, robotics, assembly line quality assurance), and 
Surveillance. 
This preferred embodiment is not intended to restrict the invention to the 
precise embodiment or embodiments described.