Target-aerosol discrimination by means of digital signal processing

• US 5,102,218 A
• Filed: 08/15/1991
• Issued: 04/07/1992
• Est. Priority Date: 08/15/1991
• Status: Expired due to Fees

• Current Assignee: United States Of America As Represented By The Secretary Of The Air Force
• Original Assignee: United States Of America As Represented By The Secretary Of The Air Force
• Inventors: Min, Kwang S., Min, Hisook L.
• Primary Examiner(s): Hellner, Mark
• Application Number:
  US07/746,690
• Time in Patent Office: 236 Days
• Field of Search: 356/5, 356/28.5, 356/342, 358/95
• US Class Current: 356/5.01
• CPC Class Codes: F42C 13/023 using active distance measurement
• CPC Class Codes: G01S 7/487 Extracting wanted echo signals , e.g. pulse detection

Abstract

For all-weather active optical proximity sensors, the technique extracts a proper target signal from the mixture of target and aerosol returns. A waveform extraction method and a spectral extraction technique are used. The waveform extraction method examines the changes of the mixed signals in the time domain by using derivatives and other pertinent parameters. The spectral extraction method extracts the target signature by bandpass filtering the transform coefficients after taking orthogonal transformations of the mixed data. Using the former, a portion of a data set is segmented to include target and aerosol returns only. The latter method is applied to the segmented portion.

An outline of the method is stated in the following.

(1) Smooth the data distribution curve.

(2) Using the waveform extraction technique, identify the critical interval which may represent the target return signal, if any.

(3) Segment the neighborhood which includes the critical interval and the aerosol return signal, if any.

(4) Find transform coefficients for the segmented interval.

(5) Apply appropriate bandpass filtering to the transform coefficients.

(6) Reconstruct a signal using the filtered transform coefficients.

Claims

1. A light detecting and ranging pulse signal processor for receiving and processing a plurality of return pulses transmitted from a laser transmitter for target-aerosol discrimination with a target at a short range of less than 30 meters by means of digital signal processing;

said system comprising;

a receiver comprised of a detector for detecting each pulse, to provide data with each pulse representing a data point having an analog value;

analog-to-digital conversion means coupled to the detector to convert the analog value of each data point to a digital value;

a digital signal processor coupled to the analog-to-digital conversion means, with the data being organized into sets of a given number of data points forming a data distribution curve;

wherein the digital signal processor comprises first and second means using a wave waveform extraction technique; and

third, fourth, fifth and sixth means using a spectral extraction technique on one of said sets at a time;

wherein

said first means comprises means for smoothing the data distribution curve;

said second means comprises means for identifying the critical interval which may represent a target return signal, if any, using first and second derivatives and the number of data points in the target return signal;

said third means comprises means for segmenting a neighborhood which includes a critical interval and an aerosol return signal, if any;

said fourth means comprises means for finding transform coefficients for the segmented interval;

said fifth means comprises means for applying appropriate bandpass filtering to the transform coefficients; and

said sixth means comprises means for reconstructing a signal using the filtered transform coefficients.

2. A method used in a light detecting and ranging pulse signal processor for receiving and processing a plurality of return pulses transmitted from a laser transmitter for target-aerosol discrimination with a target at a short range of less than 30 meters by means of digital signal processing;

said method comprising;

receiving and detecting each pulse, to provide data with each pulse representing a data point having an analog value;

converting the analog value of each data point to digital value, with the data organized into sets of a given number of data points forming a data distribution curve;

using a digital signal processor with first and second steps using a wave waveform extraction technique; and

third, fourth, fifth and sixth steps using a spectral extraction technique on one of said sets at a time;

wherein

said first step comprises smoothing the data distribution curve;

said second step comprises identifying the critical interval which may represent a target return signal, if any, using first and second derivatives and the number of data points in the target return signal;

said third step comprises means segmenting a neighborhood which includes a critical interval and an aerosol return signal, if any;

said fourth step comprises finding transform coefficients for the segmented interval;

said fifth step comprises applying appropriate bandpass filtering to the transform coefficients; and

said sixth step comprises reconstructing a signal using the filtered transform coefficients.

3. The method according to claim 2, wherein said second step comprises:

(2-1) finding the derivatives f'"'"'(n) for n in the domain, and smoothing f'"'"'(n);

(2-2) using the f'"'"'(n) distribution, finding the interval [n_r, n_f ] where n_r is the rising point of f(n) and n_f is the falling point of f(n) which follows n_r ;

a) if f'"'"'(n_r -1)<

0, f'"'"'(n_r -2)<

0, f'"'"'n_r +1)>

0, and f'"'"'(n_r +2)>

0, then n_r is the rising point of f(n);

b) if f'"'"'(n_f -1)>

f'"'"'(n_f -2)<

0, and f'"'"'(n_f +1)<

0, the n_f is the falling point of f(n);

(2-3) finding a point p such that f'"'"'(p)=max{f'"'"'(n)} for every n in the interval [n_r, n_f ], if f'"'"'(p)>

C_p for a given positive number C_p, then proceeding to the next step;

(2-4) finding f"(n) for every n in the interval [n_r, n_f ], finding h such that f"(h)=max{f"(n)} for every n in the interval [n_r, p], also finding l such that f"(l) =min{(f"(n))} for every n in the interval [p, n_f ], if f"(h)>

C_h and f"(l)<

C_l for a given positive number C_h and a given negative number C_l, then proceeding to the next step;

(2-5) let t be the number of data points for a target return and w be the number of data points in the interval [n_r, n_f ], if |w-t|<

d for a small number d, then claiming the [n_r, n_f ] is the critical interval; and

repeating steps (2-2) through (2-5) until the critical interval is identified.

4. The method according to claim 3, wherein said second step further comprises:

adapting the threshold values of the parameters C_p in step (2-3) and C_p and C_l in step (2-4) for a high density of aerosol as follows;

if an interval [n_r, n_f ] satisfies the first derivative test stated in step (2-3), then this interval represents an aerosol return signal or a target return signal, if the interval does not satisfy the second derivative test stated in step (2-4) or the width test in step (2-5), then the interval cannot be a target return signal, consequently it represents an aerosol return signal, name this interval [a_r, a_f ], testing the new interval which occurs following [a_r, a_f ] for f'"'"'(p), f"(h), and f"(l) with reduced threshold values for C_p, C_h and C_l respectively;

if the interval [n_r, n_f ] satisfies the first derivative test, the second derivative test, and the width test for each reduced parameter value, then [n_r, n_f ] claiming as a critical interval.

5. The method according to claim 3, wherein said third step comprises:

wherein segmentation is based on the following criteria;

(1) the segmented interval should exclude all the noise elements that precede or follow the aerosol and target returns;

(2) let n_s and n_e be the first and the last data points of the segmented interval respectively, let

space="preserve" listing-type="equation">delta=f(n.sub.s)-f(n.sub.e) (3.1)

selecting n_s and n_e which make delta small;

wherein for an aerosol condition, to make delta small, f(n_s) and f(n_e) are balanced using f(n_r) as a pivot where n_r is the rising point of a possible target return,

let n_p be the rising point of an interval with the following two conditions;

(1) the interval has passed the first derivative test mentioned in step (2-3), and (2) the interval immediately precedes n_r, (in other words, n_p is the starting point of the aerosol return which precedes a possible target return);

for a first case, f(n_p)<

f(n_r) and f(n_a)<

f(n_r)

since f(n_p)<

f(n_r) and f(n_a)<

f(n_r), there exists a point A in the interval [n_p, n_r ] such that

space="preserve" listing-type="equation">f(A+1)>

f(n.sub.r)

and

space="preserve" listing-type="equation">f(B)≦

f(n.sub.r) (3-2)

similarly, since f(n_a)>

f(n_r) and n_a ≦

n_r, there exists a point B in the interval [n_r, n_a ] such that

space="preserve" listing-type="equation">f(B)≦

f(n.sub.r)

and

space="preserve" listing-type="equation">f(B-1)>

f(n.sub.r) (3-3)

let

space="preserve" listing-type="equation">n.sub.s =A

and

space="preserve" listing-type="equation">n.sub.e =B (3-4)

thus the interval [A, B] is the segmentation interval [n_s, n_e ];

for a second case, f(n_p)>

f(n_r) and f(n_a)<

f(n_r),

to find a small delta in Eq. (3-1), formulate another function q(n) to replace f(n) for a certain interval;

let ps

space="preserve" listing-type="equation">q(n)=n-n.sub.p +f(n.sub.p) (3-5)

let

space="preserve" listing-type="equation">h(n)=f(n.sub.r), (3-6)

to find the intersection of q(n) and h(n), let

space="preserve" listing-type="equation">f(n.sub.f)=n-n.sub.p +f(n.sub.p) (3-7)

or

space="preserve" listing-type="equation">n=f(n.sub.r)+n.sub.p +f(n.sub.p),

replace f(n) by q(n) for the interval [f(n_r)+n_p +f(n_p), n_p ] and let

space="preserve" listing-type="equation">n.sub.s =f(n.sub.r)+n.sub.p -f(n.sub.p)

(3-8)

and

space="preserve" listing-type="equation">n.sub.e =B

where B is defined in Ed. (3-4);

so that the interval [N_s, n_e ] is the segmentation interval; and

wherein for a clear condition, the target return signal shows a sharper rising slope, the point n_p is defined simply as the rising point of the data function f(n) preceding the possible target return, and the rest oft the processes are the same as for an aerosol condition.

6. The method according to claim 5, wherein said fourth step comprises:

after the smoothed data distribution f(n) is segmented for the interval [n_s, n_e ], finding the transform coefficients F(u) of f(n) for the interval [0, m=n_e -n_s ];

wherein said fifth step comprises;

using a trapezoidal filter bandpass filtering the transform coefficients of the segmented data sets, the filter operation and the filter formulation being as follows,

let T(u) be the trapezoidal bandpass filter function and F(u) the transform coefficient of the segmented data function f(n);

G(u), the bandpass frequency, being defined as

space="preserve" listing-type="equation">G(u)=T(u)×

f(u) for u=0, 1, 2, . . . , m (4-1)

ps where m=n_e -n_s ;

the filter function T(u) is formulated where ##EQU3## whereby the bandpass filter function is adapted to the size of each segmented data set;

and wherein said fifth step comprises;

reconstructing the bandpass filtered signal gy(n) by performing the inverse transform of G(u) of Eq. (4-1).