Automated Sound Speed Classification for Optimized Search Tactics Don Delbalzo Naval Research Laboratory, Code 7183 Stennis Space Center, MS 39529 Phone: (228) 688-5458 Fax: (228) 688-5763 Email:
[email protected] Contract: N0001498WX30413
LONG-TERM GOAL My long-term goal is to provide an enhanced acoustic performance prediction capability for Navy sensors and operations. A second goal is to fully address the local sound speed profile (SSP) field by developing efficient methods to incorporate Modular Ocean Data Assimilation (MODAS) information. My working hypothesis is as follows. For a given local area, the current acoustic environment defines areas where target detection is probable and areas where it is not. Rather than having a valuable Navy platform conduct a potentially inefficient, equally-spaced, rectangular search pattern for detecting targets, the local environment and existing acoustic models can be used to derive an expedient and optimized pattern that is specific to the area. This capability is particularly relevant in shallow water areas where oceanographic, and therefore acoustic, characteristics can exhibit extreme temporal and spatial variability. MODAS Data, Korea, 8/24/98 0
Sound Speed Types for Korea 0 50
50
Depth (m)
100
100
150 200 250 300
Depth (m)
150
Province 1 Province 2 Province 3
350 400 1440
1460
1480 1500 Sound Speed (m)
1520
1540
200 Sound Speed Types for Korea with Duct Above 25m 0
250
10 20
Depth (m)
300
Province 1 350
30 40 50
Province 2 60 Province 1 Province 2 Province 3
Province 3 70
400 1440
1460
1480 1500 Sound Speed (m)
1520
1540
MODAS sound speed profiles used to create provinced sound speed field
80 1480
1490
1500
1510 1520 Sound Speed (m)
1530
1540
Provinced sound speed profiles without and with a duct
OBJECTIVES The objective of my FY98 effort is to develop a software tool that employs easily obtained environmental parameters to optimize the Navy’s acoustic search capabilities. The software program
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1998
00-00-1998 to 00-00-1998
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Automated Sound Speed Classification for Optimized Search Tactics
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incorporates a feature analysis algorithm for classifying individual SSPs and another algorithm for provincing the SSP field based on the individual classifications. APPROACH During 1998, I focused on algorithm development and validation. Specifically, the software tool developed uses both historical SSP information and actual MODAS profiles extracted from the area of interest. By using real, and easily obtained, environmental data to supplement and/or replace precalculated model SSP predictions, the search optimization tool will be especially useful for performance assessment and mission planning in regions characterized by anomalous environmental conditions. Driver Program (ssp_cla ssify.m)
Read Provin ced SSP's. (read_provprof .m )
Read Local Search Area Location s (read_searchgrid.m)
Subsequent Runs
OD al M Re
ta Da AS
Tes tM OD AS D
ata
First Run Read in MODAS SSP Field ( read _m odas.m)
Create Horizon tally B an ded Test Data F rom Pro vin ced SSP's (test _modas.m)
Reduce MO DAS SSP F ield to L ocal Search Area ( red uce_field.m)
Lo ad Previou sly Gen erated Files ( modas_sub set .jul.mat, profiles_interp.jul.m at, modas_closeloc.jul.mat )
Create Horizontally Band ed Test Data From Ran domly Select ed MODAS SSP's U sed to Create Pro vin ced SSP's ( test_mo das_rand om.m)
Dept h Interpo lat ion of Provinced an d MODAS SSP's (d ef ine_interpo lat e_depths.m , depth interp.m)
Find MODAS SSP 's Nearest t o Search Area Location s (closest_m odas_ssp.m)
Display MODAS or Test MODAS SSP's
Loop Through Each Grid Poin t in the Search Area
Apply Classification Alg orithm t o MODAS SSP (ssp _m atch.m)
Use Default SSP F ield to Fill in Un classif iab le SSP's (defau lt_classify.m)
Asso ciat e Classificatio n Parameters to Character Display Not ation (get_labels.m)
Display Search Area and Data Locat ions Display Classification Matrices (plot _srchgrid_m odasloc.m,)
Display Provin ced and MODAS SSP's and Their Gradient s by Province ( plot_sspb yprov.m plot _g rad ien t.m) )
Display Im age of MODAS SSP F ield (plot_province.m)
Mo dify Classifications o f SSPs (mod ify_estfield.m)
Flow chart of the sound speed classification software. Red circles denote user-input choices.
The feature analysis algorithm examines gradients for classifying individual profiles but can also incorporate ducts if present in the area. Importantly, the SSP categorization approach is to define key (canonical) profile types using real data. WORK COMPLETED This year’s major accomplishments include: (1) completion and validation of the classification and categorization algorithms; and (2) initial field testing of the software during the SHAREM 126 exercise, held in September 1998, in the Sea of Japan. RESULTS Laboratory testing and field application of the software program during SHAREM 126 was very successful. A library of 18 SSPs was developed for feature recognition. Three main water mass categories were identified in the SHAREM area: “hot”, “cold” and “transitional”. For each of these, mean, maximum, and minimum SSPs were derived, resulting in 9 SSPs. Although a strong surface duct was not expected during the exercise, nine additional SSPs were created by superimposing a 25-m mixed layer to the top of each SSP, thus completing the SSP library. The resulting categorization of the SHAREM SSP field adequately mirrored the three major water masses. IMPACT / APPLICATIONS Development of a tactical search optimization tool that maximizes use of MODAS information will provide valuable guidance in performance assessment and mission planning for antisubmarine and mine warfare activities. TRANSITIONS Although transition of the present effort is pending, I envision the product to be integrated with other acoustic search track algorithms to create a larger, seamless package. RELATED PROJECTS The present effort is related to other Tactical Decision Aids (TDAs) such as the Sonar In-Situ Mode Assessment System (SIMAS), Tactical Control Program (TCP), and the Integrated Carrier ASW Prediction System (ICAPS).
